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ERROR: type should be string, got "https://doi.org/10.1007/s13762 022 04079 x\n\n**REVIEW**\n\n# Plastic waste recycling: existing Indian scenario and future opportunities\n\n**R. Shanker[2] · D. Khan[2] · R. Hossain[1] · Md. T. Islam[1] · K. Locock[3] · A. Ghose[1] · V. Sahajwalla[1] · H. Schandl[3] ·**\n**R. Dhodapkar[2]**\n\nReceived: 13 December 2021 / Revised: 23 February 2022 / Accepted: 4 March 2022 / Published online: 2 April 2022\n© The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022\n\n**Abstract**\nThis review article aims to suggest recycling technological options in India and illustrates plastic recycling clusters and reprocessing infrastructure for plastic waste (PW) recycling in India. The study shows that a majority of states in India are engaged\nin recycling, road construction, and co-processing in cement kilns while reprocessing capabilities among the reprocessors\nare highest for polypropylene (PP) and polyethylene (PE) polymer materials. This review suggests that there are key opportunities for mechanical recycling, chemical recycling, waste-to-energy approaches, and bio-based polymers as an alternative\nto deliver impact to India’s PW problem. On the other hand, overall, polyurethane, nylon, and polyethylene terephthalate\nappear most competitive for chemical recycling. Compared to conventional fossil fuel energy sources, polyethylene (PE),\npolypropylene (PP), and polystyrene are the three main polymers with higher calorific values suitable for energy production.\nAlso, multi-sensor-based artificial intelligence and blockchain technology and digitization for PW recycling can prove to be\nthe future for India in the waste flow chain and its management. Overall, for a circular plastic economy in India, there is a\nnecessity for a technology-enabled accountable quality-assured collaborative supply chain of virgin and recycled material.\n\n**Keywords Informal and formal sector · Biological recycling · Chemical recycling · Mechanical recycling · Digitization ·**\nBlockchain technology\n\n\n### Introduction\n\nPlastic has evolved into a symbol of human inventiveness as\nwell as folly which is an invention of extraordinary material\nwith a variety of characteristics and capacities. Although\nIndia is a highly populated country, it is ranked 12th among\nthe countries with mismanaged plastics but it is expected\n\nEditorial responsibility: Maryam Shabani.\n\n- D. Khan\nk.debishree@neeri.res.in\n\n1 Centre for Sustainable Materials Research and Technology,\nSMaRT@UNSW, School of Materials Science\nand Engineering, UNSW Sydney, Sydney, NSW 2052,\nAustralia\n\n2 Council of Scientific and Industrial Research-National\nEnvironmental Engineering Research Institute\n(CSIR-NEERI), Nehru Marg, Nagpur 440 020, India\n\n3 Commonwealth Scientific and Industrial Research\nOrganisation (CSIRO) and Australian National University,\nCanberra, ACT​ 2601, Australia\n\n\nthat by the year 2025, it will be in 5th position (Neo et al.\n2021). Therefore, recycling or upscaling, or reprocessing of\nPW has become the urgency to curb this mismanagement\nof plastics and mitigate the negative impacts of plastic consumption and utilization from the environment. However,\nthis resource has not been given the required attention it\ndeserves after post-consumer use. Recycling or reprocessing\nof PW usually involves 5 types of processes based on the\nquality of the product manufactured upon recycling of the\nwaste, namely upgrading, recycling (open or closed loop),\ndowngrading, waste-to-energy plants, and dumpsites or\nlandfilling, as shown in Fig. 1 (Chidepatil et al. 2020). Usually, the PW is converted into lower-quality products such\nas pellets or granules, or flakes which are further utilized in\nthe production of various finished products such as boards,\npots, mats, and furniture (Centre for Science and Environment (CSE) 2021).\nPlastics have a high calorific value, with polymer energy\nvarying from 62 to 108 MJ/kg (including feedstock energy)\nwhich is much greater than paper, wood, glass, or metals\n(with exception of aluminum) (Rafey and Siddiqui 2021).\n\nV l (0123456789)1 3\n\n\n-----\n\n**Fig. 1 Different processing**\npathways for plastic waste\n(modified from Chidepatil et al.\n2020)\n\nPW mishandling is a significant concern in developing\nnations like India due to its ineffective waste management\ncollection, segregation, treatment, and disposal which\naccounts for 71% of mishandled plastics in Asia (Neo et al.\n2021). Though there are numerous sources for PW the major\nfraction is derived from the post-consumer market which\ncomprises both plastic and non-PWs and therefore, these\nwastes require to be washed and segregated accordingly\nfor conversion into the homogenous mixture for recycling\n(Rafey and Siddiqui 2021). According to a study carried out\nby the Federation of Indian Chambers of Commerce and\nIndustry (FICCI) and Accenture (2020), India is assumed to\nlose over $133 billion of plastic material value over the coming next 10 years until 2030 owing to unsustainable packaging out of which almost 75% of the value, or $100 billion,\ncan be retrieved. This review article focuses on levers and\nstrategies that could be put in place to transition India toward\na circular economy for plastics. This involves two key areas,\nthe first being reprocessing infrastructure in various states\nof India and the performance of the reprocessors in organized and unorganized sectors. The second key area for this\nstudy is an overview of the rapidly evolving area of plastic\nrecycling technologies, including mechanical recycling,\nchemical recycling, depolymerization, biological recycling,\nand waste-to-energy approaches. A brief description of the\ntechnologies is provided and their applicability to the Indian\ncontext discussed along with the role of digitization in PW\nrecycling.\n\n## 1 3\n\n\n### Research motivation and scope of the article\n\nThe research on Indian PW and its recycling pathways\naccording to the polymer types and its associated fates were\nstudied along with the published retrospective and prospective studies. Due to COVID-19, there is an exponential\nincrease in the PW and the urge to recycle this waste has\nbecome a necessity. Systematic literature studies from database collection of Web of Science (WoS) were performed\nwith keywords such as “PW recycling technologies in India”\nOR “PW management in India” OR “plastic flow in India”\nfrom 2000 to October 2021 (including all the related documents such as review papers, research papers, and reports)\nwhich in total accounted for 2627 articles only. When the\nsame keyword “plastic recycling” was searched without\ncontext to India, 5428 articles were published from 2000\nto 2021 among which only 345 articles were published by\nIndian authors. Figure 2 shows the distribution of papers on\nPW and related articles over the years. However, the number\nof review articles remains very limited concerning published\nresearch papers and reports for the same. Review articles\nplay a vital role in the substantial growth in the potential\nresearch areas for the enhancement of the proper management strategies in the respective domains. Recently, PW\nand its sustainable management necessity toward achieving\na circular economy have attracted researchers, due to its detrimental effects on humans and the environment.\n\n\n-----\n\n**Fig. 2 Yearly distribution of**\npapers related to plastic waste\nrecycling from 2000 to October\n2021\n\n\n640\n\n600\n\n560\n\n520\n\n480\n\n440\n\n400\n\n360\n\n320\n\n280\n\n240\n\n200\n\n160\n\n120\n\n\n### Reprocessing infrastructure and recycling rates for different types of plastics\n\nRecycling rates of plastics vary between countries depending upon the types of plastic. Some polymers are recycled\nmore than other types of polymers due to their respective\ncharacteristics and limitations. While PET (category 1) and\nHDPE (high-density polyethylene) (category 2) are universally regarded as recyclable, PVC (polyvinyl chloride) (category 3) and PP (category 5) are classified as “frequently\nnot recyclable” owing to their chemical characteristics, however, they may be reprocessed locally depending on practical\nconditions. LDPE (low-density polyethylene) (category 4)\nis however difficult to recycle owing to stress failure, PS\n(category 6) may or may not be recyclable locally, and other\ntypes of polymers (category 7) are not recyclable due to the\nvariety of materials used in its manufacturing (CSE 2021).\nAbout 5.5 million metric tonnes of PW gets reprocessed/\nrecycled yearly in India, which is 60% of the total PW produced in the country where 70% of this waste is reprocessed\nin registered (formal) facilities, 20% by the informal sector\nand the rest 10% is recycled at household level (CSE 2020).\nThe remaining 40% of PW ends up being uncollected/littered, which further results in pollution (water and land) and\nchoking of drains (CSE 2019a). PW is dumped into landfills\nat a rate of 2.5 million tonnes per year, incinerated at a rate\nof over 1 million tonnes per year, and co-processed as an\nalternative energy source in blast furnaces at a rate of 0.25\nmillion tonnes per year by cement firms (Rafey and Siddiqui\n2021). Thermoset plastics (HDPE, PET, PVC, etc.), which\n\n\n110 119\n85 [102 ]86 87\n76\n66\n\n35 41\n1 5 8 5 4 7 4 11 14 11 15 22\n\nResearch Paper Review Articles\n\nare recyclable, constitute 94% of total PW generated, and\nthe remaining 6% comprises other types of plastics which\nare multilayered, thermocol, etc. and are non-recyclable\n(CSE 2019b). Plastics such as PP, PS, and LDPE are partially recyclable but generally not recycled in India due to\nthe economic unviability of their recycling processes (CSE\n2020). Figure 3a shows the recycling rates of different kinds\nof plastics in India and Fig. 3b shows the percentage contribution of different recycling options in the Indian context.\n\n#### State‑wise facilities and flows of PW\n\nThe total plastic generation in India by 35 states and union\nterritories accounts for 34,69,780 tonnes/annum (~ 3.47 million tonnes/annum) in the year 2019–2020 (CPCB (Central\nPollution Control Board) 2021). Plastic processing in India\nwas 8.3 Mt in the 2010 financial year and increased to 22 Mt\nin 2020 (Padgelwar et al. 2021). Table 1 shows the state-wise\nPW generation, registered and unregistered plastic manufacturing/recycling units, and multiplayer manufacturing units\nacross the country. Furthermore, the main recycling clusters\nin India are presented in Fig. 4, wherein Gujarat (Dhoraji,\nDaman and Vapi), Madhya Pradesh (Indore), Delhi and\nMaharashtra (Malegaon, Mumbai (Dharavi and Bhandup),\nSolapur) are the main recycling hubs (Plastindia Foundation\n2018). Recycling processes and disposal methods for PW\nvary substantially across the states in India given in Table 1.\nDetails of some of the major infrastructure available in the\nstates are described in the following subsection.\n\n## 1 3\n\n\n-----\n\n**Fig. 3 a Recycling rates of**\ndifferent types of plastics in **(a)** 2.4%\nIndia (data extracted from CSE 7.6%\n2019b) and b percentage contribution of different recycling\noptions in the Indian context\n(CSE 2021)\n\n25%\n\n20%\n\nPVC HDPE\n\nThe door-to-door collection of solid waste is the most\ncommon practice for the collection of waste in almost all the\nstates. Urban Local Bodies (ULBs) of some states like Goa,\nHimachal Pradesh, Maharashtra, Uttarakhand, and West\nBengal are actively involved in the collection and segregation of waste (CPCB 2019; Goa SPCB 2020; MPCB 2020).\nFurther after collection and segregation of waste, the PW is\nsent to various disposal (landfills) and recycling pathways\n(recycling through material recovery, road construction,\nwaste-to-energy plants, RDF (refused derived fuel), etc.).\nGoa is the state where new bailing stations have been set up\nin addition to the existing facilities for the disposal of PW\n(Goa SPCB 2020). State like Kerala has taken the initiative\nfor the installation of reverse vending machines (RVMs) for\nplastic bottles in supermarkets and malls whereas Maharashtra ensures 100% collection of waste with proper segregation and transport of PW where 62% of the waste is being\nreprocessed through different methods (Kerala SPCB 2020;\nMPCB 2020). Special Purpose Vehicles (SPVs) in Punjab\nhave been effective for the collection of multilayered plastics\n(MLP) waste from different cities of the state and further\nbeing sent to waste-to-energy plants (Punjab Pollution Control Board (PPCB) 2018). Though almost all the states have\nimposed a complete ban on plastic bottles and bags, Sikkim\nwas the first state who enforce the ban into the state which\nresulted in the reduction in its carbon footprint (MoHUA\n2019). Many states such as Puducherry, Odisha, Tamil Nadu,\nTelangana, Uttar Pradesh, and West Bengal send their PW\nfor reprocessing in cement kilns (CPCB 2019). Some states\nlike Telangana have taken the initiative for source segregation of the waste from the households by separating the\nbins into dry and wet waste bins whereas the mixed waste\nis sent for further processing for road construction or in\ncement industries (Telangana State Pollution Control Board\n\n## 1 3\n\n\n(TSPCB) 2018). Along with all these facilities in different\nstates, several informal and unregistered recyclers are also\ncontributing to their best to combat PW mismanagement.\n\n#### Formal and informal sectors in India and their performance\n\nThe informal sector currently contributes 70% of PET recycling in India (Aryan et al. 2019). Approximately 6.5 tonnes\nto 8.5 tonnes per day of PW is collected by itinerant waste\nbuyers (IWBs) and household waste collectors in India, out\nof which 50–80% of PW is recycled (Nandy et al. 2015).\nKumar et al. (2018) mentioned that the average PW collected\nby a waste picker and an IWB was approximately 19 kg/d\nand 53 kg/d, respectively. According to ENF (2021), there\nare approximately 230 formal PW reprocessors in India,\nwho can recycle various types of the polymer as shown in\nFig. 5. However, the organized and unorganized sectors play\na vital role in the reprocessing of plastics in India. Table 2\nshows the distribution of organized and unorganized sectors along with the percentage growth in India. Most of the\noperations are currently related to mechanical recycling producing granules/pellets and flakes. In 30 states/UTs, there\nare 4953 registered units with 3715 plastic manufacturers/\nproducers, 896 recyclers, 47 compostable manufacturing,\nand 295 multilayered packaging units however, 823 unregistered units have been reported from different states (CPCB\n2021). However, data on reprocessing capability (material\nprocessed in terms of tonnes/year) of the individual recyclers\nare not readily available. With the limited data, it varies from\n2500 to 3000 tonnes/year whereas capacity for processing\nvarious PW varies from 600 to 26,250 tonnes/year (ENF\n2021).\n\n\n-----\n\n**Table 1 Plastic generation, plastic manufacturing, and recycling units in different states in India and status of plastic recycling and disposal in**\ndifferent states\n\n\nPossible recycling and\ndisposal methods involved\n\n\nMultilayer\nmanufacturing\nunits\n\n\nStates/UT Plastic generation (tonnes/\nannum)\n\n\nRegistered plastic manu- Unregistered plastic\nfacturing/recycling units manufacturing/recycling\nunits\n\n\nAndaman and Nicobar 386.85 – – – Recycling, Road construction\nAndhra Pradesh 46,222 Manufacturing units— – – Recycling, Road construc131 tion, Co-processing in\nCompostable units—1 cement kilns\n\nArunachal Pradesh 2721.17 – – – No information\nAssam 24,970.88 Manufacturing units—18 – 5 Road construction, Coprocessing in cement\nkilns\nBihar 4134.631 Manufacturing/Recycling Producers—225 – No information\nunits—8 Brand owners—203\nRecyclers—36\n\nChandigarh 6746.36 Recycling units—7 – – RDF processing plant\nChhattisgarh 32,850 Manufacturing units—8 – – Recycling, Co-processing\nRecycling units—8 in cement kilns, Wasteto-energy plant\nDaman Diu & Dadra 1947.7 343 – – No information\nNagar Haveli\n\nDelhi 230,525 Producers—840 – – Waste-to-energy plant\nGoa 26,068.3 Manufacturing units—35 – 1 Recycling, Co-processing\nCompostable unit—1 in cement kilns, Sanitary landfills\nGujarat 408,201.08 Manufacturing/Recycling – 10 Co-processing in cement\nunits—1027 kilns\nCompostable units—12\n\nHaryana 147,733.51 Manufacturing units—69 – 28 Road construction\nCompostable unit—1\n\nHimachal Pradesh 13,683 No information 24 79 Road construction, Coprocessing in cement\nkilns, Waste-to-energy\nplants\nJammu & Kashmir 74,826.33 259 45 – No information\nJharkhand 51,454.53 Manufacturing units—59 – – Road construction, Coprocessing in cement\nkilns, Reverse Vending\nMachines\nKarnataka 296,380 Manufacturing/Recycling 91 – Recycling, Co-processing\nunits—163 plants\nKerala 131,400 Manufacturing units— – – Recycling\n1266\nProducers—82\nRecycling units—99\nCompostable unit—1\n\nLakshadweep 46 – – – Recycling\nMadhya Pradesh 121,079 Manufacturing and Recy- – 22 Recycling, Road construccling units—164 tion, Co-processing in\nCompostable unit—1 cement kilns\n\nMaharashtra 443,724 Recycling units—62 42 – No information\nCompostable manufacturing units—6\n\nManipur 8292.8 Manufacturing units—4 – – No information\nMeghalaya 1263 4 – – Road construction\nMizoram 7908.6 – – – Recycling\n\n## 1 3\n\n\n-----\n\n**Table 1 (continued)**\n\nStates/UT Plastic generation (tonnes/\nannum)\n\n\nPossible recycling and\ndisposal methods involved\n\n\nRegistered plastic manu- Unregistered plastic\nfacturing/recycling units manufacturing/recycling\nunits\n\n\nMultilayer\nmanufacturing\nunits\n\n\nNagaland 565 Manufacturing units—4 – – Recycling, Road construction\nOdisha 45,339 Manufacturing units—13 – 3 Co-processing in cement\nkilns\nPunjab 92,890.17 Manufacturing/Recycling 48 4 Recycling\nunits—187\nCompostable units—2\nMaterial Recovery Facility—169\n\nPuducherry 11,753 Manufacturing/Recycling – 4 Road construction, Counits—49 processing in cement\nCompostable unit—1 kilns\n\nRajasthan 51,965.5 Manufacturing units—69 – 16 No information\nSikkim 69.02 – – – No information\nTamil Nadu 431,472 Manufacturing units—78 – 3 Recycling, Road construcRecycling units—227 tion, Co-processing in\ncement kilns\nTelangana 233,654.7 Manufacturing/Recycling – 2 Recycling, Road construcunits—316 tion, Co-processing in\ncement kilns\nTripura 32.1 Manufacturing units—26 – 2 No information\nRecycling units—4\n\n\nUttarakhand 25,203.03 Manufacturing/Recycling\nunits—33\nCompostable units—2\n\n\n15 28 Recycling\n\n\nUttar Pradesh 161,147.5 Manufacturing units—99 23 63 Road construction, CoRecycling units—16 processing in cement\nCompostable units—4 kilns, Waste-to-energy\n\nplant, Production of fibers and raw materials\nWest Bengal 300,236.12 Manufacturing/Recycling – 9 Road construction\nunits—157\nCompostable unit—1\n\nData sources: (Central Pollution Control Board 2019; Central Pollution Control Board 2021; CSE 2020; Goa State Pollution Control Board\n2020; Tamil Nadu Pollution Control Board 2020; Haryana State Pollution Control Board 2020; Jammu and Kashmir State Pollution Control\nBoard 2018; Kerala State Pollution Control Board 2020; Maharashtra Pollution Control Board 2020; Uttarakhand Pollution Control Board 2019;\nUttar Pradesh Pollution Control Board 2021)\n\n\nIn the Indian context, the scale of operation and quantity of material handled by the formal sector is insignificant\nwhen compared to the informal sector (Nallathambi et al.\n2018). However, data on the contribution of the informal\nsector in PW recycling in India are very limited (Kumar\net al. 2018). Formal recycling is constrained to clean, separated, pre-consumer waste in a few places in India, even if\nthe states have efficient recycling technology and resources,\nas in Gujarat and Maharashtra (TERI 2021). At present, the\ntotal numbers of organized and unorganized recycling units\nin India are 3500 and 4000, respectively (Satapathy 2017).\nThe formal recyclers face challenges in providing supply\nsecurity for reprocessed plastic materials as the current\nsupply is dominated by informal recyclers (TERI 2021). In\n\n## 1 3\n\n\nrecovering consumer waste (including PW), the informal\nsector and households play a vital role in the waste collection; approximately 6.5–8.5 Mt of PW are collected by\nthese entities, which is about 50–80% of the plastic produced\n(Nandy et al. 2015). PW collection, dismantling, sorting,\nshredding and cleaning, compounding, extrusions (pellet\nmaking) and new product manufacturing are the key activities done by the informal sector PW supply chain in India\n(WBCSD 2017).\nAmong the formal recyclers, Banyan Nation has implemented a proprietary washing technology to remove ink\nand markings from PW in the mechanical recycling process\n(Banyan Nation 2020). The recycler has integrated plastic recycling technology with data intelligence (real-time\n\n\n-----\n\n**Fig. 4 Plastic recycling clusters in India (Plastindia Foundation 2018)**\n\n**Fig. 5 Number of reprocessors** 120\naccording to polymer types\n\n104\n\nin India (ENF 2021). (Abbreviations: ABS: Acrylonitrile 100\nbutadiene styrene; HIPS: High 86\nimpact polystyrene; LLDPE: 80\nLinear low-density polyethyl- 73\nene; PA: Polyamide; PBT: Poly- 64\nbutylene terephthalate; SAN: 60\nStyrene acrylonitrile; POM:\nPolyoxymethylene; PMMA:\nPoly(methyl methacrylate); 40\nTPE: Thermoplastic elastomer)\n\n\n## 1 3\n\n\n-----\n\n**Table 2 Distribution of organized and unorganized plastic recycling units in India (Plastindia Foundation 2019)**\n\nParameters 2018 report 2019 report Percentage growth\n\nNo. of organized recycling units 3500 100 − 93%\nNo. of unorganized recycling units 4000 10,000 60%\nDirect manpower 600,000 100,000 − 83%\nIndirect manpower (including ragpickers) 1 million 1–1.5 million 50% (concerning upper limit)\nAmount of plastic waste recycled 5.5 million metric 6 million metric tonnes 8.3%\ntonnes\n\n\nlocation of informal sector PW collectors and their capacity\nfor waste processing), which has enhanced its performance\nin high-quality waste collection and recycling (Banyan\nNation 2020). The informal sector is largely involved in\nrecycling PET bottles (mainly collection and segregation).\nHorizontal turbo washers and aglow machines are widely\nused in PE granule production by the informal sector (Aryan\net al. 2019). The Alliance of Indian Waste Pickers comprises 30 organizations in 24 cities of the country, working\nin collaboration with waste pickers, acknowledging their\ncontribution, and urging for them to be integrated into the\nwaste management system. For the informal sector, a proper\ncollection network, linking GPS (Global Positioning System) to points of segregation, and tracking vehicles should\nbe considered in a consolidated framework (Jyothsna and\nChakradhar 2020).\nThe organized/formal and unorganized/informal sectors\nare not discrete and do not vie for waste; instead, they are\ninterdependent and coherent as the formal recyclers can\noperate because the informal sector performs the onerous\ntask of conveying utilizable PW to the formal sector in the\nform of aggregates, pellets, flakes and, in a few instances,\neven the finished product. Since formal commodities are\nthe ones who purchase their final goods, the informal sector relies on the formal sector. Furthermore, the informal\nsector's financial capability and ability to invest in infrastructure and equipment to manufacture goods on their own\nare restricted and therefore both communities have a mutual\nrelationship (CSE 2021).\n\n### Overview on plastic recycling technologies and their applicability to India\n\nFrom waste to material recovery, PW recycling can broadly\nbe categorized into mechanical recycling, chemical recycling, biological recycling, and energy recovery (Al-Salem\net al. 2017). The most preferable type of recycling is primary\nrecycling because of its contamination-free feature which\nfurther facilitates a smaller number of operating units resulting in the optimal amount of consumption of energy supply and resources which is further followed by secondary\n\n## 1 3\n\n\nrecycling (mechanical recycling) for recycling PW (CSE\n2021). However, processing difficulties and the quality\nof recyclates are the main drivers for seeking alternative\napproaches (Ragaert et al. 2017). Comparatively, tertiary\nrecycling or chemical/feedstock recycling is a less favored\nalternative because of high production and operational\ncosts, as well as the lack of scalable commercial technology in India whereas quaternary recycling which involves\nenergy recovery, energy from waste, or valorization of PW,\nis least preferred due to uncertainty around propriety and\nprominence of the technology, and the negative potential\nto convert land-based pollution to water and air pollution,\nbut anyhow more preferable than dumping into the landfill\n(Satapathy 2017; CSE 2021). Figure 6 shows the categorization of the recycling process of PW.\n\n#### Recycling technologies\n\n**Mechanical recycling (MR)**\n\nMechanical recycling (also known as secondary, material\nrecycling, material recovery, or back-to-plastics recycling)\ninvolves physical processes (or treatments) that convert PW\ninto secondary plastic materials. It is a multistep process\ntypically involving collection, sorting, heat treatment with\nreforming, re-compounding with additives, and extruding\noperations to produce recycled material that can substitute\nfor virgin polymer (Ragaert et al. 2017; Faraca and Astrup\n2019). It is conventionally capable of handling only singlepolymer plastics, such as PVC, PET, PP, and PS. It remains\none of the dominant recycling techniques utilized for postconsumer plastic packaging waste (PlasticsEurope 2021).\nThere are various key approaches to sorting and separating\nPW for MR, including zig-zag separator (also known as an\nair classifier), air tabling, ballistic separator, dry and wet\ngravity separation (or sink-float tank), froth flotation, and\nelectrostatic separation (or triboelectric separation). There\nare also some newer sensor-based separation technologies\navailable for PW which include plastic color sorting and\nnear-infrared (NIR) (Ministry of Housing & Urban Affairs\n(MoHUA) 2019). Fig. S1 of the supplementary material\n\n\n-----\n\n**Fig. 6 Plastic waste flow and recycling categorization (Modified from FICCI 2016; Sikdar et al. 2020; Tong et al. 2020)**\n\n\nshows the overall mechanical reprocessing infrastructure\nfor plastics.\nAfter the collected plastics are sorted, they are melted\ndown directly and molded into new shapes or are re-granulated (with the granules then directly reused in the manufacturing of plastic products). In the re-granulation process,\nplastic is melted down after being shredded into flakes, then\nprocessed into granules (Dey et al. 2020).\nDegradation and heterogeneity of PW create significant\nchallenges for recyclers involved in mechanical recycling\nas in many cases, recycled plastics do not have the same\nmechanical properties as virgin materials and therefore,\nseveral challenges emerge while recycling mono and mixed\nPW. Furthermore, difficulties in developing novel technologies to remove volatile organic compounds to improve the\nquality of recycled plastics is one of the key technological\nchallenges in mechanical recycling (Cabanes et al. 2020).\nDifferent polymers degenerate under their specific characteristics such as oxidation, light and heat, ionic radiation,\nand hydrolysis where thermal–mechanical degradation and\ndegradation during lifetime are the two ways by which it\n\n\noccurs while recycling or reprocessing of PW (Ragaert et al.\n2017). Faraca and Astrup (2019) also state that models to\npredict plastic performance based on the physical, chemical, and technical characteristics of PW will be critical in\noptimizing these processes. Other than technical challenges,\nthe mechanical recycling process possesses social and economic challenges such as sorting of mixed plastics, lack of\ninvestments and legislation, and quality of recycled products\n(Payne et al. 2019).\n\n**Chemical recycling**\n\nChemical recycling, tertiary recycling, or feedstock recycling refers to the transformation of polymers into simple\nchemical structures (smaller constituent molecules) which\ncan be utilized in a diverse range of industrial applications\nand/or the production of petrochemicals and plastics (Bhagat\net al. 2016; Jyothsna and Chakradhar 2020). This type of\nrecycling directly involves fuel and chemical manufacturers\n(Bhagat et al. 2016). Pyrolysis, hydrogenation, and gasification are some of the chemical recycling processes (Singh\n\n## 1 3\n\n\n-----\n\nand Devi 2019). The food packaging sector could be the\nmain industry to utilize outputs from the chemical recycling\nprocess (BASF 2021).\nWhen molecules, combustible gases, and/or energy are\ngenerated in a thermal degradation process, molecules, combustible gases, and/or energy are generated as multi-stream\noutputs whereas layered and complex plastics, low-quality\nmixed plastics, and polluted plastics are all viable targets\nfor chemical/feedstock recycling (CSE 2021). From an\noperational standpoint, utilizing residual chars and no flue\ngas clean-up requirements are the main advantages, while\nfrom an environmental point of view, reduction in landfilling coupled with reduced GHGs (green-house gases) and\n­CO2 (carbon dioxide) emissions are added benefits. Ease of\nuse in electricity and heat production and easily marketed\nproducts are some of the financial advantages of pyrolysis\n(Al-Salem et al. 2010). Plasma pyrolysis is a state-of-the-art\ntechnology in which thermo-chemical properties are being\nintegrated with pyrolysis (MoHUA 2019). Fig. S2 of the\nsupplementary material shows the chemical valorization of\nwaste plastics. Although, cost and catalyst reuse capability in pyrolysis processes need further investigation (TERI\n2020). Due to high energy requirements and the low price of\npetrochemical feedstock compared to monomers developed\nfrom waste plastics, chemical recycling is not yet common\nat an industry scale (Schandl et al. 2020).\nProcessing of mixed waste remains a difficult task due to\nthe intricacy in the reactions where different types of polymers reflect completely distinct spectra following degradation pathways (Ragaert et al. 2017). The presence of PVC in\nthe waste stream possesses another problem due to its density and removal of hydrochloric acid (HCl) from products\nand thus resulting in incomplete segregation (Ragaert et al.\n2017). Other than this, lack of stable waste supply, suitable\nreactor technology, and presence of inorganics in the waste\nstream possess challenges in the chemical recycling of the\nplastics (Payne et al. 2019). Lack of investments, production\nof by-products and metal-based catalysts systems contribute\nto other significant difficulties in the chemical valorization\nof waste plastics (Cabanes et al. 2020; Kubowicz and Booth\n2017).\n\n**Depolymerization** Depolymerization of the plastics is\nthe result of chemical processing where various monomer\nunits are recovered which can be reused for the production\nof new plastics manufacturing or conversion into their raw\nmonomeric forms through processes such as hydrolysis,\nglycolysis, and alcoholysis (Bhandari et al. 2021; Mohanty\net al. 2021). This process is often used to recover monomers from a recoverable resin's grade to that of virgin resin\nsuch as PET, polyamides such as nylons, and polyurethanes\nwith excellent results, as well as the possibility to restore a\nsignificant resource from commodities that are difficult to\n\n## 1 3\n\n\nrecycle commercially (MoHUA 2019). This is the process\nby which the plastic polymers are converted into sulfur-free\nliquid power sources through chemical recycling where\nthese power sources facilitate energy recovery from PWs\n(Bhandari et al. 2021). According to the studies carried out\non depolymerization of mixed waste plastics, it has been\nreported that even a small quantity, for instance, 1 mg of\nthese plastics can yield 4.5 to 5.9 cal of energy with a little\namount of energy consumption of 0.8–1 kWh/h and therefore, this process can yield additional convenience for the\nhigh-quality recycling which is recently being used for the\nPET (Bhandari et al. 2021; Ellen MacArthur Foundation\n2017; Wołosiewicz-Głąb et al. 2017). In the anoxic conditions and the presence of specific catalytic additives, the\ndepolymerization is accomplished in a specially modified\nreactor where 350 °C is the highest reaction temperature\nwhich is converted to either liquid RDF or different gases\n(reutilized as fuel) and solids (reutilized as fuel in cement\nkilns) (MoHUA 2019).\n\n**Energy recovery** Gasification of PW is performed via reaction with a gasifying agent (e.g., steam, oxygen, and air) at\nhigh temperatures (approximately 500–1300 °C) to produce\nsynthetic gas or syngas. This can subsequently be utilized\nfor the production of many products, or as fuel to generate electricity, with outputs of a gaseous mixture of carbon\nmonoxide (CO), hydrogen ­(H2), carbon dioxide ­(CO2),\nand methane ­(CH4) via partial oxidation (Heidenreich and\nFoscolo 2015; Saebea et al. 2020). The amount of energy\nderived from this process is affected by the calorific input of\nPW where polyolefins tend to display higher calorific values. Table 3 shows calorific values of various plastic polymers and conventional fuels for comparison. Due to flexibil\n**Table 3 The calorific value of popular plastics and conventional fuels**\n(Zhang et al. 2021)\n\nFuel Calorific\nvalue (MJ/\nkg)\n\nPolyethylene 43.3–47.7\nPolypropylene 42.6–46.5\nPolystyrene 41.6–43.7\nPolyvinyl chloride 18.0–19.0\nPolyethylene terephthalate 21.6–24.2\nPolyamide 31.4\nPolyurethane foam 31.6\nMethane 53\nGasoline 46\nKerosene 46.5\nPetroleum 42.3\nHeavy oil 42.5\nHousehold plastic solid waste mixture 31.8\n\n\n-----\n\nity, robustness, and advantageous economics, gasification\nalong with pyrolysis is a leading technology for chemical\nrecycling. Characterization of PW is essential for developing optimal process design, particularly for HDPE, LDPE,\nPP, PS, PVC, and PET (Dogu et al. 2021). CSIR-IIP, India\n(Council of Scientific and Industrial Research-Indian Institute of Petroleum) and GAIL, India (Gas Authority of India\nLtd.) in collaboration, have been successful in producing\nfuel and chemicals from PW where PE and PP plastics have\nbeen converted to diesel, petrochemicals, and gasoline. 1 kg\nof these plastics can yield 850 ml of diesel, 500 ml of petrochemicals, and 700 ml of gasoline, along with LPG (CSIRIIP 2018) where the process ensures 100% conversion with\nno toxic emissions and is suitable for both small- and largescale industries (CSIR-IIP 2018).\n\n**Biological recycling**\n\nBiological recycling or organic recycling involves the breaking of PW with the intervention of microorganisms such as\nbacteria, fungus, or algae to produce biogas ­(CO2 for aerobic\nprocesses and ­CH4 for anaerobic processes). PW may be\nrecycled biologically through two methods namely aerobic\ncomposting and anaerobic digestion (Singh and Ruj 2015).\nAn enzymatic approach for biodegradation of PET is considered an economically viable recycling method (Koshti et al.\n2018). Table S1 in the supplementary data shows microorganisms responsible for the PW degradation process which\ncould be utilized in the biological recycling process. Blank\net al. 2020 reported that non-degradable plastics such as\nPET, polyethylene (PE), and polystyrene (PS) can be converted to biodegradable components such as polyhydroxyalkanoates (PHA) using a combination of pyrolysis and\nmicrobiology, which is an unconventional route to a circular\neconomy. Polyaromatic hydrocarbons, polyhydroxy valerate\n(PHV) and polyhydroxyalkanoate (PHH), polylactide (PLA),\nand other aliphatic polyesters are biodegradable, whereas\nmany aromatic polyesters are highly impervious to microbial\nassault (Singh and Ruj 2015). Fig. S3 of supplementary data\nshows an overview of the biodegradation of plastics.\nOxo-degradable plastics which is one of the major classes\nof bioplastics that possess challenges due to rapid breakage\ninto microplastics when conditions (sunlight and oxygen)\nare favorable (Kubowicz and Booth 2017). The behavior of\nspecific polymers interrupts their degradation into monomers due to which the microbial activity is ineffective for\nnon-hydrolyzable manufactured polymers as the activity of\nthe microorganisms responsible for the degradation differs\nconcerning the environmental conditions (Ali et al. 2021).\nOther challenges include the consumption of energy for\nrecycling and time for degradation of the generated microplastics along with socioeconomic challenges such as more\ntime and capital investment and lack of resources (Kubowicz\n\n\nand Booth 2017). Collection and separation of bio-PW and\na lack of effective policy contribute to some other barriers\nrelated to bio-based polymers and recycling.\n\n### Techno‑economic feasibility of different recycling techniques\n\nThe techno-economic feasibility study provides a medium\nto analyze the utilization (raw materials, resources, energy,\netc.) and end-of-life trail for different recovery pathways\nfor the conversion of PW by qualitative and quantitative\napproaches in technical and financial aspects (Briassoulis\net al. 2021a). The association of technical and economic\nprospects of reprocessing technologies and related products’\nmarket tends to have a compelling impact on the formation\nof policies to reduce PW. Hence, the techno-economic feasibility study is essential for the effective management of\nPW. The disparity in melting points and treatment technologies contributes to the major challenge for the recycling of\nmixed/multilayered plastic packaging waste which affects\nthe quality of the recycled product (Larrain et al. 2021).\nTable 4 shows different parameters for techno-economic\nfeasibility for recycling technologies. Though techno-economic feasibility study facilitates the understanding inadequacy prevails in terms of sustainability. This is overcome\nby Techno-Economic Sustainability Analysis (TESA) which\nstudies alternative methods for feedstock alteration, common\nenvironmental criteria (such as mass recovery efficiency, the\nimpact of additives, and emissions from recycling facility),\nand pathways for recycling and end-of-life of plastic products (Briassoulis et al. 2021b).\n\n### Utilization of PW and recycled products in India and contribution of major players toward plastic sustainability\n\nPost-consumer PW can be utilized to produce several products after recycling, such as laying roads, use in cement\nkilns, pavement blocks, tiles, bricks, boards, and clothes.\nDue to good binding properties, when PW is in a hightemperature molten state, it can be utilized in road laying (Rokade 2012). Mixing PP and LDPE in bituminous\nconcrete significantly increases the durability and fatigue\nresistance of roads (Bhattacharya et al. 2018). Various\nindustries based in different locations of the country utilizes PP, HDPE, and LDPE waste plastics to produce reprocessed granules and further use them in the production of\nchairs, benches, dustbins, flowerpots, plastic pellets, mobile\nstands, etc. Few informal recyclers produce eco-friendly\nt-shirts and napkins from PET waste bottles whereas some\nrecyclers convert PW to office accessories, furniture, and\n\n## 1 3\n\n\n-----\n\n**Table 4 Techno-economic feasibility parameters for recycling technologies (Briassoulis et al. 2021a; CSE 2021; ElQuliti 2016; Fivga and Dimi-**\ntriou 2018; Ghodrat et al. 2019; Larrain et al. 2021; NITI Aayog- UNDP 2021; Singh and Ruj 2015; Volk et al. 2021)\n\nFeasibility parameters Mechanical Chemical Biological for bioplastic\n\n\nTECHNOLOGICAL Type of polymer PET, HDPE, LDPE, PET, PP, PVC, PE, PS,\nlaminated plastics, lowquality mixed plastics\n\nEnergy requirements 300–500 kW/month for 1200–1500 kW for\n30–50 tonnes/month 80–100 kg PW/hour\n(depends on type of technology and polymer type)\n\nTemperature requirement 100–250 °C Pyrolysis—300–900 °C\nPlasma pyrolysis—1730–9730 °C\nGasification—500–1300 °C\n\n\nBio-PET, bio-PE, bio-PP, etc.\n\n40 TJ–1500 TJ (terajoule)\n\n130–150 °C\n\n\nBiodegradability Non-biodegradable Non-biodegradable Mostly biodegradable (PHA,\nPHV, PHH, PLA)\nRaw materials cost Rs. 6–40/kg Rs. 6–40/kg Rs. 10–30/kg\nECONOMICAL Quality of processed materi- Depending on polymer type Depend on type of technol- High-quality compostable\nals ogy and polymer type bio-polymer\nCost of recyclates Rs. 20–150/kg (depends on Rs. 20–40/l (diesel/fuel) Oxo-degradable plastics—Rs.\ntype of polymers and qual- 90–120/kg Biodegradable\nity of recycled products) films/bags—Rs. 400–500/kg\n\nRecycling facilities in India 7000–10,000 15–25 5–10\n(units)\n\nCost requirements (Operat- 50–60 lakhs/annum 50–65 lakhs for 1 TPD 1–2 crores/annum\ning and capital costs) (tonnes per day) plant\n\n\ndecorative garden items. Recycle India Hyderabad, in 2015,\nbuilt houses, shelter bus stops, and water tanks with PW bottles. Further, under this initiative, thousands of chips packets\nwere weaved into ropes, tied to metal frames, and used to\ncreate dining tables. Shayna Ecounified Ltd., Delhi, with the\nCSIR-National Physical Laboratory, Delhi, converted 340\ntonnes of HDPE, LDPE, and PP waste plastics to 11 lakh\ntiles and has commercialized them to other cities such as\nHyderabad, and companies such as L’Oréal International\nand Tata Motors. Further, few recyclers convert PW such as\nmilk pouches, oil containers, shower curtains, and household plastics to poly-fuel (a mixture of diesel, petrol, etc.).\nFew of them collect PET waste and recycle it into clothes,\nautomotive parts, battery cases, cans, carpets, etc. There are\nseveral other non-government organizations (NGOs), companies, and start-ups that are involved in the recycling of PW\nand its conversion to different types of products, even after\npost-consumer use.\nUsing shredded PW, in 2015–16, the National Rural\nRoad Development Agency laid around 7,500 km of roads\nin India. In 2002, Jambulingam Street in Chennai was constructed as the first plastic road in India (TERI 2018). Plastic\nfibers can replace common steel fibers for reinforcement.\nFire-retardant composites with a wide scope of applications\ncould be developed by blending recycled plastics with fly\nash (TERI 2020). HDPE, PVC, LDPE, PP, and PS have\n\n## 1 3\n\n\nyielded conflicting performance measures, which require\nfurther investigation into the performance of the pavement,\nmethods of improving compatibilization between plastic and\nasphalt, and economic and environmental implications of\nthe process.\nFor the reduction in packing, costs and rising issues\nrelated to PW and packaging, FMCGs (fast-moving consumer goods) industries have teamed up with the Packaging\nAssociation of Clean Environment (PACE), have primarily emphasized immediate benefits including a reduction in\nsize and resource consumption where these changes have\npromoted the usage of flexible packaging and pouches over\nrigid packaging forms. Major FMCG companies like Hindustan Unilever (HUL), Nestlé, and P&G have assured that\nthey will reduce the use of virgin plastics in packaging to\nhalf the amount by the year 2025 (PRI 2021). To promote\nthe utilization of recycled plastics, HUL incorporated recycled PET and recycled HDPE in the manufacturing of personal care products (Condillac and Laul 2020). Other companies like L’Oréal and Henkel had successfully eliminated\nPVC in 2018 along with the reduced use of cellophane to\n5.5% in 2019 and reduction in the utilization of carbon black\npackaging to make carbon-free toilet cleaners, respectively\n(PRI 2021). Beverage companies like PepsiCo, Coca-Cola\nIndia, and Bisleri which use a large quantity of PET bottles,\nhave collaborated with several recyclers to upcycle the PW\n\n\n-----\n\nproducts for the production of new recycled utilities such as\nclothes and bags (Condillac and Laul 2020). Similarly, other\ncompanies like Marico and Dabur are also actively involved\nin reducing the use of virgin plastics in its packaging and for\nthe implementation of a recycling initiative where Marico in\ncollaboration with Big Bazaar is providing incentives to the\ncustomers for dropping their used plastic bottles in the stores\nand Dabur is also competing in the race to become among\nfirst Indian FMCG company to be plastic-free (Condillac and\nLaul 2020). On the other side, apart from taking initiatives\nby various FMCG companies, a lot of efforts is being done\nfor the innovation toward plastic-free packaging materials\nand therefore, Manjushree Technopack (Bengaluru, India)\nlaunched its first plant for the production of post-consumer\nrecycled polymer up to 6000 metric tonnes/year to these\nindustries. Other than this, Packmile, a packaging company\nis producing no plastic alternative such as kraft paper (which\nis biodegradable and recyclable) for Amazon India (Condillac and Laul 2020).\n\n### Role of digitization in PW recycling\n\nAs the amount of waste is increasing by each successive\nyear, technology-driven methods can be established for\ncommunities to reduce, reuse and recycle PW in an ecofriendly manner. In light of this, Recykal (in south Indian\ncity Hyderabad), a digital technology firm developed an\nend-to-end, cloud-based fully automated digital solution\nfor efficient waste management by tracking waste collection\nand promoting recycling of non-biodegradable. Its services\nassist in the formation of a cross-value channel coalition and\nthe connection of various stakeholders such as waste generators (commercial and domestic users), waste collectors,\nand recyclers, assuring that transactions between the organizations with 100% transparency and accessibility (Bhadra\nand Mishra 2021). The quantities of waste received per day\nhave risen from 20 to 30 kg in the months following to over\n10,000 to 15,000 kg recently and offer incentives based on\nthe quality of recycled products (Bhadra and Mishra 2021).\nOne such Android-based application is proposed and developed by Singhal et al. (2021), for efficient collection by pickup or drop facility incorporated in the software. Segregation,\nas well as methods for recycling different types of plastics,\nare also suggested and in return, the users are rewarded with\nthe e-coupons accordingly (Singhal et al. 2021).\nFor improvement in plastic recycling, a variety of techniques have been used and blockchain is one among them,\nand it holds promise for enhancing plastic recycling and the\ncircular economy (CE). A distributed ledger, or blockchain,\nis made up of certain immutable ordered blocks which prove\nto be an excellent approach to commence all of their customers' transactions under the same technology (Khadke et al.\n\n\n2021). One such approach is the introduction of Swachhcoin for the management of household and industrial waste,\nand their conversion into usable high-value recoverable\ngoods such as paper, steel, wood, metals, and electricity\nwith efficient and environmentally friendly technologies\n(Gopalakrishnan and Ramaguru 2019). This is a Decentralised Autonomous Organization (DAO) that is controlled unilaterally via blockchain networks which utilize a combination of techniques such as multi-sensor driven AI to establish\nan incremental and iterative chain that relies on information\ntransferred between multiple ecosystem players, analyzes\nthese inputs, and offers significant recommendations based\non descriptive algorithms which will eventually make the\nsystem entirely self-contained, economical, and profitable\n(Gopalakrishnan and Ramaguru 2019). The purpose of AI in\nthis multi-sensor infrastructure purpose is to limit unpredictability and facilitate efficient and reliable separation by training the system to identify and distinguish them appropriately\n(Chidepatil et al. 2020). Most businesses favor blockchain\ntechnology because of its decentralized architecture and low\ntrading costs along with the associated benefits of accessibility, availability, and tamper-proof structures (Khadke et al.\n2021; Wong et al. 2021).\n\n### Discussion\n\nIndia is a major player in global plastic production and manufacturing. Technology, current infrastructure, and upcoming strategies by the Indian government are combined to\nprovide detailed suggestions for policymakers and researchers in the area of achieving a circular economy. The most\nimportant barrier in Indian PW management is the lack of\nsource segregation of the waste. As in many other countries, mechanical recycling is the leading recycling route for\nIndia’s rigid plastics. The influence of thermomechanical\ndeterioration should be avoided to get high-quality recycled\nmaterial with acceptable characteristics. The development\nof advanced quality measurement techniques for technology\nsuch as nondestructive, cost-effective methods to assess the\nchemical structure and mechanical performance could be\nkey to overcoming the obstructions. For instance, the performance of MR can be partially improved through simple\npackaging design improvements, such as the use of a single polymer instead of a multilayer structure. Furthermore,\nPS and PVC could be replaced with PP for the packaging\nfilm market. There are also issues with depolymerization\nselectivity and activity, ability, and performance trade-offs\nthat may need to be addressed before these methods have\nwide applicability. Based on our assessments, Indian policymakers should consider PET, polyamide 6 (PA 6), thermosetting resins, multilayer plastic packaging, PE, PS, PP,\nand fiber-reinforced composites for chemical recycling.\n\n## 1 3\n\n\n-----\n\nAs chemical recycling is innovation-intensive, assessing\neconomic feasibility is the main challenge for developing\ncountries like India. Overall, PUR, nylon, and PET appear\nmost competitive for chemical recycling. The more problematic mixed waste streams from multilayer packaging could\nbe more suited for pyrolysis along with PE, PP, PS, PTFE\n(polytetrafluoroethylene), PA, and PMMA (poly(methyl\nmethacrylate)). Substantial investment is required for\nhydrocracking which can deal with mixed plastics. Better\nguidance on the correct chemical recycling technology for\neach Indian PW stream may require technology readiness\nlevel (TRL) assessments as proposed by Solis and Silveira\n(2020), which require an increased number of projects and\ndata available on the (chemical) process optimization. Compared to conventional fossil fuel energy sources, PE, PP,\nand PS are the three main polymers with higher calorific\nvalue, making them suitable for energy production. There\nare some challenges, however, with this technology, such\nas the identification of specific optimal biodiesel product\nproperties which can be addressed using techniques such as\nLCA (life cycle assessment) and energy-based analysis. As\nthe practical module of the Indian PW management rules\nexplicitly shows the route to oil production from waste, this\nmay indicate a focus on this technology for the country in\nthe future as chemical recycling accounts for only 0.83%\n(as shown in Fig. 3b) among all the recycling technologies.\nAlthough a relatively high cost is associated with bio-polymers at present, it is expected that production costs will\nreduce due to economies of scale in the coming years. There\nare already numerous bioplastic food packaging materials\nin the market. Since food packaging constitutes a large portion of PW in India, a significant impact could be made for\nthe country if it is switched to more sustainable bio-based\npolymers. In India, the J&K Agro Industries Development\nCorporation Ltd, in collaboration with Earth soul, has\nintroduced the first bioplastic product manufacturing facility, with 960 tonnes per year production capacity whereas\nTruegreen (Ahmedabad) can manufacture 5000 tonnes per\nyear. Some of the major manufacturing plants in India are\nBiotech bags (Tamil Nadu), Ravi Industries (Maharashtra),\nEcolife (Chennai). Recently, plant-based bio-polymer has\nbeen introduced by an Indian company named Hi-Tech\nInternational (Ludhiana) to replace single-use and multi-use\nplastic products such as cups, bottles, and straws, which is\nIndia’s only compostable plastic which implies that plastics\nproduced from this bio-polymer will initiate its degeneration within 3–4 months and can completely disintegrate after\n6 months and also, a biodegradable plastic made is converted\nto carbon dioxide and the remaining constituents transforms\ninto water and biomass (Chowdhary 2021). However, there\nare several challenges associated with this technology.\nImprovements are required to sort bioplastic from other PW\ntypes to avoid waste stream contamination. There is also a\n\n## 1 3\n\n\nneed for optimization of anaerobic digestion parameters to\nensure the complete degradation of these materials. From\nthe Indian perspective, feedstock type with their respective\ninfrastructure availability and interactions between sustainability domains is critical for policymaking issues as most of\nthe recycling sectors are operated by informal sector workers. Commercialization of laboratory-based pyrolysis and\ngasification of bioplastic streams should be developed. Due\nto contaminated collection, there is limited recyclability in\nother PW streams, which should be considered as part of\nbio-based PW management. Though India recycles 60% of\nthe total waste generated and its recycling methods are quite\neffective in solving the problem of increasing PW in India,\nthere are still some major challenges or barriers linked with\nit. For more efficient management of all the PW produced,\nstakeholders need to understand and tackle the challenges\nfaced to curb plastic pollution in the country. Different types\nof recycling technologies have their respective associated\nchallenges and barriers (including technological and social)\nwhich need to be addressed as mentioned in Table S2 of the\nsupplementary data.\nRecycled plastics and the products made from these plastics are often expensive from the virgin plastics and therefore\ncompete for their place in the market. The reason behind this\nis the easy availability of raw materials (which are waste\nfrom the petroleum industry) for the production of virgin\nplastics. Other than this, even after mentioning that 60% of\nthe PW is being recycled, a massive amount of this waste\nis found littered and unrecycled in the environment which\ncontradicts the percentage of recycling as there is a lack of\nrelevant and accurate data for the same. Furthermore, Goods\nand Services Tax (GST) also plays a vital role to build market linkages between recycled and virgin products as the\navailability of recycled products is sporadic, the revenue\nor business model tends to collapse for these products and\naffects the recyclers if the PW is being exported where the\nGST rates decreased to 5% from 18% in 2017 (CSE 2021).\nThe increased input costs due to GST and customs taxes are\nbeing transferred to secondary waste collectors by lowering\nthe cost of recycled plastics. For instance, PET bottles were\nRs. 20/kg before GST came in which decreased to Rs. 12/\nkg after GST imposition, milk packets price varied from Rs\n12/kg to Rs 8/kg and similarly, the cost of HDPE dropped by\n30% post-GST (CSE 2021). With the introduction of GST in\nthe plastic value and supply chain, the informal sectors are\nfacing huge losses due to the availability of scrap at cheaper\ncosts. Therefore, the current GST structure has affected the\nmost fragile and vulnerable section of the plastic supply\nvalue chain.\nEnormous studies have been carried out related to different techniques for recycling for various types of polymers,\nvery limited research is available on the techno-economic\nfeasibility of these technologies and therefore, this could\n\n\n-----\n\nprovide a wide scope for the relevant research in India.\nOther than this feasibility study, there is a broad range of\nopportunities and possibilities to explore and analyze the\ntechnologies in India concerning sustainability (involving\nenvironmental and social parameters) through TESA.\nSeveral published reports claim that India recycles 60% of\nthe total PW generated annually which is the highest among\nother countries such as Germany and Austria with more than\n50% recycling. India’s recycling is mostly contributed by the\ninformal sectors but has not been documented accurately by\nthe governing bodies of the country. Moreover, information\non the recycling rate of 60% varies with different sources\nand creates disparity and ambiguity of the data. As depicted\nin Fig. 3b, India recycles 94.17% of waste plastics through\nmechanical recycling, while 0.93% is chemical or feedstock\nrecycling and 5% for energy recovery and alternative uses\nsuch as making roads, boards, and tiles. Compared with\nchemical recycling, mechanical recycling is the most popular technique due to ease of operation and low-cost expenditure as compared to feedstock or chemical recycling in which\nhigh finances and operational costs are involved along with\nthe lack of availability to ascendable technology. Landfill\ndumping is sometimes favored due to improper segregation\nof waste and ease of operation by agencies employed by\nULBs. Other than mechanical and chemical recycling, bioplastics are the emerging alternative for PW in India but lag\ndue to improper legislation, high cost, and unawareness of\nthe segregation of these types of plastics. This can be facilitated if eco-labeling and a proper coding system are introduced. Though these recycling technologies are widely used\nfor reprocessing the PW, elimination of plastics from the\nenvironment is still a far-fetched dream and merely adds a\nfew more years into the end-of-life of the plastics. Therefore,\nthere is a need for affirmative legislation and strict guidelines for the use of recycled products and the exploration of\nalternatives in different sectors. Active involvement of the\ninformal sectors and inclusive growth can be ensured as their\nlivelihood is dependent on PW.\n\n### Conclusion\n\nThe circular economy is a regenerative model which requires\nthe participation of accountable stakeholders. There should\nbe continuous interaction among stakeholders to share current practices dealing with PW as part of the plastic economy. It was found that there was incomplete and indistinct\nreporting on PW generation from individual states. Information exchange via technology application should eventually\nbe an integral part of the PW management value chain. Thus,\ngeneration estimation is an essential task to set targets for\nresource recovery and recycling, which connects the “global\ncommitment” element of the circular plastic economy and\n\n\nwaste minimization. Being part of the global commitment\nto “reducing, circulating and innovating” under the “plastic pact,” a national target could be set and a mechanism is\ndeveloped. In setting a national target, the “dialogue mechanism” would further invigorate inter-and multidisciplinary\nresearch and policy directions. Consumer behavior is an\nessential task as the end-users share equal responsibilities\nas the producer circular economy. Waste management is\na complex multi-actor-based operational system built on\nknowledge, technologies, and experience from a range of\nsectors, including the informal sector. Indigenous innovation\nand research at a regional scale, such as in Gujarat, Andhra\nPradesh, and Kerala, has set an example of a circular plastic\neconomy and would help in developing a further regional\ncircular plastic economy. Efficient recycling of mixed PW\nis an emerging challenge in the Indian recycling sector. As\nplastic downcycling and recycling is an energy-intensive\nprocess, energy supply from renewable energy sources such\nas solar and wind energy can potentially reduce the ­CO2\nemissions produced. The recovery and recycling of substantial volumes of PW need emerging technological and\nspecialized equipment, which in turn necessitates a considerable capital investment. Informal sectors being prominent in\nwaste management may be deprived of recognition, technology, and scientific understanding but their skills, knowledge,\nand experience can be utilized in the value chain of plastic\nflow. Also, there is a need to formalize the informal sectors\nwith proper incentivization and other benefits as they play\na major role in plastic flow in India. Additionally, there are\nno policies or rules for the treatment of the residues from the\nresult of recycling technologies and their production units,\nwhich needs to be addressed as the number of waste residues\ndepends on the quantum of waste and technique incorporated. Universities, research organizations, and most importantly, polymer manufacturers and most important policymakers should collaborate in renewable energy integration\nand process optimization.\nFurther detailed assessment using LCA should be performed in this regard to identify the optimized solutions.\nExtended producer responsibility (EPR) and other policy\nmechanisms would be integrated sooner or later; however,\none of the fundamental aspects is being part of the circular economy. Although segmented, it is believed that the\ninformal sector is very innovative, and they could also be\ntechnologically enabled. New app development and PW\ncollection campaigns through digitalization could increase\nnon-contaminated sources of PW. Specific manufacturing\nsectors such as flexible packaging, automobiles, electrical,\nand electronics should look at the plastic problem through\nthe lens of resource efficiency and climate change ­(CO2 and\nGHGs) perspectives. The sectors should develop innovative solutions so that recycled plastics can be re-circulated\nwithin the sectors where they will be the leading consumer.\n\n## 1 3\n\n\n-----\n\nThough there are a lot of available data on different types\nof recycling of plastics and the state-wise flow of plastics\nthere is no proper information on different types of plastic polymers and their respective flow in the value chain in\ndifferent states/UTs. There is a need for the fortification of\nrecycling different technologies for different polymers and\nfor this purpose, the multi-sensor-based AI and blockchain\ntechnology can prove effective in segregation and recycling\nof the PW in a more environmentally friendly manner which\nshould be implemented in all parts of the country for efficient PW management. Furthermore, the amount of PW can\nonly be controlled by the replacement of new virgin plastics\nand existing plastics with the desired recycled plastics along\nwith citizen sensitization. Overall, for a circular plastic economy in India, there is a necessity for a technology-enabled,\naccountable quality-assured collaborative supply chain of\nvirgin and recycled material.\n\n**Supplementary Information The online version contains supplemen-**\n[tary material available at https://​doi.​org/​10.​1007/​s13762-​022-​04079-x.](https://doi.org/10.1007/s13762-022-04079-x)\n\n**Acknowledgments The authors wish to thank all who assisted in con-**\nducting this work.\n\n**Author contributions All the authors contributed to the study concep-**\ntion and design. Conceptualization and writing of the draft were done\nby Riya Shanker, Dr. Debishree Khan, Dr. Rumana Hossain, Anirban\nGhose, and Md Tasbirul Islam. The draft was revised and edited by\nKatherine Locock with the supervision of Dr. Heinz Schandl, Dr. Rita\nDhodapkar, and Dr. Veena Sahajwalla. All the authors have read and\napproved the final manuscript.\n\n**Funding The authors acknowledge project funding for “India – Aus-**\ntralia Industry and Research\nCollaboration for Reducing Plastic Waste” from CSIRO, Australia,\nthrough contract agreement.\n\n#### Declarations\n\n**Conflict of interest The authors declared that they have no conflict of**\ninterest.\n\n**Ethical approval There is no ethical approval required.**\n\n### References\n\nAl-Salem SM, Antelava A, Constantinou A, Manos G, Dutta A (2017)\nA review on thermal and catalytic pyrolysis of plastic solid waste\n[(PSW). J Environ Manag 197:177–198. https://​doi.​org/​10.​1016/j.​](https://doi.org/10.1016/j.jenvman.2017.03.084)\n[jenvm​an.​2017.​03.​084](https://doi.org/10.1016/j.jenvman.2017.03.084)\n\nAl-Salem SM, Lettieri P, Baeyens J (2010) The valorization of plastic solid waste (PSW) by primary to quaternary routes: From\nre-use to energy and chemicals. 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ERROR: type should be string, got "https://doi.org/10.1007/s12063 022 00262 y\n\n# Drivers, barriers and supply chain variables influencing the adoption of the blockchain to support traceability along fashion supply chains\n\n**Antonella Moretto[1] · Laura Macchion[2]**\n\n\nReceived: 18 February 2021 / Revised: 4 February 2022 / Accepted: 25 February 2022\n© The Author(s) 2022, corrected publication 2022\n\n\n/ Published online: 16 March 2022\n\n\n**Abstract**\nThe critical role of blockchain technology in ensuring a proper level of traceability and visibility along supply chains is\nincreasingly being explored in the literature. This critical examination must focus on the factors that either encourage or\nhinder (i.e. the drivers or barriers) the implementation of this technology in extended supply chains. On the assumption that\nthe blockchain will need to be adopted at the supply chain level, the enabling factors and the contingent variables of different supply chains must be identified and analysed. The appropriate identification of supply chain partners is becoming a\ncritical factor of success since the globalization of supply chains makes their management and control increasingly difficult.\nThis is particularly true of the fashion industry. Five blockchain providers and seven focal companies working in the fashion\nindustry were interviewed to compare their different viewpoints on this topic. The results highlight which drivers, barriers,\nand supply chain variables impact the implementation of the blockchain and specific research propositions are formulated.\n\n**Keywords Traceability · Blockchain · Fashion**\n\n\n### 1 Introduction\n\nSupply chains today are incredibly complex, comprising\nmulti-echelon and geographically dispersed companies.\nGlobalization, different international regulations, and varied cultural and human behaviors worldwide are all challenges to managing companies through their supply chains.\nThese evolutionary phenomena have made it arduous to\nacquire relevant and trustworthy information within supply\nchains and have dramatically increased the potential for\ninefficient transactions, fraud, pilferage, or simply a deterioration in supply chain performance (Hastig and Sodhi\n2020).\nThe urgent need for traceability of both product and process in supply chains has been documented in several industries, including the agri-food sector (Sun and Wang 2019;\nYadav et al. 2020; Mukherjee et al. 2021), pharmaceutical\n\n- Laura Macchion\nlaura.macchion@unipd.it\n\n1 Department of Management, Economics and Industrial\nEngineering, Politecnico Di Milano, Piazza Leonardo da\nVinci, 32 ‑ 20133 Milano, Italy\n\n2 Department of Engineering and Management, University\nof Padova, Stradella San Nicola, 3 ‑ 36100 Vicenza, Italy\n\n## 1 3\n\n\nand medical products (Chen et al. 2019) and luxury products (Choi 2019). The lack of transparency and visibility in\nall processes of the supply chain prevents customers from\nverifying the origin of the raw materials and the processes\nthat the product underwent before reaching the store shelves,\nwith a high risk of fraud and counterfeiting of products. The\ncosts involved in verifying supply chains’ intermediaries, in\nassessing their reliability and transparency in the production\nprocesses further complicates managing traceability in supply chains (Ahluwalia et al. 2020; Choi 2020). Strategic and\ncompetitive reputational issues arise from these risks and the\nlack of supply chain transparency.\nIn response to these concerns, the technological advancements of the digital era are providing companies with many\nopportunities that can be exploited in the supply chain\n(Xiong et al. 2021). The term digital supply chain refers to\ndata exchanges occurring between actors involved in a supply chain and also to how the supply chain process may be\nmanaged through a wide variety of innovative technologies\n(Büyüközkan and Göçer 2018) such as the Internet of Things\n(IoT), Big Data Analytics, cloud computing and the blockchain itself. Blockchain technology is particularly relevant\n(Casey and Wong 2017; Tapscott and Tapscott 2017; Samson\n2020) in overcoming the difficulties mentioned above due\nto its centralized database in which all the information of\n\n\n-----\n\nthe supply chain partners is recorded immutably. The literature on the use of blockchain technology in supply chains\nis quite recent (e.g. Chang et al. 2019) but has experienced\nsignificant growth in recent years thanks to the evidence\nthat emerged on the potential of this technology applied to\nsupply chains of different sectors such as food supply chains\n(Katsikouli et al. 2021; Bechtsis et al. 2021; Sharma et al.\n2021; Mukherjee et al. 2021), humanitarian supply chains\n(Baharmand et al. 2021) or pharmaceutical chains (Hosseini\nBamakan et al. 2021; Hastig and Sodhi 2020). Existing\npapers are focusing on illustrating the potential value of the\nblockchain and its interoperability with existing technology,\nsuch as IoT, and in particular, for the fashion industry, this\ntechnology has enormous potential in improving the information flows of supply chains (Agrawal et al. 2021; Wang\net al. 2020; Bullón Pérez et al. 2020; Agrawal et al. 2021;\nChoi and Luo 2019). The fashion industry is characterized\nby a multitude of international suppliers collaborating in\nthe creation of collections, and nowadays the development\nof complete traceability is certainly a relevant issue for all\ncompanies in the sector. The blockchain is characterized by\nthe possibility of ensuring traceable information and represents a technology that in the future will be massively used\nby fashion companies, even if currently there are few cases\nof application of this technology in the fashion industry\n(Ahmed and MacCarthy 2021). The fashion sector, however,\nstill presents little empirical evidence as many companies\nare still studying and evaluating blockchain technology and\nhave not yet moved on to the next phase of implementing\nthe technology. Further studies on the adoption of blockchain technology in the fashion industry are encouraged to\nevaluate the factors that may contribute to (or hinder) the\nimplementation of the blockchain system in extended fashion supply chains (Caldarelli et al. 2021). At present, there\nare still few blockchain applications, so any new studies that\ndelve into the feasibility of this tool are very useful in helping to understand the contexts in which the blockchain can\nachieve positive results for fashion companies and their supply chains (Chang et al. 2019; Queiroz and Wamba 2019).\nBearing in mind these gaps, this paper aims to investigate the adoption of the blockchain to enhance traceability\nalong supply chains. In particular, the drivers and barriers\nthat favor or hinder the introduction of blockchain technology among supply chain actors will be investigated for the\nfashion industry. The first research question (RQ1) will be:\n_Why do fashion companies adopt, or not adopt, blockchain_\n_technology as a system to improve traceability along supply_\n_chains in the fashion industry? What are the drivers and_\n_barriers to the implementation of blockchain in fashion sup-_\n_ply chains?_\nTraceability cannot be implemented at the level of a\nsingle node in the supply chain, but it affects entire fashion supply chains (Ahmed and MacCarthy 2021). For this\n\n\nreason, the implementation of blockchain technology should\nembrace the perspective of the whole supply chain by further\ninvestigating the variables that may enable or influence the\nadoption of blockchain technology at the supply chain level\nin the fashion sector. For this reason, the second research\nquestion (RQ2) is, therefore: How do supply chain variables\n_impact the adoption of blockchain technology as a system for_\n_improving traceability along fashion supply chains?_\nThese questions are tackled through the analysis of 12\ncase studies of the fashion industry, which describe fashion\ncompanies that are considering the use of blockchain technology to track their supply chain processes. The sample\nincludes both providers (five) and focal companies (seven)\nto compare their different viewpoints on the topic.\nThe paper is organized as follows. Section 2 reviews\nprevious studies focusing on blockchains and the relationship between the blockchain and traceability practices\nwithin extended supply chains. Section 3 is dedicated to the\nresearch aims, and Sect. 4 presents the methodology. Sections 5 and 6 provide a comprehensive analysis of results,\nwhile Sect. 7 highlights the concluding remarks.\n\n### 2 \u0007Literature review\n\n#### 2.1 \u0007The revolution of using blockchain technology for supply chains\n\nThe blockchain concept was proposed by the developer\nSatoshi Nakamoto and since 2009, has been fully validated\nthrough the bitcoin system implementation (Nakamoto\n2008). A blockchain refers to an open, shared, and distributed ledger that enables information disclosure and responsibility attribution and is suitable for dealing with valuable\ninformation (Pazaitis et al. 2017).\nAs stated by Fu et al. (2018), ‘The blockchain entries\n_could represent transactions, contracts, assets, identities,_\n_or practically anything else that can be digitally expressed_\n_using smart devices. New versions of blockchain technol-_\n_ogy implementation offer support for the implementation of_\n_smart contracts encoded in ledger blocks, which implement_\n_different business rules that need to be verified and agreed_\n_upon by all peer nodes from the network. When a transac-_\n_tion arrives, each node updates its state based on the results_\n_obtained after running the smart contract. Such replication_\n_process offers a great potential for control decentralization’._\nBased on a structure composed of nodes, blockchain technology can support digital integration in complex supply\nchains. The blockchain can address the limitations of traditional supply chains thanks to the features (Kouhizadeh et al.\n2021) described below.\nFirst, a distributed ledger of transactions is replicated\nto every node of the blockchain network. As already\n\n## 1 3\n\n\n-----\n\nmentioned, the distributed ledger is open to all nodes, which\nmay have restrictions depending on their permission level.\nTransactions create new blocks that are chained to the previous blocks, and everyone who has read permission can\nverify the validity of the transactions: for instance, a seller\ncan notify a buyer about a transaction, and the existence of\nthis transaction will be verified directly from the ledger. In\nthis way, all the actors in a digital supply chain can be verified (Pazaitis et al. 2017; Raval 2016).\nMoreover, the blockchain offers the possibility of developing smart contracts for automating business transactions\nand document exchanges between parties within the supply chain. Smart contracts can be developed on blockchains\nand used to automate supply chain transactions at a very\ndetailed level (Savelyev 2017). For instance, smart contracts can enable automated transactions of pre-determined\nagreements between parties. The blockchain can make the\ntransactions transparent and reliable, thus generating safe\nfinancial transactions.\nFinally, public-key cryptography is used to encrypt and\ndecrypt a transaction. This feature ensures a high level of\nsecurity while sustaining the whole architecture within the\ndigital supply chain. As a result, the blockchain can enable\nthe quick, reliable, and efficient execution of transactions\nand document exchanges securely and at a low cost (Pazaitis\net al. 2017).\nFrom the operational point of view, the adoption of a\nblockchain system can simplify supply chain processes by\nreducing, for instance, disputes over invoices. The results\nof an IBM study indicate that, worldwide, invoices for over\n100 million dollars are annually subject to dispute (IBM\n2019). According to the IBM estimations, the blockchain\ncould avoid this kind of dispute in 90–95% of cases. Purchase orders and purchase agreements, which are formalized among supply chain partners, can be registered in digital formats in a blockchain and made available only to the\nintended parties through their private keys. This drastically\nreduces the need for emails or other means of communication. With the blockchain, messages and documents are\ntransferred between supply chain members via blockchain\nnodes, with confidential data stored and made accessible\nwith a private key. If records are correctly uploaded on a\nblockchain platform, it becomes a single source of truth,\nand supply chain partners can access relevant information\nin real-time.\n\n#### 2.2 \u0007Blockchain and supply chain traceability\n\nThe identification of all transactions and information\nexchanged within a supply chain, as well as that of all suppliers collaborating in the chain, is becoming a weapon of\nsuccess: by giving evidence (and therefore enabling tracing)\nregarding the origins, supply chains are assuming a key role\n\n## 1 3\n\n\nfor consumers, who are increasingly interested in knowing\nthe details of products purchased (Morkunas et al. 2019).\nAuthors have debated concerning the interoperability of\nblockchains with IoT devices (such as the RFID), verifying\nthe benefits of an interconnection between blockchains and\nIoT identification to track products and processes. The first\nevidence in this sense comes from food supply chains. For\nexample, we cite the collaboration between the multinational\nNestlé and Walmart that have implemented successfully the\nblockchain developed by IBM (Zelbst et al. 2019). More in\ngeneral in the food sector the blockchain has demonstrated\nits important role in ensuring product safety traceability\n(Rogerso and Parry 2020). The logistics sector also experimented the potential of blockchain technology; distribution\ncompanies such as Maersk, UPS, and FedEx have indeed\nsuccessfully implemented this technology (Kshetri 2018).\nThe implementation of blockchain technology has also\nproved useful in the pharmaceutical sector, in particular\nfor products that require to be stored and distributed at a\ncontrolled temperature (Bamakan et al. 2021). Significant\nresults were also achieved in the humanitarian sector, in\nwhich blockchain technology was used for enhancing swift\ntrust, collaboration, and resilience within a humanitarian\nsupply chain setting (Dubey et al. 2020; Baharmand et al.\n2021).\nReal cases of blockchain adoption made it possible to\nverify and validate the identities of individuals, resources,\nand products in extended supply chains. Nevertheless, the\nestablishment of traceability for a network is still an open\nchallenge for many companies and sectors due to the difficulty of structuring traceability practices across company\nboundaries to identify suppliers located internationally\n(Moretto et al. 2018). In structuring traceability systems,\ncompanies must define tools and mechanisms to transmit\ninformation, focusing not only on their internal processes\nbut also on complete inter-organizational traceability that\ncan align different supply chain actors and ensure that data is\nexchanged in a standardized way. In most cases, traceability\npractices along the supply chain have been supported by\ntags, labels, barcodes, microchips, or radio-frequency identification (RFID), applied to each product (or to each batch),\nbut nowadays, digital tracking technologies are opening new\nhorizons and new possibilities. Blockchains widely enable\nthe tracking of products and service flow among enterprises\nthanks to the possibility of the access control and activity\nlogging that occurs in all nodes of the supply chain (Chang\net al. 2019). Based on this structure composed of nodes,\nthe blockchain represents a weapon that can protect every\ncompany involved from fraud and misleading information.\nEach partner in a supply chain, and every action it performs,\nare identified and tracked since the blockchain’s architecture\nensures the truthfulness of the data stored in it. Not only that,\nbut the blockchain also allows consumers to be protected\n\n\n-----\n\nfrom commercial fraud by allowing quick identification of\noriginal pieces and thus fighting the so-called grey market\n(i.e. the parallel sales market outside the official circuits of\nthe brand). In this way, the blockchain avoids, or at least\nreduces, the phenomenon of counterfeits by allowing consumers to verify information (Kshetri 2018).\nBlockchain technology also allows strengthening communication actions and the advertising campaigns of companies\nthat aim to tell the consumer the story of their products.\nThe blockchain makes it possible to check the history of the\nproduct along the entire supply chain and its use is strongly\nsupported by the greater consumer demand for tracked products. According to a recent PricewaterhouseCoopers (PwC)\nreport (2019), customers are willing to pay 5 to 10% more\nthan the list price to buy traced products.\nHowever, although many contributions detail the potential of the blockchain to support traceability systems in some\nspecific contexts (specifically in the food, pharmaceutical,\nhumanitarian, and logistics sectors), empirical evidence in\nthe fashion industry is still fragmentary. Many fashion companies are currently verifying the benefits of this technology\nfor their business and they have not yet moved on to the next\noperational phase which involves the real implementation\nof the blockchain technology (Caldarelli et al. 2021). What\nemerges from the literature review is the potential of this\ntechnology in various sectors, and, in the face of the positive\nresults, the fashion industry is working to understand the\nadvantages and limitations of the specific fashion business\n(Ahmed and MacCarthy 2021). The first results from the\nevaluation of blockchain technology in the context of fashion help to underline how this technology can lead to better\ncontrol of the fashion supply chains, characterized by high\nlevels of internationalization of production and distribution\n(Agrawal et al. 2021; Ahmed and MacCarthy 2021; Bullón\nPérez et al. 2020). The studies identify how the blockchain\ntheme for the fashion sector is closely linked to the goal of\nimproving traceability in all the procurement, production,\nand distribution of fashion products. The goal of improving traceability in the fashion supply chains is of primary\nimportance for companies in this sector, not only to know\nthe movements of physical products, the real-time stocks in\npoints of sale and distribution warehouses, the progress of\nthe subcontractors' activities but also to verify the sustainability of the entire supply chain, composed of many actors\nthat, with different roles and tasks, cooperate in the creation\nof collections (Choi and Luo 2019; Wang et al. 2020).\nThe fashion context has yet to be guided towards identifying the benefits and difficulties related to the use of blockchain technology in the fashion sector. Further evidence in\nthe fashion industry is encouraged to analyze the factors\nthat favor (or hinder) the implementation of blockchain\ntechnology in extended and complex fashion supply chains\n(Caldarelli et al. 2021).\n\n\n### 3 \u0007Research aims\n\nBlockchain technology is not yet widespread among companies, and research is still open to evaluating the new\npossibilities that blockchains can offer to various industrial sectors (Pólvora et al. 2020). Further research contributions are encouraged to identify the factors that could\ncontribute to, or that may hinder, the implementation of\nthe blockchain within supply chains (Chang et al. 2019;\nQueiroz and Wamba 2019), in particular in the fashion\nindustry (Choi et al. 2019; Caldarelli et al. 2021; Ahmed\nand MacCarthy 2021; Agrawal et al. 2021).\nThe overall goal of this research is to address the potential for using blockchain technology in fashion supply\nchains by considering the specific company variables (i.e.\nthe drivers and the barriers) that would affect its implementation. In particular, the current literature does not clarify which are the factors that a company considers to be\nfacilitators, or which to be obstacles, in their adoption of\nblockchain technology (Chang et al. 2019; Pólvora et al.\n2020; Queiroz and Wamba 2019). Fashion companies today,\nare at the stage of evaluating the relevance of blockchain\ntechnology for their business: their initial step will focus\non the identification of the main drivers and barriers in the\nadoption of blockchain technology. Current blockchain literature mainly takes a technological perspective and a more\nmanagerial point of view that would understand the drivers\nand barriers in the adoption of blockchain technology is still\nmissing. Recognizing this research gap, the first research\nquestion is formulated as follows.\n\n_RQ1: Why do fashion companies adopt, or not adopt,_\n_blockchain technology as a system to improve trace-_\n_ability along supply chains in the fashion industry?_\n_What are the drivers and barriers to the implementa-_\n_tion of blockchain in fashion supply chains?_\n\nThe literature also makes little contribution to addressing the supply chain variables that would support the\nimplementation of the blockchain in the specific fashion\ncontext. Further studies are needed to support an understanding of how to operate in making the implementation\nof blockchain technology effective and successful among\nfashion supply chain partners (Wang et al. 2019). There\nis a need to study in-depth the main variables that enable\nproper and successful implementation of blockchain technology within fashion supply chains (SCs). Industries differ\nin terms of their different SC relationships, setting the path\nfor a contingency foundation to blockchain implementation\nchoices within supply chains (Caniato et al. 2009; Pólvora\net al. 2020). Using the contingency approach emphasizes\nthat SCs can have different structures and that these may\nbe related to several contingencies, such as environment,\ntechnology, organizational goals, or the characteristics of\n\n## 1 3\n\n\n-----\n\nthe members of the SC, such as skills, knowledge, and size\n(Caniato et al. 2009). In line with the approach suggested\nby the contingency theory, the study of blockchain technology in the fashion context will have to take into account\nthe characteristics of the fashion supply chain itself. Recognizing this research gap, the second research question\nwas formulated for an in-depth investigation of specific\nfashion supply chain variables (i.e. contingent variables\nand enablers) impacting the implementation of the blockchain technology.\n\nRQ2: How do supply chain variables impact the adoption of blockchain technology as a system for improving traceability along supply chains of the fashion\nindustry?\n\n### 4 \u0007Research methodology\n\nGiven the exploratory nature of the topic under investigation, we decided to adopt a multiple case study methodology to anchor our results in the real world. The case study\nmethodology is appropriate when research is exploratory\nand the phenomenon under investigation is still poorly studied as it offers the opportunity to achieve in-depth results\nthrough direct experience (Voss et al. 2002). Multiple case\nstudies are conducted to achieve a depth of information and\nto increase the external validity of the results (Voss et al.\n2002). Although research studies are available regarding the\nimplementation of the blockchain in the financial context, a\nperspective that considers the implementation of the blockchain in manufacturing supply chains, and more specifically\nin the fashion industry, is still lacking.\n\n#### 4.1 \u0007Sample selection\n\nThe goal of the study is to investigate how company variables (drivers and barriers) and supply chain variables\n(enablers and contingent variables) impact the adoption of\nblockchain technology to improve traceability in the fashion supply chain. The literature suggests that the adoption\nof blockchain technology might differ strongly in different\nindustries (van Hoek 2019) and that the nature of the industry is one of the most impactful variables for supply chains\n(Treiblmaier 2018).\nFor this reason, the sample used in this paper is homogeneous in terms of industry, and the fashion industry was\nselected as this industry is consistently working on the\nimprovement of product traceability at the supply chain\nlevel (Choi 2019). The reasons for this attention are several. First, the phenomenon of counterfeiting heavily afflicts\nthis industry. In addition, companies are increasingly interested in verifying their supply chain partners for purposes\n\n## 1 3\n\n\nof social and environmental sustainability (Moretto et al.\n2018; Mukherjee et al. 2021). Furthermore, this industry\nis already investigating the possible contribution of blockchain technology for achieving these goals. The blockchain\nis, therefore, becoming a tool for protecting companies in\nthis context (Choi and Luo 2019; Fu et al. 2018). To mention\na few examples, companies such as Levi’s, Tommy Hilfiger,\nand LVMH are already evaluating or implementing blockchain technologies. For these reasons, the fashion supply\nchain is an interesting context in which to study the potential\nof blockchain technology (Agrawal et al. 2018).\nSimultaneously, the sample is heterogeneous in terms\nof the types of actors included, as both focal companies\nand the providers of blockchain technology were included.\nThe former were all interested in the adoption of the blockchain system within their supply chain. In particular, focal\ncompanies were included to get the perspective of supply\nchain decision-makers. Within the fashion supply chain,\nthe important changes and investments will be driven by\nthe focal company, which will push the rest of the chain in\nthe same direction. For this research, seven focal companies\nwere interviewed to discuss the roles and the responsibilities\ninvolved in the blockchain project in their company. This\npart of the sample was homogeneous in terms of size, as it\nis generally only large companies that are evaluating blockchain projects and have the financial resources to afford this\nkind of project. Furthermore, these companies are strong\nenough to influence the rest of the supply chain. Only brand\nowners were included in the sample. All the companies\nin the sample were either implementing or evaluating the\nimplementation of blockchain technology to meet their\ntraceability goals; the reason why we decided to include\ncompanies that are both implementing and evaluating the\ntechnology is that the former is potentially more aware of\nthe enablers and contingent variables whereas the latter of\ndrivers and barriers. The companies are considered anyhow\ncomparable as implementing companies are mainly in the\nearly stage in the project whereas evaluating companies have\nbeen working on these proposals for a certain amount of\ntime, so data and perception are comparable. This choice of\nthe sample will make it possible to achieve a full understanding of the drivers and barriers and also the supply chain variables that influence the adoption of blockchain technology\nin the fashion industry.\nIn addition to representatives from the fashion industry,\nblockchain providers are included in the sample to introduce the perspective of actors who are in the position to talk\nwith several companies, and who have a breadth of perspective on the main drivers, barriers, enablers, and contingent\nvariables addressed by their customers. The providers were\nasked to present their understanding of the viewpoints of\ntheir fashion customers. For the providers to be eligible for\nthe research, they needed to work explicitly with fashion\n\n\n-----\n\ncompanies. This part of the sample is heterogeneous in terms\nof company size, as both large companies and small startups\nare emerging to support fashion companies in their adoption\nof blockchain technology. Five blockchain providers were\ninterviewed for the study, and they spoke from the position of the technology expert and also from the perspective\nof sales and commercial managers who are in contact with\ncustomers in the fashion industry.\nA total of 12 case studies were thus included in the\nresearch (Tables 1 and 2): five technology providers who\nsupport companies in blockchain implementation and seven\nfocal companies that are evaluating blockchain implementation in their respective supply chains. The number of\ncase studies is considered sufficient to reach saturation\n(Yin 2003).\n\n#### 4.2 \u0007Data collection\n\nTo collect the data, semi-structured interviews were conducted, and for this purpose, a semi-structured interview\nprotocol was developed. A research protocol increases\nresearch reliability and validates the research by guiding\ndata collection. Furthermore, a protocol provides essential\ninformation on how to carry out case studies by standardizing the procedures used to collect the data (Yin 2003).\nDue to the exploratory purpose of this study, open questions were asked and the protocol developed did not follow\na rigid pattern but allowed the conversation to be natural so\nthat the characteristics of the framework would be shaped\nby the answers given in the interviews. The protocol was\nrevised in the course of the interviews to incorporate the\ninsights gathered.\nTwo separate interview protocols were designed, one for\nthe focal companies and one for the providers. The former\nwas composed of (1) an introduction to the company (e.g.,\ncompany name, role of the person interviewed, number of\nemployees, turnover, description of the supply chain in terms\nof sourcing, making and delivery and the global scope of the\nSC for the focal company); (2) a description of the traceability system already in place with the focal company (e.g.\nreasons for adoption of a traceability system, technologies\nadopted, impact on processes, main drawbacks, etc.); (3) an\nevaluation of the main drivers and barriers to the adoption of\n\n**Table 1 Sample composition–Providers**\n\n**Company** **Location** **Revenue**\n\n_Provider 1_ Italy 39 Million $\n_Provider 2_ Italy Around 100.000€\n_Provider 3_ Italy 46 Billion $\n_Provider 4_ Italy 4 Million €\n_Provider 5_ Italy 2 Million €\n\n\nblockchain technology; (4) the characteristics of the supply\nchain and how these variables influence the implementation of the blockchain. The interview protocol for the providers included (1) an introduction to the company (name\nand role of the person interviewed, number of employees,\nturnover, description of the services offered to companies);\n(2) a description of the blockchain technology that they are\nselling to their customers; (3) an analysis of the main reasons\nfor fashion customers implementing blockchain technology,\nincluding an investigation of drivers and barriers; (4) an\nanalysis of how the individual supply chain features impact\ncompanies’ adoption of blockchain technology.\nThe data collection stage involved multiple investigators and interviewers and all the interviews were recorded\nand transcribed (Eisenhardt 1989). Trick questions were\nincluded to verify the information and to identify any bias.\nThe whole data collection process was conducted in 2019.\nData collected through direct interviews were then combined with secondary data, such as white papers, company\nwebsites, documents provided by the company, case studies\npresented in conferences or specific workshops, etc.\nAfter the interview, each case was analyzed on its own.\nThe data collected through the direct interviews were then\ncategorized onto a spreadsheet. It was then analyzed and\ntriangulated with secondary data, such as the companies’\ndocuments, newspapers, and reports on both the focal companies and the providers. In empirical studies, a combination\nof different sources makes it possible to understand all facets\nof the complex phenomenon studied (Harris 2001).\n\n#### 4.3 \u0007Data analysis\n\nThe data analysis involved three stages: a within-case analysis, a cross-case analysis, and a theory-building stage. For\nthis data analysis, the research team met many times after\nthe initial site visits to develop a strategy for synthesizing the\ndata. In cases where some data were missing or unclear, the\nrespondents were contacted again by phone for clarification.\nTo maintain the narrative of the findings, a within-case\nanalysis was conducted to identify each company’s peculiarities (its drivers and barriers), while the main supply chain\nvariables (enablers and contingent variables) for each case\nwere highlighted. Several quotations from informants have\nbeen included in the within-case analysis, as reported along\nwith the description of the results in the paper. In particular, open coding was adopted for the within-case analysis,\nand labels and codes were identified based on transcripts of\nthe interviews. The within-case analysis involved following\nseveral steps: reading the transcripts of the interviews twice\nto take notes and grasp the general meaning of the interview. Through this process, the most frequent words used\nin each case were identified, and these were used to create\nthe coding labels. Finally, data interpretation was performed\n\n## 1 3\n\n\n-----\n\n**Table 2 Sample composition –**\n**Company** **Location** **Revenue** **Number of** **Degree of globalization**\nFocal companies\n**employees**\n\n_Focal Company (FC) 1_ Italy 54 Billion € 150.000 Stores in more than 150 countries\nGlobal supply network\n_Focal Company (FC) 2_ Italy 60 Million € 260 Global customers\nMainly local suppliers\n_Focal Company (FC) 3_ Italy 150 Million € 1400 Global customers\nLocal and global suppliers are\nequally important\n_Focal Company (FC) 4_ Italy 3 Billion € 6.500 Stores in more than 150 countries\nGlobal supply network\n_Focal Company (FC) 5_ Italy 1 Billion € 3.800 Stores in more than 150 countries\nGlobal supply network\n_Focal Company (FC) 6_ Italy 1,5 Billion € 4.000 Stores in more than 100 countries\nGlobal supply network\n_Focal Company (FC) 7_ Italy 1,5 Billion € 6500 Stores in more than 150 countries\nGlobal supply network\n\n\nwhere each case was taken individually and its variables\nwere described and interpreted. This included examining the\nfinal results to conclude the within-case analysis.\nThese coding labels were then used to perform the crosscase analysis (Annex A). The cross-case analysis was initially jointly performed for the focal companies and providers to combine their different points of view and to raise\ndifferences during the discussion. The purpose of the crosscase analysis was to identify both commonalities and differences among the cases. The cross-case comparisons helped\nto extract the common patterns. The cross-case analysis was\nperformed independently by two researchers and then the\nresults were compared to find similarities and differences\nand to increase the descriptive validity. In the case of any\nmisalignment, a revision of results was performed to arrive\nat a common classification for each case.\nFinally, the theory-building stage was completed, where\ninterpretation and abstraction were performed. This involved\niterating data and theory to design a new framework for characterizing the design of decentralized two-sided platforms\nthat are built upon blockchain technology. Results of this\nstep are provided in the Table reported in the Result section.\n\n### 5 \u0007Drivers and barriers for blockchain technology\n\n#### 5.1 \u0007Drivers for blockchain technology\n\nThe analysis of the within-cases allowed us first of all to\nidentify two main groups of drivers for the blockchain technology: the internal and the external. In terms of the internal drivers, companies presented decisions taken within the\n\n## 1 3\n\n\ncompany to improve internal performance metrics such as\nefficiency and effectiveness. In terms of external drivers,\ncompanies presented the incentives or requests obtained\nfrom external actors, which could be either the supply chain\nor the customers. This distinction was made particularly\nclear by the providers, who illustrated the different requests\nreceived from some of their customers, as indicated in a\nquote from Provider 2: ‘For us, it is particularly important\n_to understand why a customer is approaching the block-_\n_chain. Some of them are mainly interested in the possibility_\n_to exploit traceability at a lower cost or through the auto-_\n_mation of some steps, so mainly with an internal perspec-_\n_tive. Some others are, actually, more focused on the external_\n_perspectives: either for specific requests of the customers or_\n_retailers or for the willingness to onboard on the project the_\n_overall supply chain. But this is an important distinction,_\n_guiding potentially different approaches’._\nBased on these insights, the cross-case analysis considered\nthree different variables, i.e. the internal drivers, the external drivers (the supply chain), and the external drivers (the\ncustomers), as reported in Annex A. We noticed that almost\nall of the companies have listed some elements in all three\ngroups of drivers. Internal drivers are mentioned strongly by\nproviders whereas focal companies are stressing more the\nimportance of external drivers, especially supply chain ones.\nThis difference could depend on the fact that providers are\nalso considering the perspective of companies that at the end\ndecided to not move forward in the adoption of the blockchain technology; focal companies, on the contrary, strongly\nunderstand the importance to generate value along the supply\nchain or for accomplishing the request of customers.\nHaving compared the different cases, their commonalities\nand differences were considered and are combined in Table 3.\n\n\n-----\n\nThe first group concerns internal drivers, meaning the\nreasons that push the individual company to implement\nblockchain technology. In particular, companies presented\neither efficiency- or effectiveness-oriented reasons for their\nadoption of the blockchain. These companies highlighted\nstrongly the benefits expected in terms of reduction of costs\nto be achieved through greater business efficiency (in terms\nof the reduction of insurance costs or bureaucracy costs),\ngenerally to be achieved through an extensive process of\nautomation. Several companies also emphasized as important the need to reduce the cost of compliance. This was\nexpressed by the manager of Provider 2, who reported: ‘In\n_Castel Goffredo there is a district where 60% of European_\n_socks are produced. One of the most interesting topics that_\n_came up with them is the management of compliance. Each_\n_of these companies, of which many are subcontractors for_\n_other brands like Zara, have a series of certificates that_\n\n_[they] must produce. But they come to need 15 different_\n_certificates for each company, so every 2/3 days they have_\n_an audit, which involves dedicating people and wasting time._\n_This is a big problem for them because the certifications are_\n_different, but they also have many common points. Maybe_\n_they have to produce one for a brand and a similar one for_\n_another brand. Thanks to a blockchain and a smart con-_\n_tract, they could reduce these kinds of costs’. The cost of_\ncompliance was probably the most frequently cited driver\nfor the blockchain, and also in the literature. This driver was\ncited by all the providers, illustrating that this is the main\npoint emphasized by the providers in terms of what matters to their customers. This point, especially in the fashion\nindustry, could represent an important element especially\nfor smaller companies, with several customers and request\nto accomplish.\nAlthough this driver was strongly presented in the case\nstudies, and especially by the providers, it is interesting that\nseveral other drivers were also emphasized. In terms of the\ninternal drivers, several case studies spoke of the importance\nof using blockchain technology to increase effectiveness,\nin particular, due to improvements in the decision-making\nprocess, as information is always required immediately and\nmust be easily available. This was supported by an additional\ndriver linked to data integrity and data safety, as companies\nneed to be sure of the validity of the data that they use for\ndecision-making. This driver is, anyhow, not specific to the\nindustry, but presented also in literature as one of the main\nadvantages of the blockchain technology independently from\nthe area of application.\nHowever, the most recurrent driver, specific to fashion\nproducts, is the possibility of reducing counterfeit products.\nThis was highlighted by almost all the focal companies, all\nof whom are potentially strongly impacted by this issue. Provider 2 gave an example of this when they reported that one\nof their customers had suffered damage due to counterfeit\n\n## 1 3\n\n\n-----\n\nproducts that equaled 10% of their total revenue. FC3\nreported: ‘We are part of a blockchain project sponsored\n_by the government. The main reason why the government_\n_pushed this project was a willingness to protect Made in_\n_Italy’. This is a relevant driver for the industry, that was also_\nmentioned for example for food products in other domains.\nThe second group of drivers pertains to external drivers\nand includes the supply chain drivers, where other supply\nchain actors play an important role. This is a perspective\njust partially investigated in existing literature, for example\nconsidering the logistics industry. The first group of supply chain drivers concerns the willingness to increase visibility along the overall chain, thanks to the trust demonstrated in the sharing of data among different actors. This\nwas expressed by Provider 1: ‘I think generally, a block_chain is solving a problem of trust. It is solving a problem_\n_in which multiple different actors, within a specific kind of_\n_system, whether it is a supply chain system, or whether it_\n_is a government, like a political system, or different kind_\n_of social system, where different actors have incentives to_\n_anticipate in the system and some of the actors have incen-_\n_tives to cheat, not be transparent, maybe gain more out of_\n_the system. Blockchain essentially enforces trust onto a sys-_\n_tem so individual actors can’t take advantage or manipu-_\n_late the system for their advantage’. What the blockchain_\ndoes is create controlled data shared by multiple companies.\nEvery company has its information system, making incorrect data modifications impossible. The blockchain makes\npossible a process in which multiple organizations interact\nwith each other and, at the same time, it ensures that only\ncorrect data are exchanged through this interaction. Data\nare stored on the blockchain in a way that means they are\nnon-falsifiable and cannot be tampered with. The reason\nfor the blockchain increasing trust is not that data are automatically true, but that accountability for what is reported\nis clear. A good example of this is reported by Provider 3:\n_‘I can also write false information because the blockchain_\n_does not validate the data per se, so if I write the tempera-_\n_ture that a sensor detects while I have a warehouse full of_\n_sushi and the temperature is at 40 degrees but I write 0,_\n_the blockchain records 0. However, the fact remains that I_\n_digitally sign cryptographically what I am writing and I also_\n_take responsibility for what I am writing. So if a garment is_\n_made of merino wool and I declare that it is made of merino_\n_wool, this remains written, and therefore, there is this kind_\n_of advantage’._\nSome of the other companies also reported drivers that\nare consistent with the features of the blockchain itself: the\nblockchain is agnostic, or interoperable in terms of data,\nand so it makes it possible to achieve benefits such as having common communication layers among all levels of the\nchain and obtaining disintermediation of the network. These\ndrivers are valid for the fashion industry but aligned with\n\n## 1 3\n\n\nthe main drivers of the technology itself, as presented in\nliterature streams about blockchain technology.\nAnother group of supply chain drivers concerns the use\nof the blockchain as an extension of best practices along\nthe chain. Several companies stated that they are studying\nthis new technology as their main competitors are doing the\nsame: this point was highlighted by several focal companies, whereas it was quite neglected by the providers. If this\nshould become the standard, the late joiners might experience some damage either because they are late or simply\nbecause they are perceived as not being innovative. The\ndifference existing between focal companies and providers\nis interesting to highlight and is making this variable particularly critical for the industry under investigation, where\ninnovation represents definitively a critical success factor.\nVery interesting is what was mentioned by companies such\nas FC3, who said they want to use the blockchain to stress\nmore ethical behaviors along the entire chain.\nCompanies also expressed their willingness to adopt the\nblockchain because of the requests of their customers.\nThis created the third group of drivers. The customers of\nthe fashion industry can be divided into end consumers and\nretailers. This difference is a peculiarity of this industry,\nwhere retailers and end consumers might play a relevant, but\ndifferent role. In terms of the end consumers, the companies\nwant to become increasingly transparent concerning them.\nIn particular, some consumers are especially interested in\nbuying from open companies, and so the companies are willing to demonstrate the validity of what they offer in terms\nof the quality of the product, its authenticity, the features of\nthe products, etc. This topic emerges as particularly critical\nin this industry, due to the strong scandals that happened in\nthe past. On the one hand, the application of the blockchain\nto the production portion of the supply chain will make it\npossible to verify exactly which actors collaborate in the\nproduction of a product, with evident benefits in terms of\nproduct authenticity and also the protection of social and\nenvironmental sustainability (for instance by ensuring the\norigin of raw materials purchased at the international level).\nIt enables the suppliers to be controlled in a more precise\nway as regards the stringent laws in the environmental field\nand concerning guarantees that must be provided about child\nlabor and more generally, about the safety and contracts of\ntheir workers.\nOn the other hand, the blockchain will make it possible\nto follow the products during all their distribution steps all\nacross the world. This will guarantee the authenticity of the\nproducts available in shops, and it will also work as a certification for consumers. Focal companies, in particular, are\nreinforcing the importance of using technology to support\nthe story and the validity of the history of their products.\nThis perspective is comparable to what is presented also in\nthe literature about food products.\n\n\n-----\n\nIn terms of the retailers, they may push companies\ntowards a more transparent approach and so the focal companies will need to respond to these requests. This is mainly\nachieved through accountability towards the end consumer.\nA good example was reported by Provider 1: ‘I think that\n_money is the main driver for the economic sustainability._\n_And so, it might not be the customers like you and me, but it_\n_might be the customer like the big department stores. Maybe_\n_these department stores don’t want to work more with you._\n_Creating more transparency, people can make better deci-_\n_sions on where they source’._\nThirdly, several companies presented the coherence of\nthis approach by providing typical critical success factors\n(CSFs) of fashion companies, especially the high-end ones,\nsuch as telling the story, increasing brand awareness, and\npresenting the company as innovative and open towards\nits consumers. Proof of the products’ authenticity will add\nfurther security to the claims made by the brands: it will\nassure the consumers that information on the final product and certifications are verified by the company and its\nsuppliers. This helps in the prevention of false claims and\nincludes the field of sustainability where the risk of ‘greenwashing’ is always present (concerning both environmental\naspects and social sustainability). This is a point strongly\nstressed especially by focal companies, willing to find new\nlevers to differentiate proper sustainability and just minimal levers.\nThese results are summarized in the following research\nproposition:\n\n_RP1: The implementation of blockchain technology to_\n_improve traceability along the fashion supply chain_\n_is driven by three main groups of factors: to increase_\n_internal efficiency and effectiveness at the process_\n_level, to be aligned with the requests emerging at the_\n_fashion supply chain level, and to increase the level_\n_of trust communicated to end consumers and fashion_\n_retailers._\n\n#### 5.2 \u0007Barriers to blockchain technology\n\nBridging the digital and physical worlds by making the products’ path accessible to the customers through a blockchain\nsystem is not easy in any situation, and this is why some of\nthe barriers are discussed here.\n\n**Table 4 Barriers to blockchain technology**\n\n\nThe within-case analysis enabled two main groups of barriers to be identified: those that were strongly linked to the\ntechnology and those that were more oriented to cultural\napproaches and to the readiness of the industry to accept\nthis new way of working. The former was mainly described\nby the providers, who saw the technology as the critical element, whereas the focal companies were more focused on\nindustry-specific elements. This result could depend on the\nsample composition: focal companies are already implementing in the late stage of evaluation of the technology,\nthereby being quite sure of the willingness to introduce this\ntechnology. On the contrary, technology providers have the\nperspective of both adopters and not adopters and in this\ncase, technological barriers appear more relevant and complicated to overcome.\nThe cross-case analysis was performed considering these\ndifferent approaches and it is summarized in Table 4.\nThe first group of barriers is technology-specific. First,\nwas the theme of the investments needed to support the\ndevelopment of a blockchain system as the blockchain is\nstill perceived as an expensive technology. This was particularly regarded as an issue due to the risk that it would\nincrease the costs of the final product. For example, FC5\nsaid, ‘The reason why blockchain is deeply discussed within\n_my company is that the cost is still particularly high, espe-_\n_cially in comparison to other traceability systems. If we need_\n_to transfer this cost in the prices of the products, marketing,_\n_and salespeople are not aligned and not willing to accept_\n_this additional point whether they are not able to see the_\n_value for the customers’. Moreover, the blockchain is seen_\nas a complex technology, difficult to understand and motivate, for example, FC3 mentioned, ‘For me, it, was not easy\n_to understand how the technology works and so to trust the_\n_technology. Now I got it but the problem is still not com-_\n_pletely solved as now it is a matter of understanding which_\n_are the data to properly share.’ This barrier is not industry-_\nspecific but connected to the technology itself. In this vein,\nsolutions identified in other industries could also become a\nlever to overcome this technology in the fashion domain too.\nThe second group of barriers is called industry-specific\nas they relate to specific features of the fashion industry, such\nas the generally low level of digitalization in the supply chain\n(thereby requiring a big jump, especially for small companies), which is also related to a generally low technological culture in the industry. Moreover, at present, there is no\n\n\n**Technology specific** **Industry-specific**\n\n- difficult to understand how the technology works - low level of digitalization in the supply chain\n\n- the high cost of the technology - missing a shared technological standard in the industry\n\n - missing a technological culture in the industry\n\n - collaboration among different SC partners\n\n## 1 3\n\n\n-----\n\ntechnological standard, and several companies are worried\nabout this. For example, FC1 reported, ‘Today, the biggest\n_problem is not so much to use the blockchain, but to use it_\n_in the same way because if everyone makes his [own] block-_\n_chain fragment there is also a big race for who will be the_\n_winner-take-all’. Finally, to use the blockchain it is necessary_\nto have strong collaboration among the supply chain partners,\nbut the overall level of collaboration in the fashion industry\nis often poor, and this could reduce the feasibility of adopting blockchain technology. This is something presented as\nparticularly critical by focal companies, especially those in\nthe evaluating phase. To overcome this barrier is relevant to\nexpand the adoption of blockchain technology in this domain.\nThese results are summarized in the following research\nproposition:\n\n_RP2: The implementation of blockchain technology to_\n_improve traceability along the fashion supply chain_\n_is halted by two main groups of factors: a low under-_\n_standing of the newly emerging technology in the_\n_fashion industry and the perception that the fashion_\n_industry is not yet ready from either a technological_\n_or a cultural point of view._\n\n### 6 \u0007Supply chain variables and the impact on blockchain technology\n\nExploratory case studies were used to understand if and\nhow the characteristics of the supply chain might impact\nthe blockchain.\nWhat the cases suggest is that two different groups of\nsupply chain variables could influence the adoption of\n\n\nblockchain technology. First, there are the enablers, considered to be elements existing within the supply chain that\ncould support and exploit the adoption of blockchain technology. Second, there are contingent variables, described\nas the contextual factors of the supply chain, which could\nimpact the potential benefits achievable through blockchain\ntechnology as well as the possibility of implementing it.\nThese two groups of variables were used to perform the\ncross-case analysis reported in Annex A and summarized in\nTable 5. In analyzing the data reported in Annex A, we could\nnotice that there is quite a good consensus about the enablers\nidentified in different cases; these enablers are pretty in line\nwith the main barriers previously identified, addressing that\nthese variables could reduce the risks and the uncertainty\ngenerated by the technology. On the other hand, reading data\nof the cross-case analysis, some differences among the case\ncould be highlighted in terms of contingent variables. Providers are focusing more on fixed parameters, such as the\nsupply chain complexity and the features of the industry,\nwhereas focal companies are strongly presenting the relationships existing. This dichotomy again provides evidence\nof which are the elements influencing the adoption since the\nbeginning and which are the most relevant points presented\nduring the implementation, with a more practical and business perspective.\n\n#### 6.1 \u0007Supply chain contingent variables for blockchain technology\n\nThe case studies highlight several contingent variables that\ncould influence the adoption as well as the success of blockchain technology. Cases are quite aligned in the identification of variables to consider but have different perspectives\n\n\n**Table 5 Supply chain enablers and contingent variables of blockchain technology**\n\n**Contingent variables** **Enablers**\n\n\nSUPPLY CHAIN COMPLEXITY\n\n- the size of the companies (easier to use with big suppliers, more relevant\nwith small ones)\n\n- number of nodes involved (the higher the number of nodes the higher the\nsafety of the system)\n\n- globalization of the supply chains (the more the supply chain is global the\ngreater the need to bring information to the consumers)\n\n- level of vertical integration (less relevant when production activities are\nowned)\nTYPE OF RELATIONSHIP\n\n- duration of the relationships with suppliers (best used with stable\nsuppliers)\n\n- supplier commitment towards the company (adoptable with committed\nsuppliers)\nINDUSTRY​\n\n- level of regulation (less valuable when the regulations are already super\nstrong and are monitoring everything, but proper regulations might be an\nenabler factor)\n\n- positioning (adaptable with high-end products)\n\n## 1 3\n\n\n\n- proper supply chain traceability system already in place (with the\nappropriate units of analysis, single product or container)\n\n- need to integrate blockchain with other technologies, such as IoT\n\n- willingness to collaborate with other actors in the chain\n\n\n-----\n\nabout the possible positive or negative influence of the variables. This is something very specific for the industry under\ninvestigation and not investigated in current literature. The\nmost frequently mentioned, and also most controversial element, pertained to supply chain complexity. This result\nhighlights the complexity of the supply chain as an important element fostering or reducing the effectiveness of the\nadoption of the new technology. Discussion on this point\nvaries widely as some companies address the supply chain\ncomplexity as being the greatest difficulty to introducing\nblockchain technology, with related costs and risk of failure (e.g., FC5). In contrast, other companies say that it is\nbecause of the high level of supply chain complexity that it\nis so important to exploit the traceability of the supply chain,\nand in this way, the potential value of blockchain technology is boo.\nIn this group, four main elements could be identified,\nwhich are consistent with the literature about supply chain\ncomplexity. First, the size of the company matters, but the\nimpact of this factor is controversial from the companies’\npoints of view. On the one hand, the blockchain may offer its\nstrongest contribution when small suppliers are involved, as\ntheir inclusion is critical to providing reliable and trustworthy data. On the other hand, these companies are also those\nwhere the industry-specific above are stronger, and so the\npossibility of involving them is more challenging.\nThe second element concerns the number of nodes\ninvolved: some companies indicated that the higher the number of nodes involved the higher the safety of the blockchain\nsystem. This is confirmed by the fact that it is easier to verify the validity of data provided when the number of actors\ninvolved is low, as it is easy to use alternative methods. At\nthe same time, other companies pointed out that when the\nnumber of nodes to be involved is high, the complexity in\nimplementing the technology and therefore the related costs\nincrease, thereby reducing the feasibility of the project.\nThirdly, the globalization of the supply chain was considered and discussed. Here again, contrasting opinions were\ngiven as some companies said that the more global the supply chain, the more difficult but also necessary it became\nto provide reliable information to the consumers. This is\nsomething very peculiar for this industry and with the sample analyzed, considering that all the focal companies considering present a high level of upstream and downstream\nglobalization, as illustrated in Table 2. Again, in terms of the\nnumber of nodes involved, the more global the supply chain,\nthe higher the costs of the technology.\nFinally, the level of vertical integration was mentioned. In\nkeeping with the opinions reported regarding the number of\nnodes involved, the contribution of the blockchain is higher\nif the level of vertical integration is low, as within a single\ncompany other methods, such as the more traditional centralized database, are sufficient.\n\n\nAccording to these insights, the following research proposition was formulated:\n\n_RP3: Supply chain complexity influences the imple-_\n_mentation of blockchain technology to increase trace-_\n_ability as the higher the supply chain complexity (in_\n_terms of size of the companies involved, number of_\n_nodes, globalization of the fashion supply chain, and_\n_level of vertical integration) the higher is the relevance_\n_of traceability along the fashion supply chain, but also_\n_the higher is the difficulty in implementing the block-_\n_chain technology._\n\nThe second contingent variable relates to the type of\n**relationship existing between the supply chain partners.**\nBlockchain technology is most effective with suppliers who\nhave been adopted for a long period, whereas in the case\nof a spot relationship, the cost and time required to integrate a new supplier into the blockchain would be greater\nthan the value to be obtained. This is a definitive and critical point for the fashion industry, as most of their products\nlast for not more than one season. Suppliers will likely be\nextensively revised for each collection, thereby reducing the\nnumber of actors that can be meaningfully involved in the\nblockchain. At the same time, suppliers must be committed\nto the relationship. The combination of these two elements\nwas illustrated by FC4: ‘There are big companies with fixed\n_and stable suppliers and therefore they can contractually_\n_manage this integration. When you have so many suppliers,_\n_even small ones that go in rotation, [it] is much more dif-_\n_ficult. We are perhaps big names, but we have volumes that_\n_are not comparable to someone else. And so the difficulty lies_\n_in keeping the supplier bound and performing what you ask_\n_him. We have productions in Asia where we are very small_\n_and we have to get in line with the others. In sneakers, if you_\n_talk about Adidas, Puma, or Nike, we are 0. The volume, in_\n_that case, is king.’_\nAccording to these insights, the following research proposition was formulated:\n\nRP4: Blockchain technology is easier to implement\nin the fashion supply chain with long-lasting relationships, where there is a high level of collaboration and\ntrust.\n\nFinally, some contingent factors are specific to the indus**try. From this perspective, two main contingent variables**\nwere highlighted by the interviews: the level of regulation\nand the product positioning. Regulations can play a role in\ndriving the adoption of the blockchain, but at the same time,\nthey can render the technology useless. For example, Provider 2 gave the example of the pharma industry, which is\nalready strongly regulated in terms of traceability and so it\nis less valuable for it to use blockchain technology as the\nachievable benefits would be little different. In this case, the\n\n## 1 3\n\n\n-----\n\nfashion industry can have a good potentiality, considering\nstill a limited level of regulation about the topic, but a growing relevance and perceived urgency.\nFor the latter, product positioning, the cost of the investment and the level of data to be shared are the same, independent of the type of product considered. To mitigate the\nbarriers related to the cost of the technology while exploiting\nthe drivers related to customers, there is greater potential\nwhen the technology is adopted for high-end products. This\nis a typical relevant variable for the industry, in discriminating among several strategic decisions.\nAccording to these insights, the following research proposition was formulated:\n\n_RP5: Blockchain technology is easier to implement in_\n_a regulated industry, such as the fashion one, where_\n_there is a strong need for traceability, which is not yet_\n_achieved, and for high-end products._\n\n#### 6.2 \u0007Supply chain enablers for blockchain technology\n\nIn terms of the enablers, the cases highlighted that some\nelements can make strengthen or ease the impact of both\ndrivers and barriers on the implementation of blockchain\ntechnology. In particular, the case studies highlighted how\nessential it is for fashion companies to evaluate the application of blockchains first of all, in guaranteeing the trace**ability of their products. Knowing where products come**\nfrom and what paths they have taken before arriving in the\nstores is useful both for brands, to check their supply chain,\nand for the customers who get additional information on\nthe product purchased. The major goal for the application\nof the blockchain in the field of fashion, therefore, becomes\nto trace and retrace every single passage of a product, from\nthe raw materials until the final store. The blockchain is not\nonly a tool that facilitates traceability, but it also enables the\nsharing of data. Most of the companies agreed that a proper\nsupply chain traceability system should be in place, whether\nthe companies wanted to exploit the benefits of blockchain\ntechnology. This was a point of agreement between the providers and the focal companies and differed from the initial\nexpectations that the use of the blockchain was to foster\ntraceability along the supply chain. This result is not always\ncompletely aligned with the insights of the literature, where\nthe relevance of blockchain to foster visibility is often presented. It is interesting to consider what FC7 reported: ‘We\n_already have in place a traceability system that was devel-_\n_oped several years ago. This is fundamental, as without a_\n_proper system it is irrelevant. Our driver is to increase vis-_\n_ibility along the supply chain.’_\nThe second element highlighted concerns the **possi-**\n**bilities offered by other technologies on the market. In**\n\n## 1 3\n\n\nparticular, correct verification requires a critical revision of\nthe other technologies available on the market that allow\ninformation sharing (for example, QR code, NFC, and the\nRFID system) to understand if they can meet the goals of\nbrand transparency. A relevant question that companies will\nhave to ask themselves is whether smart labels, such as NFC\ntags or custom plug-ins for e-commerce, could convey sufficient information to consumers for their business purposes.\nAlso, if the existing technologies are insufficient and the\nblockchain might provide a real contribution, it is necessary\nto understand how to integrate the blockchain with other\nexisting technologies to include existing data in ensuring\nreliable information.\nThe third and last enabler is the collaboration among\nall supply chain partners. Blockchain development inevitably requires that content and data will be collected from\nmultiple sources and suppliers and that information will be\nconstantly updated. This means involving each participant\nalong the supply chain in a long-term collaboration project,\nwhich must be grounded on mutual trust. The development\nof a blockchain project must foresee, at least initially, the\ncreation of support for companies in the network that will\nco-participate in the transparency project promoted by the\nbrand, without forgetting that the hostilities or reticence of\nsuppliers who may not want to collaborate with the other\nsuppliers will also have to be managed.\nAccording to these insights, the following research proposition was formulated:\n\n_RP6: The impact of drivers to foster the implementa-_\n_tion of blockchain technology and of the barriers to_\n_interfere with the implementation of blockchain tech-_\n_nology along the fashion supply chain depend on an_\n_already existing traceability system, on the possibility_\n_of integration with other technologies, and collabora-_\n_tion between supply chain partners._\n\n#### 6.3 \u0007Detailed research framework\n\nResults of the paper are summarized in a research framework\nas depicted in Fig. 1.\nShreds of evidence of the case studies and the summary\nof the detailed research framework provided above are also\nnecessary to offer some guidance about steps and phases that\ncompanies should perform to introduce blockchain technology in the fashion supply chain.\nThe driver of traceability along the supply chain, which\nis pushing companies towards blockchain projects, reveals\nhow strong is the need of companies to develop common\ndatabases to collect accurate supply chain information\nabout traceability and sustainability. This first need to be\nfulfilled becomes the first question to which companies\nmust answer in the process of defining the technology that\n\n\n-----\n\n**Fig. 1 – Detailed Research**\nFramework\n\nsupports such information sharing: “Does a company need\na database to collect and share data with Supply Chain\npartners?”. If companies respond negatively to this question, blockchain technology cannot and must not be taken\ninto consideration. A negative answer can be justified, for\nexample, by companies that are not very advanced concerning the issue of traceability and sustainability and that\nmanage the SCs still in “watertight compartments” among\nthe different SC partners.\nOn the contrary, if the response is positive, the company\nwill have to understand how much this point is relevant\nfor other actors of the supply chain and wonder about how\nmany partners will have to participate in information-sharing\nactivities. In particular, if the technology is not relevant for\nexternal partners and the exchange of information will be\nlimited between a dyad of partners, a blockchain will be a\nsuperstructure, which, would entail considerable costs and a\nconsiderable development commitment. In this case, a centralized database, managed directly by the focal company\nand accessible to the partners, could be a more streamlined\nsolution.\nAfter identifying the number of participants in the datasharing project, the type of relationship to be established\nand the kind of relationship willing to maintain should also\nbe analyzed. Considering that in a blockchain the partners will have to exchange sensitive data, it is necessary\nto understand the level of trust to be established. If the\nrelationship with the identified partners is not of full confidence the blockchain project must be discarded; alternatively, multiply the copies of the centralized databases\n\n\nin such a way that partners can access but not have full\ncontrol over all data. Blockchain technology contemplates\nthat a partner can change data for all connected partners,\nbut if this is not supported by trust, the blockchain project\ncannot continue.\nSubsequently, the operative aspects at the production\nlevel must be analyzed. Which transactions will have to be\nconnected and which production process must be linked in\nthe eventual blockchain? In other words, which production\nprocess must be traceable and traceable must be defined\nprecisely to comply with the traceability drivers that have\nencouraged the evaluation of a blockchain project. If the\nneed for traceability were not so strong, the blockchain project would not make sense. Probably for these companies,\nthe traceability of the supply chain is not so strong as to justify investments in new technology, but other less expensive\nprocesses are sufficient. Instead, if the traceability of the\nproduction processes along the entire supply chain will be\na very strong need of the company then the blockchain will\nbe the ideal solution.\n\n### 7 \u0007Conclusion\n\n‘Blockchain’ is one of the keywords for the future. When\nit was born, more than ten years ago, it was linked only\nto the bitcoin economy. Today, the decentralized database\nwhere transactions between users are recorded is not only\nlinked to banks’ transactions, but it is playing a significant\nrole within supply chains. International competition and the\n\n## 1 3\n\n\n-----\n\nadvent of innovative technologies are just some of the critical challenges that the fashion industry faces today. These\nchallenges require new ways of operating and accordingly,\nrequire changes in the supply chain processes.\nAlthough explored in other industries, literature is still\nquite preliminary at presenting what fashion companies specifically can do to implement blockchain technologies. For\nthis reason, this paper aims to understand the main drivers,\nbarriers, enablers, and contingent variables that explain the\nadoption of blockchain technology in the fashion industry.\nTo tackle this goal, the research was based on multiple case\nstudies, conducted through interviews with five blockchain\nproviders and seven fashion focal companies. Through\nanalysis of the case studies, the main groups of drivers (i.e.\ninternal drivers, supply chain drivers, and customer drivers),\nbarriers (i.e. technology and industry-specific), enablers, and\ncontingent variables (i.e. supply chain complexity, industry,\nand type of relationships with suppliers) were identified.\nAlthough exploratory, from an academic point of view\nthis work contributes to the schematization of the discussion\non the blockchain, identifying drivers and barriers for the\nfashion context and illustrating how the main features of the\nindustry may influence technology adoption. This industry\nhas some peculiarities and a great relevance, to justify a\nfocus in the existing literature and in trying to understand\nwhich principles valid in other industries could be replicated\nto fashion one. Moreover, current literature is just partially\nconsidering how supply chain variables could influence the\nadoption of blockchain technology to increase the visibility\nalong the supply chain; this paper, with a specific focus on\nthe fashion industry, tries to address which might be these\nareas of influence, contributing to the literature. Moreover,\nthe results hint at additional areas for investigation. Technology appears to offer a potentially valuable tool in the field\nof sustainability where previously, companies developed\nto control and audit systems based on internal protocols.\nThese were developed ad hoc by each brand or, in more\nadvanced cases, supported by certifications of environmental\n\n## 1 3\n\n\nand social sustainability. The blockchain will unquestionably\nmake it possible to see, in real-time, which actors in the\nsupply chain process the final products, and more generally, it will make it possible to provide guarantees on the\nsub-working activities through which these products have\npassed. In the fashion sector, it is common practice for suppliers to make use of sub-suppliers for production processes\nthat require highly specialized skills. The blockchain is\nincreasingly available for all sectors that need to certify the\nquality and origin of their products and raw materials. The\npotential of this technology lies in its ability to obtain greater\nconsumer confidence and to guarantee products in terms of\nsustainability and all that happens along the fashion supply\nchain. This will allow brands to provide verified information\non the materials, processes and, people behind their products. This topic is particularly relevant especially for fashion\ncompanies and further research could be necessary too.\nFrom the managerial point of view, this perspective is\na hot issue. This guide can be a useful tool for directing\ndiscussion on the feasibility of a blockchain project. This\nresearch offers valuable and original contributions to practitioners who are thinking about the drivers and barriers to\nnew blockchain projects, while the research also identifies\nconcrete questions that managers can use to check whether\nblockchain technology meets the needs of their particular\nproduction context.\nHowever, the paper does have some limitations, which\nopen opportunities for further investigation. First, the paper\ndoes consider both providers and focal companies but there\nis no proper discussion of the differences between the two\ngroups of actors. Additional research might also include\nthe viewpoint of the suppliers and compare the perspectives reported by different actors in the chain. Second, the\npaper illustrates the main drivers and barriers towards the\nadoption of the blockchain. The benefits and the costs to the\ncompanies are not discussed: further study might involve\nan action research project to assess the impacts in terms of\nperformance.\n\n\n-----\n\n### Annex A: Drivers, Barriers, Enablers, and Contingent variables\n\n**Case** **Internal drivers** **External drivers** **External drivers** **Barriers** **Enablers** **Contingent variables**\n**(company)** **(supply chain)** **(customers)**\n\n\n_Provider 1_ - business efficiency\nthrough breaking\ndown data silos\n\n - reduction of the\ncosts of compliance\n\n - improving internal\ndecision making\n\n_Provider 2_ - data safety\n\n - reduction of counterfeit products\n\n - reduction in the cost\nof compliance\n\n_Provider 3_ - process automation\n(e.g., through smart\ncontracts)\n\n - business efficiency\nand reduction of\ninternal costs\n\n\n\n- trust: reduction of\nopportunistic behaviors in the supply\nchain\n\n- reduction of information asymmetries at\ndifferent stages of the\nsupply chain – >\n\n-reduction of bounded\nrationality – >\n\n- authenticity and consistency of data\n\n- increase in efficiency\nat the supply chain\nlevel\n\n- shared communication\nlayers: blockchain is\nagnostic in terms of\nthe format of data\n\n- adoption by main\ncompetitors\n\n- decentralization and\ndisintermediation in\nthe network\n\n- trust: sharing of data\namong different actors\nof the supply chain\n\n- accountability for\nwhat is reported by\ndifferent actors in the\nchain\n\n- shared communication\nlayers: blockchain is\nagnostic in terms of\nthe format of data\n\n\n\n- supply chain complexity (globalization, number of\nactors involved, size\nof companies)\n\n- level of regulation\n(less valuable when\nthe regulation is\nalready super strong\nand is monitoring\neverything)\n\n- number of nodes\ninvolved (the higher\nthe number of nodes\nthe higher the safety\nof the system)\n\n- market globalization\n\n- level of regulation\n(proper regulations\nmight be an enabling\nfactor)\n\n## 1 3\n\n\n\n- proper supply chain\ntraceability system\nalready in place\n(with appropriate\nunits of analysis, a\nsingle product or\ncontainer)\n\n- proper supply chain\ntraceability system\nalready in place\n\n- willingness to collaborate with other\nactors in the chain\n\n\n\n- low level of digitalization in the supply\nchain\n\n- definition of the\ngovernance and the\ncentral authority\n\n- difficulty to understand which data\nare appropriate\nto share through\nthe blockchain, to\navoid the risk of\ndata overflow\n\n- missing a technological culture\n\n- difficult to understand how the\ntechnology works\n\n- the cost of blockchain is going to\nimpact the cost to\ncustomers\n\n\n\n- providing customers\nwith data to understand whether the\nprice is representative of the value of\nthe products\n\n- allowing retailers\nto decide to source\nfrom reliable suppliers\n\n- stronger communication with customers\nfor reasons of brand\nawareness\n\n- desire to assure the\nauthenticity and\nthe ownership of\nproducts to end\nconsumers\n\n- providing customers\nwith data to understand whether the\nprice is representative of the value of\nthe product\n\n- marketing desire:\npresent the company\nas innovative and\nwilling to share data\nwith customers\n\n- desire to assure\ntraceability of the\nsupply chain to\nassure sustainable and ethical\nbehaviors\n\n\n_Provider 4_ - reduction of coun- - trust of data provided\nterfeit products by other supply chain\nactors\n\n - accountability for\nwhat different actors\nare responsible for\ndoing\n\n\n-----\n\n**Case** **Internal drivers** **External drivers** **External drivers** **Barriers** **Enablers** **Contingent variables**\n**(company)** **(supply chain)** **(customers)**\n\n\n_Provider 5_ - trust of data provided\nby other supply chain\nactors\n\n - accountability for\nwhat different actors\nare responsible for\ndoing\n\n\n\n- global supply chains\n(the more the supply\nchain is global the\ngreater the need to\nbring information to\nthe consumers)\n\n- SC complexity\n\n- duration of relationships with suppliers\n(best used with\nstable suppliers)\n\n- positioning: the\nmethod is better\nsuited to luxury\nproducts as a product\ncannot cost 5$, and it\nis also necessary to\nshare all the data\n\n- global supply chains\n(to insert data of\nglobal markets such\nas North Korea,\nChina, or Bangladesh)\n\n- supply chain complexity (difficult to\nimplement when\nthere is high SC\ncomplexity)\n\n\n\n- the high cost of the - need to integrate\ntechnology blockchain with\nother technologies,\nsuch as IoT\n\n - willingness to collaborate with other\nactors in the chain\n\n- missing a techno- - willingness to collogical standard laborate with other\nactors in the chain\n\n - proper supply chain\ntraceability system\nalready in place\n\n - willingness to\ncollaborate with\nother actors in the\nsupply chain\n\n- difficult to understand which data\nare appropriate\nto share through\nthe blockchain, to\navoid the risk of\ndata overflow\n\n- the high cost of the\ntechnology\n\n- missing a shared\ntechnological\nstandard in the\nindustry\n\n\n_FC 1_ - business efficiency\nand reduction\nof internal costs\n(e.g., reduction of\ninsurance costs, of\nbureaucracy costs)\n\n_FC 2_ - Simplify the internal processes of\ndata traceability\n\n_FC 3_ - reduction of counterfeit products\n\n - process automation and business\nefficiency and\nreduction of\ninternal costs\n(e.g., reduction of\ninsurance costs, of\nbureaucracy costs)\n\n - reduction of logistics risks\n\n - reduction of the\ncost of compliance\n\n## 1 3\n\n\n\n- trust of data provided\nby other supply chain\nactors\n\n- accountability for\nwhat is reported by\ndifferent actors in the\nchain\n\n- trust of data provided\nby other supply chain\nactors\n\n- accountability for\nwhat is reported by\ndifferent actors in the\nchain\n\n- accountability for\nwhat is reported by\ndifferent actors in the\nchain\n\n- sharing of ethical\nprinciples along the\nsupply chain\n\n\n\n- desire to assure the\nauthenticity and\nthe ownership of\nproducts to end\nconsumers\n\n- marketing desire:\npresent the company\nas innovative and\nwilling to share data\nwith customers\n\n- the desire of new\nconsumers to have\nmore open companies\n\n- providing customers\nwith reliable data\nabout the product\nand the company\n\n- providing customers\nwith data to understand whether the\nprice is representative of the value of\nthe product\n\n- providing end\ncustomers with\nreliable data about\nthe product and the\ncompany\n\n- providing end\ncustomers with\nreliable data about\nthe product and the\ncompany\n\n\n-----\n\n**Case** **Internal drivers** **External drivers** **External drivers** **Barriers** **Enablers** **Contingent variables**\n**(company)** **(supply chain)** **(customers)**\n\n\n_FC 4_ - reduction of counterfeit products\n\n - reduction of the\ncost of compliance\n\n - process automation and business\nefficiency and\nreduction of internal costs\n\n\n\n- duration of the relationships with suppliers (best used with\nstable suppliers)\n\n- supplier commitment\ntowards the company\n\n- level of vertical integration (less relevant\nwhen production\nactivities are owned)\n\n- duration of the\nrelationships with\nsuppliers\n\n- global supply chains\n(more relevant but\nmore challenging for\nglobal supply chains)\n\n- supply chain complexity (globalization, number of\nactors involved, size\nof companies)\n\n- supply chain complexity (globalization, number of\nactors involved, size\nof companies)\n\n## 1 3\n\n\n\n- proper supply chain\ntraceability system\nalready in place\n\n- willingness to\ncollaborate with\nother actors of the\nsupply chain\n\n- proper supply chain\ntraceability system\nalready in place\n\n- need to integrate\nblockchain with\nother technologies,\nsuch as IoT\n\n- proper supply chain\ntraceability system\nalready in place\n\n- proper supply chain\ntraceability system\nalready in place\n\n\n\n- missing a shared\ntechnological\nstandard in the\nindustry\n\n- the high cost of the\ntechnology\n\n- a collaboration\namong different SC\npartners (identify\nthe partners who\nare willing to\ncollaborate in this\nproject)\n\n- a collaboration\namong different SC\npartners\n\n- low level of digitalization in the supply\nchain\n\n\n\n- accountability for\nwhat is reported by\ndifferent actors in the\nchain\n\n\n_FC 5_ - reduction of coun- - trust of data provided\nterfeit products by other supply chain\nactors\n\n - sharing of ethical\nprinciples along the\nsupply chain (verify\nthe origin of raw\nmaterials and production activities;\n\n - verify the sustainability (both social\nand environmental) of\nthe upstream supply\nchain)\n\n - the main competitors\nare evaluating the BC\n(great debate in the\nfashion sector)\n\n\n_FC 6_ - data safety\n\n - reduction of counterfeit products\n\n\n\n- trust of data provided\nby other supply chain\nactors\n\n- sharing of ethical\nprinciples along the\nsupply chain (verify\nthe origin of raw\nmaterials and production activities;\n\n- verify the sustainability (both social\nand environmental) of\nthe upstream supply\nchain)\n\n- the main competitors\nare evaluating the BC\n(great debate in the\nfashion sector)\n\n\n\n- providing customers\nwith data to understand whether the\nprice is representative of the value of\nthe product\n\n- confirm to customers the history of\nproducts (such as the\norigin of raw materials and production\nactivities)\n\n- confirm to customers the history of\nproducts (such as the\norigin of raw materials and production\nactivities)\n\n- storytelling about\nthe product for the\nconsumer\n\n- map the finished\nproduct lots that are\nshipped around the\nworld\n\n\n_FC 7_ - trust of data provided\nby other supply chain\nactors at the international level\n\n - the main competitors\nare evaluating the BC\n(great debate in the\nfashion sector)\n\n\n-----\n\n**Acknowledgements The authors thank AiIG- Associazione italiana di**\nIngegneria Gestionale for supporting the project for young researchers\n(BANDO “Misure di sostegno ai soci giovani AiIG”).\n\n**Funding Open access funding provided by Università degli Studi di**\nPadova within the CRUI-CARE Agreement.\n\n#### Declarations\n\n**Conflicts of interest The authors have no competing interests to de-**\nclare that are relevant to the content of this article.\n\n**Open Access This article is licensed under a Creative Commons Attri-**\nbution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long\nas you give appropriate credit to the original author(s) and the source,\nprovide a link to the Creative Commons licence, and indicate if changes\nwere made. 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ERROR: type should be string, got "https://doi.org/10.1007/s10916 020 01620 8\n\n\n**SYSTEMS-LEVEL QUALITY IMPROVEMENT**\n\n\n# Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories\n\n**Davide Calvaresi[1]** **· Michael Schumacher[1]** **· Jean-Paul Calbimonte[1]**\n\n\nReceived: 6 May 2020 / Accepted: 16 July 2020\n© The Author(s) 2020\n\n\n/ Published online: 2 August 2020\n\n\n**Abstract**\nPatients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after\nsurgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can\nproduce an individual burden to the patient, who is often at home without the full support of healthcare professionals.\nAlthough technological solutions –in the form of mobile apps and wearables– have been proposed to mitigate these issues,\nit is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized\nand effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient\ntrajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized\nsupport. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to\neffectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present\na novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology\nfor modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used\nin order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This\npaper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture,\nincluding an example of its use through a case scenario for cancer survivors support.\n\n**Keywords Patient trajectories · Semantic modeling · Agent-based modeling**\n\n\n## Introduction\n\nThe importance of sustained support over extended periods\nof time is particularly important for patients, especially for\nrehabilitation, chronic diseases, or other conditions such as\nthose affecting cancer survivors. In these situations, patients\nare often left at home, expected to continue their lives and\nactivities, while dealing with potential complications and\nissues inherent to their health conditions [27]. To support\nthem effectively in this delicate phase, healthcare providers\n\nThis article is part of the Topical Collection on Healthcare\n_Intelligent Multi-Agent Systems (HIMAS2020)_\nGuest Editors: Neil Vaughan, Sara Montagna, Stefano Mariani,\nEloisa Vargiu and Michael I. Schumacher\n\n� Jean-Paul Calbimonte\n[jean-paul.calbimonte@hevs.ch](mailto: jean-paul.calbimonte@hevs.ch)\n\n1 University of Applied Sciences and Arts Western Switzerland,\nHES-SO Valais-Wallis, TechnoPole 3, CH-3960,\nSierre, Switzerland\n\n\nneed to have a sufficient understanding of the individual\npathways of each patient, as well as the potential risks\nand courses of action [19]. Each patient may respond differently to treatments, depending on a series of factors,\nincluding demographics, health conditions, psychological\naspects, social and emotional characteristics, etc. Although\nit is undoubtedly complicated and even expensive to have\nsuch a detailed picture of each patient’s situation using traditional approaches, nowadays, the use of digital solutions for\npersonal data monitoring and coaching opens the ways for\npersonalized healthcare. Such solutions include the usage\nof artificial intelligent (AI) techniques —including machine\nlearning (ML) based data analytics— through the exploitation of large volumes of personal health data acquired\nfrom patients going through different health pathways.\nThe concept of illness trajectories [31], describing the\ndifferent events and situations a patient experiences through\na given illness, can be broadened to what is called a patient\ntrajectory [3]. Beyond the scope of an illness, a patient trajectory encompasses contextual data from the patient, even\nbefore diagnosis, and may include multiple co-morbidities,\nas well as emotional and social indicators, self-reported\n\n\n-----\n\noutcomes, and wellness monitoring observations during\nand after treatment [14, 41]. The usage of data analytics based on ML techniques applied to this vast body of\ndata can provide a number of features including: patient\nstratification, identification of unusual behavior patterns,\nprediction of wellness and distress parameters, assessment\nof home exercise performance, improvement of adherence\nto treatment, identification and prevention of risk situations. On the one hand, the information contained in these\ntrajectories requires managing and integrating (potentially)\nvery diverse types of data, ranging from electronic health\nrecords [8, 18] to self-reported observations [20] or sensor\nmeasurements recorded by a wearable device [10]. The data\n_variety and distribution aspects are, therefore, fundamental_\nproblems to be addressed. On the other hand, as a consequence, the management of this information requires taking\ninto account specific concerns regarding data distribution,\nreuse conditions, sharing among different care structures,\nconfidentiality & privacy. In particular, the agent-oriented\napproach characterizes the majority of assistive systems\noperating with distributed and heterogeneous data [12].\nAgent-based systems can ensure a high-degree of personalization [4], autonomy, distributed collaborative/competitive\nintelligence, and security.\nTherefore, in the context of patient trajectory analytics,\nthe main high-level requirements are: to handle broad-scope\ninformation, heterogeneous data-sources, and distributed\ndata producers and consumers. These requirements entail\nscientific challenges related to (i) the modeling of patient\ntrajectories under heterogeneity constraints; and (ii) the\ndesign of decentralized digital infrastructures for analyzing\nand sharing these trajectories. In this paper, we propose\naddressing these two challenges by introducing an agentbased modeling approach that relies on the use of semantic\nmodeling of patient trajectories. The rationale behind this\ndesign is that ontology models can effectively help to\ndescribe events and circumstances of a patient with respect\nto her health condition, while autonomous agents can\nrepresent her interests facing other agents, which may\nact on behalf of other patients, healthcare providers, and\ndata analytics processes. The agent paradigm, in this case,\nguarantees that patients (through their agents) can establish\nand negotiate how and what data is collected from them,\nwhich data sources can be considered, which data is shared\nand with whom, or what kind of processing is allowed. In\nthe same way, healthcare professionals may request through\ntheir agents, what kinds of data are requested form a patient\ntrajectory, which kind of data analytics are necessary, and\nwhat other collaborations or cooperation mechanisms are\nneeded with other physicians, nurses or other personnel.\nThe main contributions of this work can be summarized\nas follows: we (i) identify the main challenges for decentralized analytics of patient trajectories (“Challenges in patient\n\n\ntrajectories: Modeling and analytics”); (ii) establish a set of\ndesign principles of agent interaction models for patient trajectories represented through ontologies (“Patient trajectory\nagents: Design principles”); (iii) propose a multi-agent\narchitecture that complies with those principles (“Agentbased architecture for patient trajectory management”); and\n(iv) provide an example of how this approach can be applied\nin the context of cancer survivor trajectories (“Case study\nscenario: Trajectories of cancer survivors” and “Cancer\nsurvivors support with τ Agents”).\n\n## Case study scenario: Trajectories of cancer survivors\n\nCancer is one of the main causes of death worldwide,\nand diagnosed cases are expected to increase significantly\nin the next decades [9]. Although the different forms of\ncancer affect a large portion of the population, including\nmillions of patients in working age, recent advances in\nearly detection and treatment are already showing promising\nresults [34]. In Europe, more than 50% of cancer patients\nsurvive five years or more after diagnosis, and a number\nof them are able to return to work and daily life activities,\nalthough experiencing side-effects and other conditions due\nto their treatment [29]. These patients endure different\nphysical and psychological issues after cancer treatment has\nceased, potentially during long-term periods. These issues\nare known to affect the quality of life (QoL) significantly\nand include reduced physical activity, increased fatigue,\nfear of cancer recurrence, emotional distress, etc. [24, 38].\nAlthough there is evidence that specific changes in behavior\ncan lead to better outcomes for survivors [21] –e.g., changes\nin diet, moderate exercise, cognitive therapies– in practice,\nit is difficult to adapt these recommendations to individual\nneeds, preferences, expectations, and motivation factors.\nUnderstanding the trajectory of cancer survivors can constitute a fundamental starting point in order to provide useful\nand personalized suggestions or support [26]. Trajectory\ninformation can be acquired from several sources, including\nthe EHR of each patient, self-reported information, behavior questionnaires, or wearable data. Events in the trajectory\ncan be used to identify associations between symptoms,\nand events, such as therapies, interventions, admissions, readmissions, etc. (Fig. 1). Trajectories can be used to assess\nrisks as well as to establish predictive models associating symptoms, diseases and outcomes. As we can see in\nFig. 1, the trajectory of a patient has a direct incidence not\nonly on her physical well-being but also on the social and\npsychological aspects of her life. Therefore, the trajectory\ninformation can help coping with disease sequels and issues\naffecting physiological and physical characteristics, while\nalso supporting a broader scope of quality of life aspects.\n\n\n-----\n\n**Fig. 1 Schematic view of a**\npatient trajectory over time, with\nrespect to general well-being\nand distress. Notice that the\ntrajectory can be analyzed for\ndifferent aspects, e.g. physical,\npsychological, social\n\nAn additional difficulty for managing cancer survivor\ntrajectories is the need to share data among different institutions and entities, entailing an inherently distributed scenario, while guaranteeing privacy requirements. Survivors\nare generally at home, and a lot of the information produced at this point is acquired through apps, self-reported\noutcomes and other instruments. Moreover, EHR data may\ncome from different hospitals and clinics where the patient\nwas treated, e.g. for chemotherapy, physiotherapy, radiotherapy, or surgery, even in different geographical locations.\nWithout coordination mechanisms, the patient is left with\nthe burden of managing her own data, and having to use adhoc procedures for sharing it among clinical and medical\nprofessionals.\n\n## Challenges in patient trajectories: Modeling and analytics\n\nThe modeling of patient trajectories is not straightforward,\ngiven the diversity of information sources, and the broad\nscope of data that they may include, from demographics\nto physiological or psychological observations. We can\nsummarize these challenges according to the following\naspects:\n\n**Trajectory information heterogeneity A fundamental issue**\nfor the modeling of trajectories is related to the vast number\nof information that can potentially be integrated. Depending\non the objectives of the analytics to be performed, trajectories must be able to include different types of data.\nFor example, in Table 1, we identify items form EHR\nand other sources that could be relevant for the trajectory of a cancer survivor [14, 41]. The degree of heterogeneity requires the usage of models that incorporate\nsemantics, potentially spanning very different aspects: diagnostics, treatments, medication, laboratory, imaging, quality\nof life, etc.\n\n**Patient data sources Trajectory information may be acquired**\nfrom different repositories and devices. Models must define\n\n\ninteraction mechanisms for acquisition, negotiation, and\nexchange of trajectory data from heterogeneous sources\n(see Table 1). For example, cancer survivor data may\ninclude retrospective information extracted from EHR\nrecords in one or more hospitals and clinics. It may also\ncomprise continuous measures from a wearable device\n(e.g., for physical activity), or even chatbot interactions and\nquestionnaire responses (e.g., emotional assessment).\n\n**Trajectory data integration & aggregation In order to**\nanalyze trajectories, it is necessary to combine not only\ndifferent data sources but also from large numbers of\npatients. Using machine learning or other AI techniques, it\nis then possible to extract relevant insights, derive patterns,\nand classify trajectory trends. The acquisition of these data\nrequires protocols for establishing the conditions on which\ndata will be used, how it will be processed, and what\noutcomes might be obtained.\n\n**Life-long dynamic trajectories Trajectories can span several**\nyears, and may also include live data collected daily\n(or instantaneously) through sensing devices. Trajectory\nanalysis must be able to cope with this dynamicity and\nincorporate on-demand analytics that adapts through time\nand according to the evolution of the patient pathway.\nFor example, trajectory predictions can help dramatically\nimproving quality-of-life indicators in cancer survivors.\n\n**Data analytics explainability Although AI-based analytics**\nhave shown impressive results for classification, prediction,\nand pattern identification, they often lack in terms of\nunderstandability and interpretability. Patient trajectory\nanalytics should be able to provide explainable outcomes,\npotentially combining and reconciling complementary\npredictors. In particular, for cancer survivors explanations\ncan lead to stronger motivation and self-efficacy regarding\na therapy or treatment.\n\n**Privacy and confidentiality Given the sensitive nature of**\ntrajectory data, privacy has to be guaranteed along the\nprocess of acquisition, exchange, processing, and storage.\n\n\n-----\n\n**Table 1 Relevant aspects for**\npatient trajectories of cancer\nsurvivors from different\nsources\n\n\nAspects Potential parameters Source\n\nDemographics age, gender, marital status, employment, etc. EHR\nGeneral indicators BMI, weight, height, blood pressure, etc. EHR +\nMonitoring\nDiagnosis Cancer type, disease stage, tumor location, EHR\ntime after diagnosis, etc.\nTreatment surgery, ostomy, radiation, chemotherapy, etc. EHR\nCo-morbidities hypertension, diabetes, CVD, chronic lung disease, EHR\nhigh cholesterol\nSymptom burden fatigue, sleep disturbances, depression, pain, Self-reported +\ncognitive dysfunction, insomnia Monitoring\nQuality of life physical, psychological and social functioning Self-reported\n\n\nFollowing current regulations in privacy (e.g., GDPR in the\nEU), patients’ rights must be respected, e.g., granting access\nto selected data, accepting or rejecting consent conditions,\ndeleting personal data partially/entirely, or obtaining one’s\npersonal data collections.\n\n## Patient trajectory agents: Design principles\n\nTo address the challenges described in “Challenges in\npatient trajectories: Modeling and analytics”, we propose\nthe representation of trajectories using semantic models\nand embedding interactions in a multi-agent environment\naccording to the following design principles.\n\n**Ontology-based trajectory modeling Our model proposes**\nusing ontologies to represent trajectories, as well as\nconnected aspects, including illnesses, admission/discharge\nevents, periodical observations, diagnosis, etc. As a result,\ntrajectories can be represented as knowledge graphs with\nprecise semantics and upon which reasoning and analytics\ncan be applied [6, 7]. The advantages of using ontologies\nare numerous, as they provide semantics-by-design, allow\novercoming heterogeneity, facilitate the interconnection of\ndiverse sources, and can be used as the backbone of logicbased reasoning. In particular, this paper focuses on the\nuse of the widely used schema.org [22] vocabulary (see\nFig. 2), which contains a set of medical concepts related to\ntrajectory aspects, including symptoms, medical conditions,\ntherapies, diagnosis, etc.\n\n**Standard semantic vocabularies Several ontologies have**\nbeen standardized, especially in the health domain. These\ninclude medication standards, laboratory codes, diagnosis,\nbiomedical concepts, among many others. Moreover,\ngeneric health vocabularies, such as the schema.org medical\nterms, can be used to have a common way of referring to\ntrajectories and their related concepts. Our architecture, as\n\n\nseen later, is based on the use of standard semantic models,\ni.e., RDF and ontologies in the health domain. As seen in\nFig. 2, the popular schema.org vocabulary contains standard\nterms, which can be complemented with specific medical\nontologies like MeSH [32] or ICD-10 [33]. Moreover, as\nseen in Fig. 3, we can use these terms to represent the\ndifferent events and stages in the patient trajectory, e.g.,\nsymptoms, therapies, surgical procedures, conditions, etc.\n\n**Agent-based entity modeling. The multi-agent paradigm**\nenables decentralized interactions among entities concerned\nwith patient trajectories. These include the patient itself,\nwhich includes her behaviors, goals, and knowledge. Data\nacquisition processes can also be modeled as agents,\ncoordinating trajectory building with other agents that\nimplement analytics processing, confidentiality negotiation,\nor aggregation on behalf of a clinical institution (e.g.,\nfor a research study). We propose modeling all entities\nintervening in the generation, processing, and consumption\nof trajectory information.\n\n**Multi-agent behaviors for trajectory interactions Interac-**\ntions among agents managing trajectories can be governed\nthrough dynamic behaviors, considering changes that may\noccur during the period of observation or study. These\nbehaviors may include ML or other AI-based processing\nof trajectory data; or in a meta-level, the negotiation of\nexchange of trajectories. Regarding data aggregation, the\nbehavior of an agent representing a clinical study may\nrequire managing interactions within a cohort of patients\nor the request for crowd-sourced data. In all of these, the\ndecentralized nature of these behaviors makes it possible to\navoid top-down governance schemes, which are unfeasible\nin multi-party clinical studies and support environments.\n\n**Negotiation in trajectory processing The** multi-agent\nparadigm includes the possibility of incorporating negotiation mechanisms at different levels of trajectory analysis.\n\n\n-----\n\n**Fig. 2 Excerpt from schema.org [22] of relevant medical concepts for patient trajectories. For simplicity, empty boxes represent unspecified types**\n\n\nFor example, a processing agent using ML techniques may\nrequire detailed EHR records for training, which could\npotentially clash with a patient agent’s goal regarding data\nanonymity. A negotiation could be established to comply\nwith both parties’ expectations. Other negotiation protocols\ncan be set up, for instance, by coaching agents, which may\npropose different treatment strategies to a patient agent. A\ndialogue between the two parties can then be established\nin order to agree on the most suitable strategy to follow\njointly. Our model considers these negotiation patterns a\nfundamental element in the decentralized management of\npatient trajectories.\n\n**Personaldataprivacyinteractions Agents must be designed**\nto comply with existing regulations for data privacy (e.g.,\nGDPR). In this regard, it is fundamental to consider semantic models representing personal data handling concepts,\nincluding consent, purpose, processing, legal basis, controllers, and recipients, among others [36]. Agents can,\ntherefore, exchange patient trajectory data, only if consent\n\n\nrequirements are met, and according to the legal constraints\nreflected with these semantic vocabularies.\n\n## Agent-based architecture for patient trajectory management\n\nThis section presents a conceptual architecture of an agentbased approach for patient trajectory management, relying\non the use of ontology-driven data models. The central element in this architecture is the τ Agent, which s a patient\ntrajectory management agent (Fig. 4). Agents of this type\ncan play different specific roles, such as a patient agent,\na processing agent, coaching agent, aggregator agent, and\nacquisition agent. A τ Agent is characterized by a set\nof goals, beliefs, and behaviors; and includes a specialized knowledge graph of patient trajectory data (partial,\ncomplete and/or aggregated). Moreover, it employs a set of\nchannels for communication with other τ Agents, a scheduler for establishing task allocation strategies, a set of\n\n\n**Fig. 3 Schematic view of a patient trajectory, aligning with schema.org medical concepts: symptoms, conditions, therapies, surcial procedures, etc**\n\n\n-----\n\n**Fig. 4 Schematic view of τ** Agents for managing patient trajectories\n\nstandard ontologies for trajectory and medical data representation, and (optionally) a set of ML analytics components.\n_τ_ Agent goals may differ according to the assumed\nrole [39]. For a patient τ Agent, the goals may be related, for\ninstance, to quality of life indicators. For example, a goal\nof an agent acting on behalf of cancer survivor, could be\nto retain moderate physical activity over a certain period,\nin order to reduce risk factors of recurrence. Conversely, a\ncoaching agent may define goals regarding the adherence\nof its assigned patients to their individual treatments or\ntherapies. This could be measured using different indicators,\ne.g., through quantitative instruments.\nSimilarly, beliefs can be defined differently according\nto the agent role. In general, beliefs include metadata of\nother agents (e.g., patient agents subscribed to a coaching\nagent, or potential trajectory contributors for training a\nML agent), health vocabularies, constraints, and privacy\npolicies. These beliefs can be crucial later on, for example,\nduring a negotiation among different agents. For instance,\na coaching agent belief set can be periodically updated in\norder to follow the evolution of a patient trajectory, so that\nfuture support actions are adapted to the current situation.\nBehaviors may require access to different functionalities. In\nthe case of processing τ Agents, this may include gateways\nfor machine learning methods or reasoning over the\ntrajectory knowledge graphs. All communication channels\nin τ Agents use RDF [16] as underlying representation\nmodel (Figs. 4 & 5).\nIn Fig. 5 we provide a detailed example of interactions\namong τ Agents assuming different roles. A patient agent\nacting on behalf of a human may solicit data from data\nacquisition agents, i.e., those gathering data from sensors\n\n\nin the patient environment. Upon negotiation of the data\nacquisition terms, sensor agents may periodically send data\nto the patient agent, which can then construct its own\ntrajectory, which will be part of its own beliefs. Then,\nan aggregator agent may request, through a negotiation\nprotocol, data to several patient agents. To accept or\nreject this request, the different privacy regulations and\npreferences, as well as usage and consent information,\nare fundamental. Patient agents agreeing to aggregate\ntheir data, will probably expect further processing to\nproduce actionable feedback. Precisely, a processing agent\nmay then use the aggregated trajectories to create (e.g.,\nprediction) models using ML techniques. The outcomes of\nthe processing of patient trajectories can then be used by a\ncoaching agent to provide support and recommendations to\nthe patients that initially contributed their data.\nAs can be seen, this conceptual architecture emphasizes\non the decentralized nature of patient trajectory interactions.\n_τ_ Agents can respond to entirely different goals, even\nleading to potential conflicts that would require negotiation\nto be solved. Moreover, the approach also encourages\nsupport for different levels of commitment within the\nagent environment. This responds to the personalized\nrequirements of patient support systems. For example,\ncancer survivors may have different levels of adherence to\ntreatment and very different illness pathways.\nInteractions among τ Agents can be embedded in\nstandard agent protocols such as FIPA [1]. For example,\nas seen in Fig. 6, a coaching agent may require prediction\nresults from a processing agent, regarding potential\noutcomes of a given patient. This request can be encoded\nas a Request Interaction Protocol, to which the processing\nagent may agree or refuse. In case of acceptance, the\n\n\n-----\n\n**Fig. 5 Interactions among**\n_τ_ Agents assuming different\nroles. All interactions rely on the\nusage of semantic RDF\nmessages\n\nprediction data can be transmitted. All interactions are\nencoded in RDF in the proposed architecture.\n\n## Cancer survivors support with τ Agents\n\nTo illustrate the different interactions among τ Agents, we\npresent excerpts of semantically annotated data representing\nexcerpts and parts of patient trajectories, for the case\nscenario of colorectal cancer survivors.\nConsider a patient who has survived colon cancer and\nis now following a long-life support program. His patient\nagent is in charge of managing his patient trajectory, and\nfor this purpose, it collects EHR information available\nfrom agents representing the different hospitals and clinics\nwhere he was treated. Moreover, and assuming that the\nsupport program includes the usage of wearable devices\nthat monitor physical activity, stress, and behavior, the\npatient trajectory can be completed with live data integrated\ncontinuously.\nIn Listing 1, we illustrate how we can represent a set of\nsymptoms from a patient, using the schema.org vocabulary.\nIn the example, the patient symptoms are encoded as\nMedicalSymptom instances, with codes referring to a\nspecific medical coding system (in this case, the ICD-10\n\n**Fig. 6 τ** Agent interaction\nfollowing the FIPA request\ninteraction protocol\n\n\nstandard). These symptoms, i.e., fatigue, rectal bleeding, and\ndiarrhea, can be integrated as part of the patient trajectory\nand could be used later for stratification or classification.\nThe symptomatic and diagnosis information is only one\nsmall part of the patient trajectory. Additional information\ncan be appended, including the colon cancer diagnose itself\n(Listing 2), treatments such as a colonoscopy, epidemiology,\nrisk factors, stage of cancer, etc. Many of these pieces of\ninformation can be used in different ways during a support\nprogram. Just as an example, considering that risk factors\nsuch as polyps or smoking habits can be linked to future\nrecurrence of cancer, the coaching agent may choose to\npropose actions that reduce those risks. Notice that we can\nuse different coding systems, as in the case of risk factors,\nwhere the MeSH [32] standard is employed.\nFurthermore, during the program, a cancer survivor may\nsuffer not only from physical problems but also from\npsychological issues. As an example, consider that the\npatient suffers from anxiety, mainly due to the fact of having\nfear of recurrence. Using a self-reported questionnaire (e.g.,\nthrough a mobile app), or supported by wearable devices\nthat compute stress levels, and anxiety symptom can be\nestablished, encoded with ICD-10 in Listing 3.\nHaving this information, the coaching agent can propose\nactions, in this case potential therapies and activities that\n\n\n-----\n\n**Listing 1 Example of symptoms**\nencoded with ICD-10 and\nfollowing schema.org\nrepresented in RDF Turtle\nformat. All prefixes omitted for\nbrevity\n\n**Listing 2 Example of colorectal**\ncancer details described with\nschema.org\n\n**Listing 3 Example of a medical**\ncondition –anxiety– for a cancer\nsurvivor\n\n**Listing 4 Example of potential**\ntherapies for a cancer survivor\n–flexibility exercises and\npsychological group therapy\n\n\n-----\n\ncould help the patient dealing with his conditions. As\nan example, in Listing 4 we include both an exercise\ntherapy (flexibility) and psychological therapy (group\npsychotherapy).\n\n## Discussion and related work\n\nThe proposed conceptual architecture is based on two\nfundamental ideas: (i) the use of semantic representation\nmodels, and (ii) the multi-agent paradigm. Both show\ncomplementary properties allowing the establishment of\ndecentralized networks of potentially independent agents,\nwhich can establish cooperation and negotiation mechanisms to achieve their goals. Although at this stage, the\nproposed model does not materialize into an implementation, it already establishes the main guiding principles that\nshould be observed. In particular, we can emphasize on the\n_τ_ Agent basic structure, the types of roles that can be implemented, the usage of RDF for inter-agent communication,\nthe reliance on standard vocabularies such as schema.org,\nand of medical ontologies like ICD-10 or MeSH. We believe\nthat this approach can lead to promising results, especially\nfor use-cases where patient trajectories can be exploited\nusing large volumes of data while maintaining personal data\npreferences and guarantees. We identify several aspects in\nwhich further research is required in order to address the\nchallenges identified above, and we relate them to existing\nwork in the literature.\n\n**Ontology agreement Matching terms among ontologies**\nis a long-studied topic, and in this case, it will be\nnecessary to align concepts from different vocabularies, and\neven data models [25]. For example, patient trajectories\ncould be specified both using schema.org and the FHIR[1]\n\nspecifications. Moreover, a large number of medical specific\ncodes can make it hard to overcome potential coding\ndiscrepancies. Several works in the literature have used\nontology-based approaches for health data integration [17,\n30]. However, only few works include the modeling\nof interactions, negotiation, and collaboration among\nintelligent and autonomous systems [11], as in τ Agents.\n\n**Agentautonomy We presented different profiles for τ** Agents,\nincluding specialized sensor data acquisition agents. Nevertheless, given that it is often the case that sensing and\nwearable devices have limited computation capabilities, it\nbecomes challenging to deploy intelligent agents on such\nplatforms. Although there have been recent proposals on\nhow to adapt multi-agent systems to these environments,\ne.g., incorporating real-time support [12] or scheduling\n\n[1http://hl7.org/fhir](http://hl7.org/fhir)\n\n\nstrategies [13], the integration of these data into semantic\ntrajectories remains to be implemented.\n\n**Implementation The** implementation of the proposed\nagent-based model is one of the key aspects to consider\nin the immediate future. This implementation will need to\nconsider the communication interactions as described earlier in the paper and using ontologies such as schema.org as\na first-class citizen. Nevertheless, given the open nature of\nsemantic vocabularies, it is at the same time advantageous\nfor extensibility purposes, but problematic as the number of\nmodels to integrate can be incompatible or hard to align. The\nimplementation will also consider the issues of agent discovery, negotiation implementation, and publishing patient\ntrajectories. Previous works have explored the integration of\nhealth agents through semantic services [11] and ontologybased approaches [23, 40], although lacking the concept of\npatient trajectories.\n\n**Recommendation & support The proposed architecture**\nserves as a platform for eHealth support. Therefore, the\nhigh-level challenge is to provide useful recommendations\nand advice. We plan to implement the use-case for\ncancer survivors, following the principles and examples\nshown in this paper. Beyond existing works in the area,\nincluding eHealth support and Semantic Web architectures\nfor patient support [5, 23], we combine both the modelling\nof trajectories and of agents’ behaviors. An additional\nchallenge will be to effectively assess the adequacy and\naccuracy of the recommendation with respect to the\nsurvivors’ needs, goals, and expectations.\n\n**Explainability A general challenge regarding data analytics,**\nand especially when using ML techniques, is explainability.\nThis is even more important in eHealth, where decisions can\nhave vital consequences. In this case, future work should\nalso consider not only the of symbolic knowledge from ML\npredictors but also the integration of heterogeneous knowledge and negotiation among explainability agents [15].\nAgents may need to have reliable explanations of analysis\nand decisions taken regarding a trajectory, before choosing\na behavior change strategy [2].\n\n**Evaluation and validation Several indicators must be con-**\nsidered for evaluation of this approach, including not only\nperformance metrics for communication and decision making but also considering the effectiveness of negotiations,\naccuracy of data analytics, response time of agent interactions, compliance to privacy policies, etc. While a number of\nontology-based medical system have been evaluated in the\nlast decade [28, 35, 37, 40], the incorporation of trajectory\nand agent-based modelling requires a thorough assessment,\ne.g. by running pilot studies.\n\n\n-----\n\n## Conclusions\n\nIn this paper, we presented a novel approach based on multiagent systems for managing patient trajectories, which are\nrepresented and exchanged using semantic models. We\nidentified first a set of challenges in this context, for which\nwe proposed a corresponding set of design principles. In\nturn, these principles guide our proposal for a conceptual\narchitecture that defined what we call τ Agents, which can\nassume different roles. Furthermore, we exemplified how\nthis architecture can be used to acquire patient trajectory\ndata, aggregate them, and apply AI algorithms to provide\ninput for coaching agents. The entire concept has been used\nto illustrate a concrete use-case, i.e., for cancer survivorship\nsupport. Finally, we have proposed a research agenda that\ncontinues addressing the different challenges described in\nthe paper, targeting not only scientific but also societal\nimpact through the development of decentralized eHealth\napplications.\n\n**Funding Information Open access funding provided by University of**\nApplied Sciences and Arts Western Switzerland (HES-SO). This work\nis partially supported by the H2020 project PERSIST: Patient-centered\nsurvivorship care plan after cancer treatment (GA 875406).\n\n### Compliance with Ethical Standards\n\n**Conflict of interests The authors declare that they have no conflicts of**\ninterest.\n\n**Ethical approval This article does not contain any studies with human**\nparticipants or animals performed by any of the authors.\n\n**Open Access This article is licensed under a Creative Commons**\nAttribution 4.0 International License, which permits use, sharing,\nadaptation, distribution and reproduction in any medium or format, as\nlong as you give appropriate credit to the original author(s) and the\nsource, provide a link to the Creative Commons licence, and indicate\nif changes were made. The images or other third party material in\nthis article are included in the article’s Creative Commons licence,\nunless indicated otherwise in a credit line to the material. 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ERROR: type should be string, got "https://doi.org/10.1007/s10796 023 10443 0\n\n# A Fully Privacy-Preserving Solution for Anomaly Detection in IoT using Federated Learning and Homomorphic Encryption\n\n**Marco Arazzi[1]** **· Serena Nicolazzo[2]** **· Antonino Nocera[1]**\n\nAccepted: 17 October 2023\n© The Author(s) 2023\n\n**Abstract**\nAnomaly detection for the Internet of Things (IoT) is a very important topic in the context of cyber-security. Indeed, as\nthe pervasiveness of this technology is increasing, so is the number of threats and attacks targeting smart objects and their\ninteractions. Behavioral fingerprinting has gained attention from researchers in this domain as it represents a novel strategy to\nmodel object interactions and assess their correctness and honesty. Still, there exist challenges in terms of the performance of\nsuch AI-based solutions. The main reasons can be alleged to scalability, privacy, and limitations on adopted Machine Learning\nalgorithms. Indeed, in classical distributed fingerprinting approaches, an object models the behavior of a target contact by\nexploiting only the information coming from the direct interaction with it, which represents a very limited view of the target\nbecause it does not consider services and messages exchanged with other neighbors. On the other hand, building a global\nmodel of a target node behavior leveraging the information coming from the interactions with its neighbors, may lead to\ncritical privacy concerns. To face this issue, the strategy proposed in this paper exploits Federated Learning to compute a\nglobal behavioral fingerprinting model for a target object, by analyzing its interactions with different peers in the network.\nOur solution allows the training of such models in a distributed way by relying also on a secure delegation strategy to involve\nless capable nodes in IoT. Moreover, through homomorphic encryption and Blockchain technology, our approach guarantees\nthe privacy of both the target object and the different workers, as well as the robustness of the strategy in the presence of\nattacks. All these features lead to a secure fully privacy-preserving solution whose robustness, correctness, and performance\nare evaluated in this paper using a detailed security analysis and an extensive experimental campaign. Finally, the performance\nof our model is very satisfactory, as it consistently discriminates between normal and anomalous behaviors across all evaluated\ntest sets, achieving an average accuracy value of 0.85.\n\n**Keyword Internet of Things, Federated Learning, Blockchain, Autonomy, Reliability, Machine Learning, Privacy,**\nHomomorphic Encryption\n\n\n### 1 Introduction\n\nThe massive distribution of smart and interconnected devices\nis making us spectators and actors, at the same time, of a new\nworld of application scenarios inside the Internet of Things\n(IoT,hereafter).However,asthepervasivenessandautonomy\nof smart things grow, cyber attacks are becoming more and\nmore dangerous and complex (Adat et al., 2018), demanding\nsecurity approaches based on always improved and sophis\nThese authors contributed equally to this work.\n\n### B Antonino Nocera\nantonino.nocera@unipv.it\n\nExtended author information available on the last page of the article\n\n\nticated techniques. This crucial aspect has to be tackled\nbecause security and privacy concerns act as inhibitors of this\nmarket’s future expansion and evolution (Al-Sarawi et al.,\n2020).\nA recent solution to make IoT more robust to possible\nsecurity threats and misuse is the computation of devices\n_fingerprint, used to detect the object anomalies caused by_\nattacks, hardware deterioration, or malicious software modifications (Sánchez et al., 2021). Previous strategies in this\ncontext leveraged features derived from device information\n(i.e., device name, device type, manufacturer information,\nserial number, and so forth) and other basic networking data\nto model the identity of an IoT node (Oser et al., 2018; Kohno\net al., 2005). More recent approaches, based on Machine\nLearning (ML, hereafter) and Deep Learning (DL, hereafter)\n\n## 1 3\n\n\n-----\n\ntechniques, aim at modeling a complete profile of a thing,\ncomposed not only of device and network information but\nalso of the hidden and unique patterns in the behavior that\na node reveals when it interacts with other peers. This so\ncalled behavioral fingerprint is more difficult to be forged by\na malicious adversary, increasing the probability of detecting\npotential misbehavior that may arise due to cyber attacks,\nsystem faults, or misconfigurations (Aramini et al., 2022;\nBezawada et al., 2018; Ferretti et al., 2021; Celdrán et al.,\n2022).\nMost of the approaches based on behavioral fingerprinting fall into two different groups. The first set is composed\nof centralized solutions in which a single hub is in charge\nof training and executing ML algorithms to assess the fingerprint of all the devices of the network. Therefore, due to\nthe use of end-to-end encryption, these solutions cannot take\ninto consideration features obtainable by analyzing private\nmessage payloads exchanged between every pair of nodes\n(Hamad et al., 2019; Miettinen et al., 2017). A second group\nconsists of distributed approaches in which a comprehensive\nprofile can be built, but only concerning a single node point\nof view (i.e., the ML model is trained and executed by a node,\nbased on its direct interactions with a target node) (Aramini\net al., 2022; Ferretti et al., 2021).\nTo overcome these limitations, in this paper, we face the\nchallenge of designing a global model for behavioral fingerprinting considering the information from multiple nodes\nwithout centralizing the solution in a single super-node. To\ndo so, we leverage the novel paradigm of Federated Learning (FL, for short) (Yang et al., 2019). Generally, FL is a\ndistributed collaborative AI approach that allows the training of models through the coordination of multiple devices\nwith a central server, acting as an aggregator, without the\nneed to share the actual datasets (Nguyen et al., 2021).\nIn particular, in an IoT scenario, an aggregator can coordinate multiple objects, called workers, to perform neural\nnetwork training. The main steps can be summarized as follows. First, the aggregator initializes a shared global model\nwith random parameters and broadcasts it to the worker\nnodes.Secondly,forseveraliterations,eachworkercomputes\nits individual model update, leveraging its local dataset. Once\nthe gradient is computed the aggregator receives all model\nupdates and combines them into an aggregated global model.\nFinally, this global update will be downloaded by the workers to compute their next local update. The steps above are\nrepeated until the global training is complete.\nIn our paper, we apply this approach to an IoT scenario\nin which devices with different computational capabilities\ncan cooperate. In particular, the worker devices, in charge of\ntraining local ML models, should be powerful devices with\nsufficient computational capability, memory, and stability.\nThe role of the aggregators, instead, is distributed among\nmultiple devices that can have high or medium computational\n\n## 1 3\n\n\ncapabilities. Observe that, each aggregator collects information from workers to create a global model for one or more\ntargets, but a target node can have only one aggregator. In\nthis way, FL can be simply applied to an IoT environment\nin the form of a “distributed aggregation” architecture, that\ninvolves multiple aggregation servers receiving local learning model updates from their associated devices (Khan et al.,\n2021).\nThis approach presents several points of strength. First off,\nglobal behavioral fingerprints can be computed for a target\nnode by considering aspects captured and modeled by all its\npeers. This strategy allows for enhanced learning accuracy\nrates. Approach scalability is also improved due to the distributed learning nature of FL. Moreover, the raw data are not\nrequired for the training on the aggregator side, thus minimizing the leakage of sensitive information to external third\nparties.\nHowever, the application of this strategy can introduce\nfurther privacy concerns arising from the exposure of sidechannel information. For instance, all the workers involved\nin the learning task would expose their interactions with the\ntarget, and the aggregator would know the identity of the\nmonitored objects.\nIn this paper, we try to face this further issue by designing a Secure Multi-party Computation (SMC, for short)\nscheme based on Homomorphic Encryption (HE, for short)\nanditsproperties.Unlikeconventionalencryptionalgorithms\nsuch as Advanced Encryption Standard (AES) or RivestShamir-Adleman (RSA), HE has been designed to perform\noperations over encrypted data (Gentry, 2009), proving endto-end IoT dataflow privacy. In general, HE has been applied\nto IoT scenarios to securely store data in public clouds, where\ncomputations, such as the training and execution of ML algorithms, can be performed without deciphering and accessing\nthe user’s data (Kim et al., 2018). In our approach, we make\nuse of HE during a safe starting phase. We assume that this\nphase has a sufficient duration to gather enough data to train\nML models in an environment in which the target node is\nfree from possible attacks. Specifically, the main steps of\nthis stage can be summarized as follows.\nEvery node with sufficient computation capability to train\nan ML model contacts the target node (for which it wants to\ncompute the behavioral fingerprint) to exchange a message\ncontainingthenecessaryidentifierparametersencryptedwith\na homomorphic hash function.\nAfter this step, the worker nodes query the Blockchain\nto discover the identity of the aggregator node for the considered target. In our solution, we leverage Blockchain and\nsmart contracts technology for a number of tasks to make\nit fully distributed. In particular, Blockchain is exploited to\nimplement a reputation mechanism to: (i) monitor aggregator nodes at a global level and (ii) store malicious nodes’\ninformation resulting from the application of our strategy.\n\n\n-----\n\nTo achieve this goal, our approach leverages a consolidated\npractice, indeed, Blockchain smart contracts are already\nbeing used to control and secure IoT devices (Christidis &\nDevetsikiotis, 2016; Khan & Salah, 2018), and, in addition,\nlightweight adaptations of a Blockchain have been designed\ntosupportresource-constrainedsmartthings(Corradinietal.,\n2022). As for the reputation mechanism, although this function is orthogonal to our approach, several proposals can be\nused to provide forms of trust in an IoT network (Corradini\net al., 2022; Dedeoglu et al., 2019; Pietro et al., 2018). Nevertheless, in our solution, we adapt an existing schema by\nallowing nodes to assign a trust score (i) to their peers based\non the analysis of their behavior through the proposed behavioral fingerprinting model, and (ii) to an aggregator according\nto its performance during the training phase.\nWith that said, leveraging information exchanged through\na refined use of HE properties, worker nodes can identify a\ncommon aggregator and, this last can, then, group together\nthe ones with common learning tasks. In our solution, the\nsteps above are carried out by maintaining private all the side\ninformation, as a matter of fact, to realize a fully privacypreserving solution, neither the aggregator must know the\nidentity of the target node, nor the different workers should\nknow each other. Finally, as stated before, in our heterogeneous IoT environment all these devices, even less powerful\nones, can benefit from our approach by delegating several\ntasks of our schema to more capable devices. In our strategy,\nalso this additional facility must be privacy-preserving.\nThe outline of this paper is as follows. In Section 2, we\nillustrate the literature related to our approach. In Section\n3, we give a general overview of our reference IoT model\nand describe the proposed framework in detail. In Section\n4, we analyze our security model. In Section 5, we present\nthe set of experiments carried out to test our approach and\nshow its performance. Finally, in Section 6, we discuss the\nlimitations of our paper, draw our conclusions, and present\npossible future works related to our research efforts. In the\nfollowing, we list the main challenges faced and describe the\ninsightful contributions provided.\n\n#### 1.1 Challenges and Contribution\n\nAs described above, the challenges faced by our proposal and\nits main contributions are numerous and we can summarize\nthem as follows:\n\nDynamic threat landscape. IoT devices are constantly\n\n updated and released. Nevertheless, vulnerability exploitation is developed at a similarly high pace. This makes the\nthreats against this context highly dynamic and difficult\nto foresee. We tackle this issue by proposing a behavioral fingerprinting model able to monitor the hidden and\nunique patterns of the behavior of a node in a network.\n\n\nThis tailored countermeasure appears suitable for a constantly changing attack surface.\nIncrease security. We improve the accuracy of behav\n ioral fingerprinting models by building a comprehensive\nobject profile. Indeed, adopting a solution based on FL\nallows us to evaluate the behavior of an object across different services and leverage the interaction with multiple\npeers.\nSolution scalability. Scalability is an issue that affects\n\n various aspects of behavior monitoring approaches, especially in the context of IoT. We face this problem by\nadopting a FL strategy aiming at distributing the monitoring tasks across the nodes of the network.\nLack of interaction data. IoT devices generate traffic by\n\n infrequent user interactions. FL strategy empowers nodes\nwith global models generated from the aggregation of\ndifferent contributions.\nAutonomy. The IoT scenario demands a growing num\n ber of tasks carried out without the need for human\nintervention. We leverage Blockchain and smart contract\ntechnology for several steps in our approach to distribute\nthe computation and increase object autonomy.\nPrivacy of data. IoT devices exchange sensitive informa\n tion, hence the privacy aspects related to behavioral data\nandcorrespondingmodelsplayakeyrole.WeadoptFLto\nsecure data during the training of behavioral fingerprinting models. More importantly, we take a step forward in\nmaintaining the private identity of target nodes and workers leveraging a homomorphic encryption-based strategy.\nIoT device heterogeneity. Many IoT devices have lim\n ited capabilities in terms of available memory, computing\nresources, and energy and, therefore, they are not capable\nof performing complex algorithms. Through our secure\ndelegation solution also less capable devices can benefit\nfrom our approach in a privacy-preserving way.\n\n### 2 Related Works\n\nWith the growing complexity and pervasiveness of IoT-based\nsolutions, the surface and the impact of possible attacks\nagainst this scenario are increasing as well (Hassija et al.,\n2019; Li et al., 2015). In the last years, researchers have\nstudied novel countermeasures to the most disparate type\nof threats to IoT devices (Buccafurri et al., 2016; Kozlov\net al., 2012; Sicari et al., 2016; Tweneboah-Koduah et al.,\n2017), and the latest ones are involving also Machine Learning and Deep Learning techniques (Al-Garadi et al., 2020;\nCauteruccio et al., 2019). In this context, a recent trend is\nto develop ML and DL algorithms to model peculiar characteristics of target objects to detect compromised devices\nwithin a network. The ensemble of these features, that an\nIoT device possesses and reveals when it interacts with other\n\n## 1 3\n\n\n-----\n\nobjects over a network, represents the so called fingerprint.\nClassical device fingerprinting comprehends soft identities,\nsuch as: device name, device type, manufacturer information, serial number, network address, and other features that\ncan be derived from different types of networking information. For instance, the authors of (Oser et al., 2018) identified\n19 features that can be used to assess the security level of\nan object directly from the data-link header of 802.11 messages. Also physical layer information is used, for instance,\nthe work illustrated in (Radhakrishnan et al., 2014) focuses\non the analysis of the physical aspects of devices, like interarrival times of different packets, to fingerprint them. An\nevolution of such an approach that cannot be very easily\ncloned by a malicious adversary, is represented by behavioral fingerprinting (Aramini et al., 2022; Bezawada et al.,\n2018; Celdrán et al., 2022; Ferretti et al., 2021). This type of\ntechnique leverages application-level information to extract\nfeatures concerning the interaction among the devices and,\nhence, their networking behavior. In particular, in (Bezawada et al., 2018) the authors leverage a number of features\nextracted from the network traffic of the device to train an\nML model that can be used to detect similar device types. The\nwork presented in (Celdrán et al., 2022) illustrates a detection\nframework that applies device behavioral fingerprinting and\nML to detect anomalies and classify different threats, such as:\nbotnets, rootkits, backdoors, and ransomware affecting real\nIoT spectrum sensors. As for the work presented in (Aramini\net al., 2022), it describes an enhanced behavioral fingerprinting model consisting of a fully decentralized scenario, where\nit is possible to exploit the features derived from the analysis\nof packet payloads (for instance, different types of devices\nand their traffic characteristics) and message content as well.\nStill, there exist challenges in terms of the performance of\nML-based fingerprinting solutions able to detect a forged or\ncorrupted smart thing in the network. The causes are related\nto scalability, security, and privacy issues and also to the fact\nthat an object can model the behavior of another object concerning its single point of view (i.e., the ML algorithm used\nis thought to evaluate only the services and messages from\nthe interaction of the two things) (Sánchez et al., 2021).\nHence, a new perspective that can comprehend the whole\nbehavior of an object is demanding. Moreover, classical ML\ntechniques require centralized data collection and processing that may not be feasible in IoT application scenarios\ndue to the high scalability of modern IoT networks, growing data privacy concerns, and heterogeneity of devices. To\nface these issues and allow a collaborative ML approach,\nFederated Learning (Khan et al., 2021; Nguyen et al., 2021;\nYang et al., 2019) solutions have emerged with the aim of\ndistributing ML algorithm execution without the need for\ndata sharing. For instance, (Rey et al., 2022) shows a framework that uses FL to detect malware affecting IoT devices\nusing multi-layer perceptron and autoencoder neural net\n## 1 3\n\n\nwork architectures. Whereas the authors of (Preuveneers\net al., 2018) studied FL to design an intrusion detection\nsystem. This work also includes Blockchain technology to\nmitigate the problems faced in adversarial FL, however it\ndoes not focus specifically on IoT devices. Also the authors\nof (Nguyen et al., 2019) used FL, their aim is to build a\ndistributed system for detecting compromised IoT devices\nthrough an anomaly detection-based approach. It consists of\na simple fingerprint of the device based on network packets\nable to monitor changes caused by network attacks. All the\nabove works exploit FL for a different goal concerning ours.\nTo the best of our knowledge, no previous works have used\nFL for behavioral fingerprinting computation.\nTill now we described how the problem of scalability and\nperformances of behavioral fingerprinting computation can\nbe faced through FL. But other challenges arise in this new\nIoT scenario, for instance, the privacy of data exchanged by\nthings.\nTo face the risk of privacy leakage of sensitive information in the IoT caused by the centralized servers’ architecture\nand the weakness and heterogeneity of devices and security\nprotocols, researchers have begun to exploit the potentiality\nof Homomorphic Encryption (Peralta et al., 2019; Shrestha\n& Kim, 2019). For instance, the work presented in (Peralta\net al., 2019) shows a possible application of HE to perform\ncomputations in the cloud maintaining data privacy, and it\nalso reviews a number of challenges in this context, such as\ncomputational cost and lack of interoperability, which will\nrequire further research efforts. However, recently, research\nadvances have made it possible to implement practical homomorphiccryptosystems,atleastinMobileenvironments(Ren\net al., 2021; Shafagh et al., 2017). In particular, the encryption primitive used is the hash function and the operation\nwe exploit is XOR. Homomorphic Hashing, first introduced\nby Bellare, Goldreich, and Goldwasser (Bellare et al., 1994)\nhas been used for disparate application scenarios (Kim &\nHeo, 2012; Lewi et al., 2019; Yao et al., 2018). In particular, (Kim & Heo, 2012) proposes a device authentication\nprotocol for smart grid systems based on the properties of\nthis function to decrease the amount of computation on\na smart meter. Whereas, the approach presented in (Yao\net al., 2018) proposes a homomorphic hash and Blockchainbased authenticated key exchange in the context of social\nnetworks. Facebook researchers design a scheme based on\nHomomorphic Hashing to secure update propagation in the\ncontext database replication, ensuring consistency (Lewi\net al., 2019).\nIn our approach, we leverage the properties of Homomorphic Hashing, in particular, related to the XOR operation,\nto allow the aggregator node, during the safe starting phase\nof our framework design, to identify groups of objects able\nto compute the device fingerprint of a target object, without\nrevealing the identity of the target object itself. To the best\n\n\n-----\n\nof our knowledge, the way we design this algorithm is novel\nand has never been used before.\nA novel research direction to monitor the behavior of\nobjects in IoT networks in a distributed way and provide\nsome forms of trust or authentication is Blockchain (Ali et al.,\n2021; Chen et al., 2022; Dedeoglu et al., 2019; Hammi et al.,\n2018; Nofer et al., 2017; Pietro et al., 2018). In particular, the\nauthors of (Pietro et al., 2018) present a framework based on\nthe concept of Islands of Trust, that are portions of the IoT\nnetwork where trust is managed by both a full local PKI and\na Certification Authority. Service Consumers generate transactions forming an Obligation Chain first locally accepted\nby Service Providers and, then, shared with the rest of the\nnetwork. Also the work presented in (Hammi et al., 2018)\nexploits a similar concept of secure virtual zones (called bubbles) obtained through Blockchain technology, where objects\ncan identify and trust each other. Both the work presented in\n(Corradini et al., 2022; Dedeoglu et al., 2019) try to overcome Blockchain limitations proposing a light architecture\nfor improving the end-to-end trust making this technology\nfeasible to limited IoT devices. The proposal illustrated in\n(Dedeoglu et al., 2019) leverages some gateway nodes calculating the trust for sensor observations based on some\nparameters, such as: nodes reputation, data received from\nneighboring nodes, and the observation confidence. to compute the trustworthiness of a node, if the neighboring sensor\nnodes are associated with different gateway nodes, then, the\ngateway nodes are in charge of computing and sharing the\nevidence with their neighbors’ gateway nodes. This architecture is not fully distributed and secure delegation is not\nperformed; indeed, more powerful nodes are used as gateways. Whereas the work presented in (Corradini et al., 2022)\ndescribes a framework based on a two-tier Blockchain able\nto provide security and autonomy of smart objects in the\nIoT by implementing a trust-based protection strategy. This\nwork leverages the idea of communities of objects and relies\non a first-tier Blockchain to record transactions evaluating\nthe trust of an object in another one of the same community\nor of a different community. After a certain time interval,\nthese transactions are aggregated and stored in the secondtier Blockchain to be globally available. In our approach the\nuse of Blockchain technology is limited to keeping trace of:\n_(i) the identity of the device in charge to act as an aggrega-_\ntor for a target node; (ii) the evaluation of the behavior of\naggregator after the aggregation task to enable the aforementioned FL approach; and (iii) the identity of objects for the\nanomaly detection task. Hence, differently from the abovecited approaches, the core of the strategy is not performed\nthrough Blockchain.\nAnother functionality provided by this paper is the possibility for the less capable devices to benefit and participate in\nour FL approach through secure delegation. This algorithm\nhas been mentioned in the H2O framework (Ferretti et al.,\n\n\n2021), without developing a detailed implementation of it.\nThanks to this paradigm, the training and inference phases\nof our model can be obtained through a privacy-preserving\ncollaborative delegation approach in which power devices\ncooperate and provide support to less powerful ones to implement the solution without revealing the features of the model.\nIn the following, we summarize the comparison with the\nmost important works introduced above based on the different functionalities provided by our approach, namely:\n\nAnomaly Detection: a capability to identify action\n\n sequences that deviate significantly from the expected\nbehavior.\nReputation Model: a functionality that allows a node in\n\n the network to compute a reliability score of another node\nbasedontrustvaluesandaccordingtoitsneighbors’opinion, even if they have not been in contact before.\nPrivacy: the implementation of measures and strategies\n\n to protect the identity of the node during the computation\nof behavioral fingerprint models.\nSecure Delegation: a mechanism allowing devices to del\n egate tasks to more capable peers, by preserving the\nprivacy of the involved nodes’ identity.\n\nWith the letter ‘x’ we denote that the corresponding property\nis provided by the cited paper (Table 1).\n\n### 3 Description of Our Approach\n\nThis section is devoted to the description of our proposal.\nIn particular, in the next subsections, we provide a general\noverview of our approach along with its underlying model;\nwe illustrate our Secure Multi-party computation strategy to\nform groups of co-workers for an FL task; after that, we detail\nour FL-based behavioral fingerprinting solution; finally, we\nsketch the adaption of an existing reputation model into our\nscenario.\n\n#### 3.1 General Overview\n\nThis section details the architectural design of our FL-based\napproach. In particular, we will describe the system actors\nand how they interact with each other during the model\ntraining and evaluation processes. Table 2 reports all the\nabbreviations and symbols used throughout this paper.\nAs typically done in the literature, our model for the considered IoT scenario is based on a directed graph G\n=\n⟨N _, E⟩, where N is the set of nodes and E is the set of_\nedges representing relationships between pairs of nodes. In\nparticular, a link is built if two nodes got in touch in the past\nexchanging one or more messages. Observe that the direc\n## 1 3\n\n\n-----\n\n**Table 1 Comparison of our approach with related ones**\n\nApproach Approach Type Anomaly Reputation Privacy Secure\n\n(Bezawada et al., 2018; Celdrán et al., 2022; Fingerprint x - - Oser et al., 2018; Radhakrishnan et al., 2014)\n\n(Aramini et al., 2022) Fingerprint x - - x\n\n(Preuveneers et al., 2018; Rey et al., 2022) FL, Blockchain x - - \n(Nguyen et al., 2019) Fingerprint, FL, Blockchain x - - \n(Kim & Heo, 2012) HE x - x \n(Yao et al., 2018) HE, Blockchain x - x \nDedeoglu et al. (2019); Hammi et al. (2018); Blockchain x x - Pietro et al. (2018)\n\nFerretti et al. (2021) Fingerprint, Consensus x x - \nOur approach FL, Fingerprinting\n\nConsensus, Delegation, HE x x x x\n\n\n**Table 2 Summary of the main**\nsymbols and abbreviations used\nin our paper\n\n## 1 3\n\n\nSymbol Description\n\nFL Federated Learning\n\nSMC Secure Multi-party Computation\n\nHE Homomorphic Encryption\n\n_N_ The set of IoT nodes of the network\n\n_Nl_ The set of basic devices, a subset of N\n\n_Nm_ The set of devices with medium computation power, a subset of N\n\n_N_ _p_ The set of powerful devices, a subset of N\n\n|N | Cardinality of the set of IoT nodes\n\n_ni_ An IoT device of N\n\n_ci_ A worker device of N\n\n_idci_ The id of a worker device ci\n_b_ A target node\n\n_ab_ An aggregator node for b\n\n_idab_ The id of an aggregator ab\n_�b_ List of workers training a model on b\n\n_�n_ the set of neighbor nodes of n\n\n_H_ Homomorphic Hash Function\n\n_η, ξ_ Nonces\n\n_t_ The size of a sequence of input symbols of the deep learning model\n\n_di_ Delegate node of ci for a task\n\n_thw_ Threshold for mispredicted symbols\n\n_Tci,b_ Trust score assigned by a node ci towards a target node b\n\n_FP_ _wb_ Behavioral fingerprinting function of b during an observation window wb\n_Rb[ω]_ Reputation of b after each time period ω\n\n_τ_ Tolerance value\n\nm A generic Machine Learning evaluation metric\n\n_φban_ Ban interval\n\n\n-----\n\ntion of the link identifies the node that starts to communicate\nduring the message exchange. The group of peers a node ni\nhas been interacting with is the set of neighbors of ni and can\nbe defined as �ni = {n j ∈ _N : (ni_ _, n j_ _) ∈_ _E}._\nMoreover, in our model, N is partitioned into three subsets\naccording to the different object capabilities, thus resulting\nin N = N _p ∪_ _Nm ∪_ _Nl_ . The subset of powerful devices\n_N_ _p includes all the devices with sufficient capabilities in_\nterms of memory and computational strength to perform the\nmore demanding tasks of our approach (e.g., the training\nML/DL models). The second set Nm is composed of devices\nwith medium computational and memory capabilities, due to\ntheir battery constraints or power stability. The last set Nl is\ncomposed of less capable nodes with basic functionalities.\nSince they have limited computational power, they can rely\non delegation to more powerful nodes to participate in our\nframework.\nAs stated in the Introduction, the proposal described in\nthis paper focuses on the computation of behavioral fingerprinting models via FL. To do so, our strategy assumes the\nexistence of an initial phase, called the safe starting phase,\nin which several actors can train ML/DL models to learn the\nbehavior of target nodes in an environment free from possible attacks to these targets (i.e., no attacks are performed\non any involved target node capable of altering its behavior).\nDuring this phase, IoT nodes can play one of the following\nroles:\n\n_Worker. It is in charge of training a local behavioral fin-_\n\n gerprinting model of a target node. Since training such a\nmodel is the more demanding task in our solution in terms\nof computational and memory capability, these nodes\nbelong to the N _p set._\n_Aggregator. They are in charge of aggregating the local_\n\n contributions of the different workers of an FL task to\ncompute a global model for a target. This task is less\ncomputationally demanding than the previous one, hence\nit can be taken over by nodes belonging to N _p_ _Nm (See_\n∪\nSection 5.2 for details on the performance).\n_Target. They are the monitored nodes for which the_\n\n behavioral fingerprinting has to be computed. There are\nno requirements in terms of computational power for\nthem, hence they can belong to any subset of nodes\ndefined above (N _p ∪_ _Nm ∪_ _Nl_ ).\n\nDuring this phase, less and medium-capable nodes belonging\nto Nm _Nl can participate in the scheme leveraging a secure_\n∪\ndelegation approach. In particular, they can entrust nodes in\n_N_ _p to carry out actions on their behalf exchanging data in a_\nprivacy-preserving way. The details of this task are described\nin Section 3.3.\nSubsequently, in the fully operational phase, also referred\nto as inference phase, learned models are used by all the\n\n\nactors to infer possible anomalies on the monitored targets.\nThis phase is less impacting than the training one in terms of\ncomputational requirements, hence all the objects belonging\nto N _p_ _Nm can actively participate in this phase. It is worth_\n∪\nnoting that, also during this phase, less capable nodes belonging to Nl can entrust nodes in N _p_ _Nm for the inference_\n∪\nof behavioral fingerprinting models, through the aforementioned secure delegation strategy.\nThe last actor of our approach is the Blockchain. This technology provides a shared ledger to record trusted information\naccessible to all the nodes over the network. In particular, we\nleverage smart contracts running on the Blockchain to automatically execute predefined actions when certain conditions\nare met. Since smart contracts are stored on the Blockchain,\ntheir code and execution history are visible to all participants\nin the network enhancing transparency in transactions. In\nparticular, we leverage this paradigm to keep track of several\naspects, namely:\n\nThe information necessary to discover the identity of\n\n aggregators for target nodes. In our approach, neither\nthe workers know each other nor the aggregator knows\nthe identity of the target. For these reasons, we design\nour framework to include Blockchain technology, thus\nremoving the need of a trusted central authority or counterpart to keep information private.\nThe trust scores assigned by workers to estimate the reli\n ability of an aggregator. As a matter of fact, the use\nof Blockchain for this task enhances trust and prevents\nmanipulation of scores. Through smart contracts’, code is\nexecuted automatically to compute these complex measures starting from trust scores.\nThe identity of corrupted objects resulting from the mon\n itoring activity of nodes owing behavioral fingerprint\nmodel towards target peers. Once our anomaly detection framework has detected a change in the behavior of\na node, it is important to publish this information in an\nimmutable and trusted ledger accessible by every node\nof the network.\n\nFigure 1 shows the general architecture of our solution\nillustrating the different actors of the model. In particular,\n_c1, c2, c3 are three worker nodes, b is the target node, and_\n_ab is the aggregator for b. The right part of this figure shows_\nthe Blockchain exploited during a number of steps of our\napproach. It is worth noting that, the interactions between the\naggregator and the workers take place only during the safe\n_starting phase to train the behavioral fingerprinting model_\nof the target. In the subsequent phase, nodes communicate\nwith each other and can leverage both trained models and\nthe information stored in the Blockchain to evaluate the\nbehavior of a contact. It is worth observing that, in our scenario, an anomaly in the behavior of a node can be caused\n\n## 1 3\n\n\n-----\n\n**Fig. 1 The general architecture**\nof our solution\n\nby either a hardware malfunction, an environmental change,\nor an ongoing cyber attack. For the estimation of a change\nin the observed node behavior, a true positive will be signaled if the number of unexpected actions as predicted by our\nmodels exceeds a certain threshold. This happens also in the\ncase of some external causes (like environmental changes).\nMoreover, our strategy leverages a mechanism to estimate\ntrust scores on the basis of the detected behavioral anomalies\nand compute nodes’ reputations. If the reputation of a node,\ncomputed by aggregating all the trust contributions towards\nit, goes under a reference threshold, it will be isolated by\nthe other peers and, therefore, it is technically banned from\nthe system (for, at least, a time φban). At this point, system\nadministrators can decide to restore the node or retrain its\nbehavioral fingerprinting models, especially if the external\ncause is known and under control.\nIn the next sections, we will describe our approach in\ndetail.\n\n#### 3.2 A Secure Multi-Party Computation Strategy to Identify Federated Learning Co-Workers\n\nThis section is devoted to the definition of a privacypreserving strategy to identify the correct aggregator for a\nspecific target and, hence, define groups of workers that can\ncollaborate on an FL task. As said above, in our approach,\neach FL task is focused on the construction of a behavioral\nfingerprinting model for a target node of the network.\nIn practice, given a target node b, the above reasoning\ninvolves two actions that must be carried out to configure the\nFL task: (i) the identification of the aggregator for a target\nnode, and (ii) the creation of the group of workers for the\n\n## 1 3\n\n\nsubsequent training task. It is worth noting that these tasks\nare performed by keeping the identities of the involved actors\nprivate. To do so, we develop a privacy-preserving strategy\nfor group formation and identity exchange based on a Secure\nMulti-party Computation (SMC) strategy.\nIt is important to underlying that, as stated above, the\nactions above are performed during a safe starting phase,\nin which no attacks occur against the target b. We assume\nthat such a phase is admissible and, typically, it can coincide\nwith the system setup period or any subsequent maintenance\naction involving b.\nGiven a node ci _N_ _p aiming at learning the behavioral_\n∈\nfingerprints of b. Let idci be the identifier of ci, and let η be\na private nonce generated by b. Finally, let _() be a homo-_\n_H_\nmorphic hash function preserving the XOR operation (Lewi\net al., 2019). Our solution would enforce the following steps.\nFirst, ci contacts b to exchange a message containing\ninformation about idci and a nonce generated by b, say η.\nA suitable payload is generated by b crafting the identifier of\n_ci and η, through a bitwise XOR operation. The result of the_\nXOR operation is transformed by b using the homomorphic\nhash function, thus obtaining the final payload H(idci ⊕ _η)._\nAfter receiving the first contact from ci, b proceeds by\nidentifying its referring aggregator. In our scenario, any node\nof N _p_ _Nm can play the role of the aggregator, provided_\n∪\nthat it is associated with a sufficient trust score. The details\nconcerning the trust mechanism are reported in Section 3.4.\nIn any case, the eligible aggregators along with their trust\nscores are stored in the underlying Blockchain. Once b has\nidentified its aggregator ab, it will create a new transaction\nin the Blockchain to publish this information. However, our\nsolution requires that the association between b and ab can\nonly be disclosed by b to the nodes it wishes to involve in\n\n\n-----\n\nthe subsequent FL task. This would confer to b the capability\nof filtering out unwanted workers from the learning task of\nits behavioral fingerprinting model. To do so, b computes a\nsecret by applying again the homomorphic hash function to\na payload composed of the bitwise XOR between the public\nidentifier of the selected aggregator idab and its private nonce\n_η. Consequently, the public transaction on the Blockchain_\ngenerated by b does not save the plain identifier of its aggregator, but the secret H(idab ⊕ _η)._\nAt this point, when ci wants to gather the identity of the\naggregator selected by b, it will retrieve the transaction generated by b from the Blockchain, containing H(idab ⊕ _η),_\nand it will carry out the following computation. First, it performs a bitwise XOR operation with: (i) the hash received\nby the target, namely H(idci ⊕ _η); (ii) H(idab ⊕_ _η); and (iii)_\nthe hash of its own identity H(idci ). For the properties of\nhomomorphic hashing concerning the XOR operation, we\nhave the following equation:\n\n_H(idci ⊕_ _η) ⊕_ _H(idab ⊕_ _η) ⊕_ _H(idci )_\n\n= H(idab ⊕ _η) ⊕_ _H(η ⊕_ _idci ⊕_ _idci )_\n\n= H(idab ⊕ _η) ⊕_ _H(η)_\n\n= H(idab ⊕ _η ⊕_ _η)_\n\n= H(idab _)_ (1)\n\n\n**Algorithm 1 Discovering Aggregator identity**\n\n**Data: ci ∈** _N_ _p, b ∈_ _N_, ab ∈ _N_ _p ∪_ _Nm_ ; /* node, target\nnode, aggregator node for b */\n_η, H;_ /* nonce of b, homomorphic hash\nfunction for XOR */\n_L = {idx_ _, x ∈_ _N_ }; /* list of the aggregator\nidentifiers in the Blockchain */\n_H(idab ⊕_ _η);_ /* secret for the aggregator of\ntarget node in the Blockchain */\n**Result: idab**\n_ci contacts b;_\n_ci ←_ _H(idci ⊕_ _η) from b;_\n_ci computes H(idci ) ;_\n_ci computes_ _H[�](idab_ _) = H(idci ⊕_ _η) ⊕_ _H(idab ⊕_ _η) ⊕_ _H(idci ) ;_\n**foreach idx ∈** _L do_\n\n_ci computes H(idx_ _);_\n**if H(idx** _) ==_ _H[�](idab_ _) then_\n\n_idab = idx_\n**end**\n**end**\n\n\nNow, ci can retrieve from the Blockchain the list of available\naggregators. For each identifier in such a list, c1 can apply\n_() to it and compare the result with the value from the_\n_H_\nprevious computation. The search for the correct aggregator\nwill be completed when a match is found. Algorithm 1 summarizes the steps above for the privacy-preserving discovery\nof idab . Observe that, the computational complexity of such\nan algorithm is O(|L|), where |L| is the number of possible\naggregators in the system.\nAfter this step, ci is now equipped with the identity of the\naggregator ab for the target b, hence ci is ready to contact ab\nto notify its intention to train a model for b.\nThe steps carried out by ci are repeated by any other node\n_c j of N_ _p interested in a model for b. Our solution does not_\nenforce any restriction on the number of FL tasks an aggregator could be involved in. Indeed, as will be shown in Section\n5, the computational complexity required for the aggregation\nis not very high and, therefore, can be easily executed by any\nnode of N _p_ _Nm. However, ab must identify and synchro-_\n∪\nnize all the nodes related to a specific FL task (i.e., a task\ndedicated to a given target b). Again, our solution enforces\nthat ab must not know the identity of b and, therefore, the\nidentification of the groups of workers can be performed as\nshown in Algorithm 2. In particular, given a list of nodes\n_�ab = ⟨c1, c2, ..., cn⟩_ that contacted the aggregator ab, the\nidentification of the groups of workers is done through an\niterative algorithm. For each worker ci ∈ _�ab the aggregator_\n\n\ncomputes the hash of its identity H(idci ) and performs a bitwise XOR operation with the secret previously received from\n_ci (i.e., H(idci ⊕_ _η)). Due, once again, to the homomorphic_\nproperty of the hash function for the bitwise XOR, this will\nresult in the following.\n\n_�ci = H(idci )_ ⊕ _H(idci ⊕_ _η) = H(idci ⊕_ _idci ⊕_ _η) = H(η)_\n\nNow, for each other node c j ∈ _�ab \\ ci_, the aggregator\nperforms a XOR operation between �ci and the secret previously received by c j, say H(idc j ⊕ _η[′]). Thus obtaining:_\n\n_�ci ⊕H(idc j ⊕η[′]) = H(η)⊕H(idc j ⊕η[′]) = H(η⊕idc j ⊕η[′])_\n\nNow, if η = η[′] holds, then the previous computation will\nbe equal to H(idc j ). Since we assumed that different targets\nwill always have different nonces (no collision between generated nonces), this result would mean that ci and c j share\nthe same target and, hence, they belong to the same working\ngroup �b. Observe that, ab can directly compute H(idc j )\nfor c j to verify the equality between the results of the computation above and the identifier of c j . The computational\ncomplexity for the group identification algorithm is O(�ab _),_\nwhere �ab is the number of nodes that contacted ab for an\naggregation task.\nThe sequence diagram in Fig. 2 summarizes all the steps\nperformed during the safe starting phase of our approach.\n\n#### 3.3 Distributed Behavioral Fingerprinting via Federated Learning\n\nThis section is devoted to the description of the Federated Learning strategy for the computation of behavioral\n\n## 1 3\n\n\n-----\n\n**Fig. 2 The sequence diagram of all the FL setup steps performed during the safe starting phase of our solution**\n\n\nfingerprinting models. Practically speaking, FL is a distributed collaborative machine learning approach that allows\nalgorithm training across multiple decentralized devices\nholding local data samples without sharing the actual datasets\n(Koneˇcn`y et al., 2015). Recently, this paradigm has been\ninvestigated for building intelligent and privacy-enhanced\nIoT applications (Nguyen et al., 2021; Sánchez et al., 2021).\nAlthough few works leverage this strategy for anomaly\ndetection in IoT, they are focused on building classical device\nfingerprints based on basic parameters, like usage of CPU,\n\n**Algorithm 2 Training groups identification**\n\n**Data: ci ∈** _N_ _p, b ∈_ _N_, ab ∈ _N_ _p ∪_ _Nm_ ; /* node, target\nnode, aggregator node for b */\n_η, H, �ab_ ; /* nonce of b, homomorphic hash\nfunction, set of nodes that contacted\n_ab */_\n_��ab = {H(idc j ⊕_ _η[′]) : c j ∈_ _�ab_ }; /* The set of\nsecrets sent by the nodes of �ab to ab\n*/\n**Result: ab ←** _�b;_ /* List of nodes that will\ntrain a model on b */\n_ci ∈_ _�b;_\n_ci −→_ _H(idci ⊕_ _η) to ab;_\n_ab computes H(idci );_\n_ab computes_\n_H(idci ) ⊕_ _H(idci ⊕_ _η) = H(idci ⊕_ _idci ⊕_ _η) = H(η);_\n**foreach c j ∈** _�ab do_\n\n_ab computes H(η) ⊕_ _H(idc j ⊕_ _η[′]) = H(η ⊕_ _idc j ⊕_ _η[′]);_\n_ab computes H(idc j );_\n**if H(η ⊕** _idc j ⊕_ _η[′]) = H(idc j ) then_\n\n_c j ∈_ _�b_\n**end**\n**end**\n\n\nmemory, and so on (Sánchez et al., 2022, 2021). The novelty\nof our contribution concerns the fact that we aim to construct\na global device behavioral profile taking into account all the\ninteractions over the network, even across different services,\na node may provide.\nConsider, for instance, the example shown in Fig. 3 about\na smart thermostat. This device can detect multiple metrics,\nsuch as the temperature and humidity of the room in which\nit is located; it can connect to other smart devices via Bluetooth or directly to the Internet allowing the owner to monitor\nthe home situation, remotely. Moreover, it can control the\nhome heating system according to the detected temperature.\nFinally, it could also communicate with a central home alarm\nsystem in the case in which a fire or anomaly temperatures\nhave been detected. Hence, this device holds interfaces with\nthe actors it interacts with, providing different services to\neach of them. This means that the communications and the\nmessages it exchanges can be very different according to the\nservice it is providing.\nClassical decentralized behavioral fingerprinting solutions (Aramini et al., 2022; Bezawada et al., 2018; Ferretti\net al., 2021) consider only a single interaction sequence to\nbuild a profile of a target node and they neglect a comprehensive point of view coming from the messages exchanged\nbetween the target and its other neighbors. Hence in the\nexample shown above, the home heating system will build\nan ML model of the thermostat, which will differ from the\none trained by the home alarm system or any other smart\ndevice.\nOur strategy leverages FL to build behavioral fingerprinting models combining the perspectives of different workers\n(neighbors of a target node) in a global profile. Ultimately,\n\n\n## 1 3\n\n\n-----\n\n**Fig. 3 Smart Thermostat**\ninteractions in a domotic\nenvironment\n\nthis would depict the behavior of the target device in a more\ngeneral way.\nNevertheless, the global model is fed with the single interaction sequences, for which we leverage an adaptation of\nthe behavioral fingerprinting solution described in (Aramini\net al., 2022). Observe that, according to our fully distributed\narchitecture, a worker has always access to payload data as\nit is the intended recipient of the communication with the\ntarget. Therefore, we can follow the solution described in\n(Aramini et al., 2022), thus including payload-based features in our strategy. These additional features allow also\nfor the protection against cyber-physical attacks, in which an\nattacker tries to jeopardize sensing data to alter the behavior\nof the cyber-physical environment. In addition to payloadbased features, to characterize the behavior of an object this\napproach considers also classical network parameters (i.e.,\nsource port type, TCP flag, encapsulated protocol types, the\ninterval between arrival times of consecutive packets, and\npacket length) altogether with features derived from the payload.Thenitproceedsbymappingthesequenceofexchanged\npackets in a sequence of symbols and leverages a Gated\nRecurrent Unit (GRU) neural network composed of 2 layers of 512 and 256 neurons, respectively, a fully connected\nlayer with size 128, and an output classification layer. The\nchoice of a GRU as the reference model, instead of more\ncomplex architectures (such as LSTM), is due to the need of\nsolving the trade-off between the solution accuracy and the\ncomputational complexity of training behavioral fingerprinting models for IoT nodes. The objective of the deep learning\nmodel is to classify the next symbol given a sequence of input\nsymbols of size t [1].\n\n1 Observe that, the value of t can be fixed based on the dynamicity\nof the object-to-object interactions. In our experiment (see Section 5)\n\n\nIn the remainder of this section, we illustrate how we\napply FL in our approach. In the previous sections, we\nfocused on the description of the setup tasks crucial for\nthe privacy-preserving execution of our scheme, namely: (i)\nthe identification of the aggregator device for a target node,\nand (ii) the creation of groups of workers for FL training\ntask. At this point, since all the roles have been assigned,\nthe aggregator first initializes a global model with random\nlearning parameters. Secondly, each worker gets in contact with the aggregator to receive the current model and,\nafter this step, it computes its local model update. To do so,\neach node leverages its own dataset gathered from the direct\ninteraction sequence with the target node. At each training\nepoch, once the local contribution is computed, the worker\ncan forward it to the aggregator that is in charge of combining all the local model updates and, hence, it constructs an\nenhanced global model with better performance, still ensuring protection against privacy leakages. The last two steps are\nperformed iteratively until the global training is complete.\nFigure 4 sketches the steps described above focusing on\nthe communication between one of the involved workers and\nthe aggregator.\n\n**3.3.1 Leveraging Secure Delegation**\n\nIt is worth observing that, because of the high heterogeneity\nof devices in an IoT network, not all the nodes are equipped\nwith sufficient computational and memory capability to execute the training phase of our approach. Hence, we resort to\na secure delegation mechanism according to which less powerful devices in Nl _Nm can delegate such tasks to powerful_\n∪\n\nfollowing the results described in (Aramini et al., 2022), we set this\nvalue to 10.\n\n## 1 3\n\n\n-----\n\n**Fig. 4 Detailed view of the interaction between a worker and the aggregator during the training of a FL model**\n\n\ndevices in N _p. In the recent literature, some theoretical mod-_\nels and ontologies have been designed for the identification of\nreliable IoT devices for secure delegation, tackling the issue\nof incomplete task requests owned by resource-constrained\nIoT devices (Khalil et al., 2021). Of course, any existing\nsecure delegation strategy could be adopted in our approach.\nHowever, for the sake of completeness, we describe a naive\napproach in which both the training and the subsequent inference phases can benefit from delegation.\nIn particular, in the following, we describe the two scenarios above, separately. We start with the training phase and\nwe consider the situation in which a less capable device, say\n_ci_, is involved as a worker in the construction of a behavioral\nfingerprinting model for a target b. We assume that, due to\nthe lightweight nature of the operations described in Section\n3.2, any node can perform the setup steps for the configuration of the FL task (see the experiments on the performance\nof IoT nodes on these tasks in Section 5.2). In practice, ci can\nexecute both Algorithms 1 and 2 to identify the aggregator\nfor b and become a member of the working group to build its\nbehavioral fingerprinting model. Secure delegating is, hence,\nneeded in the subsequent steps involving the training of the\nlocal ML model.\nAccording to our strategy, given a cryptographic salted\nhash function _(v, s) (Rana et al., 2022), in which v is the_\n_H∫_\nvalue to be hashed and s is the salt, the secure delegation of\nthe training phase requires the following steps:\n\ncollection of interaction packets with the target b;\n\n feature extraction and mapping with the corresponding\n\n symbols (as described before);\npre-processing of the symbol sequence to guarantee pri\n vacy;\nupload of the training set in a shared data bucket linked\n\n in the Blockchain;\nidentification of a trusted delegated node in the network;\n\n interaction with the delegated node to start the training.\n\n \n## 1 3\n\n\nFirst, ci collects a sequence of interaction packets during\nits communication with b. Adopting the approach described\nin (Aramini et al., 2022), it, then, extracts both payload-based\nand network-based features from such a sequence. It, then,\nmaps each unique combination of these features to a corresponding symbol. At this point, a sequence of interaction\npackets is replaced by a sequence of symbols.\nNow, without losing information, to protect the privacy\nof the communications between the worker ci and b, our\napproach imposes that each symbol of such a sequence can be\nconverted into its hash representation using the salted secure\nhash function mentioned above. In this way, only the source\nnode ci can know the mapping between the original symbol sequence and the hashed one. This facility is enabled at\nthe FL task level, i.e. once a node ci expresses its need for\na secure delegation, the whole FL task will be adjusted to\nwork with a converted set of symbols. To do so, ci communicates its need to use secure delegation to the aggregator\n_ab. The latter will, then, generate a salt s that will be sent to_\nall the workers involved in the FL task having b as a target.\nAt this point any packet sequence ⟨ _pkt1, pkt2, · · ·, pktm⟩_\nwill be converted, first into a sequence of symbols according to the values of the considered features of each packet,\nnamely ⟨sy1, sy2, · · ·, sym⟩. Then, each node will apply the\nsecure salted hash function to obtain the hashed sym_H∫_\nbol sequence ⟨H∫ _(sy1, s), H∫_ _(sy2, s), · · ·, H∫_ _(sym, s)⟩._\nObserve that, while the first transformation can be done by\nany node in the network and, hence, knowing a sequence of\nsymbols it is possible to derive information about the original\npacket sequence, due to the property of the adopted cryptographic salted hash function, it is not possible to invert the\nhashed symbol sequence into its original packet sequence.\nAs a consequence, only the nodes involved in the FL task,\nwhich know the salt s, can obtain the hashed symbols from\na sequence of packets and, hence, exploit the trained model.\nAs for the identification of a trusted delegated node,\nour approach can leverage any existing state-of-the-art trust\n\n\n-----\n\nmodel for IoT. In Section 3.4, we provide an overview of a\npossible trust scheme and extend it to include support for the\nidentification of aggregators. The only requirement is that ci\ncan estimate the reliability of its peers so as to identify the\ncorrect delegate di for its task.\nAt this point, ci can share its privacy-preserving training set with di to start the training phase. To do so, we\nleverage IPFS as a global file system in which nodes can\nupload their data. Moreover, the links to IPFS folders are\nsharedthroughtransactionsontheBlockchain.Ofcourse,our\nprivacy-preserving strategy does not require additional security mechanisms on IPFS to protect the training set. Indeed,\nas stated above, any node in the network could use these data\nto train a model, however, only the node involved in the specific FL task will know the salt s and, hence, can perform\nthe mapping between the hashed symbol sequence and the\nreal packet one. With that said, di can carry out the training\ntask for ci by receiving the initialized global model of the FL\ntask from it. At each epoch, di will return the local model\nupdates to ci and it will receive the updated global model for\nthe following training epoch.\nAfter the training phase, ci will receive the final version\nof the trained model from ab. However, if the delegation\nembracesalsothemodelinference,thedelegatednoderetains\nthe trained model to support ci also for model inference.\nIn particular, the secure delegation for the inference phase\nworks as follows. First, ci collects the packet sequence from\nits direct interaction with b. Then, it converts this sequence\ninto the corresponding symbol sequence and, hence, applies\n_H∫_, using the same salt s obtained by ab during the training phase, to build the hashed symbol sequence. This last\ncan, then, be used by di as input to the trained behavioral\nfingerprinting model.\n\n**3.3.2 Exploiting behavioral fingerprints for Anomaly**\n**Detection**\n\nThe steps described above focus on the creation of deep\nlearning models that, given an input symbol sequence, are\ncapableofclassifyingitsnextsymbol.Theadvantagebrought\nabout by our solution is that to estimate the behavior of a\nnode, it considers not only a single point-to-point interaction between two peers, but a community-oriented general\nperspective of the target node. However, although the performance of such a classifier is extremely high as will be shown\nin Section 5, using a single prediction to identify a change\nin the behavior of a node is not adequate and could lead to\nfalse predictions. To avoid this issue, as suggested by the\nrelated literature (Aramini et al., 2022; Nguyen et al., 2019),\nwe adopt a window-based strategy. Specifically, given an\nobservation window, say wb, our approach exploits the afore\n\nmentioned classifier to identify mispredicted symbols. As for\nthe estimation of a change in the observed node behavior, a\ntrue positive will be signaled if the number of mispredicted\nsymbols exceeds a threshold thw. Such a threshold should\nbe suitably tuned to dampen the, even low, false prediction\nrate of the underlying classifier. Practically speaking, if the\noverall confidence of the classifier is 0.80, to dampen the\nprediction errors, thw should be fixed to a value greater than\n20% of the window size. Of course, the choice of the correct\nvalue for thw, although its lower bound can be established\nby the reasoning above, strongly depends on the dynamics of\nthe IoT scenario under analysis. Indeed, a greater thw implies\na slower detection of behavior changes for the target nodes\n(Aramini et al., 2022).\n\n#### 3.4 The Underlying Trust Model\n\nInthissection,wesketchtheunderlyingtrustmodelexploited\nby our solution. Indeed, in the previous sections, we stated\nthat an IoT node can select suitable aggregators and/or delegated nodes by leveraging the information stored in the\nBlockchain about node reliability. Behavioral fingerprinting\ncan be a key factor in the construction of enhanced reputation\nmodels. Indeed, it can be used to estimate anomalous actions\nthat can be grounded on security attacks or device malfunctions. The definition of a model to estimate trust scores and\ncompute nodes’ reputations is an orthogonal study concerning our approach; therefore, to build our solution, we can\nleverage existing proposals to provide forms of trust in an\nIoT network (Corradini et al., 2022; Dedeoglu et al., 2019;\nPietro et al., 2018).\nIn particular, in our proposal, we adopt the approach of\n(Corradinietal., 2022)toestimatetrustandreputationscores.\nIn the following, we briefly sketch the adaptation of such an\napproach into our application scenario. Specifically, in our\ncontext, a trust score can be assigned by a node ci towards a\ntarget node b, for which it holds a behavioral fingerprinting\nmodel, as follows:\n\n_Tci_ _,b = 1 −_ _FP_ _wb_ _(ci_ _, b)_\n\nHere, FP _wb is a function that exploits the behavioral fin-_\ngerprinting model of b to estimate changes in its behavior\nduring an observation window wb. This function can naively\nrecord the number of mispredictions registered during wb\nand compute the ratio between such a number and the total\nlength of the packet sequence exchanged by ci and b during\n_wb. As done in (Corradini et al., 2022), such trust scores can_\nbe published by the monitoring node ci in the Blockchain.\nTherefore, given a fixed time period ω > wb, let T Sb[ω] [be]\nthe set of trust transactions published by any node holding\n\n## 1 3\n\n\n-----\n\na fingerprinting model towards b. Moreover, let Tb[ω] [be the]\naverage trust score in T Sb[ω][. The reputation after each time]\nperiod ω can be computed as follows.\n\n\n_Rb[ω]_ [=]\n\n\n�α · Rb[ω][−][1] + (1 − _α) · Tb[ω]_ if|T Sb[ω][| ̸=][ 0]\n_Rb[ω][−][1]_ otherwise\n\n\nIn this equation, again as stated in (Corradini et al., 2022),\n_α is a parameter introduced to tune the importance of past_\nbehavior observations concerning new ones.\nAs an additional trust contribution, we design a specific\ntrust score for aggregators. An aggregator can be also evaluated based on its honesty in constructing global models\nduring FL tasks. To do so, we introduce an additional check\nthat the involved workers can perform during the training\nepochs. Given a normalized performance metric m, at each\nepoch e, a worker ci can compare the value of m for the\nlocal model, say ml, and for the global one returned by the\naggregator ab for this epoch, namely m _g. In practice, such_\nan additional trust score can be formulated as follows.\n\n_Tci_ _,ab = |ml −_ _m_ _g| · (1 −_ _τ)_\n\nHere, τ is a tolerance value introduced to absorb the\nexpected variations in the values of the chosen metric\nbetween the global and local models. Finally, as for the metric m, it can be any evaluation metric typically adopted for\nmachine learning models, such as the accuracy, the preva_lence, the f-measure, and so forth._\n\n### 4 Security Model\n\nThis section is devoted to the security model underlying our\nsolution. In particular, we introduce both the attack model\nand the security analysis proving that our approach is robust\nto possible attacks.\n\n#### 4.1 Attack Model\n\nWe start this section with a preliminary assumption according to which our approach is applied to a scenario already in a\nstationary situation, or fully operational phase, with enough\nnodes available to carry out all the steps required by our\nscheme. For this reason, we do not consider the initial startup stage, which can be characterized by an IoT network not\nyet active or complete. Moreover, as stated in Section 3, we\nassume the existence of a safe starting phase in which the\nnodes are configured and the behavioral fingerprinting models can be trained.\nIn the following, we list the assumptions useful for analyzing the security properties of our model.\n\n## 1 3\n\n\n**A.1 There exists an initial safe phase in which behavioral**\nfingerprinting models are built in the absence of attacks\non target nodes.\n**A.2 An attacker cannot control the majority of the**\nworkers by training a behavioral fingerprinting model\nassociated with a target.\n**A.3 An attacker has no additional knowledge derived**\nfrom any direct physical access to IoT objects.\n**A.4 The exploited Blockchain technology is compli-**\nant with the standard security requirements commonly\nadopted for Blockchain applications.\n**A.5 The nonces and identifiers of nodes are generated**\nstarting from different key spaces. Moreover, no pair of\nidentifiers or nonces can collide.\n\nAs stated above, our model ensures a list of security properties (SP, in the following), as follows:\n\n**SP.1 Resistance to attacks on Federated Learning.**\n**SP.2 Resistance to attacks on the SMC strategy to identify**\nFL co-workers.\n**SP.3 Resistance to attacks on the Blockchain and the**\nSmart Contract technology.\n**SP.4 Resistance to attacks on the Reputation Model.**\n**SP.5 Resistance to attacks on the IoT network.**\n\n#### 4.2 Security Analysis\n\nThis section presents the analysis of the security properties\nlisted above to prove that our approach can ensure them.\nIn the following, we provide a detailed description of such\nanalysis for each of the properties listed above.\n\n**4.2.1 SP.1 - Resistance to attacks on Federated Learning**\n\nOur approach leverages Federated Learning during the safe\n_starting phase in which the behavioral fingerprinting mod-_\nels have to be trained for target nodes. For Assumption A.1,\nduring this stage models computation is performed in the\nabsence of attacks against target nodes. However, both the\nworkers and the aggregator nodes can be forged or attacked.\nAs for the first case, the large threat surface of the Federated Learning scenario makes this new type of distributed\nlearning system vulnerable to many known attacks targeting\nworker nodes (Jere et al., 2020). In general, these security\nattacks focus on poisoning the model or preventing its convergence. In our approach, we can consider the protection\nagainst these attacks as an orthogonal task. Indeed, in the\ncurrent scientific literature, there exist several countermeasures that FL aggregators can adopt to identify misbehaving\nworkers and, hence, discard their contributions. Examples\nof such strategies are, for instance, the robust aggregation\nfunctions AGRs, such as Krum, Trimmed Mean, and so forth\n\n\n-----\n\n(Blanco-Justicia et al., 2021). These represent lightweight\nheuristics that can be easily adopted in our scenario to provide robustness against common attacks.\nConsideringthesecondcaseinwhichtheaggregatornodes\nare corrupted, our approach natively supports a countermeasure to possible attacks targeting them. Indeed, in Section\n3.4, we include a facility in the underlying trust model to\nevaluate their honesty. The trust score, used to assess the\nquality of its aggregation behavior, is computed by analyzing the performance of partial local models and the global one\ngenerated by the aggregator during each epoch. If this value\ngoes under a reference reliability threshold, the aggregator\ncannot be contacted by other nodes in the future. To avoid\nthe permanent removal from the system of a node, we could\nhypothesize a ban interval, say φban, after which the default\nreputation value will be restored. Of course, for critical scenarios, φban can also be infinite. Therefore, no advantage is\nobtained by the attacker if, after a malicious behavior, the\nnode is forbidden to interact with the network for a possibly\nlong period.\n\n**4.2.2 SP.2 - Resistance to attacks on the SMC strategy to**\n**identify FL co-workers**\n\nIn our scenario, during the phase related to the formation\nof the groups of workers for FL tasks (see Section 3.2), a\nmalicious node can try to contact a victim node, say b, to\ndiscover its secret nonce η. Holding this value the attacker\ncan infer the identities of the workers for the victim b. To do\nthis, it performs a cryptographic attack exploiting the properties of HE. Indeed, it queries multiple times b trying to guess\n_η and analyzing the result. In particular, it sends to b a value_\nthat is not its identifier but a guessing value for η, say η[′]. If\nit succeeds in the guessing of η (i.e. η[′] = η) b will return\n_H(η[′]_ ⊕ _η) = 0. At this point, the attacker can violate the_\nSMC scheme and break our privacy-preserving algorithm.\nThis attack can then be used to implement active eavesdropping, as a malicious node can sense the messages exchanged\nbetween two nodes and try to oust the intended target node\nto take some advantage.\nThis attack cannot happen thanks to the Assumption A.5,\nindeed the nonce and the identifier of the nodes have to be\nchosen in different key spaces. Therefore, an attacker cannot\nguess the nonce of the victim by forging a suitable identifier\nas shown above.\n\n**4.2.3 SP.3 - Resistance to attacks on the Blockchain and the**\n**Smart Contract technology**\n\nThis category of attacks tries to exploit known vulnerabilities\nof the Blockchain and the Smart Contract technology. This\nnew paradigm has been widely used in a variety of applications in recent years, but it still presents open issues in terms\n\n\nof security (Idrees et al., 2021; Kushwaha et al., 2022; Singh\net al., 2021).\nTheapproachpresentedinthispaperdoesnotfocusonfacing security challenges on Blockchain, instead, it leverages\nthis technology to equip the network with a secure public\nledger able to support some functionalities. In particular,\nwe exploit Blockchain and Smart Contracts to keep trace\nof (i) the information necessary to discover the identity of\naggregators for target nodes; (ii) the trust scores assigned by\nworkers to estimate the reliability of an aggregator; and (iii)\nthe identity of corrupted objects resulting from the monitoring activity of workers towards target nodes.\nTherefore, also because our proposal does not aim at\nextending existing Blockchain solutions, we do not consider vulnerabilities and possible direct attacks to it. In other\nwords, for Assumption A.4, we presuppose that the underlying Blockchain solution guarantees the standard security\nrequirements already adopted for common Blockchain applications (Singh et al., 2021), thus it can be considered\nsecure.\n\n**4.2.4 SP.4 - Resistance to attacks on the Reputation Model**\n\nOur strategy includes also a contribution to the computation\nof a trust score to evaluate the trustworthiness of IoT nodes.\nAnyway, although in our approach we described a simple\nadaptation of an existing trust model (Corradini et al., 2022)\ninto our scenario, this task can be considered orthogonal to\nour strategy. Therefore, for our security analysis related to\nthe trust model we can rely on the analysis conducted in\n(Corradini et al., 2022).\nAnyway, just to give a few examples of attacks targeting\nthe trust model of our approach, we consider in the following\nhow our schema proves to be robust against two of the most\npopular attacks on reputation systems, namely the Whitewashing and Slandering (or Bad-mouthing) attack.\nThe former occurs when a malicious node tries to exit\nand rejoin the network to delude the system and clean its\nreliability.\nOur strategy is based on a community-oriented general\nperspective of the trustworthiness of a target node. Indeed,\nto assess the reliability of a node, we adopt a window-based\nstrategy leveraging our behavioral fingerprinting models.\nSpecifically, trust scores are computed based on the rate of\nmispredicted symbols inside an observation window. At this\npoint, if the reputation of the node, computed by aggregating\nall the trust contributions towards it, goes under a reference\nthreshold, it will be isolated by the other peers and, therefore,\nas explained above it is technically banned from the system\n(for, at least, a time φban). Moreover, as an additional security mechanism, if a device is banned multiple times, φban\ncan be incremented at every ban until the object removal is\npermanent.\n\n## 1 3\n\n\n-----\n\nObserve that, in IoT, one of the main issues is related\nto the difficulty of mapping a unique identifier with an\nobject. Therefore, in some cases, an attacker could still\nperform a Whitewashing attack by exiting the system and\nre-introducing his/her device with a different (forged) identifier. To face this situation, we can adopt a pessimistic attitude\napproach, which imposes that newly introduced devices will\nstart in a banned state (no other node will interact with it) for\na time φban, and only after this period they can be part of the\nnetwork. In this way, attempting a whitewashing by forging\na new identifier for a device would result again in the node\nbeing banned for φban time, and no advantage is obtained.\nAs for Slandering or Bad-mouthing attacks, they occur\nwhen an intruder tries to distort the innocent nodes’ reputation by attesting a negative opinion of them. In our approach,\na Slandering or Bad-mouthing attack can happen if a worker\nlies about the result of the behavioral fingerprinting model of\na monitored node computing a false negative trust score for\nthat node.\nIf this threat is performed by a single node, only its local\ncontribution to the trust score is impacted. Hence, the global\ntrust score will not be compromised because it will be balanced by the honest contributions of the other nodes testing\nthe behavioral fingerprinting model for the victim.\nMoreover, these attacks can be performed also in a distributed fashion, through some colluding nodes trying to\npoison the trust score of a victim with multiple negative\ntrust contributions. Anyway, for Assumption A.2, an attacker\ncannot control the majority of workers holding a behavioral\nfingerprinting model for a target. It is worth noting that this\nassumption is commonly accepted for distributed domain\nscenarios,inwhichthemajorityofusersornodesinanetwork\nor a system can be considered honest at any time (Cramer\net al., 1997; Rottondi et al., 2016; Zwierko et al., 2007).\nAs an additional consideration, our approach preserves the\nprivacy of the identity of the nodes forming the group of\nworkers for an object thanks to HE. Hence, the components\nof the group do not know each other, also an attacker cannot have this information from additional knowledge derived\nfromanydirectphysicalaccesstoIoTobjectsforAssumption\nA.3. For all these reasons, our approach can be considered\nrobust against Slandering or Bad-mouthing attacks.\n\n**4.2.5 SP.5 - Resistance to attacks on the IoT network**\n\nAs for attacks undermining network and node availability, we\nconsider the two most popular ones, namely DoS and Sleep\nDeprivation attacks.\nDuring a Denial of Service (DoS) an attacker introduces\na large amount of (dummy) transactions in the network to\noverflow it and affect its availability. In our approach, this\nattack could also result in the impossibility for nodes to run\nthe FL algorithm and check peers’ behavior. For this reason,\n\n## 1 3\n\n\nany existing solution aiming at preventing DoS attacks in\nIoT could be exploited in our approach, such as the ones\npresented in (Abughazaleh et al., 2020; Baig et al., 2020;\nHussain et al., 2020). It is worth explaining that, however,\nour approach does not add any advantage to an adversary\nperforming such a category of attacks.\nA form of DoS attack specific to the IoT environment is\nknown as Sleep Deprivation Attack (SDA, hereafter) whose\nobjective is to undermine the power of the node to consume\nits battery life and power it off (excluding the victim from the\nnetwork). As for this attack, our approach natively supports\na countermeasure. Indeed, the alteration in the behavior of\nan attacked node can be detectable by our behavioral fingerprinting models. Therefore, our approach can prevent SDA,\nbecause once a change in the behavior of the attacked node is\ndetected, the other nodes can safely discard all the requests\ncoming from it.\n\n### 5 Experiments\n\nThis section deals with the analysis of our experimental campaign useful for validating our approach. In particular, in\nthe next subsections, after the description of our dataset, we\nreport in detail the performance evaluation of our solution\nto build a global behavioral fingerprinting model using FL,\nthe results of our solution for anomaly detection, and, finally,\nthe tests to assess the performance of the overall approach in\nterms of execution times.\n\n#### 5.1 The Dataset\n\nTo validate our proposal, we started from a dataset publicly available online concerning IoT traffic collected by a\n[centralized network hub. The dataset is available at https://](https://iotanalytics.unsw.edu.au/attack-data.html)\n[iotanalytics.unsw.edu.au/attack-data.html and has been orig-](https://iotanalytics.unsw.edu.au/attack-data.html)\ninally produced by the authors of (Hamza et al., 2019). It\ncontains about 65 GB of data describing daily IoT traffic\n(i.e., traffic generated by smart devices, such as light sensors,\nmotion sensors, and so forth). The original dataset contains\nboth data generated in the absence of cyber attacks, as well\nas traffic generated when some attack is deployed on the IoT\nnodes. Interestingly, this same dataset has been adopted in\n(Aramini et al., 2022) to test the performance of the original behavioral fingerprinting model which is extended in this\nproposal. The authors of (Aramini et al., 2022) also enhanced\nthis dataset to simulate the collection of traffic from the IoT\nnodes, directly (no central hub collector); thus, granting that\npayload data is accessible from monitoring nodes. Because\nin our scenario, we are also focusing on a fully distributed\ncontext, we adopt the extended version of the above dataset\ngenerated in (Aramini et al., 2022). Some statistics about our\nreferring data are, then, reported in Table 3.\n\n\n-----\n\n**Table 3 Statistics of the dataset considered in our study**\n\nCommunication Type Min # of packets Max # of packets\n\nBenign 12,793 97,256\n\nBenign with payload 4,670 39,000\n\nMalign 6,971 89,148\n\nMalign with payload 2,196 8,694\n\n#### 5.2 Performance Analysis of our Global Behavioral Fingerprinting Model\n\nTo assess the performance of our approach to build a global\nbehavioral fingerprinting model using FL, we performed a\ncomparison analysis between our solution and the baseline\napproach proposed in (Aramini et al., 2022). Indeed, the\napproach of (Aramini et al., 2022) started from the results\nreported in (Nguyen et al., 2019) and demonstrated that,\nby exploiting additional features related to the payload, it\nis possible to improve the solution performance. Indeed, the\nauthors of (Aramini et al., 2022) proposed a fully distributed\nbehavioral fingerprinting model, which, however, is focused\non just a point-to-point vision of a node towards a target\npeer. Our approach, instead, extends this idea by considering that in IoT a node can participate in multiple services,\nthus showing different behavioral patterns according to them.\nTherefore, we aim to build a global model considering all\nsuch patterns to represent the complete behavior of a target\nnode, and we leverage Federated Learning for this objective.\nWith that said, we start our comparison by analyzing the\nperformance of our model and the model of (Aramini et al.,\n2022) for 12 nodes monitoring 3 different targets. As for\nour approach, we extracted from the original dataset groups\nof nodes having communications with the same targets; in\nthis way, we could build our Federated Learning scenario. In\nparticular, after analyzing all the communications available\nin the dataset, we were able to set the number of workers to\n4. Hence, for each target, we obtained a global model built\naccording to our strategy and 4 point-to-point models built\naccording to the strategy of (Aramini et al., 2022). As for\nthe training data, we used the communication sequences but\nwe kept the 20% of them for the subsequent testing. Indeed,\nonce the models have been built, to compare the obtained\nperformance, we used the test set of each involved node,\nindependently. Of course, the point-to-point (P2P) models\nare trained and tested on the data of the same communication\n(direct testing), whereas our global model (GM) is trained on\nglobal data and, then, tested on the individual test sets of the\ninvolved nodes; thus, we can expect a slight reduction in\nthe performance. However, we argue that such a reduction\nis negligible. The results of this experiment are reported in\nTable 4 where we analyzed prediction accuracy results and\n\n\n**Table 4 Comparison of the performance of our approach (GM) and the**\nsolution of (Aramini et al., 2022) (P2P) with direct testing in terms of\nprediction accuracy\n\nModel _c1_ _c2_ _c3_ _c4_\n\nTarget 1 P2P **0.78** 0.75 **0.86** **0.83**\n\nGM 0.77 **0.76** 0.82 **0.83**\n\nTarget 2 P2P 0.81 **0.82** **0.85** **0.83**\n\nGM **0.82** 0.80 0.75 **0.83**\n\nTarget 3 P2P 0.82 **0.89** 0.74 **0.84**\n\nGM **0.86** **0.89** **0.79** **0.84**\n\nin which c1, c2, c3, and c4, for each target node, act as both\nindividual nodes building P2P models of the target behavior\nand the workers of the Federated Learning task building the\nglobal model GM.\nBy analyzing this table we can see that, as expected,\nthe point-to-point models achieve sometimes slightly better performance when tested against a test set derived by the\nsame communication from which the training set has been\nextracted. However, our hypothesis is also correct as the performance reduction of our approach is negligible (less than\n1%, on average).\nHowever, the characteristic of our global model is just the\ncapability of being generally valid for any communication\ntowards a target node (also for communications related to\ndifferent services). To test this aspect, we proceeded with\na similar experiment as above, but we performed a crosstesting and assessed the performance of each point-to-point\nmodel (P2Pc1, P2Pc2, P2Pc3, and P2Pc4 ) and our global one,\non every test set available from the different involved nodes.\nWe reported the results of this experiment in Table 5.\nIn practice, in our testbed, each client owns a dataset referring to its individual communications with the shared target\nnode. From these datasets, for each client, we extracted a test\nset namely, Test-set c1, Test-set c2, Test-set c3, and Test-set\n_c4, respectively. At this point, differently from the previous_\nexperiment, the cross-testing consisted in applying all the\nP2P models and our global one on all the available test sets\nfrom the clients. Of course, when a P2P model, say P2Pc1,\nis applied to the test set belonging to the client that built\n\n**Table 5 Comparison of the performance of our approach and the solu-**\ntion of (Aramini et al., 2022) with cross testing\n\n#Model Test-set c1 Test-set c2 Test-set c3 Test-set c4\n\nP2Pc1 **0.82** _< 0.01_ _< 0.01_ _< 0.01_\n\nP2Pc2 _< 0.01_ **0.89** _< 0.01_ _< 0.01_\n\nP2Pc3 _< 0.01_ _< 0.01_ **0.74** _< 0.01_\n\nP2Pc4 _< 0.01_ _< 0.01_ _< 0.01_ **0.84**\n\nGM **0.86** **0.89** **0.79** **0.84**\n\n## 1 3\n\n\n-----\n\nthis model, c1 in this case, the experiment implies a direct\ntesting, thus returning the optimal performance for that specific model. With this experiment, we aim at demonstrating\nthat, because the communications of different clients with the\nsame target node may concern different services, local P2P\nmodels are not a general solution to monitor the behavior of\na node.\nAs a matter of fact, by inspecting Table 5, we can clearly\nsee that the point-to-point models return satisfactory accuracy results only when applied to the test set generated by\nthe same communication of the original training set (direct\ntesting). The last row of this table, instead, shows the performance of our global model which is very satisfactory\nacross every considered test set. This confirms our intuition\nthat classical behavioral fingerprinting approaches, such as\n(Aramini et al., 2022) and (Nguyen et al., 2019), reach very\nsatisfactory performance assessing the behavior of a node\nconcerning only a single target communication type (i.e.,\ncommunications generated for a specific service or action).\nOur approach, on the other hand, allows for the construction of consistent and complete behavioral fingerprints of an\nIoT node. In practice, the models built by our approach are\nmore stable and can be used to characterize the behavior of\na target node in general, and not just for a specific single\nservice/action it may offer/perform.\n\n#### 5.3 Windows-Based Anomaly Detection with Behavioral Fingerprint\n\nAs described in Section 3.3.2, our approach exploits behavioral fingerprinting models to detect anomalies on target\nnodes by leveraging a window-based mechanism. In particular, once again, our solution is based on the strategy originally\ndescribed (Nguyen et al., 2019) and (Aramini et al., 2022).\nThe proposed strategy works by computing the misprediction rate of the next symbol inside an observation window.\nAs seen in Section 3.4, the misprediction rate is defined as\nthe ratio between the number of symbols inside the windows not predicted by our behavioral fingerprinting model\nas plausible ones in the analyzed sequence and the overall number of symbols in the observation window. Clearly,\nthe choice of the right size for such a window plays a key\nrole. Intuitively, larger windows imply a more stable anomaly\ndetection capability, as any noise, even the one caused by\nthe errors in the predictions introduced by our model, would\nbe smoothed out (smaller oscillations in the misprediction\ncurve). Of course, the larger the window the slower the detection of possible anomalies, since more symbols (and, hence,\nmore packets) would be required to detect it. A possible,\nstrategy for identifying the correct size is to use the difference\nbetween the maximum and minimum peaks of the misprediction curve. Indeed, a lower difference would imply better\nstability. At this point, to find the optimal solution we can rely\n\n## 1 3\n\n\non the Kneedle algorithm (Satopaa et al., 2011). Specifically,\nit seeks to find the elbow/knee in the misprediction curve,\nwhich corresponds to the point where the curve has the most\nvisible change from high slope to low slope. In Fig. 5, we\nshow the application of this algorithm in our context.\nAs shown in this figure, in our scenario, a possible optimal\nconfiguration for the window is 100 symbols.\nWith this setting, we performed a further experiment to\ndemonstrate the capability of our solution to detect anomalies in the behavior of an IoT node and we compared the\nobtained performance with those obtained by related pointto-pointmodels.Specifically,wefocusedagainonthetestbed\nintroduced in the experiment described in Section 5.2, in\nwhich we considered 4 different point-to-point behavioral\nfingerprinting models (P2P models, for short), according to\nthe strategy of (Aramini et al., 2022), built by 4 IoT nodes,\nnamely c1, c2, c3, and c4, and targeting the same node b.\nMoreover, we simulated an FL task involving the same 4\nnodes and built a global model for b (GM, for short) according to our approach. Of course, each involved monitoring\nnode, c1, . . ., c4, collects the portion of traffic originated by\n_b towards it and creates its training and test sets. At this_\npoint, we analyzed the performance of the window-based\nanomaly detection strategy using both the P2P models and\nthe GM model as underlying fingerprinting models. To do\nso, we fixed a threshold of 0.5 (i.e., 50% of the symbols in a\nwindow), so that a misprediction rate higher than this threshold in a window would correspond to the detection of an\nanomalous behavior. Moreover, we simulated the situation\nin which the first 280 packets from b are benign and after\nthat, the node performs an attack. To simulate the attack, we\n\n**Fig.5 ApplicationoftheKneedlealgorithmtoidentifythebestwindow**\nsize\n\n\n-----\n\nused the malign traffic for this node contained in our original\ndataset (see Section 5.1). The obtained results are visible in\nFig. 6.\nAs shown in this figure, the anomaly detection strategy\nusing P2P models works only when the traffic analyzed is\nderived from the test set of the node that built the underlying P2P model. Instead, when it is applied to different test\nsets it cannot distinguish between normal and anomalous\nbehaviors. When our GM model is used instead, the anomaly\ndetection strategy achieves very good performance across all\nthe different test sets (see the subplots in the last line of\nFig. 6). This allows for the construction of a solid anomaly\n\n\ndetection solution for IoT nodes, which is agnostic on the\nspecific services the monitored nodes could be involved in.\n\n#### 5.4 Analysis of Execution Times\n\nThis section is devoted to the tests performed to validate\nthe feasibility and effectiveness of our proposal in terms of\nexecution times. Indeed, our approach is designed for an\nIoT scenario, typically characterized by many heterogeneous\ndevices.\nWe start by considering our privacy-preserving schema for\nthe identification of the correct aggregator of a node (Algo\n\n**Fig. 6 Performance of the window-based anomaly detection strategy using both P2P and GM models to monitor a common target**\n\n\n## 1 3\n\n\n-----\n\n**Table 6 Average execution times of Algorithm 1 on different device**\ntypes\n\nDevice Type Average MPC Time\n\nDesktop PC 49.6 ms\n\nRaspberry Pi4 185.3 ms\n\n1 core ARM1176 (QEMU) 774 ms\n\nrithm 1), and for the creation of groups of workers for the FL\ntasks (Algorithm 2). Both cases share a similar strategy and\nare based on the computation of bitwise XOR operations on\nhashed value through homomorphic hashing. Therefore, we\nfocus here on Algorithm 1, which is based on Equation 1,\nand, hence, test the feasibility of this computation on different types of devices. For this experiment, we considered the\nsame Federated Learning scenario analyzed in the previous\nexperiment and derived from the original dataset. Moreover,\nwe considered 3 types of device, namely: (i) a desktop personal computer equippedwithaRyzen75800xOcta-core3.8\nGHz base, 4.7 GHz boost processor, and 32GB of RAM, (ii)\na Raspberry Pi4 with a Quad-core Cortex-A72 processor and\n8GB of RAM, and (iii) a single-core ARM1176 CPU with\n512MB of RAM, emulated with the QEMU virtualization\nenvironment[2]. We executed Algorithm 1 on each considered\ndevice type and reported the results in Table 6.\nBy inspecting this table, we can conclude that our privacypreserving scheme is feasible for all the considered device\ntypes. The computation is, in general, carried out in less than\n1 second with a maximum value of 774 milliseconds for the\nless capable considered device type.\nAfter that, we focused on the computational requirements\nfor the aggregator in our solution. Aggregators coordinate\nFederated Learning tasks and, during each training epoch,\naggregate the gradient updates produced by the workers to\nbuild the global model.\nTo evaluate the execution times of the aggregation task,\nwe considered, again, the 3 types of device and the Federated\nLearning task mentioned above. Hence, we measured the\ntime required, on average, to aggregate the gradient updates\nof the local models (i.e., of the local GRU deep learning\nmodels described in Section 3.3) during the epochs of such\na Federated Learning task. The result of this experiment is\nreported in Table 7.\nThis result confirms again that both our secure multi-party\ncomputationandtheaggregationtaskcanbeexecutedbyvery\nheterogeneous devices including those with limited computational capability (such as a node equipped with a single\ncore ARM1176 and 512MB of RAM).\nAs a final evaluation of execution times, we focused on\nthe performance of the inference of a trained instance of\n\n[2 https://www.qemu.org/](https://www.qemu.org/)\n\n## 1 3\n\n\n**Table 7 Average aggregation time for different device types**\n\nDevice Type Average Aggregation Time\n\nDesktop PC 118ms\n\nRaspberry Pi4 241ms\n\n1 core ARM1176 (QEMU) 755ms\n\nour behavioral fingerprinting model. In particular, we analyzed the impact of our secure delegation strategy in such a\ntask to validate its feasibility. Therefore, we executed model\ninferences with and without the secure delegation strategy\nand computed the execution times for batches of consecutive\nsymbols of variable sizes. The obtained results are reported\nin Fig. 7.\nThis figure shows that the performance reduction introduced by our secure delegation strategy is about 16.6% on\naverage.Althoughsuchadifferenceisnotnegligible,thevery\nlow general inference times of our model make the inclusion\nof the delegation strategy still feasible across all the possible\nscenarios.\n\n### 6 Discussion and Conclusion\n\nIn recent years, IoT devices have grown in number and\ncomplexity to empower new applications with enhanced\npossibilities in monitoring, decision-making, and automation contexts. Clearly, in this scenario, privacy and security\naspects become a major concern.\nThis paper provides a contribution to this setting by\ndesigning a novel distributed framework for the computation\n\n**Fig. 7 Inference time with and without our secure delegation strategy**\n\n\n-----\n\nof global behavioral fingerprints of objects. Indeed, classical behavioral fingerprints are based on Machine Learning\nsolutions to model object interactions and assess the correctness of their actions. Still, scalability, privacy, and intrinsic\nlimitations of adopted Machine Learning algorithms represent the main aspects to be improved to make this paradigm\nentirely suitable for the IoT environment. Indeed, in classical distributed fingerprinting approaches, an object models\nthe behavior of a target contact by exploiting only the information coming from the direct interaction with it, which\nrepresents a very limited view of the target because it does\nnot consider services and messages exchanged with other\nneighbors. However, building global models with information coming from several interactions of nodes with the target\nmay lead to critical privacy concerns.\nTo face this issue, we assumed a comprehensive perspective analyzing the hidden patterns of the behavior of a node\nin the interactions with all its peers over a network. To do\nso, we designed a solution based on Federated Learning that\nbenefits from a distributed computation of behavioral fingerprintsinvolvingdifferentworkingnodes.Thankstothisnovel\nML strategy, besides enriching the fingerprinting model with\ninformation coming from different interactions of multiple\nnodes, our approach addresses also several aspects related\nto the security and privacy of data exchanged among the\ninvolved actors. Moreover, it guarantees the scalability of the\nproposed solution and very satisfactory accuracy results of\nthe anomaly detection schema making our approach suitable\nto the constantly changing attack surface that characterizes\nthe modern IoT. Furthermore, our solution considers the\nintrinsic heterogeneity of the entities involved in the considered scenario, allowing less capable nodes to participate\nin the framework, by relying on a secure delegation strategy for both the training and the inference of FL models in\na privacy-preserving way. Finally, through the properties of\nHomomorphic Encryption and the Blockchain technology,\nour approach guarantees the privacy of both the target object\nand the different contributors, as well as the robustness of\nthe solution in the presence of security attacks. All these features lead to a secure fully privacy-preserving solution whose\nrobustness and correctness have been evaluated in this paper\nthrough a detailed security analysis. Moreover, an extensive\nexperimental campaign showed that the performance of our\nmodel is very satisfactory, and we can distinguish between\nnormal and anomalous behavior across every considered test\nset, reaching a 0.85 value of accuracy on average. Furthermore,theverylowgeneralinferencetimesofourmodelmake\nthe inclusion of the delegation strategy still feasible across\nall the possible scenarios with a performance reduction of\nonly 16.6%, on average.\nWhile this work has provided valuable insights into the\npotential of our solution for anomaly detection in IoT, several\nlimitations should be acknowledged. Firstly, our framework\n\n\nneeds a sufficient total number of heterogeneous nodes to\nperform its operations properly. Moreover, even if secure delegation can be applied, still an adequate number of powerful\nnodes with sufficient computational capability, memory, and\nstability should be present to train local ML models. Furthermore, the effectiveness of our approach, which is based\non FL, heavily relies on frequent communications between\nthe aggregator and the workers in the training phase. In\nan IoT scenario, this might lead to longer training times\nand potentially hinder convergence. Anyway, a number of\nrecent studies have already tackled the issue of training distributed machine learning models for resource-constrained\nIoT devices (Imteaj et al., 2021). Our work can leverage one\nof the existing studies on the application of FL to IoT since\nthis part is orthogonal to our work.\nWe plan to expand the research described in this proposal\nwith further investigations in the next future. For instance, we\nareplanningtostudyasolutiontobuild,stillinacollaborative\nand distributed way, the behavioral fingerprinting of objects\nin the network but also taking into account an optimized\norchestrationoftheirworkload.Inparticular,thankstosecure\ndelegation, this solution should allow a better distribution of\nthe workload, generated by FL tasks, among the nodes of\nthe network, according to power consumption minimization,\nService Level Agreement (SLA, for short) requirements, and\nthe reliability of the nodes.\n\n**Acknowledgements This work was supported in part by the project**\nSERICS (PE00000014) under the NRRP MUR program funded by the\nEU-NGEU, and by the Italian Ministry of University and Research\nthrough the PRIN Project “HOMEY: a Human-centric IOE-based\nframework for supporting the transition towards industry 5.0” (code\n2022NX7WKE).\n\n**Funding Open access funding provided by Università degli Studi di**\nPavia within the CRUI-CARE Agreement.\n\n**Availability of data and materials The dataset used in this paper is**\n[publicly available in the repository: https://iotanalytics.unsw.edu.au/](https://iotanalytics.unsw.edu.au/attack-data.html)\n[attack-data.html and has been originally produced by the authors of](https://iotanalytics.unsw.edu.au/attack-data.html)\n(Hamza et al., 2019). In this paper, we also adopted the algorithms\nproposed in Aramini et al. (2022) to generate payload data.\n\n#### Declarations\n\n**Conflict of interest/Competing interests The authors declare that they**\nhave no conflict of interest or competing interests that are relevant to\nthe content of this article.\n\n**Open Access This article is licensed under a Creative Commons**\nAttribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as\nlong as you give appropriate credit to the original author(s) and the\nsource, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material\nin this article are included in the article’s Creative Commons licence,\nunless indicated otherwise in a credit line to the material. 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Electronic Notes in Theoretical Computer\n_Science, 168, 109–126._\n\n**Publisher’s Note Springer Nature remains neutral with regard to juris-**\ndictional claims in published maps and institutional affiliations.\n\n**Marco Arazzi is currently a Ph.D. Student in Computer Engineering at**\nthe same University. From March to July 2023, he worked as a Visiting Researcher in the Cyber Security group of the Delft University of\nTechnology (TU Delft). His research interests include Data Science,\nMachine Learning, Social Network Analysis, the Internet of Things,\nPrivacy, and Security. He is the author of 10 scientific papers in these\nresearch fields.\n\n**Serena Nicolazzo is currently a Type-A Temporary Research Fel-**\nlow (RTDA) at the University of Milan. She got a PhD in Information Engineering at the University Mediterranea of Reggio Calabria in\n2017. Her research interests include Data Science, Security, Privacy,\nand Social Network Analysis. She is involved in several TPCs and editorial boards of prestigious International Conferences and Journal in\nthe context of Data Science and Cybersecurity and she is the author of\nabout 40 scientific papers. She was a Visiting Researcher at Middlesex\nUniversity of London and is actively collaborating with the Polytechnic University of Marche, the University of Pavia, and the University\nCollege of London.\n\n**Antonino Nocera is an Associate Professor at the University of Pavia.**\nHe received his PhD in Information Engineering at the Mediterranea\nUniversity of Reggio Calabria in 2013. His research interests span\nseveral research contexts including Artificial Intelligence, Data Science, Security, Privacy, Social Network Analysis, Recommender Systems, Internet of Things, Cloud Computing, and Blockchain. In these\nresearch fields, he published about 90 scientific papers. He is involved\nin several TPCs of prestigious International Conferences in the context of Data Science and Cybersecurity and is an Associate Editor\nof Information Sciences (Elsevier) and of the IEEE Transactions on\nInformation Forensics and Security.\n\n## 1 3\n\n\n-----\n\n### Authors and Affiliations\n\n**Marco Arazzi[1]** **· Serena Nicolazzo[2]** **· Antonino Nocera[1]**\n\nMarco Arazzi\nmarco.arazzi01@universitadipavia.it\n\nSerena Nicolazzo\nserena.nicolazzo@unimi.it\n\n## 1 3\n\n\n1 Department of Electrical, Computer and Biomedical\nEngineering, University of Pavia, Via A. Ferrata, 5, Pavia\n27100, PV, Italy\n\n2 Department of Computer Science, University of Milan, Via\nCeloria, 18, Milan 20133, MI, Italy\n\n\n-----\n\n"

ERROR: type should be string, got "https://doi.org/10.1007/s10796 023 10411 8\n\n# Examining the Acceptance of Blockchain by Real Estate Buyers and Sellers\n\n**William Yeoh[1]** **· Angela Siew Hoong Lee[2] · Claudia Ng[2] · Ales Popovic[3] · Yue Han[4]**\n\nAccepted: 24 May 2023\n© The Author(s) 2023\n\n**Abstract**\nBuying and selling real estate is time consuming and labor intensive, requires many intermediaries, and incurs high fees.\nBlockchain technology provides the real estate industry with a reliable means of tracking transactions and increases trust\nbetween the parties involved. Despite the benefits of blockchain, its adoption in the real estate industry is still in its\ninfancy. Therefore, we investigate the factors that influence the acceptance of blockchain technology by buyers and sell­\ners of real estate. A research model was designed based on the combined strengths of the unified theory of technology\nacceptance and use model and the technology readiness index model. Data were collected from 301 real estate buyers and\nsellers and analyzed using the partial least squares method. The study found that real estate stakeholders should focus on\npsychological factors rather than technological factors when adopting blockchain. This study adds to the existing body of\nknowledge and provides valuable insights to real estate stakeholders on how to implement blockchain technology.\n\n**Keywords Blockchain · Real estate · Adoption · Factors · Partial least squares method**\n\n\n### 1 Introduction\n\nReal estate is very different from other assets due to high\ntransaction costs, long-term commitment, regulations,\nand other constraints (Dijkstra, 2017). Buying or selling\nreal estate is often time consuming and labor intensive,\nrequires multiple intermediaries, and incurs high fees.\nHigh expenses include costs associated with time delays,\n\nWilliam Yeoh\n\nwilliam.yeoh@deakin.edu.au\n\nAngela Siew Hoong Lee\nangelal@sunway.edu.my\n\nClaudia Ng\nngclaudia95@gmail.com\n\nAles Popovic\nales.popovic@neoma-bs.fr\n\nYue Han\nhany@lemoyne.edu\n\n1 Deakin University, Geelong, Australia\n\n2 Sunway University, Sunway City, Malaysia\n\n3 NEOMA Business School, Mont-Saint-Aignan, France\n\n4 Le Moyne College, Syracuse, USA\n\n\noutdated technologies, and complex data-sharing mecha­\nnisms (Latifi et al., 2019). In addition, the real estate indus­\ntry faces information costs, such as the cost of coordinating\ntrusted information between dispersed parties in relation\nto contract enforcement information (Sinclair et al., 2022).\nBlockchain technology could help the real estate industry\neliminate inefficiencies and inaccuracies (Deloitte, 2019).\nAccording to transaction cost theory, adopting blockchain\ntechnology has the potential to lower real estate transaction\ncosts and enable lower ex-post transaction costs by reducing\nverification time (Dijkstra, 2017). Combining transparent\nreal estate markets with more effective real estate transac­\ntion processes and lower transaction costs could create more\nliquid real estate markets (Dijkstra, 2017).\n\nBlockchain is a decentralized network that provides a\n\nhigh level of transparency and trust without the need for\na central authority to vouch for accuracy (Akram et al.,\n2020; Kamble et al., 2019). The risk of fraud is mitigated\nby cryptographic signatures that make it virtually impos­\nsible to alter or forge anything registered on the blockchain\n(Mansfield-Devine, 2017). Blockchain can reduce effort\nwhile increasing the efficiency and effectiveness of real\nestate transactions. It provides the real estate industry with a\nreliable and transparent means to seamlessly track and trace\nprocesses (Compton & Schottenstein, 2017). Karamitsos\n\n\n-----\n\net al. (2018) concluded that blockchain for the real estate\nindustry could increase trust between companies involved\nin the real estate ecosystem and eliminates the need for\nintermediaries because transactions are automatically veri­\nfied and validated.\n\nExisting literature explores the benefits and applica­\n\ntions of blockchain for the real estate industry (e.g., Kona­\nshevych, 2020; Latifi et al., 2019; Sinclair et al., 2022;\nWouda & Opdenakker 2019; Yapa et al., 2018). However,\ndespite numerous studies examining the benefits of block­\nchain, there is little research on how buyers and sellers per­\nceive and accept blockchain technology in the real estate\nindustry. Given that blockchain is an emerging technology\n(Akram et al., 2020), the real estate industry is still in the\nearly stages of its adoption. More targeted studies need\nto be conducted on the adoption of blockchain in the real\nestate industry (Saari et al., 2022) because understanding\nblockchain adoption can help alleviate the concerns of real\nestate buyers and sellers, leading to broader adoption in the\nindustry. In addition, this understanding can help real estate\nstakeholders and policymakers make informed decisions\nabout how to allocate scarce resources and create relevant\npolicies to enable blockchain implementation (Alalwan et\nal., 2017; Martins et al., 2014). To address this gap in the\nliterature, we aim to investigate the factors that influence\nthe behavioral intentions of real estate buyers and sellers in\nrelation to the use of blockchain technology. We synergisti­\ncally combine the unified theory of acceptance and use of\ntechnology (UTAUT) model and the technology readiness\nindex (TRI) model to develop a research model and test it\nwith real estate buyers and sellers through an online survey.\n\nThis work provides both theoretical and practical contri­\n\nbutions. It is one of the first studies to investigate the adop­\ntion of blockchain technology in the real estate industry. It\nfills a gap in the literature by providing a comprehensive\nunderstanding of new technology adoption by integrating\nthe UTAUT and TRI models. The model presented in this\npaper demonstrates the importance of psychological fac­\ntors in technology acceptance studies and provides a new\nresearch stream for future studies. The implications for\npractitioners are threefold. First, a greater focus on psycho­\nlogical factors positively influences technology acceptance.\nSecond, emphasizing the holistic benefits of technology in\nan ecosystem promotes technology acceptance. Third, form­\ning a consortium to facilitate the technology implementa­\ntion environment is beneficial when stakeholders consider\nnew technologies.\n\nThe remainder of this paper is organized as follows. Sec­\n\ntion 2 provides an overview of blockchain for real estate and\nintroduces the theoretical basis of this research. Section 3\nprovides the research model that connects the two theories\nand the hypotheses. The research method is then described\n\n\nin Sect. 4, followed by the analysis of the results in Sect. 5.\nSection 6 discusses the main findings of the study, the con­\ntributions of these findings to the literature, and the practical\nimplications of the findings. Section 7 concludes the paper\nand suggests avenues for future research.\n\n### 2 Background\n\n#### 2.1 Blockchain Technology and the real Estate Industry\n\nUnlike traditional databases that are stored in a single loca­\ntion and controlled by a single party, blockchain is a distrib­\nuted database that can store any information (e.g., records,\nevents, or transactions) (Mougayar, 2016). Blockchain can\nbe referred to as a metatechnology because it integrates\nseveral other technologies, such as software development,\ncryptographic technology, and database technology (Mou­\ngayar, 2016). Zyskind and Nathan (2015) revealed that the\ncurrent practice of collecting private information by third\nparties poses the risk of security breaches. The main advan­\ntage of blockchain is that it can protect permanent records\nfrom data manipulation and infiltration. It also partially\nguarantees anonymity, transparency, transactions, and data\nauthentication (Mougayar, 2016).\n\nIn recent years, the real estate industry has considered\n\nusing blockchain technology for registering, managing,\nand transferring property rights (Crosby et al., 2016; Swan,\n2015). Real estate industry players have recognized that\nblockchain-based smart contracts can help them reap the\nbenefits of operational efficiency, automation, and transpar­\nency. Smart contracts are decentralized agreements driven\nby programming codes that are automatically executed\nwhen certain conditions are met (Swan, 2015). For exam­\nple, if an apartment sale is handled through a smart contract,\nthe seller gives the buyer the door code for the apartment\nonce payment is received. The smart contract is executed\nand automatically releases the door code on settlement day.\nBy using smart contracts, not only are these agreements\nautomatically enforced, but they are also legally binding.\nIn addition, the blockchain ensures that all actions and cor­\nrespondence between buyers and sellers are recorded immu­\ntably, providing all parties with an indisputable record of\npayments and records (Liebkind, 2020).\n\nAccording to transaction cost theory, smart contracts\n\nexpedite the registration, administration, and transfer of\nproperty rights while reducing ex-ante and ex-post transac­\ntion costs (Crosby et al., 2016; Kosba et al., 2016; Swan,\n2015). Smart contracts have recently become more popu­\nlar because they can replace lawyers and banks involved in\nasset transactions according to predefined aspects (Fairfield,\n\n\n-----\n\n2014). The use of blockchain in real estate transactions\ncould make the transfer of money between parties faster,\neasier, and more efficient (Compton & Schottenstein, 2017).\nBlockchain application in the form of cryptocurrencies has\nemerged as a medium of exchange for real estate transac­\ntions, with examples in Tukwila (United States), Essex\n(United Kingdom), and Sabah (Malaysia) (Vanar, 2018).\n\nBlockchain technology can transform key real estate\n\ntransactions such as buying, selling, financing, leasing, and\nmanagement transactions. Karamitsos et al. (2018) found\nthat the benefits of using blockchain for real estate are that it\nincreases trust between entities involved in real estate devel­\nopment and eliminates the need for intermediaries because\ntransactions are automatically verified and validated.\nAccording to Deloitte (2019), most executives consider cost\nefficiency the biggest benefit of blockchain use. Table 1 pro­\nvides a summary of the benefits of blockchain for the real\n\n\nestate industry. The table demonstrates that blockchain can\nreduce transaction complexity, increase security, and mini­\nmize opportunism in real estate transactions.\n\n#### 2.2 UTAUT\n\nThe UTAUT model suggests that four constructs—perfor­\nmance expectancy, effort expectancy, social influence, and\nfacilitating conditions—are the most important determi­\nnants of intention to use information technology (Venkatesh,\n2003). These constructs comprise the most influential con­\nstructs derived from eight models: the technology accep­\ntance model (TAM); the theory of reasoned action (TRA);\nthe motivational model (MM); the theory of planned behav­\nior (TPB); the combined TAM + TPB (CTT); the model of\npersonal computer utilization (MPCU); innovation diffusion\ntheory (IDT); and social cognitive theory (SCT) (Venkatesh,\n\n\n**Table 1 Advantages of block­**\nchain for the real estate industry\n\n\nSecuring digital prop­\nerty records and rights\nsystem\n(Altynpara, 2023;\nLiebkind, 2020; Latifi\net al., 2019; Sinclair\net al., 2022; Wouda &\nOpdenakker 2019; Yapa\net al., 2018)\n\nProcessing real estate\ntransactions and smart\ncontracts\n(Latifi et al., 2019; Sin­\nclair et al., 2022; Wouda\n& Opdenakker. 2019;\nYapa et al., 2018)\n\nImproving pre-purchase\ndue diligence\n(Altynpara, 2023;\nWouda & Opdenakker,\n2019; Yapa et al., 2018)\n\nRemoving\nintermediaries\n(Yapa et al., 2018;\nAltynpara 2023; Latifi\net al., 2019)\n\nEnabling real estate\ninvestments to\nbecome liquid through\ntokenization\n(Altynpara, 2023; Latifi\net al., 2019)\n\n\nAdvantages Descriptions\n\n\n\n- Blockchain ledger entries can record any data structure, including property\ntitles, identity, and certification, and allow their digital transfer via smart\ncontracts.\n\n- Blockchain can establish transparent and clear timelines for property owners.\n\n- Blockchain can automatically guarantee the legitimacy of the transfer of title.\n\n- Owners can trust that their deed is accurate and permanently recorded if prop­\nerty ownership is stored and verified on the blockchain because the verifiable\ntransactional history guarantees transparency.\n\n- Blockchain serves as a single irrefutable point of truth, which can greatly ben­\nefit fraud detection and prevention, regulatory compliance, and due diligence.\n\n- Blockchain’s trustless nature allows for direct transactions between buyers\nand sellers, eliminating the need for external supervision of transactions.\n\n- The process can be further bolstered by implementing smart contracts that\nensure a buyer–seller transaction will occur only if certain conditions are met.\n\n- Smart contracts enable the real estate to reap the benefits of deal automation\nand transparency.\n\n- With blockchain, trust will be in a decentralized network of actors rather than\nin individual actors.\n\n- Property documents can be kept digitally in blockchain-based platforms.\n\n- These digital documents can contain all the required property data and easily\nbe searched anytime.\n\n- The required data concerning the desired property is always accessible to\nevery purchaser or property owner, or others involved.\n\n- Blockchain allows all paperwork to be completed automatically and can mini­\nmize the possibility of annoying paper errors and inaccuracies.\n\n- Blockchain enables realty data to be shared among a peer-to-peer network.\n\n- Blockchain enables real estate brokers to receive additional monitoring of this\ndata and reduce their fees because data can be accessed easily.\n\n- Blockchain eliminates the need for intermediaries (e.g., title companies, attor­\nneys, assessment experts, realtors/real estate agents, and escrow companies) by\nharnessing smart contracts.\n\n- Blockchain can become an absolute realty mediator because it can perform\ntasks from managing a highly secure database of property records to automati­\ncally conducting every payment.\n\n- Blockchain enables real estate investments to become liquid because it\nprovides transparent records for the desired property, secure multisignature\ncontracts, and eliminates the need to perform tedious paperwork tasks.\n\n- Tokenization refers to the issuance of blockchain tokens acting as the digital\nrepresentation of an asset or a fraction of an asset.\n\n- Tokenizing properties can bring greater liquidity to the sector, increase trans­\nparency, and make the investment in real estate more accessible.\n\n\n-----\n\n2003). Performance expectancy refers to the extent to which\nusers expect that using the system will help them improve\ntheir job performance. This construct has four root con­\nstructs: perceived usefulness (from TAM/TAM2 and CTT);\nextrinsic motivation (from MM); relative advantage (from\nIDT); and outcome expectancy (from SCT). Effort expec­\ntancy refers to the degree of ease associated with using the\nsystem. This construct is derived from perceived ease of use\n(TAM/TAM2); complexity (MPCU); and ease of use (IDT).\nFinally, social influence indicates how significant the indi­\nvidual considers the use of the new system to be. This con­\nstruct is represented in the UTAUT model as a “subjective\nnorm” in TRA, TAM2, TPB, and CTT, as “social factors”\nin MPCU, and as an “image” in IDT. The UTAUT model is\nvaluable in various research areas, such as continuous use of\ncloud services (Wang et al., 2017) and behavioral intention\nand use in social networking apps (Ying, 2018). In addi­\ntion, the UTAUT model is more successful than the previ­\nous eight models in explaining up to 70% of use variations\n(Venkatesh, 2003).\n\n#### 2.3 TRI\n\nThe TRI refers to the propensity of people to adopt and use\nnew technologies to achieve their goals. The TRI can be\nused to gain a deeper understanding of people’s willingness\nto adopt and interact with technology, particularly com­\nputer and internet-based technology. Parasuraman (2000)\nnoted that TRI can be viewed as a general state of mind\nthat results from a gestalt of mental promoters and inhibitors\nthat combine to determine a person’s propensity to use new\ntechnologies. The TRI has four dimensions: optimism, inno­\nvativeness, discomfort, and insecurity. Optimism is consid­\nered an indicator of a positive attitude toward technology and\nrepresents the belief that technology can bring efficiency,\nbetter control, and flexibility. Innovativeness refers to users’\ninclination to pioneer technology. Discomfort describes a\nlack of power and a feeling of being overwhelmed when\nusing technology. Insecurity refers to worries or distrust of\nthe technology and its capabilities. In the four dimensions,\nthe technology motivators are optimism and innovativeness,\nwhile the technology barriers are insecurity and discomfort.\nPattansheti et al. (2016) combined TRI with TPB and TAM\nto explain the adoption behavior of Indian mobile banking\nusers, and the results suggested that the integrated constructs\nwere useful indicators. Larasati and Widyawan (2017) used\nTRI in conjunction with TAM to analyze enterprise resource\nplanning implementation in small- and medium-sized enter­\nprises and found that the combined constructs in TAM and\nTRI provided a better understanding of enterprise resource\nplanning implementation.\n\n\n### 3 Research Model and Hypotheses\n\nThis study builds a research model based on UTAUT and\nTRI to investigate how real estate buyers and sellers per­\nceive the use of blockchain technology. The UTAUT model\npresents four primary constructs that influence final inten­\ntion: performance expectancy, effort expectancy, social\ninfluence, and facilitating conditions; these four constructs\nwere included in the proposed model. Given that blockchain\nis still a relatively new technology that is not yet widely\nused in the real estate industry, the four constructs of TRI\nwere adopted (innovativeness, optimism, discomfort, and\ninsecurity) to explain the willingness of real estate buyers\nand sellers to use this technology.\n\nUsing the UTAUT model alone has the disadvantage of\n\nneglecting the psychological aspects of the user (Napitupulu\net al., 2020). Previous research has demonstrated that user\nreadiness based on personality traits is critical in driving\ntechnology acceptance (Parasuraman, 2000). The TRI is\nincluded in our study to consider characteristics that explain\na person’s willingness to use technology. However, some\nresearchers believe that TRI alone does not adequately\nexplain why certain individuals adopt new technologies\nbecause individuals with high technology readiness do\nnot always adopt new technologies (Basgoze, 2015; Tsi­\nkriktsis, 2004). Some previous studies have integrated the\nTAM model with the TRI model to combine variables on\ncognitive aspects and psychological traits of technology\nuse (Adiyarta et al., 2018). However, there are few studies\nthat examine two perspectives (technology readiness and\ntechnology acceptance) simultaneously. Examining both\ntheories of technology readiness and acceptance simultane­\nously can provide a deeper description of technology adop­\ntion (Rinjany, 2020). Therefore, this study integrates the\nUTAUT with the TRI to complement the strengths of the\ntwo models and compensate for the weaknesses of the mod­\nels. The TRI examines user readiness, while the UTAUT\nmodel examines technology acceptance factors.\n\nSince 2020, the COVID-19 pandemic has affected the\n\nway organizations operate and accelerated the adoption of\ndigital technologies by several years (LaBerge et al., 2020).\nBecause many of these changes that occurred during the\npandemic (e.g., social distancing and contactless transac­\ntions) could be long term, we also include the influence of\nthe pandemic (PAND) in the research model to test whether\nthe pandemic influences respondents’ behavioral intentions\nto adopt blockchain. We define pandemic influence as the\ninfluence of an epidemic that occurs in a large area and\naffects most people. For example, physical distancing is\npracticed to suppress disease transmission, which leads to\na contactless, paperless approach to conducting real estate\ntransactions that do not require physical contact between\n\n\n-----\n\nreal estate stakeholders becoming a priority. The research\nmodel proposed in this study is presented in Fig. 1.\n\n#### 3.1 Performance Expectancy\n\nPerformance expectancy (PEXP) is the extent to which a\nperson believes that the use of technology will help them\nimprove their job performance (Venkatesh, 2003). This\nmeans that the more a user believes that a technology will\nimprove their job performance, the greater the intention\nto use it (Williams et al., 2015). A person’s motivation to\naccept and use a new technology depends on whether they\nperceive certain benefits will arise from use of the technol­\nogy in their daily lives (Davis, 1989). Blockchain has been\nshown to create high expectations for improvements in real\nestate transactions, such as promoting process integrity, net­\nwork reliability, faster transactions, and lower costs (Latifi\net al., 2019). In addition, blockchain provides liquidity in\nthe real estate market and eliminates intermediaries through\nsmart contracts. Previous studies have reported that the\nintention of individuals to accept a technology depends sig­\nnificantly on the expectation of performance (Alalwan et al.,\n2017; Riffai et al., 2012; Weerakkody et al., 2013). In this\nstudy, PEXP refers to the perception of a real estate buyer or\nseller that using blockchain would improve overall perfor­\nmance, including speeding up the registration and transfer\nof property rights, reducing the complexity of transactions\nwith multiple parties, and eliminating the need for interme­\ndiaries in real estate transactions. Therefore, we hypothesize\nthe following:\n\n_H1: Performance expectancy positively affects the inten­_\n\n_tion to use blockchain technology in the real estate industry._\n\n**Fig. 1 Research model**\n\n\n#### 3.2 Effort Expectancy\n\nEffort expectancy (EEXP) refers to the ease of using a tech­\nnology (Venkatesh, 2003). Individuals are less likely to use\na technology if they perceive it to be difficult or if it requires\nmore effort than to use than existing methods. Effort expec­\ntancy is closely related to performance expectancy, with\nthe former being closer to efficiency expectancy and the\nlatter being closer to effectiveness expectancy (Brown et\nal., 2010). In this study, the ease of use and complexity of\nblockchain can also be conveyed by the amount of time and\neffort required by the buyer and seller. That is, individuals\nwill be satisfied with their experience with the technology\nif they perceive that it requires little effort and is low in\ncomplexity. Previous studies have demonstrated the impact\nof effort expectancy on the adoption of new technologies,\nincluding the blockchain (Kamble et al., 2019; Pattansheti\net al., 2016). Previous research has also demonstrated that\nsmart contracts in blockchain can minimize human effort\nby using predefined rules (Francisco & Swanson, 2018). In\nthis study, EEXP refers to the extent to which the real estate\nbuyer or seller feels that the blockchain is easy to use in\nreal estate transactions. Users need to understand that the\nblockchain is a distributed ledger and that the smart contract\nis simply a program stored on the blockchain that automati­\ncally executes transactions when certain conditions are met,\nand they need to learn to connect the computer system to the\nblockchain network. Therefore, we propose the following\nhypothesis:\n\n_H2: Effort expectancy positively affects the intention to_\n\n_use blockchain technology in the real estate industry._\n\n\n-----\n\n#### 3.3 Social Influence\n\nSocial influence (SINF) is the extent to which an individual\nperceives how significant others consider using the new\nsystem (Venkatesh, 2003). Previous research has found that\nsocial influence is exerted through the opinions of family,\nfriends, and colleagues (Irani et al., 2009; Venkatesh &\nBrown, 2001). Other studies have also demonstrated that\nsocial influence factor can lead to higher intention to use\nwhen users have higher normative pressure and volume\n(Granovetter, 1978; Markus, 1987). The importance of\nsocial influence in accepting new technologies has also been\nhighlighted in studies focusing on areas such as adopting\nmobile government services (Zamberi & Khalizani, 2017)\nand internet-based banking (Martins et al., 2014). In our\nstudy, _SINF refers to how much an individual values the_\nopinions of people around them regarding the use of block­\nchain in real estate transactions. Therefore, we hypothesize\nthe following:\n\n_H3: Social influence positively affects the intention to use_\n\n_blockchain technology in the real estate industry._\n\n#### 3.4 Facilitating Conditions\n\nFacilitating conditions (FCON) are defined as the extent to\nwhich an individual believes that an organizational and tech­\nnical infrastructure is in place to support the use of a system\n(Venkatesh, 2003). Facilitating conditions, such as network\nconnectivity, hardware, and user support, have a significant\nimpact on technology adoption and use (Queiroz & Wamba,\n2019; Tran & Nguyen, 2021). Because blockchain is highly\ninterconnected, it requires technical resources to enable its\nuse. Insufficient resources negatively impact blockchain\nusage (Francisco & Swanson, 2018). For example, if there\nis a lack of support from the blockchain organization, users\nmight opt for other supported systems. In contrast, if users\nfeel that the blockchain organization provides sufficient\ntechnical support and resources, they are more likely to\nadopt blockchain effortlessly. From the perspective of this\nstudy, facilitating conditions emphasize the availability of\nthe technical infrastructure and the awareness of real estate\nbuyers and sellers about the resources available to support\nthe use of blockchain technology in the real estate industry.\nTherefore, we hypothesize the following:\n\n_H4: Facilitating conditions positively affect the intention_\n\n_to use blockchain technology in the real estate industry._\n\n#### 3.5 Innovativeness Users\n\nInnovativeness (INNO) refers to the user’s propensity to\nbe a pioneer in the field of technology. This factor helps\nto increase individuals’ willingness to accept and use\n\n\ntechnology (Parasuraman, 2000). Individuals with high lev­\nels of innovativeness are eager to try new technologies to\nunderstand new features and uses. Therefore, they are more\nmotivated to adopt new technologies and enjoy the experi­\nence of learning them (Kuo et al., 2013). Their willingness\nto learn, understand, and use new technologies increases\ntheir adoption of technology (Turan et al., 2015). In addi­\ntion, innovative individuals tend to be more open to new\nideas and creations in general (Kwang & Rodrigues, 2002).\nThis is also confirmed by the fact that innovativeness has\nbeen found to be a major factor influencing the intention\nto use technology (e.g., Buyle et al., 2018; Qasem, 2020;\nZmud, 1990). In our study, INNO refers to the motivation\nand interest of real estate buyers and sellers to use block­\nchain for real estate transactions. Therefore, we propose the\nfollowing hypothesis:\n\n_H5: Innovativeness positively affects the intention to use_\n\n_blockchain technology in the real estate industry._\n\n#### 3.6 Optimism\n\nOptimism (OPTI) is considered an indicator of a positive\nattitude toward technology. Parasuraman (2000) found that\nindividuals who are optimistic about technology can achieve\nmore benefits from technology in relation to control over\nlife, flexibility, and efficiency. Scheier (1985) also found\nthat confident and optimistic people are usually more likely\nto believe that good things will happen than bad things. The\nmindset of such people influences their attitude toward tech­\nnology acceptance and risk perception (Costa-Font, 2009).\nThese individuals have positive strategies that directly affect\ntheir technology acceptance (Walczuch et al., 2007). That is,\noptimistic people tend to focus less on negative things and\naccept technologies more readily. In this study, OPTI refers\nto the beliefs and positive attitudes of real estate buyers and\nsellers toward blockchain in real estate transactions. There­\nfore, we propose the following hypothesis.\n\n_H6: Optimism positively affects the intention to use_\n\n_blockchain technology in the real estate industry._\n\n#### 3.7 Discomfort\n\nDiscomfort (DISC) describes feelings of lack of control and\nbeing overwhelmed when using technology. It is a barrier\nthat lowers individuals’ willingness to use and accept tech­\nnology (Parasuraman, 2000). Individuals who have high\nlevels of discomfort with new technology are more likely to\nfind the technology difficult to use (Walczuch et al., 2007).\nDiscomfort indicates a low level of technological mastery,\nwhich leads to a reluctance to use the technology, ultimately\nmaking the individual uncomfortable with the technol­\nogy (Rinjany, 2020). As a result, they may continue to use\n\n\n-----\n\ntraditional methods to accomplish their daily tasks. Previous\nstudies (Kuo et al., 2013; Rahman et al., 2017) have found\nthat discomfort affects an individual’s perceived ease of use\nand directly influences their intention to use the technology.\nGiven that blockchain is a new and disruptive technology,\nit is reasonable to assume that some discomfort will arise\namong individuals in relation to adopting this technology.\nIn our research, DISC refers to the uneasiness of real estate\nbuyers and sellers toward the use of blockchain in real estate\ntransactions. Therefore, we hypothesize:\n\n_H7: Discomfort negatively affects the intention to use_\n\n_blockchain technology in the real estate industry._\n\n#### 3.8 Insecurity\n\nInsecurity (ISEC) refers to concern about or distrust of tech­\nnology and distrust of its capabilities. Similar to discomfort,\nit is a barrier that lowers a person’s willingness to use and\naccept technology (Parasuraman, 2000). Individuals who\nfeel less secure about technology tend to have little confi­\ndence in the security of newer technologies. Therefore, they\nmay require more security to use new technology (Parasura­\nman & Colby, 2015). Distrust and pessimism about new\ntechnology and its performance can make an individual\nskeptical and uncertain about the performance of the tech­\nnology (Rinjany, 2020). Individuals with higher levels of\ninsecurity are more likely to be skeptical of new technolo­\ngies and may not even be motivated to try them, even if\nthey could benefit from using them (Kamble et al., 2019).\nBecause blockchain is considered a new technology, some\nindividuals are expected to be skeptical about it. In this study,\n_ISEC refers to the distrust and uncertainty of real estate buy­_\ners and sellers about using blockchain in real estate transac­\ntions. Therefore, we hypothesize the following:\n\n_H8: Insecurity negatively affects the intention to use_\n\n_blockchain technology in the real estate industry._\n\n#### 3.9 Pandemic Influence\n\nThe COVID-19 virus triggered a global pandemic that has\naffected all aspects of daily life and the economy. We con­\nsider the pandemic influence (PAND) has positively affected\nthe use of technology in the real estate industry. According\nto Deloitte (2019), processes in the real estate industry are\ncurrently mainly paper based, and due diligence processes\ngenerally occur offline. Many real estate transactions (e.g.,\nsigning the letter of intent to purchase, purchase agreement,\nand land title registration) require face-to-face contact with\nstakeholders such as the buyer or seller, attorneys, and real\nestate agents, and require ink signatures back and forth on\npaper, with numerous intermediaries involved. Kalla et al.\n(2020) demonstrated that blockchain-based smart contracts\n\n\ncould streamline complex application and approval pro­\ncesses for loans and insurance. Other benefits include elimi­\nnating processing delays caused by traditional paper-based\npolicies and eliminating intermediaries, which typically\nrequire the physical presence of a person. As social distanc­\ning and digitization of various aspects of businesses become\nthe norm to contain the spread of the virus (De et al., 2020),\nwe hypothesize the following:\n\n_H9: The impact of the pandemic positively affects the_\n\n_intention to use blockchain technology in the real estate_\n_industry._\n\n### 4 Research Method\n\nWe developed a questionnaire based on previous literature\nto test the research model. The questionnaire was created\nusing Google Forms. The participants in the survey were\nbuyers and sellers of real estate in Malaysia. A five-point\nLikert scale was used, ranging from “strongly disagree”\nto “strongly agree”. Respondents were told they were not\nrequired to participate in the survey and that they had per­\nmission to withdraw at any time without penalty. Partici­\npants were also assured that all their data would be kept\nconfidential. Table 2 provides the details of the measure­\nment items.\n\nTo promote content validity, an information sheet for par­\n\nticipants at the beginning of the questionnaire included the\nguidelines for the questionnaire and a request for partici­\npants to submit their responses only if they were buyers or\nsellers of real estate. The online questionnaire was sent to\n1,000 individuals, and a total of 301 valid responses were\ncollected, giving a response rate of 30.1%. Table 2 pro­\nvides the details of the measurement items. The items were\nadapted from previous literature.\n\n### 5 Results\n\nTable 3 provides the demographics of the survey partici­\npants. The gender distribution among the respondents was\nequal, and half of the survey respondents were younger than\n35 years of age. Notably, half of the respondents owned one\nor two properties (56.1%), followed by 17.6% who owned\nthree or four properties, while only 4% owned five or more\nproperties.\n\n#### 5.1 Measurement Model\n\nMeasurement models indicate the relationships between\nconstructs and the corresponding indicator variables, and\nthe distinction between reflective and formative measures\n\n\n-----\n\n**Table 2 Details of measurement**\nitems\n\n\nPerformance\nExpectancy\n(PEXP)\n\nEffort\nExpectancy\n(EEXP)\n\nSocial\nInfluence\n(SINF)\n\nFacilitating\nConditions\n(FCON)\n\nInnovativeness\n(INNO)\n\nOptimism\n(OPTI)\n\nDiscomfort\n(DISC)\n\nInsecurity\n(ISEC)\n\nBehavioral\nIntention\n(BINT)\n\nPandemic\nInfluence\n(PAND)\n\n\nConstruct Item Indicator\n\n\nPE01 I would find blockchain technologies useful in real estate processes.\n\nPE02 Using blockchain technologies accomplishes real estate processes more\nquickly.\n\nPE03 Using blockchain technologies increases productivity in real estate processes.\n\nPE04 Using blockchain would improve performance in real estate processes.\n\nPE05 Using blockchain will help minimize transaction delays.\n\nEE01 I feel that blockchain would be easy to use.\n\nEE02 I think blockchain is clear and understandable.\n\nEE03 I think it will be easy for me to remember and perform tasks using blockchain.\n\nEE04 I feel blockchain will be easier to use compared to the conventional practices\nof managing real estate processes.\n\nEE05 I would find blockchain flexible to interact with.\n\nSI01 People around me believe using blockchain in real estate processes is a wise\ndecision.\n\nSI02 I am more likely to use blockchain in real estate processes if people around\nme are using it.\n\nSI03 If people around me are exploring the use of blockchain, it puts pressure on\nme to use it.\n\nFC01 I know how blockchain works.\n\nFC03 I have the knowledge necessary to use blockchain.\n\nIN01 I am open to learning new technology such as blockchain.\n\nIN02 I believe that it would be beneficial to replace conventional practices with\nblockchain.\n\nOP01 Blockchain would give me more control over certain aspects in the real estate\nprocesses.\n\nOP02 Blockchain can transform the real estate industry for the better.\n\nOP03 Blockchain can solve current issues faced in the real estate industry.\n\nDI01 It will be difficult to understand and apply the concept of blockchain in real\nestate.\n\nDI02 I think blockchain is too complex.\n\nDI03 There should be caution in replacing important people-tasks with blockchain\ntechnology.\n\nDI04 Blockchain is too complicated to be useful.\n\nIS01 I consider blockchain safe to be applied in real estate.\n\nIS02 I am confident that sending information over blockchain is secure.\n\nIS03 I feel confident storing and accessing data on blockchain.\n\nBI01 I predict that I will use blockchain in real estate processes in the future.\n\nBI02 I intend to use blockchain in real estate processes in the future.\n\nBI03 I will continuously see blockchain being used in real estate processes in the\nfuture.\n\nBI04 If available, I prefer blockchain to be used in real estate processes.\n\nPAN01 I feel that blockchain could help minimize real estate sales procedures that\nrequire human contact (e.g., Smart Contracts).\n\nPAN02 If blockchain was implemented, it would help reduce the possible negative\neffects that the pandemic may have caused on the real estate economy.\n\nPAN03 During a pandemic, real estate sales processes would be more efficient with\nblockchain because it could substitute attorneys and banks involved based on\npredefined aspects.\n\nPAN04 I would feel more comfortable proceeding with selling/buying a property if\nblockchain was integrated in real estate processes.\n\n\nis crucial in assigning meaningful relationships in the struc­\ntural model (Anderson & Gerbing, 1988). In this research,\nall ten constructs are reflective. The quality of the reflective\nmeasurement model is determined by the following factors:\n\n\n(1) internal consistency; (2) convergent validity; (3) indica­\ntor reliability; and (4) discriminant validity.\n\nThe traditional criterion for measuring internal consis­\n\ntency is Cronbach’s alpha (Hair et al., 2010). However, this\nmeasure is sensitive to the number of items on a scale and\n\n\n-----\n\n**Table 3 Respondent demographics**\nCategory Item Frequency Percentage\n\nGender Male 156 51.8\n\nFemale 145 48.2\n\nAge < 26 76 25.2\n\n26–35 75 24.9\n\n36–45 56 18.6\n\n46–55 61 20.3\n\n - 55 33 11\n\nNumber of real 0 25 8.3\nestate properties 0 (to purchase 42 14\nowned within the next\n\ntwo years)\n\n1 or 2 169 56.1\n\n3 or 4 53 17.6\n\n≥ 5 12 4\n\n\n**Table 4 Cronbach’s alpha, composite reliability, and AVE values**\nConstruct Cronbach’s Composite reli­ Average\nalpha ability (CR) variance\n\nextracted\n(AVE)\n\n_BINT_ 0.911 0.938 0.790\n\n_DISC_ 0.821 0.881 0.651\n\n_EEXP_ 0.919 0.939 0.756\n\n_FCON_ 0.853 0.931 0.872\n\n_INNO_ 0.729 0.878 0.783\n\n_ISEC_ 0.886 0.93 0.815\n\n_OPTI_ 0.834 0.901 0.751\n\n_PAND_ 0.845 0.895 0.682\n\n_PEXP_ 0.899 0.926 0.714\n\n_SINF_ 0.734 0.848 0.650\n\nNote: BINT refers to behavioral intention\n\nunderestimates internal consistency reliability. Thus, it may\nbe used as a more conservative measure. Because of the\nlimitations of Cronbach’s alpha, it may be technically more\nbeneficial to utilize composite reliability, which considers\nthe different outer loadings of the indicator variables (Hair\net al., 2017). Its interpretation is the same as for Cronbach’s\nalpha. The composite reliability of the construct should be\nbetween 0.70 and 0.95 (Grefen et al., 2000).\n\nGiven that Cronbach’s alpha is a conservative measure\n\nof reliability, and composite reliability tends to overestimate\nthe internal consistency reliability, which could result in\nrelatively high reliability estimates, both criteria should be\nconsidered and reported (Hair et al., 2017). Table 4 presents\nthe Cronbach’s alpha values, composite reliability, and aver­\nage variance extracted (AVE) values of all ten constructs.\nThe Cronbach’s alpha and composite reliability values were\nwithin the threshold range of 0.70–0.95.\n\nConvergent validity is the extent to which a measure cor­\n\nrelates positively with alternative measures within the same\nconstruct. The common measure to establish convergent\nvalidity on the construct level is the AVE. The guideline for\n\n\n**Table 5 Outer loadings**\nConstruct Item Loadings\n\n_BINT_ BI01 0.867\n\nBI02 0.928\n\nBI03 0.849\n\nBI04 0.909\n\n_DISC_ DI01 0.753\n\nDI02 0.877\n\nDI03 0.715\n\nDI04 0.869\n\n_EEXP_ EE01 0.886\n\nEE02 0.875\n\nEE03 0.876\n\nEE04 0.846\n\nEE05 0.866\n\n_FCON_ FC01 0.932\n\nFC03 0.936\n\n_INNO_ IN01 0.848\n\nIN02 0.921\n\n_ISEC_ IS01 0.859\n\nIS02 0.919\n\nIS03 0.928\n\n_OPTI_ OP01 0.837\n\nOP02 0.913\n\nOP03 0.848\n\n_PAND_ PAN01 0.815\n\nPAN02 0.796\n\nPAN03 0.855\n\nPAN04 0.836\n\n_PEXP_ PE01 0.877\n\nPE02 0.870\n\nPE03 0.880\n\nPE04 0.854\n\nPE05 0.737\n\n_SINF_ SI01 0.779\n\nSI02 0.845\n\nSI03 0.793\n\nmeasuring convergent validity is that the AVE of the con­\nstruct should be higher than 0.50. As presented in Table 4,\nthe AVE value of all ten constructs meets the guideline\nthreshold value of > 0.50.\n\nIndicator reliability represents how much variation in an\n\nitem is explained by the construct and is referred to as the\nvariance extracted from the item. To measure a construct’s\nindicator reliability, the following guidelines are applied:\n(1) the indicator’s outer loadings should be higher than 0.70\n(Hair et al., 2010); and (2) indicators with outer loadings\nbetween 0.40 and 0.70 should be considered for removal\nonly if the deletion leads to an increase in composite reli­\nability and AVE above the suggested threshold value (Hair\net al., 2017). Table 5 presents the outer loadings of all con­\nstructs. All values appear to be higher than the suggested\nthreshold value of 0.7. Hence, no removal of constructs was\nrequired.\n\n\n-----\n\n**Table 6 Discriminant validity**\nConstruct _BINT_ _DISC_ _EEXP_ _FCON_ _INNO_ _ISEC_ _OPTI_ _PAND_ _PEXP_ _SINF_\n\n_BINT_ **0.889**\n\n_DISC_ −0.291 **0.807**\n\n_EEXP_ 0.538 −0.346 **0.870**\n\n_FCON_ 0.449 −0.258 0.497 **0.934**\n\n_INNO_ 0.590 −0.142 0.387 0.330 **0.885**\n\n_ISEC_ −0.692 0.300 −0.466 −0.430 −0.536 **0.903**\n\n_OPTI_ 0.673 −0.175 0.569 0.442 0.569 −0.561 **0.867**\n\n_PAND_ 0.647 −0.156 0.465 0.281 0.558 −0.607 0.604 **0.826**\n\n_PEXP_ 0.605 −0.208 0.584 0.356 0.543 −0.522 0.695 0.533 **0.845**\n\n_SINF_ 0.508 −0.104 0.404 0.329 0.439 −0.446 0.485 0.457 0.582 **0.806**\n\n**Fig. 2 Structural model**\n\n\nDiscriminant validity refers to how a construct is genu­\n\ninely distinct from other constructs by empirical standards.\nTo check the discriminant validity, the square roots of the\nAVEs were compared with the correlation for each of the\nconstructs. The common guideline for assessing discrimi­\nnant validity is that the construct’s square root AVE should\nbe higher than the correlations between the specific construct\nand all the other constructs in the model (Zmud, 1990).\n\nTable 6 presents the discriminant validity result. The\n\ndiagonal items in the table signify the square roots of the\n\n\nAVEs—a measure of variance between the construct and its\nindicators—while the off-diagonal items signify the corre­\nlation between constructs. As presented in Table 6, all the\nsquare roots of the AVEs (bold) are higher than the correla­\ntion between the constructs, indicating that all the constructs\nin Table 6 satisfy discriminant validity and can be used to\ntest the structural model.\n\n\n-----\n\n**Table 7 VIF values**\nConstruct VIF\n\n_DISC_ 1.33968\n\n_EEXP_ 2.55515\n\n_FCON_ 1.77895\n\n_INNO_ 1.57217\n\n_ISEC_ 1.69459\n\n_OPTI_ 2.45746\n\n_PAND_ 1.84538\n\n_PEXP_ 2.13851\n\n_SINF_ 1.53758\n\n#### 5.2 Common Method bias\n\nBecause of the self-report nature of the data collection\nmethod used in this study, common method bias may be an\nissue. The potential for common method bias was assessed\nand managed using the following measures. First, Pavlou\nand El Sawy (2006) asserted that common method bias\nresults in very high correlations (i.e., r > 0.90). The high­\nest correlation among the constructs in this study exceeded\n0.90, indicating there is a concern that this study may be\naffected by common method bias. Thus, the Harman onefactor test was performed in which all the variables were\nloaded into an exploratory factor analysis. Harman’s onefactor test reveals problematic common method bias if an\nexploratory factor analysis returns eigenvalues that depict\nthat the first factor accounts for more than 50% of the vari­\nance among the variables. The test result of this study indi­\ncates that the highest factor explained 27.9% of the variance\namong all variables, which is acceptable according to Pod­\nsakoff and Organ’s (1986) criterion. Based on Liang et al.\n(2007), we included a common method factor in the model.\nThe coefficients for the measurement and structural mod­\nels did not alter significantly after controlling the common\nmethod factor. Thus, we conclude that common method bias\ndoes not pose a significant threat to the results of this study.\n\n#### 5.3 Structural Model\n\nThe structural model represents the underlying structural\ntheories of the path model. The assessment of the structural\n\n\nmodel involves examining the model’s predictive capabili­\nties and the relationships between the constructs. Figure 2\nabove illustrates the structural model proposed in this study.\nThe steps for structural model assessment are as follows:\n(1) examine structural model for collinearity; (2) assess the\nsignificance of the path coefficients; (3) assess the level of\n_R[2]; (4) assess the f[2] effect size; and (5) assess the predictive_\nrelevance Q[2].\n\nThe first step is to assess the collinearity between the\n\nconstructs. Variance inflation factor (VIF) values of 5 or\nabove in the construct indicate collinearity (Hair et al.,\n2017). Table 7 demonstrates that all VIF values of the con­\nstructs are below 5, which means there is no collinearity\nissue in our study.\n\nThe significance of a coefficient ultimately depends on\n\nits standard error obtained through the bootstrapping pro­\ncedure. Bootstrapping computes the empirical t-values and\n_p-values for all structural path coefficients. Given that our_\nstudy is exploratory, the significance level is assumed to be\n10%. The bootstrapping analysis was run using a two-tailed\ntest. Hence, the critical value is 1.65 for t-statistics and 0.1\nfor _p-values (Hair et al.,_ 2010). To assess the significance\nof the path coefficients, the guidelines are as follows: (1)\n_t-value should be higher than the critical value; (2) p-value_\nshould be lower than 0.1 (significance level = 10%).\n\nAs presented in Table 8, _PEXP has a nonsignificant_\n\npositive effect on _BINT (β = 0.052,_ _t_ = 0.750, _p_ = 0.454).\nSimilarly, EEXP also has a nonsignificant positive effect on\n_BINT (β = 0.046, t_ = 0.971, p = 0.332). Therefore, neither H1\nnor H2 is supported.\n\n_SINF has a more substantial nonsignificant positive effect_\n\non BINT (β = 0.076, t = 1.460, p = 0.145) than the previous\nconstructs, but it did not satisfy the minimum threshold. The\nsame is true for FCON, with a stronger but nonsignificant\npositive effect on _BINT (β = 0.067,_ _t_ = 1.450, _p_ = 0.148).\nHence, neither H3 nor H4 are supported.\n\nThe effect of _INNO on_ _BINT (β = 0.115,_ _t_ = 2.168,\n\n_p_ = 0.009) is significantly positive. In addition, _OPTI has_\na significant positive effect on _BINT (β = 0.204,_ _t_ = 3.431,\n_p_ = 0.001). Therefore, both H5 and H6 are supported.\n\n\n**Table 8 Path coefficients**\nHypothesis Path Path coefficient (β) _t-statistics_ _p-values_ Hypothesis supported\n\nH1 _PEXP -> BINT_ 0.052 0.75 0.454 No\n\nH2 _EEXP -> BINT_ 0.046 0.971 0.332 No\n\nH3 _SINF -> BINT_ 0.076 1.46 0.145 No\n\nH4 _FCON -> BINT_ 0.067 1.45 0.148 No\n\nH5 _INNO -> BINT_ 0.115 2.618 0.009 Yes\n\nH6 _OPTI -> BINT_ 0.203 3.431 0.001 Yes\n\nH7 _DISC -> BINT_ −0.078 2.251 0.025 Yes\n\nH8 _ISEC -> BINT_ −0.273 5.05 0 Yes\n\nH9 _PAND -> BINT_ 0.179 3.389 0.001 Yes\n\n\n-----\n\n**Table 9 R[2] value for behavioral intention**\nDependent construct _R square_\n\n_BINT_ 0.657\n\n**Table 10 Effect size f[2] values**\nConstruct _f[2]_\n\n_BINT_ –\n\n_DISC_ 0.015\n\n_EEXP_ 0.003\n\n_FCON_ 0.009\n\n_INNO_ 0.021\n\n_ISEC_ 0.105\n\n_OPTI_ 0.046\n\n_PAND_ 0.045\n\n_PEXP_ 0.003\n\n_SINF_ 0.01\n\n**Table 11 Predictive relevance coefficient Q[2]**\n\nConstruct _Q²_\n\n_BINT_ 0.507\n\nIn contrast, _DISC has a significant negative effect on_\n\n_BINT (β =_ −0.078, t = 2.251, p = 0.025). Likewise, the effect\nof _ISEC on_ _BINT is significantly negative (β_ = −0.273,\n_t_ = 5.050, p = 0.000). Thus, H7 and H8 are both supported.\n\nFinally, it is observed that PAND has a significant posi­\n\ntive effect on BINT (β = 0.179, t = 3.389, p = 0.001). Hence,\nH9 is supported.\n\nHigher levels of the _R[2] value indicate higher levels of_\n\npredictive accuracy. Table 9 demonstrates that the proposed\nmodel accounted for 65.7% of the variance in behavioral\nintention.\n\nOther than evaluating the _R² values, changes in the_ _R²_\n\nvalue when a specified exogenous construct is excluded\nfrom the model can be used to assess whether the excluded\nconstruct has a substantial influence on the endogenous\nconstructs. This measure is referred to as the ƒ² effect size.\nGuidelines for determining ƒ² are that values of 0.02, 0.15,\nand 0.35, respectively, represent small, medium, and large\neffects of the exogenous latent variable (Cohen, 1988).\nEffect size values of less than 0.02 indicate that there is no\neffect. Table 10 presents the f[2] value for each variable. The\nvalues range from 0.003 to 0.105. _EEXP,_ _PEXP,_ _FCON,_\n_SINF, and DISC have f[2] values less than 0.02, indicating no_\neffect. In contrast, _INNO,_ _PAND,_ _OPTI, and_ _ISEC have_ _f[2]_\nvalues between 0.02 and 0.15, meaning these variables have\na medium effect.\n\nThe predictive relevance Q[2] indicates the model’s out-of\nsample predictive power or predictive relevance (Geisser,\n1975; Stone, 1974). A path model that exhibits predictive\nrelevance accurately predicts data not used in the model\nestimation. In the structural model, Q² values greater than 0\nsuggest that the model has predictive relevance for a specific\n\n\nendogenous construct, whereas values of 0 and below indi­\ncate a lack of predictive relevance. As shown in Table 11,\nthe Q[2] value is 0.507, thus exceeding the minimum thresh­\nold of zero, which means that the model has predictive rel­\nevance for the construct.\n\n### 6 Discussions\n\nThis study combined UTAUT and TRI to develop a research\nmodel with nine hypotheses to understand the factors influ­\nencing blockchain acceptance in the real estate indus­\ntry. Given that user readiness factors are explained by the\nTRI and technology adoption factors are explained by the\nUTAUT model, we integrated the UTAUT model with the\nTRI to complement the strengths and compensate for the\nweaknesses of each model. Data were collected from real\nestate buyers and sellers, the people most involved in and\naffected by buying or selling real estate. To the best of our\nknowledge, this study is one of the first to address the accep­\ntance of blockchain by real estate buyers and sellers. Previ­\nous studies have examined either the technological aspect\nor the application of blockchain to real estate, with few\nstudies specifically examining the adoption of blockchain\nin the real estate industry (Konashevych, 2020; Wouda &\nOpdenakker, 2019).\n\n#### 6.1 Findings\n\nThis study revealed several interesting findings. The study\ndemonstrates that four measures from the TRI model,\nnamely innovativeness, optimism, discomfort, insecurity,\nand an additional measure, pandemic influence, are the most\nimportant factors affecting blockchain acceptance in the real\nestate industry. In contrast, four measures from the UTAUT\nmodel, namely performance expectancy, effort expectancy,\nsocial influence, and facilitating conditions, did not signifi­\ncantly influence the intentions of real estate buyers and sell­\ners to use blockchain technology.\n\nThe results indicate that innovativeness positively influ­\n\nences the intention to use blockchain technology. This result\nis consistent with previous studies (Buyle et al., 2018;\nQasem, 2020; Rahman et al., 2017) that have demonstrated\nthat innovativeness has a strong influence on technology\nuse intention. This can be explained by innovative indi­\nviduals generally being more open to new ideas (Kwang\n& Rodrigues, 2002). Innovativeness promotes eagerness to\nlearn, understand, and use new technologies, thus increasing\ntechnology acceptance (Turan et al., 2015). Optimism also\nhas a positive influence on the intention to use blockchain.\nThis finding is consistent with findings from recent studies\n(Koloseni & Mandari, 2017; Qasem, 2020; Rahman et al.,\n\n\n-----\n\n2017). Optimistic individuals tend to have positive percep­\ntions of technology (Napitupulu et al., 2020). Our findings\nsuggest that optimism increases the likelihood that individu­\nals perceive blockchain as a technology that will improve\nthe real estate industry.\n\nThe present study shows that discomfort hinders the\n\nintention to use blockchain technology, in contrast to some\nprevious studies that found discomfort was insignificant in\ninfluencing blockchain adoption (Kamble et al., 2019; Pat­\ntansheti et al., 2016). However, our finding is consistent\nwith other studies that have observed that discomfort nega­\ntively affects perceived ease of use, which directly affects\ntechnology adoption intentions (Kuo et al., 2013; Rahman\net al., 2017). Given that blockchain is known as a disruptive\ntechnology, some respondents reported feeling uncomfort­\nable that they cannot use the technology properly. Our study\nsuggests that uncertainty affects the intention to use block­\nchain. This contrasts with a previous study of blockchain\nadoption, which found that uncertainty had an insignificant\neffect on perceived ease of use or usefulness on the intention\nto use blockchain. Most subjects did not consider the use of\nblockchain to be doubtful (Kamble et al., 2019). However,\nblockchain is seen as a new, emerging technology, particu­\nlarly when considering its implementation in sectors such as\nreal estate. As a result, uncertainty and doubt are widespread\namong respondents.\n\nThe results suggest that the influence of the pandemic\n\nhas a positive effect on individuals’ intentions to use block­\nchain technology. During the COVID-19 pandemic, block­\nchain with smart contracts was able to simplify complicated\napplication and approval processes for loans and insur­\nance that were affected and extended during the lockdown\nperiods (Pérez-Sánchez et al., 2021). That is, blockchain\ncan mitigate the adverse effects of a pandemic situation\nin the real estate industry by creating smart contracts for\nreal estate (Redolfi, 2021). Our study suggests that perfor­\nmance expectancy does not influence the intention to use\nblockchain. Furthermore, similar to previous studies, effort\nexpectancy has no influence on intention to use, implying\nthat effort expectancy is insignificant in determining the\nintention to use blockchain technology (Batara et al., 2017;\nEckhardt et al., 2009). Effort expectancy and performance\nexpectancy are closely related, with the former being more\nassociated with efficiency expectancies and the latter more\nwith effectiveness expectancies (Brown et al., 2010).\n\nThis study also found that social influence does not\n\naffect the intention to use blockchain, which confirms a\nrecent study that found that social influence has no signifi­\ncant effect on blockchain adoption intention (Alazab et al.,\n2021). This result suggests that others’ experiences with\nblockchain acceptance do not influence real estate buyers\nand sellers. Moreover, we found that conducive conditions\n\n\ndo not significantly influence behavioral intention. Previous\nresearch has found that enabling conditions influence block­\nchain adoption in supply chains in the United States but not\nin India (Queiroz & Wamba, 2019). Our study also suggests\nthat facilitating conditions play an important role in deter­\nring blockchain adoption in other developing countries such\nas Malaysia. Our research suggests that blockchain adoption\nby real estate buyers and sellers is mainly determined by\nthe psychological aspects and personality traits measured by\nTRI rather than by the aspects of the system or technology\nthat the UTAUT measures.\n\n#### 6.2 Implications for Theory\n\nThis study provides a broader view of new technology\nadoption and highlights the importance of integrating the\nUTAUT and TRI models. Although UTAUT is a valuable\nmodel in various research areas (Venkatesh, 2003; Wang et\nal., 2017; Ying, 2018), the psychological aspects of the user\nare not considered in the model (Napitupulu et al., 2020).\nOur analysis demonstrates that it may be beneficial and\nsignificant to theorize about effects that are currently miss­\ning from the original UTAUT model. Integrating the con­\nstructs of the TRI model with the constructs of the UTAUT\nmodel not only enables us to examine technology readiness\nand acceptance simultaneously but also stimulates further\nresearch to improve existing models and deepen the study\nof technology adoption.\n\nPrior studies have not attached significant importance\n\nto individual factors and major global events in influenc­\ning technology adoption and have neglected the importance\nof psychological factors as antecedents to intention to use\ninformation technology and systems (Adiyarta et al., 2018;\nNapitupulu et al., 2020). This study provides evidence that\nthe four psychological measures of the TRI model (innova­\ntiveness, optimism, discomfort, and insecurity) all signifi­\ncantly affect blockchain adoption in the real estate industry.\nIn addition, this paper shows that major global events, such\nas the COVID-19 pandemic, influence real estate buyers’\nand sellers’ behavioral intentions to use blockchain tech­\nnology. These findings provide new directions for future\nresearch, not only for the study of blockchain adoption in\nthe real estate industry but also for the general study of tech­\nnology adoption.\n\n#### 6.3 Implications for Practice\n\nThis paper also has important implications for practitio­\nners. The first implication is that it would be beneficial for\nblockchain and real estate stakeholders to focus more on\npsychological factors than technological factors when imple­\nmenting blockchain. They can conduct pre-implementation\n\n\n-----\n\nstudies, such as surveys or focus groups, to understand per­\nsonal characteristics and address potential psychological\nconcerns, which will help improve the efficiency of technol­\nogy adoption when implementing revolutionary blockchain\ntechnology.\n\nThe second implication for real estate stakeholders is that\n\nemphasizing the holistic benefits of blockchain technology\nto the real estate ecosystem, including buyers and sellers,\nis more likely to drive technology adoption than outlining\nblockchain’s features. As our study shows, people are more\nexperienced in using various new technologies in today’s\ninternet age. Therefore, performance expectancy and effort\nexpectancy were not found to be critical in influencing users’\nintentions to use blockchain. In contrast, knowledge of the\nholistic benefits may contribute to psychological factors that\npositively impact technology adoption, such as innovative­\nness and optimism, and mitigate the negative psychological\nfactors, such as discomfort and insecurity.\n\nThe third implication is that stakeholders in the real\n\nestate industry, such as professional associations, govern­\nment agencies, financial institutions, brokers, and lawyers,\nshould collaborate to establish a blockchain network so that\nreal estate settlements can be conducted online with smart\ncontracts and blockchain-based streamlined processes. The\nthree implications of this study can also provide stakehold­\ners in sectors other than real estate with insights into adopt­\ning new technologies.\n\n#### 6.4 Limitations and Future Research\n\nLike any other study, this study has limitations that provide\nfurther research opportunities. First, our model was tested in\nMalaysia, which is a developing country. Future studies can\napply a comparative research approach and test our model\nin developed countries. Second, our study is limited to the\nreal estate industry. Researchers can further investigate\nthe acceptance of blockchain technology by applying our\nresearch model to other sectors or industries.\n\n### 7 Conclusion\n\nBased on the UTAUT and TRI models, this paper concep­\ntualized and empirically examined the factors that influence\nintentions to use blockchain technology in the real estate\nindustry. Data were collected from 301 real estate buyers and\nsellers and analyzed using the partial least squares method.\nThe results showed high internal consistency and reliability,\nindicating that the study has high predictive accuracy. The\nstudy concluded that the intention of real estate actors to\nuse blockchain is significantly influenced by the following\nfactors: innovativeness, optimism, discomfort, insecurity,\n\n\nand pandemic influence. Thus, our empirical investigation\nshows that the model we propose, which reformulates the\ntheses of the original UTAUT model, can provide a useful\nalternative for understanding blockchain acceptance and\nuse.\n\n**Acknowledgements This material is based upon work supported**\nby the National Natural Science Foundation of China under Grants\n72172163.\n\n**Funding Open Access funding enabled and organized by CAUL and**\nits Member Institutions\n\n#### Declarations\n\n**Declaration of interest The authors declare that they have no known**\ncompeting financial interests or personal relationships that could have\nappeared to influence the work reported in this paper.\n\n**Open Access** This article is licensed under a Creative Commons\nAttribution 4.0 International License, which permits use, sharing,\nadaptation, distribution and reproduction in any medium or format,\nas long as you give appropriate credit to the original author(s) and the\nsource, provide a link to the Creative Commons licence, and indicate\nif changes were made. The images or other third party material in this\narticle are included in the article’s Creative Commons licence, unless\nindicated otherwise in a credit line to the material. If material is not\nincluded in the article’s Creative Commons licence and your intended\nuse is not permitted by statutory regulation or exceeds the permitted\nuse, you will need to obtain permission directly from the copyright\n[holder. To view a copy of this licence, visit http://creativecommons.](http://creativecommons.org/licenses/by/4.0/)\n[org/licenses/by/4.0/.](http://creativecommons.org/licenses/by/4.0/)\n\n### References\n\nAdiyarta, K., Napitupulu, D., Nurdianto, H., Rahim, R., & Ahmar, A.\n\n(2018, May). User acceptance of e-government services based on\nTRAM model. In IOP Conference Series: Materials Science and\n_Engineering (Vol. 352, p. 012057). 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Retrieved March 14, 2023 from [https://](https://www2.deloitte.com/us/en/pages/financial-services/articles/blockchain-in-commercial-real-estate.html)\n[www2.deloitte.com/us/en/pages/financial-services/articles/](https://www2.deloitte.com/us/en/pages/financial-services/articles/blockchain-in-commercial-real-estate.html)\n[blockchain-in-commercial-real-estate.html](https://www2.deloitte.com/us/en/pages/financial-services/articles/blockchain-in-commercial-real-estate.html)\n\n**Publisher’s Note Springer Nature remains neutral with regard to juris­**\ndictional claims in published maps and institutional affiliations.\n\nSpringer Nature or its licensor (e.g. a society or other partner) holds\nexclusive rights to this article under a publishing agreement with the\nauthor(s) or other rightsholder(s); author self-archiving of the accepted\nmanuscript version of this article is solely governed by the terms of\nsuch publishing agreement and applicable law.\n\n**William Yeoh is an Associate Professor at Deakin Business School,**\nDeakin University. His scholarship has been published in leading\njournals, including 7A* and 24A Australian Business Deans Coun­\ncil (ABDC) ranked journal publications and in all top Information\nSystems Conference proceedings (i.e., ICIS, HICSS, ECIS, PACIS,\nAMCIS, ACIS), and has been supported by AUD1.2 Million from var­\nious funding bodies and industries. He has been recognised for excel­\nlence in teaching, research, and service, receiving Educator of the Year\nGold Award (a national award from the Australian Computer Society\nACS - Australia’s peak ICT professional association), Deakin ViceChancellor’s Award for Value Innovation, Deakin Faculty Research\nExcellence Award, and two-time internationally-competitive IBM\nFaculty Awards.\n\n\n**Angela Siew-Hoong Lee is a Professor and an Associate Dean at**\nSchool of Engineering and Technology, and the Head of Department\nof Computing and Information Systems at Sunway University. Prof\nAngela Lee has been developing data science curriculum for more than\n10 years and she is the key person to introduce Data Science degree at\nSunway University. She was recently awarded the SAS Global Forum\nDistinguished Educator Award 2021. She regularly speaks at data sci­\nence conferences. Angela has developed many innovative ways to\nuse analytics and data science tools from the most elementary level\nto advanced analytics. She teaches Social Media Analytics, Visual\nAnalytics, Advanced Analytics and Business Intelligence and has pub­\nlished many international journal papers in the area of churn analytics,\nsentiment analysis and predictive analytics.\n\n**Claudia Ng received her Bachelor of Data Analytics and Master of**\nScience by Research from Sunway University. She is a data analyst at\na Malaysian bank.\n\n**Aleš Popovič is a Full Professor of Information Systems at NEOMA**\nBusiness School in France. He seeks to find research that is relevant\nand useful to both the academic and practitioner communities. His\nareas of research interest are focused on the study of how ISs provide\nvalue for people, organisations, and markets. He studies IS value in\norganisations, IS success, behavioural and organizational issues in IS,\nand IT in inter-organizational relationships. Dr. Popovič has published\nhis research in a variety of academic journals, such as Journal of the\nAssociation for Information Systems, European Journal of Information\nSystems, Journal of Strategic Information Systems, Decision Support\nSystems, Information & Management, Information Systems Frontiers,\nGovernment Information Quarterly, and Journal of Business Research.\n\n**Yue Han is an Associate Professor of Information Systems in the**\nMadden School of Business at Le Moyne College. Her main research\nareas include crowdsourcing, collective intelligence, knowledge reuse\nfor innovation, and information diffusion in social media. She also\nstudies the implementation of business intelligence and artificial intel­\nligence. She has published papers in various information systems jour­\nnals and conferences such as Information Systems Research, Journal\nof the Association for Information Systems, International Conference\non Information Systems, and ACM SIGCHI Conference on ComputerSupported Cooperative Work & Social Computing.\n\n\n-----\n\n"