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The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain Case
9th IFAC Conference on Manufacturing Modelling, Management and 9th IFAC IFAC Conference Conference on on Manufacturing Manufacturing Modelling, Modelling, Management Management and and Control 9th online at www.sciencedirect.com 9th IFAC Conference on Manufacturing Modelling, Management and Control Berlin, Germany, August 28-30, 2019 Available Control 9th IFAC Conference on Manufacturing Modelling, Management and Control Berlin, Germany, August 28-30, 2019 2019 Berlin, Germany, August 28-30, Control Berlin, Germany, August 28-30, 2019 Berlin, Germany, August 28-30, 2019
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IFAC PapersOnLine 52-13 (2019) 1715–1720
The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain The Role of Social Influence in Blockchain The Brazilian Supply Chain CaseAdoption: The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain Case Case Case Case Samuel Fosso Wamba*. Maciel M. Queiroz. **
Fosso M. ** Samuel Fosso Wamba*. Wamba*. Maciel M. Queiroz. Queiroz. ** Toulouse, CO 31068 France *Toulouse Business School - Samuel TBS, Information, OperationsMaciel and Management Sciences, Samuel Fosso Wamba*. Maciel M. Queiroz. ** Toulouse, *Toulouse Business School TBS, Information, Operations and Management Sciences, CO *Toulouse Business School - Samuel TBS, Operations and Management Sciences, CO 31068 31068 France France Wamba*. Maciel M. Queiroz. ** Toulouse, (Tel: +33 5Information, 61 Fosso 29 50 54; e-mail: [email protected]). *Toulouse Business School TBS, Information, Operations and Management Sciences, Toulouse, CO 31068 France (Tel: +33 5 61 29 50 54; e-mail: [email protected]). (Tel: +33 5 61 29 50 54; e-mail: [email protected]). **Paulista University PostgraduateOperations Program inand Administration São Paulo,Toulouse, CO 04026-002 Brazil *Toulouse Business School--UNIP, TBS, Information, Management, Sciences, CO 31068 France (Tel: +33 5Postgraduate 61 29 50 54; Program e-mail: [email protected]). **Paulista University UNIP, in Administration Administration São Paulo, Paulo, CO CO 05508-030 04026-002 Brazil Brazil **Paulista University -- UNIP, in São Paulo, CO 04026-002 Brazil **University of São(Tel: Paulo, Architecture and Ocean Engineering,,, São +33Naval 5Postgraduate 61 29 50 54; Program e-mail: [email protected]). **Paulista University - UNIP, Postgraduate Program in Administration , São Paulo, Paulo, CO CO 05508-030 04026-002 Brazil Brazil **University of Naval Architecture and Engineering, **University of São São Paulo, Paulo, Naval Architecture and Ocean Ocean Engineering,, São São (e-mail:[email protected]) **Paulista University - UNIP, Postgraduate Program in Administration São Paulo, Paulo, CO CO 05508-030 04026-002 Brazil Brazil **University of São Paulo, Naval(e-mail:[email protected]) Architecture and Ocean Engineering, São Paulo, CO 05508-030 Brazil **University of São Paulo, Naval(e-mail:[email protected]) Architecture and Ocean Engineering, São Paulo, CO 05508-030 Brazil (e-mail:[email protected]) (e-mail:[email protected]) Abstract: The aim of this study is to understand the blockchain adoption behaviour in Brazilian supply Abstract: The study is blockchain adoption behaviour Brazilian supply Abstract: The aim aim of ofthethis this study is to to understand the blockchain adoption behaviour ininto Brazilian supply chains. Specifically, study aims to understand unlock the the potential of social influence; takingin consideration Abstract: The aim ofthethis study is to understand the blockchain adoption behaviour ininto Brazilian supply chains. Specifically, study aims to unlock the potential of social influence; taking consideration chains. study aims to understand unlock the the potential of social influence; taking consideration workersSpecifically, from Brazilian supply We proposed a model using constructs frominainto unified theory of Abstract: Thethe aim ofthethis study ischains. to blockchain adoption behaviour Brazilian supply chains. Specifically, the study aimschains. to unlock the potential of social influence; taking consideration workers from the Brazilian supply We aa model using constructs from aainto unified of workers from thethe Brazilian supply We proposed proposed model usinganalysed constructs frompartial unified theory of acceptance and use technology (UTAUT). The models were using leasttheory squares chains. Specifically, the of study aimschains. to unlock the potential of social influence; taking into consideration workers from thethe Brazilian supply chains. We proposed a modelwere usinganalysed constructs frompartial a unified theory of acceptance and use (UTAUT). models using least squares acceptance and use of of technology technology (UTAUT). The models were using least squares structuralfrom equation modelling (PLS-SEM) andproposed theThe findings indicated the power ofpartial social influence in workers thethe Brazilian supply chains. We a model usinganalysed constructs from a unified theory of acceptance and the use of technology (UTAUT). The models were analysed using partial least squares structural modelling (PLS-SEM) and findings indicated the social influence in structural equation modelling (PLS-SEM) and the theThe findings indicated the power ofpartial social influence in predicting equation other constructs. In addition, this study shows thepower mediation effect ofsquares effort acceptance and theUTAUT use of technology (UTAUT). models were analysed usingof least structural equation modelling (PLS-SEM) and the findings indicated the power of social influence in predicting other constructs. In this study shows the mediation effect of predicting other UTAUT constructs. In addition, addition, this study shows thepower mediation effect of effort expectancyequation and UTAUT themodelling facilitating conditions relationship between social andeffort the structural (PLS-SEM) andinthethefindings indicated the of influence social influence in predicting other UTAUT constructs. In addition, this study shows the mediation effect of effort expectancy and the facilitating conditions in the relationship between social influence and the expectancy and UTAUT the tofacilitating conditions in the relationship between social influence andeffort the behaviouralother intention adopt blockchain. ourthis findings essential theoretical and managerial predicting constructs. In Finally, addition, studybring shows the mediation effect of expectancy and the tofacilitating conditions in the relationship between social influence and the behavioural adopt Finally, our bring essential theoretical and behavioural intention adopt blockchain. Finally, our findings findings bringbetween essential social theoretical and managerial managerial implications.intention Copyright © 2019blockchain. IFAC expectancy and the tofacilitating conditions in the relationship influence and the behavioural intention to © adopt blockchain. Finally, our findings bring essential theoretical and managerial implications. Copyright 2019 IFAC implications.intention Copyright 2019blockchain. IFAC behavioural to © adopt Finally, our findings bring essential theoretical and managerial implications. Copyright ©UTAUT, 2019 IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rightsperformance reserved. Keywords: Blockchain, supply chain, social influence, facilitating conditions, implications. Copyright ©UTAUT, 2019 IFAC Keywords: Keywords: Blockchain, UTAUT, supply supply chain, chain, social social influence, influence, facilitating facilitating conditions, conditions, performance performance expectancy, Blockchain, effort expectancy. Keywords: Blockchain, UTAUT, supply chain, social influence, facilitating conditions, performance expectancy, effort expectancy, Blockchain, effort expectancy. expectancy. Keywords: UTAUT, supply chain, social influence, facilitating conditions, performance expectancy, effort expectancy. expectancy, effort expectancy. discussion about blockchain adoption behaviour in supply 1. INTRODUCTION discussion blockchain adoption behaviour in discussion about blockchain adoption behaviourinsights in supply supply chains. Ourabout results also provide interesting for 1. 1. INTRODUCTION INTRODUCTION discussion about blockchain adoption behaviourinsights in supply chains. Our results also provide interesting for chains. Our results also provide interesting insights for 1. INTRODUCTION managers and practitioners. discussion about blockchain adoption behaviour in supply Blockchain technologies (Nakamoto, 2008) are some of the chains. Our results also provide interesting insights for 1. INTRODUCTION managers and practitioners. Blockchain technologies (Nakamoto, 2008) are some of the managers and practitioners. chains. Our results also provide interesting insights for Blockchain technologies (Nakamoto, 2008) are some of the most paramount cutting-edge technologies. Recently, because managers and practitioners. Blockchain technologies (Nakamoto, 2008) are some of the most paramount cutting-edge technologies. Recently, because managers and practitioners. most paramount cutting-edge technologies. Recently, because of the highlytechnologies disruptive capacity and the application Blockchain (Nakamoto, 2008) are some of the most paramount cutting-edge technologies. Recently, because 1.1 Blockchain Technologies: from bitcoins to all business of the highly disruptive capacity and the of theparamount highly disruptive capacity and the application application adherence to vast business models (Larios-Hernández, 2017; most cutting-edge technologies. Recently, because 1.1 Blockchain 1.1 Blockchain Technologies: Technologies: from from bitcoins bitcoins to to all all business business of the highly disruptive capacity and the application 2017; models adherence vast business models (Larios-Hernández, adherence toFilippi vast business models (Larios-Hernández, 1.1 Blockchain Technologies: from bitcoins to all business Pazaitis, Deto and capacity Kostakis, 2017), blockchain 2017; of the highly disruptive and the application models models adherence toFilippi vast business models2017), (Larios-Hernández, 2017; 1.1 Blockchain Technologies: from bitcoins to all business Pazaitis, De and Kostakis, blockchain Pazaitis, DetoFilippi and Kostakis, 2017), blockchain models technologies have attracted attention in the supply chains adherence vast business models (Larios-Hernández, 2017; Since the introduction of blockchain technologies in the Pazaitis, De Filippi and Kostakis, 2017), blockchain models technologies have attracted attention in the supply chains Since the introduction introduction of blockchain blockchain technologies in the the technologies have attracted attention in the supply chains field (Kshetri, 2018; Min, 2018). A key characteristic of Pazaitis, De Filippi and Kostakis, 2017), blockchain Since the of in crypto-currency field (Nakamoto, 2008),technologies many other studies technologies have attracted attention in the supply chains Since the introduction of blockchain technologies in the field 2018; Min, A characteristic of crypto-currency field (Nakamoto, 2008), many other studies field (Kshetri, (Kshetri, 2018; Min,is2018). 2018). Atokey key characteristic of blockchain technologies related the decentralized model technologies have attracted attention in the supply chains crypto-currency field (Nakamoto, 2008), many other studies have been conducted in different research fields (Lei et al., Since the introduction of blockchain technologies in the field (Kshetri, 2018; Min,is2018). Atokey characteristic of crypto-currency field (Nakamoto, 2008), many other studies blockchain technologies related the decentralized model have been conducted in different fields (Lei et blockchain technologies related the decentralized model that central intermediary is no more needed to validate the fielda(Kshetri, 2018; Min,is2018). Atokey characteristic of have been conducted inThere different research fields (Leistudies et al., al., 2017; Y. Chen, 2018).(Nakamoto, is noresearch consensus definition for crypto-currency field 2008), many other blockchain technologies is related to the decentralized model have been conducted in different research fields (Lei et al., that a central intermediary is no more needed to validate the 2017; Y. Chen, 2018). There is no consensus definition for that a central intermediary is no more needed to validate the transactions.technologies Additionally, transactions use cryptographic blockchain is the related to the decentralized model 2017; Y. Chen, 2018). There is no consensus definition for blockchain in extant literature. In this study, we follow the have been conducted in different research fields (Lei et al., that a central Additionally, intermediary is notransactions more needed tocryptographic validate the 2017; Y. Chen, 2018). There is no consensus definition for transactions. the use blockchain in extant literature. In this study, we follow the transactions. Additionally, the transactions use cryptographic techniques that cannot permit their modification. that a central intermediary is no more needed to validate the blockchain in extant literature. In this study, we follow the compact definition provided by de Leon et al. (2017), in 2017; Y. Chen, 2018). There is no consensus definition for transactions. Additionally, thetheir transactions use cryptographic blockchain in extant literature. In this study, we follow the techniques that cannot permit modification. compact definition provided by de Leon et al. (2017), in techniques that cannot permit their modification. Consequently, blockchain is known as a tamper-proof transactions. Additionally, the transactions use cryptographic compact definition provided by de Leon et al. (2017), in which blockchain is defined as “a digital information blockchain in extant literature. In this study, we follow the techniques thatblockchain cannot permit their modification. compact definition provided by asde “a Leon et al.information (2017), in Consequently, is as tamper-proof which blockchain is defined digital Consequently, is known known as aa business tamper-proof technology that thatblockchain will remodel traditional models. techniques cannot permit their modification. which blockchain is defined as “a digital information recording method capable of recording data using a logbook compact definition provided by de Leon et al. (2017), in Consequently, blockchain is known as a business tamper-proof which blockchain is defined as “a data digital information technology will traditional models. recording method capable of recording recording using a logbook logbook technology that thatblockchain will remodel remodel traditional models. Consequently, is known as a business tamper-proof recording method capable of data using a approach; and characterised by the following: 1-Ordered, 2which blockchain is defined as “a digital information technology that remodel traditional business promises models. to recording method capable of recording data using a logbook Considering thewill chains, blockchain approach; and3-Sound characterised by the the following: following: 1-Ordered, 2technology that willsupply remodel traditional business promises models. approach; and characterised by 1-Ordered, Incremental, (cryptographically verifiable up to 2a method capable of recording data using a logbook Considering the supply chains, Considering the safety, supplysupply chains, blockchain promises to to recording approach; and characterised by the following: 1-Ordered, 2improve product chainblockchain visibility, transparency, Incremental, 3-Sound (cryptographically verifiable up to a Incremental, 3-Sound (cryptographically verifiable up to a Considering the supply chains, blockchain promises to given block) and 4-Digital.” (de Leon et al., 2017, p. 288). approach; and characterised by the following: 1-Ordered, 2improve product safety, supply chain visibility, transparency, improve product safety, chainblockchain visibility, Incremental, 3-Sound (cryptographically verifiable up to a traceability ofthe operations, cooperation, as welltransparency, as improve Considering supplysupply chains, promises to given block) and 4-Digital.” (de Leon et al., 2017, p. 288). given block) and 4-Digital.” (de Leon et al., 2017, p. 288). improve product safety, supply chain visibility, transparency, Incremental, 3-Sound (cryptographically verifiable up to a traceability of operations, cooperation, as as improve traceability ofothers operations, cooperation, as well welltransparency, as improve givendefinition block) andas4-Digital.” (de Leonimplies et al., 2017, p. 288). trust, among (Kshetri, 2018). addition, with smart The improve product safety, supply chain In visibility, mentioned(de above that p. blockchain traceability of operations, cooperation, as well as improve given block) and 4-Digital.” Leon et al., 2017, 288). trust, among others (Kshetri, 2018). In addition, addition, with smart as implies that trust, among (Kshetri, 2018). In with smart contracts, significant reductions on cost of the The traceability ofothers operations, cooperation, astransactions well as improve The definition definitionoperate as mentioned mentioned above implies network that blockchain blockchain technologies in a above peer-to-peer model trust, among others (Kshetri, 2018). In addition, with of smart The definition as mentioned above implies that blockchain contracts, significant reductions on cost transactions the technologies operate in a peer-to-peer network model contracts, significant reductions on cost transactions of the operations expected. The2018). blockchain integration with The trust, amongareothers (Kshetri, In addition, with smart technologies operate in a peer-to-peer network model (Christidis and Devetsikiotis, 2016; Mengelkamp et al., definition as mentioned above implies that blockchain contracts, significant reductions on cost transactions ofwith the (Christidis technologies operate in a peer-to-peer network model operations are expected. The blockchain integration and Devetsikiotis, 2016; Mengelkamp et al., operations are expected. The blockchain integration with supply chains is still nascent, and the extant literature contracts, significant reductions on cost transactions of the technologies (Christidis and Devetsikiotis, 2016; Mengelkamp et al., 2018). This characteristic implies that, in a decentralized operate in a peer-to-peer network model operations are expected. The blockchain integration with 2018). (Christidis and Devetsikiotis, 2016; Mengelkamp et al., supply chains is nascent, and the literature This characteristic implies that, in a decentralized supply chains is still stillstudies nascent, and the extant extant literature concerning empirical this technology is scarce operations are expected. The ofblockchain integration with (Christidis 2018). This characteristic implies that, in a decentralized network, there are no intermediaries (Aste, Tasca and Di and Devetsikiotis, 2016; Mengelkamp et al., supply chains is stillstudies nascent, and the extant is literature 2018). This characteristic implies that, in a Tasca decentralized concerning empirical of this scarce network, there are no intermediaries (Aste, and Di concerning empirical studies of Mainly this technology technology scarce 2018). (Fosso chains Wamba al., 2018). reporting supply isetstill nascent, and the studies extant is literature network, there are no intermediaries (Aste, Tasca and Di Matteo, 2017; Scott, Loonam and Kumar, 2017). Also, the This characteristic implies that, in a decentralized concerning empirical studies of Mainly this technology is scarce network, there Scott, are noLoonam intermediaries (Aste,2017). TascaAlso, and the Di (Fosso et al., 2018). studies reporting Matteo, 2017; and Kumar, (Fosso Wamba Wamba et (Kamble, al., 2018). studies reporting blockchain adoption Gunasekaran and Arha, 2018; network, concerning empirical studies of Mainly this technology is scarce Matteo, 2017; Scott, Loonam and Kumar, 2017). Also, the transactions accomplished by the network members there are no intermediaries (Aste, Tasca and are Di (Fosso Wamba et al., 2018). Mainly studies reporting Matteo, 2017; Scott, Loonam and Kumar, 2017). Also, the blockchain adoption (Kamble, Gunasekaran and Arha, 2018; transactions accomplished byand thestored network members are blockchain (Kamble, Gunasekaran and Arha, 2018; Queiroz andadoption FossoetWamba, 2019a) are available. Inreporting order to Matteo, (Fosso Wamba al., 2018). Mainly studies transactions accomplished by the network members are performed cryptographically into linked blocks 2017; Scott, Loonam and Kumar, 2017). Also, the blockchain adoption (Kamble, Gunasekaran and Arha, 2018; transactions accomplished byandthestored network members are Queiroz and Fosso are In to performed cryptographically into linked blocks Queiroz and Fosso Wamba, 2019a) are available. available. In order order to transactions contribute to fillingWamba, this gap,2019a) this study aims answer the blockchain adoption (Kamble, Gunasekaran andtoArha, 2018; performed cryptographically and stored into linked blocks (Kshetri, 2017). Due to the tamper-proof (Kano and accomplished by the network members are Queiroz and Fosso Wamba, 2019a) are available. In order to performed cryptographically and stored into linked blocks contribute to Fosso fillingWamba, this gap, gap, thisrole study aims toinfluence answer the (Kshetri, Due the (Kano and contribute to filling this this study aims answer followingand question: What is2019a) the socialto in Queiroz areofavailable. In orderthe to performed (Kshetri, 2017). Due to to there the tamper-proof (Kano and Nakajima, 2017). 2018) reflected, no modification allowed cryptographically and istamper-proof stored into linked blocks contribute to filling What this gap, thisrole study aims toinfluence answer the (Kshetri, 2017). Due to the tamper-proof (Kano and following question: is the of social in Nakajima, 2018) reflected, there is no modification allowed following question: What is the role of social influence in blockchaintoadoption? the constructs from the (Kshetri, contribute filling thisDrawing gap, thisonstudy aims to answer Nakajima, 2018) reflected, there is no modification allowed after the transactions have been validated. However, it is 2017). Due to the tamper-proof (Kano and following question: What is the on rolethe of constructs social influence in Nakajima, 2018) reflected, there is no modification allowed blockchain adoption? Drawing from the after the transactions have been validated. However, it is blockchain adoption? Drawing on the constructs from the UTAUT (Venkatesh et al., 2003), we proposed and validated following question: What is the role of social influence in Nakajima, after the transactions have been validated. However, it is possible to recover transaction history because of the link 2018) reflected, there is no modification allowed blockchain adoption? Drawing on the constructs from the after the totransactions have beenhistory validated. However, itlink is UTAUT (Venkatesh et 2003), we proposed and possible recover transaction because of the UTAUT (Venkatesh et al., al., 2003),on wethe proposed and validated validated a model taking into consideration the Brazilian supply chains. blockchain adoption? Drawing constructs from the after possible to recover transaction history because of the link between blocks (Hou, Wang and Liu, 2018). and it has been the transactions have been validated. However, it is UTAUT (Venkatesh et al., 2003),the weBrazilian proposedsupply and validated possible to recover transaction history because of the link aUTAUT model taking into consideration chains. between (Hou, Wang and Liu, it been aOur model taking into consideration Brazilian supply chains. possible results contribute thethe stimulation anvalidated in-depth (Venkatesh ettowards al., 2003), we proposedof and between blocks blocks (Hou,transaction Wangparadigms andhistory Liu, 2018). 2018). and it has has been traceability inbecause the and supply chains to the recover of the link aOur model taking into consideration the Brazilian of supply chains. modifying between blocks (Hou, Wangparadigms and Liu, 2018). and it haschains been results contribute towards stimulation an modifying the traceability in the supply results contribute towards the thethe stimulation an in-depth in-depth aOur model taking into consideration Brazilian of supply chains. between modifying the traceability paradigms in the supply chains blocks (Hou, Wang and Liu, 2018). and it has been Our results contribute towards the stimulation of an in-depth modifying the traceability paradigms in the supply chains Our results contribute towards the Federation stimulation of an in-depth modifying the traceability paradigms 2405-8963 © IFAC (International of Automatic Control) by Elsevier Ltd. All rights reserved. in the supply chains Copyright © 2019, 2019 IFAC 1740Hosting Peer review© of International Federation of Automatic Copyright 2019 IFAC 1740 Copyright ©under 2019 responsibility IFAC 1740Control. 10.1016/j.ifacol.2019.11.448 Copyright © 2019 IFAC 1740 Copyright © 2019 IFAC 1740
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(Biswas, Muthukkumarasamy and Tan, 2017; Lu and Xu, 2017).
1.2 Blockchain and Supply Chain Management Considering the integration of blockchain into the supply chain management (SCM) field, there exist many challenges and opportunities. It is clear that the blockchain applications in the SCM are in the early stages. However, there are various actions regarding blockchain implementation ongoing in different SCM contexts (Kshetri, 2018). In this outlook, blockchain has the potential to remodel any SCM across the world. Emerging literature reported various benefits that the SCM will attain with blockchain technologies. For example, with smart contract utilization, the complexity of the processes as well as the costs of transactions can be improved (Kim and Laskowski, 2017; Tian, 2017). Traceability through the supply chains will be significantly improved, implying more efficiency in the prevention of fraud (R. Y. Chen, 2018). Moreover, the process with more transparency and confidence (Lu and Xu, 2017) will be implicated in more cooperation between supply chain members (Aste, Tasca and Di Matteo, 2017), trust (Kshetri, 2018), accountability as well as security (Biswas, Muthukkumarasamy and Tan, 2017) among others. Despite these critical benefits, the literature concerning empirical studies (Fosso Wamba et al., 2018) about blockchain in the supply chain context is scarce. However, scholars have recently employed considerable efforts in order to understand the dynamics of blockchain adoption in the supply chain contexts (Francisco and Swanson, 2018; Kamble, Gunasekaran and Arha, 2018; Queiroz and Fosso Wamba, 2019). 2. HYPOTHESES AND RESEARCH MODEL The hypotheses of this study was derived with the support of the technology acceptance model lens. Specifically, we used the constructs social influence, facilitating conditions, performance expectancy, effort expectancy, as well as the behavioural intention from the classical unified theory of acceptance and use of technology (UTAUT) model (Venkatesh et al., 2003).
2.1 Unified Theory of Acceptance and Use of Technology (UTAUT) The unified theory of acceptance and use of technology (UTAUT) was proposed by Venkatesh et al. (2003) as a unification of the eight previous models regarding behaviour in the technology systems usage. In this original version, the UTAUT has the following four constructs as the main predictors of the behavioural and technology intentions to adopt usage behaviour (Venkatesh et al., 2003); social influence, facilitating conditions, performance expectancy, and effort expectancy. Additionally, the model is moderated by four main variables; namely gender, age, experience, and voluntariness of use. However, recent studies have
discontinued the use of these moderators, since the adherence is not suitable for all types of contexts (Dwivedi, Rana, Janssen, et al., 2017; Dwivedi, Rana, Jeyaraj, et al., 2017). Following this line of thought, we decided to use only the four primary constructs.
2.2 Behavioural Intention Behavioural intention refers to “the degree to which a person has formulated conscious plans to perform or not perform some specified future behaviour” (Warshaw & Davis, 1985, p. 214). In technology adoption literature, it has been studied exhaustively (Davis, 1989; Venkatesh et al., 2003; Venkatesh, Thong and Xu, 2012; Queiroz and Fosso Wamba, 2019a), but not sufficiently, due to the exponential growth of new technologies.
2.3 The Impact of Social Influence Social influence construct is defined as “the degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003, p.451). Facilitating conditions refer to “the degree to which an individual believes that an organizational and technical infrastructure exist to support the use of the system” (Venkatesh et al., 2003, p. 453). Performance expectancy is related to “the degree to which an individual believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al., 2003, p. 447). Effort expectancy is defined as “the degree of ease associated with the use of the system” (Venkatesh et al., 2003, p. 450). Social influence can be exerted by family, friends (Rana et al., 2017), and peers. Considering the SCM context, there are several interactions between the workers, his/her internal colleagues and (external) supply chain members. In this regard, we argue that the worker's interaction in the SCM can affect the perception of the organizational technical infrastructure. Moreover, social influence will impact directly on the performance expectancy with blockchain in the SCM operations. Blockchain literature already reported that efficiency in the process and the worker’s performance would be improved by blockchain (Kshetri, 2018). In addition, it is clear that with blockchain applications in the SCM, workers hope to perform their tasks by minimizing the effort. For instance, to minimize the complexity of the activities, a smart contract could be used (Kim and Laskowski, 2017), same as the effort with traceability (Lu and Xu, 2017; Tian, 2017). This, consequently, influences the worker’s productivity and performance. We postulate the following hypotheses: H1. Social influence has a positive effect on facilitating conditions. H2. Social influence has a positive effect on performance expectancy. H3. Social influence has a positive effect on effort expectancy.
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2.4 Facilitating Conditions Facilitating conditions are represented mainly by organizational and IT infrastructure (e.g., systems, internet speed, cloud integration, computers, among others). This construct has been used successfully in the literature concerning technology adoption (Venkatesh et al., 2003; Venkatesh, Thong and Xu, 2012; Sabi et al., 2016; Rana et al., 2017). Following this research stream, it is expected that facilitating conditions exert high influence on blockchain adoption in supply chains (Francisco and Swanson, 2018). Hence, we hypothesize that:
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H7. Effort expectancy as a mediator influences the relationship between social influence and behavioural intention to adopt blockchain. H8. Facilitating conditions as a mediator influences the relationship between social influence and behavioural intention to adopt blockchain. H9. Performance expectancy as a mediator influences the relationship between social influence and behavioural intention to adopt blockchain.
H4. Facilitating conditions have a positive effect on behavioural intention to adopt blockchain.
2.5 Performance Expectancy Previous literature on technology adoption had already emphasized the power of performance expectancy (Venkatesh et al., 2003; Riffai, Grant and Edgar, 2012; Venkatesh, Thong and Xu, 2012). In blockchain-supply chain integration, significant improvement is expected in the worker’s performance (Kshetri, 2018). Therefore, we postulate the following:
Fig. 1. Proposed research model.
H5. Performance expectancy has a positive effect on behavioural intention to adopt blockchain. 3. METHODOLOGY 2.6 Effort Expectancy Similar to performance expectancy, effort expectancy is a construct that explores the ease of use of the system. It has been well exploited in previous studies (Venkatesh et al., 2003; Batara et al., 2017). Regarding the blockchain context, it is expected that the complexity of the various activities will drop significantly. Thus, enabling more efficiency across supply chain activities (Aste, Tasca and Di Matteo, 2017; Veuger, 2018). Hence, we hypothesize that: H6. Effort expectancy has a positive effect on behavioural intention to adopt blockchain.
2.7 Mediators Considering the blockchain dynamics, in this study, we identified effort expectancy, facilitating conditions, and performance expectancy as mediator variables in the relationship between social influence and blockchain adoption. In the original UTAUT, these variables predict behavioural intention directly. However, to the best of our knowledge, prior literature concerning blockchain adoption, has not yet proposed these mediations. Thus, we hypothesize the following:
We applied a questionnaire survey in a Brazilian supply chain context and sent the questionnaire to senior supply chain specialists via LinkedIn (Sibona, Cummings and Scott, 2017; Queiroz and Telles, 2018). The items of the survey instrument were adapted from previously validated studies (Venkatesh et al., 2003; Venkatesh, Thong and Xu, 2012). We measured the scales by a seven-point Likert, ranging from “strongly disagree” to “strongly agree”. Of the 500 organizations the questionnaire was sent to, we received 138 useful responses, totalizing a 27.6% response rate. Of 138 respondents, the majority were male (86.96%). In terms of the age range of respondents, majority of the respondents fell between the ages of 42-49 (32.61%), followed by 34-41 (26.09%). In terms of the highest level of education, those with a postgraduate degree (specialization level) were at 47.83%.
3.1 Nonresponse, Common Method Bias and Endogeneity To establish the nonresponse bias between the respondents (early and late), we performed a t-test (Tsou and Hsu, 2015). Based on the fact that we used single respondents, we also assessed common method bias (Podsakoff and Organ, 1986). Moreover, we evaluated the possible problem of endogeneity by Ramsey regression equation error test (Lai, Sun and Ren, 2018) due to the recursivity of the structural model. Thus, none of these possible problems affected our model.
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4. RESULTS AND ANALYSIS
Table 3. Path coefficients results
In order to ensure the reliability of the results, Table 1 shows the reliability indicators. Firstly, the Cronbach's alpha and composite reliability were in line with the 0.70 literature cutoffs (Nunnally, 1978; Hair Jr. et al., 2017), confirming the reliability of the questionnaire. We assessed the convergent validity by factor loadings, in which all values were greater than the threshold 0.70 (Fornell and Lacker, 1981; Hair Jr. et al., 2017). We also used the average variance extracted to analyse the convergent validity. The values obtained were higher than the cut-off 0.50, indicating convergent validity (Fornell and Lacker, 1981). Finally, we performed the Fornell-Lacker criterion to assess the discriminant validity (Table 2). The results highlighted in bold showed that the items from constructs are distinct from each other. Therefore, confirming the discriminant validity.
H P β SD t p H1 SINF -> FCON 0.622 0.057 10.907 0.000 H2 SINF -> PEXP 0.593 0.065 9.077 0.000 H3 SINF -> EEXP 0.526 0.074 7.159 0.000 H4 FCON -> BINT 0.403 0.111 3.618 0.000 H5 PEXP -> BINT 0.005 0.086 0.040 0.968 H6 EEXP -> BINT 0.267 0.097 2.759 0.006 Note: H=Hypothesis; P=Path; β=standardized coefficient; SD=Standard deviation; t=t-statistics; p=p-values.
Table 1. Reliability indicators of the model VARIABLE CA BINT 0.959 EEXP 0.902 FCON 0.863 PEXP 0.903 SINF 0.837 Note: CA=Cronbach’s alpha; CR AVE = Average variance extracted.
CR 0.973 0.932 0.908 0.933 0.902 = composite
AVE 0.924 0.775 0.713 0.778 0.755 reliability;
Also, we performed a mediation test following the extant literature (Baron and Kenny, 1986; Hair Jr. et al., 2017). The results reported in Table 4 show that both, effort expectancy and facilitating conditions are good mediators in the relationship between social influence and blockchain behavioural intention. Following the previous results of the performance expectancy in H5, the result of the mediation also showed the non-significant effect of the performance expectancy on the relation between social influence and blockchain behavioural intention. Table 4. Mediation test analysis H
P
β
SD
t
p
H7 SINF -> EEXP -> BINT 0.143 0.062 2.288 0.022 H8 SINF -> FCON -> BINT 0.254 0.085 2.949 0.003 H9 SINF -> PEXP -> BINT 0.005 0.052 0.039 0.969
Table 2. Discriminant validity results VARIABLE BINT EEXP FCON PEXP SINF
BINT 0.961 0.521 0.570 0.343 0.726
EEXP 0.880 0.630 0.397 0.527
FCON
0.844 0.594 0.622
PEXP
0.882 0.592
SINF
4.2 Regression analysis of the model
0.869
The proposed model based on the UTAUT constructs (Venkatesh et al., 2003), showed good power of prediction. The results reported in Table 5 show that our model explains 35.4% of the variation in blockchain adoption between Brazilian supply chain specialists. This result is in line with others studies on technology adoption (Lancelot and Oliveira, 2013; Hajli et al., 2017). Also, social influence explained 27.3%, 38.2%, and 34.5% in the variance of the effort expectancy, facilitating conditions and performance expectancy, respectively.
4.1 Analysis of the Structural Model We used SmartPLS 3 to assess the hypotheses of the proposed model (Ringle, Christian M., Wende, Sven, & Becker, 2015; Hair Jr. et al., 2017). Table 3 reports the results of the hypotheses. The results of the H1 (β = 0.622, p = 0.000), H2 (β = 0.593, p = 0.000), and H3 (β = 0.526, p = 0.000) strongly support the positive effect of social influence on facilitating conditions, performance expectancy, and effort expectancy, respectively. Also, H4 (β = 0.403, p = 0.000) and H6 (β = 0.267, p = 0.006) were supported, indicating the positive effect on the facilitating conditions and effort expectancy, respectively, on blockchain behavioural intention. Surprisingly, H5 showed a non-significant effect (β = 0.005, p = 0.968) of the performance expectancy on blockchain behavioural intention. Thus, H5 was rejected. This result contrasts a recent study of blockchain adoption in India and the U.S. (Queiroz and Fosso Wamba, 2019a), showing that there are meaningful differences between countries in blockchain adoption.
Table 5. Regression analysis results Dependent Variable
R Square
R Square Adjusted
BINT
0.368
0.354
EEXP
0.278
0.273
FCON
0.387
0.382
PEXP
0.350
0.345
5. DISCUSSION AND CONCLUSIONS 5.1 Theoretical Implications In this study, we proposed and validated a model based on UTAUT constructs (Venkatesh et al., 2003) to understand the
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blockchain behavioural intention in the Brazilian supply chains. The model accounted for 35.4% of the variation in intention to adopt blockchain. Since we modelled the social influence as a predictor of the facilitating conditions, performance expectancy, and effort expectancy, our findings represent an interesting contribution to the theory. Firstly, we showed the strong effect of social influence in facilitating conditions, performance expectancy, and effort expectancy. Secondly, contrary to recent literature (Queiroz and Fosso Wamba, 2019), performance expectancy was found to have a non-significant effect on blockchain intention to adopt in Brazilian supply chains. Thirdly, this was in contrast to results achieved from a similar construct (perceived usefulness) in blockchain adoption in India supply chains (Kamble, Gunasekaran and Arha, 2018). Furthermore, we showed the mediation effect of effort expectancy and facilitating conditions on the relationship between social influence and behavioural intention to adopt blockchain.
5.2 Managerial Implications From a practical lens, our findings suggest valuable implications and insights for managers and practitioners. Firstly, we confirmed the positive effect of the social influence in supply chains toward blockchain adoption. Secondly, we showed that in Brazilian supply chains, the findings from performance expectancy emphasizes that the workers are not sure about the job improvement/productivity increase by blockchain applications. This implies that managers have to be careful and develop strategies to improve the awareness of the blockchain benefits in the SCM. Lastly, managers and practitioners involved in blockchain projects need to consider the particularities of the countries (Kamble, Gunasekaran and Arha, 2018; Queiroz and Fosso Wamba, 2019).
5.3 Limitations and Future Research This study has the following limitations: i) Due to the fact that blockchain adoption literature is scarce, the generalizations of our findings need to be carefully analysed; ii) The respondents awareness of blockchain can be inferred from the quality of the responses; and iii) Although we did not use the moderators variables of the UTAUT, it can help to understand nuances in the sample. These limitations unlock potential future studies regarding blockchain adoption in supply chains and other contexts. For example, it needs more studies to understand the different behaviours in blockchain adoption between countries. Also, the positive effect of social influence in the other UTAUT constructs can be tested in others countries. In the same line of thought, the non-effect of the performance expectancy needs more investigation, same as the proposed mediation relationships.
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The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain The Role of Social Influence in Blockchain The Brazilian Supply Chain CaseAdoption: The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain The Role of Social Influence in Blockchain Adoption: The Brazilian Supply Chain Case Case Case Case Samuel Fosso Wamba*. Maciel M. Queiroz. **
Fosso M. ** Samuel Fosso Wamba*. Wamba*. Maciel M. Queiroz. Queiroz. ** Toulouse, CO 31068 France *Toulouse Business School - Samuel TBS, Information, OperationsMaciel and Management Sciences, Samuel Fosso Wamba*. Maciel M. Queiroz. ** Toulouse, *Toulouse Business School TBS, Information, Operations and Management Sciences, CO *Toulouse Business School - Samuel TBS, Operations and Management Sciences, CO 31068 31068 France France Wamba*. Maciel M. Queiroz. ** Toulouse, (Tel: +33 5Information, 61 Fosso 29 50 54; e-mail: [email protected]). *Toulouse Business School TBS, Information, Operations and Management Sciences, Toulouse, CO 31068 France (Tel: +33 5 61 29 50 54; e-mail: [email protected]). (Tel: +33 5 61 29 50 54; e-mail: [email protected]). **Paulista University PostgraduateOperations Program inand Administration São Paulo,Toulouse, CO 04026-002 Brazil *Toulouse Business School--UNIP, TBS, Information, Management, Sciences, CO 31068 France (Tel: +33 5Postgraduate 61 29 50 54; Program e-mail: [email protected]). **Paulista University UNIP, in Administration Administration São Paulo, Paulo, CO CO 05508-030 04026-002 Brazil Brazil **Paulista University -- UNIP, in São Paulo, CO 04026-002 Brazil **University of São(Tel: Paulo, Architecture and Ocean Engineering,,, São +33Naval 5Postgraduate 61 29 50 54; Program e-mail: [email protected]). **Paulista University - UNIP, Postgraduate Program in Administration , São Paulo, Paulo, CO CO 05508-030 04026-002 Brazil Brazil **University of Naval Architecture and Engineering, **University of São São Paulo, Paulo, Naval Architecture and Ocean Ocean Engineering,, São São (e-mail:[email protected]) **Paulista University - UNIP, Postgraduate Program in Administration São Paulo, Paulo, CO CO 05508-030 04026-002 Brazil Brazil **University of São Paulo, Naval(e-mail:[email protected]) Architecture and Ocean Engineering, São Paulo, CO 05508-030 Brazil **University of São Paulo, Naval(e-mail:[email protected]) Architecture and Ocean Engineering, São Paulo, CO 05508-030 Brazil (e-mail:[email protected]) (e-mail:[email protected]) Abstract: The aim of this study is to understand the blockchain adoption behaviour in Brazilian supply Abstract: The study is blockchain adoption behaviour Brazilian supply Abstract: The aim aim of ofthethis this study is to to understand the blockchain adoption behaviour ininto Brazilian supply chains. Specifically, study aims to understand unlock the the potential of social influence; takingin consideration Abstract: The aim ofthethis study is to understand the blockchain adoption behaviour ininto Brazilian supply chains. Specifically, study aims to unlock the potential of social influence; taking consideration chains. study aims to understand unlock the the potential of social influence; taking consideration workersSpecifically, from Brazilian supply We proposed a model using constructs frominainto unified theory of Abstract: Thethe aim ofthethis study ischains. to blockchain adoption behaviour Brazilian supply chains. Specifically, the study aimschains. to unlock the potential of social influence; taking consideration workers from the Brazilian supply We aa model using constructs from aainto unified of workers from thethe Brazilian supply We proposed proposed model usinganalysed constructs frompartial unified theory of acceptance and use technology (UTAUT). The models were using leasttheory squares chains. Specifically, the of study aimschains. to unlock the potential of social influence; taking into consideration workers from thethe Brazilian supply chains. We proposed a modelwere usinganalysed constructs frompartial a unified theory of acceptance and use (UTAUT). models using least squares acceptance and use of of technology technology (UTAUT). The models were using least squares structuralfrom equation modelling (PLS-SEM) andproposed theThe findings indicated the power ofpartial social influence in workers thethe Brazilian supply chains. We a model usinganalysed constructs from a unified theory of acceptance and the use of technology (UTAUT). The models were analysed using partial least squares structural modelling (PLS-SEM) and findings indicated the social influence in structural equation modelling (PLS-SEM) and the theThe findings indicated the power ofpartial social influence in predicting equation other constructs. In addition, this study shows thepower mediation effect ofsquares effort acceptance and theUTAUT use of technology (UTAUT). models were analysed usingof least structural equation modelling (PLS-SEM) and the findings indicated the power of social influence in predicting other constructs. In this study shows the mediation effect of predicting other UTAUT constructs. In addition, addition, this study shows thepower mediation effect of effort expectancyequation and UTAUT themodelling facilitating conditions relationship between social andeffort the structural (PLS-SEM) andinthethefindings indicated the of influence social influence in predicting other UTAUT constructs. In addition, this study shows the mediation effect of effort expectancy and the facilitating conditions in the relationship between social influence and the expectancy and UTAUT the tofacilitating conditions in the relationship between social influence andeffort the behaviouralother intention adopt blockchain. ourthis findings essential theoretical and managerial predicting constructs. In Finally, addition, studybring shows the mediation effect of expectancy and the tofacilitating conditions in the relationship between social influence and the behavioural adopt Finally, our bring essential theoretical and behavioural intention adopt blockchain. Finally, our findings findings bringbetween essential social theoretical and managerial managerial implications.intention Copyright © 2019blockchain. IFAC expectancy and the tofacilitating conditions in the relationship influence and the behavioural intention to © adopt blockchain. Finally, our findings bring essential theoretical and managerial implications. Copyright 2019 IFAC implications.intention Copyright 2019blockchain. IFAC behavioural to © adopt Finally, our findings bring essential theoretical and managerial implications. Copyright ©UTAUT, 2019 IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rightsperformance reserved. Keywords: Blockchain, supply chain, social influence, facilitating conditions, implications. Copyright ©UTAUT, 2019 IFAC Keywords: Keywords: Blockchain, UTAUT, supply supply chain, chain, social social influence, influence, facilitating facilitating conditions, conditions, performance performance expectancy, Blockchain, effort expectancy. Keywords: Blockchain, UTAUT, supply chain, social influence, facilitating conditions, performance expectancy, effort expectancy, Blockchain, effort expectancy. expectancy. Keywords: UTAUT, supply chain, social influence, facilitating conditions, performance expectancy, effort expectancy. expectancy, effort expectancy. discussion about blockchain adoption behaviour in supply 1. INTRODUCTION discussion blockchain adoption behaviour in discussion about blockchain adoption behaviourinsights in supply supply chains. Ourabout results also provide interesting for 1. 1. INTRODUCTION INTRODUCTION discussion about blockchain adoption behaviourinsights in supply chains. Our results also provide interesting for chains. Our results also provide interesting insights for 1. INTRODUCTION managers and practitioners. discussion about blockchain adoption behaviour in supply Blockchain technologies (Nakamoto, 2008) are some of the chains. Our results also provide interesting insights for 1. INTRODUCTION managers and practitioners. Blockchain technologies (Nakamoto, 2008) are some of the managers and practitioners. chains. Our results also provide interesting insights for Blockchain technologies (Nakamoto, 2008) are some of the most paramount cutting-edge technologies. Recently, because managers and practitioners. Blockchain technologies (Nakamoto, 2008) are some of the most paramount cutting-edge technologies. Recently, because managers and practitioners. most paramount cutting-edge technologies. Recently, because of the highlytechnologies disruptive capacity and the application Blockchain (Nakamoto, 2008) are some of the most paramount cutting-edge technologies. Recently, because 1.1 Blockchain Technologies: from bitcoins to all business of the highly disruptive capacity and the of theparamount highly disruptive capacity and the application application adherence to vast business models (Larios-Hernández, 2017; most cutting-edge technologies. Recently, because 1.1 Blockchain 1.1 Blockchain Technologies: Technologies: from from bitcoins bitcoins to to all all business business of the highly disruptive capacity and the application 2017; models adherence vast business models (Larios-Hernández, adherence toFilippi vast business models (Larios-Hernández, 1.1 Blockchain Technologies: from bitcoins to all business Pazaitis, Deto and capacity Kostakis, 2017), blockchain 2017; of the highly disruptive and the application models models adherence toFilippi vast business models2017), (Larios-Hernández, 2017; 1.1 Blockchain Technologies: from bitcoins to all business Pazaitis, De and Kostakis, blockchain Pazaitis, DetoFilippi and Kostakis, 2017), blockchain models technologies have attracted attention in the supply chains adherence vast business models (Larios-Hernández, 2017; Since the introduction of blockchain technologies in the Pazaitis, De Filippi and Kostakis, 2017), blockchain models technologies have attracted attention in the supply chains Since the introduction introduction of blockchain blockchain technologies in the the technologies have attracted attention in the supply chains field (Kshetri, 2018; Min, 2018). A key characteristic of Pazaitis, De Filippi and Kostakis, 2017), blockchain Since the of in crypto-currency field (Nakamoto, 2008),technologies many other studies technologies have attracted attention in the supply chains Since the introduction of blockchain technologies in the field 2018; Min, A characteristic of crypto-currency field (Nakamoto, 2008), many other studies field (Kshetri, (Kshetri, 2018; Min,is2018). 2018). Atokey key characteristic of blockchain technologies related the decentralized model technologies have attracted attention in the supply chains crypto-currency field (Nakamoto, 2008), many other studies have been conducted in different research fields (Lei et al., Since the introduction of blockchain technologies in the field (Kshetri, 2018; Min,is2018). Atokey characteristic of crypto-currency field (Nakamoto, 2008), many other studies blockchain technologies related the decentralized model have been conducted in different fields (Lei et blockchain technologies related the decentralized model that central intermediary is no more needed to validate the fielda(Kshetri, 2018; Min,is2018). Atokey characteristic of have been conducted inThere different research fields (Leistudies et al., al., 2017; Y. Chen, 2018).(Nakamoto, is noresearch consensus definition for crypto-currency field 2008), many other blockchain technologies is related to the decentralized model have been conducted in different research fields (Lei et al., that a central intermediary is no more needed to validate the 2017; Y. Chen, 2018). There is no consensus definition for that a central intermediary is no more needed to validate the transactions.technologies Additionally, transactions use cryptographic blockchain is the related to the decentralized model 2017; Y. Chen, 2018). There is no consensus definition for blockchain in extant literature. In this study, we follow the have been conducted in different research fields (Lei et al., that a central Additionally, intermediary is notransactions more needed tocryptographic validate the 2017; Y. Chen, 2018). There is no consensus definition for transactions. the use blockchain in extant literature. In this study, we follow the transactions. Additionally, the transactions use cryptographic techniques that cannot permit their modification. that a central intermediary is no more needed to validate the blockchain in extant literature. In this study, we follow the compact definition provided by de Leon et al. (2017), in 2017; Y. Chen, 2018). There is no consensus definition for transactions. Additionally, thetheir transactions use cryptographic blockchain in extant literature. In this study, we follow the techniques that cannot permit modification. compact definition provided by de Leon et al. (2017), in techniques that cannot permit their modification. Consequently, blockchain is known as a tamper-proof transactions. Additionally, the transactions use cryptographic compact definition provided by de Leon et al. (2017), in which blockchain is defined as “a digital information blockchain in extant literature. In this study, we follow the techniques thatblockchain cannot permit their modification. compact definition provided by asde “a Leon et al.information (2017), in Consequently, is as tamper-proof which blockchain is defined digital Consequently, is known known as aa business tamper-proof technology that thatblockchain will remodel traditional models. techniques cannot permit their modification. which blockchain is defined as “a digital information recording method capable of recording data using a logbook compact definition provided by de Leon et al. (2017), in Consequently, blockchain is known as a business tamper-proof which blockchain is defined as “a data digital information technology will traditional models. recording method capable of recording recording using a logbook logbook technology that thatblockchain will remodel remodel traditional models. Consequently, is known as a business tamper-proof recording method capable of data using a approach; and characterised by the following: 1-Ordered, 2which blockchain is defined as “a digital information technology that remodel traditional business promises models. to recording method capable of recording data using a logbook Considering thewill chains, blockchain approach; and3-Sound characterised by the the following: following: 1-Ordered, 2technology that willsupply remodel traditional business promises models. approach; and characterised by 1-Ordered, Incremental, (cryptographically verifiable up to 2a method capable of recording data using a logbook Considering the supply chains, Considering the safety, supplysupply chains, blockchain promises to to recording approach; and characterised by the following: 1-Ordered, 2improve product chainblockchain visibility, transparency, Incremental, 3-Sound (cryptographically verifiable up to a Incremental, 3-Sound (cryptographically verifiable up to a Considering the supply chains, blockchain promises to given block) and 4-Digital.” (de Leon et al., 2017, p. 288). approach; and characterised by the following: 1-Ordered, 2improve product safety, supply chain visibility, transparency, improve product safety, chainblockchain visibility, Incremental, 3-Sound (cryptographically verifiable up to a traceability ofthe operations, cooperation, as welltransparency, as improve Considering supplysupply chains, promises to given block) and 4-Digital.” (de Leon et al., 2017, p. 288). given block) and 4-Digital.” (de Leon et al., 2017, p. 288). improve product safety, supply chain visibility, transparency, Incremental, 3-Sound (cryptographically verifiable up to a traceability of operations, cooperation, as as improve traceability ofothers operations, cooperation, as well welltransparency, as improve givendefinition block) andas4-Digital.” (de Leonimplies et al., 2017, p. 288). trust, among (Kshetri, 2018). addition, with smart The improve product safety, supply chain In visibility, mentioned(de above that p. blockchain traceability of operations, cooperation, as well as improve given block) and 4-Digital.” Leon et al., 2017, 288). trust, among others (Kshetri, 2018). In addition, addition, with smart as implies that trust, among (Kshetri, 2018). In with smart contracts, significant reductions on cost of the The traceability ofothers operations, cooperation, astransactions well as improve The definition definitionoperate as mentioned mentioned above implies network that blockchain blockchain technologies in a above peer-to-peer model trust, among others (Kshetri, 2018). In addition, with of smart The definition as mentioned above implies that blockchain contracts, significant reductions on cost transactions the technologies operate in a peer-to-peer network model contracts, significant reductions on cost transactions of the operations expected. The2018). blockchain integration with The trust, amongareothers (Kshetri, In addition, with smart technologies operate in a peer-to-peer network model (Christidis and Devetsikiotis, 2016; Mengelkamp et al., definition as mentioned above implies that blockchain contracts, significant reductions on cost transactions ofwith the (Christidis technologies operate in a peer-to-peer network model operations are expected. The blockchain integration and Devetsikiotis, 2016; Mengelkamp et al., operations are expected. The blockchain integration with supply chains is still nascent, and the extant literature contracts, significant reductions on cost transactions of the technologies (Christidis and Devetsikiotis, 2016; Mengelkamp et al., 2018). This characteristic implies that, in a decentralized operate in a peer-to-peer network model operations are expected. The blockchain integration with 2018). (Christidis and Devetsikiotis, 2016; Mengelkamp et al., supply chains is nascent, and the literature This characteristic implies that, in a decentralized supply chains is still stillstudies nascent, and the extant extant literature concerning empirical this technology is scarce operations are expected. The ofblockchain integration with (Christidis 2018). This characteristic implies that, in a decentralized network, there are no intermediaries (Aste, Tasca and Di and Devetsikiotis, 2016; Mengelkamp et al., supply chains is stillstudies nascent, and the extant is literature 2018). This characteristic implies that, in a Tasca decentralized concerning empirical of this scarce network, there are no intermediaries (Aste, and Di concerning empirical studies of Mainly this technology technology scarce 2018). (Fosso chains Wamba al., 2018). reporting supply isetstill nascent, and the studies extant is literature network, there are no intermediaries (Aste, Tasca and Di Matteo, 2017; Scott, Loonam and Kumar, 2017). Also, the This characteristic implies that, in a decentralized concerning empirical studies of Mainly this technology is scarce network, there Scott, are noLoonam intermediaries (Aste,2017). TascaAlso, and the Di (Fosso et al., 2018). studies reporting Matteo, 2017; and Kumar, (Fosso Wamba Wamba et (Kamble, al., 2018). studies reporting blockchain adoption Gunasekaran and Arha, 2018; network, concerning empirical studies of Mainly this technology is scarce Matteo, 2017; Scott, Loonam and Kumar, 2017). Also, the transactions accomplished by the network members there are no intermediaries (Aste, Tasca and are Di (Fosso Wamba et al., 2018). Mainly studies reporting Matteo, 2017; Scott, Loonam and Kumar, 2017). Also, the blockchain adoption (Kamble, Gunasekaran and Arha, 2018; transactions accomplished byand thestored network members are blockchain (Kamble, Gunasekaran and Arha, 2018; Queiroz andadoption FossoetWamba, 2019a) are available. Inreporting order to Matteo, (Fosso Wamba al., 2018). Mainly studies transactions accomplished by the network members are performed cryptographically into linked blocks 2017; Scott, Loonam and Kumar, 2017). Also, the blockchain adoption (Kamble, Gunasekaran and Arha, 2018; transactions accomplished byandthestored network members are Queiroz and Fosso are In to performed cryptographically into linked blocks Queiroz and Fosso Wamba, 2019a) are available. available. In order order to transactions contribute to fillingWamba, this gap,2019a) this study aims answer the blockchain adoption (Kamble, Gunasekaran andtoArha, 2018; performed cryptographically and stored into linked blocks (Kshetri, 2017). Due to the tamper-proof (Kano and accomplished by the network members are Queiroz and Fosso Wamba, 2019a) are available. In order to performed cryptographically and stored into linked blocks contribute to Fosso fillingWamba, this gap, gap, thisrole study aims toinfluence answer the (Kshetri, Due the (Kano and contribute to filling this this study aims answer followingand question: What is2019a) the socialto in Queiroz areofavailable. In orderthe to performed (Kshetri, 2017). Due to to there the tamper-proof (Kano and Nakajima, 2017). 2018) reflected, no modification allowed cryptographically and istamper-proof stored into linked blocks contribute to filling What this gap, thisrole study aims toinfluence answer the (Kshetri, 2017). 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All rights reserved. in the supply chains Copyright © 2019, 2019 IFAC 1740Hosting Peer review© of International Federation of Automatic Copyright 2019 IFAC 1740 Copyright ©under 2019 responsibility IFAC 1740Control. 10.1016/j.ifacol.2019.11.448 Copyright © 2019 IFAC 1740 Copyright © 2019 IFAC 1740
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(Biswas, Muthukkumarasamy and Tan, 2017; Lu and Xu, 2017).
1.2 Blockchain and Supply Chain Management Considering the integration of blockchain into the supply chain management (SCM) field, there exist many challenges and opportunities. It is clear that the blockchain applications in the SCM are in the early stages. However, there are various actions regarding blockchain implementation ongoing in different SCM contexts (Kshetri, 2018). In this outlook, blockchain has the potential to remodel any SCM across the world. Emerging literature reported various benefits that the SCM will attain with blockchain technologies. For example, with smart contract utilization, the complexity of the processes as well as the costs of transactions can be improved (Kim and Laskowski, 2017; Tian, 2017). Traceability through the supply chains will be significantly improved, implying more efficiency in the prevention of fraud (R. Y. Chen, 2018). Moreover, the process with more transparency and confidence (Lu and Xu, 2017) will be implicated in more cooperation between supply chain members (Aste, Tasca and Di Matteo, 2017), trust (Kshetri, 2018), accountability as well as security (Biswas, Muthukkumarasamy and Tan, 2017) among others. Despite these critical benefits, the literature concerning empirical studies (Fosso Wamba et al., 2018) about blockchain in the supply chain context is scarce. However, scholars have recently employed considerable efforts in order to understand the dynamics of blockchain adoption in the supply chain contexts (Francisco and Swanson, 2018; Kamble, Gunasekaran and Arha, 2018; Queiroz and Fosso Wamba, 2019). 2. HYPOTHESES AND RESEARCH MODEL The hypotheses of this study was derived with the support of the technology acceptance model lens. Specifically, we used the constructs social influence, facilitating conditions, performance expectancy, effort expectancy, as well as the behavioural intention from the classical unified theory of acceptance and use of technology (UTAUT) model (Venkatesh et al., 2003).
2.1 Unified Theory of Acceptance and Use of Technology (UTAUT) The unified theory of acceptance and use of technology (UTAUT) was proposed by Venkatesh et al. (2003) as a unification of the eight previous models regarding behaviour in the technology systems usage. In this original version, the UTAUT has the following four constructs as the main predictors of the behavioural and technology intentions to adopt usage behaviour (Venkatesh et al., 2003); social influence, facilitating conditions, performance expectancy, and effort expectancy. Additionally, the model is moderated by four main variables; namely gender, age, experience, and voluntariness of use. However, recent studies have
discontinued the use of these moderators, since the adherence is not suitable for all types of contexts (Dwivedi, Rana, Janssen, et al., 2017; Dwivedi, Rana, Jeyaraj, et al., 2017). Following this line of thought, we decided to use only the four primary constructs.
2.2 Behavioural Intention Behavioural intention refers to “the degree to which a person has formulated conscious plans to perform or not perform some specified future behaviour” (Warshaw & Davis, 1985, p. 214). In technology adoption literature, it has been studied exhaustively (Davis, 1989; Venkatesh et al., 2003; Venkatesh, Thong and Xu, 2012; Queiroz and Fosso Wamba, 2019a), but not sufficiently, due to the exponential growth of new technologies.
2.3 The Impact of Social Influence Social influence construct is defined as “the degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003, p.451). Facilitating conditions refer to “the degree to which an individual believes that an organizational and technical infrastructure exist to support the use of the system” (Venkatesh et al., 2003, p. 453). Performance expectancy is related to “the degree to which an individual believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al., 2003, p. 447). Effort expectancy is defined as “the degree of ease associated with the use of the system” (Venkatesh et al., 2003, p. 450). Social influence can be exerted by family, friends (Rana et al., 2017), and peers. Considering the SCM context, there are several interactions between the workers, his/her internal colleagues and (external) supply chain members. In this regard, we argue that the worker's interaction in the SCM can affect the perception of the organizational technical infrastructure. Moreover, social influence will impact directly on the performance expectancy with blockchain in the SCM operations. Blockchain literature already reported that efficiency in the process and the worker’s performance would be improved by blockchain (Kshetri, 2018). In addition, it is clear that with blockchain applications in the SCM, workers hope to perform their tasks by minimizing the effort. For instance, to minimize the complexity of the activities, a smart contract could be used (Kim and Laskowski, 2017), same as the effort with traceability (Lu and Xu, 2017; Tian, 2017). This, consequently, influences the worker’s productivity and performance. We postulate the following hypotheses: H1. Social influence has a positive effect on facilitating conditions. H2. Social influence has a positive effect on performance expectancy. H3. Social influence has a positive effect on effort expectancy.
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2.4 Facilitating Conditions Facilitating conditions are represented mainly by organizational and IT infrastructure (e.g., systems, internet speed, cloud integration, computers, among others). This construct has been used successfully in the literature concerning technology adoption (Venkatesh et al., 2003; Venkatesh, Thong and Xu, 2012; Sabi et al., 2016; Rana et al., 2017). Following this research stream, it is expected that facilitating conditions exert high influence on blockchain adoption in supply chains (Francisco and Swanson, 2018). Hence, we hypothesize that:
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H7. Effort expectancy as a mediator influences the relationship between social influence and behavioural intention to adopt blockchain. H8. Facilitating conditions as a mediator influences the relationship between social influence and behavioural intention to adopt blockchain. H9. Performance expectancy as a mediator influences the relationship between social influence and behavioural intention to adopt blockchain.
H4. Facilitating conditions have a positive effect on behavioural intention to adopt blockchain.
2.5 Performance Expectancy Previous literature on technology adoption had already emphasized the power of performance expectancy (Venkatesh et al., 2003; Riffai, Grant and Edgar, 2012; Venkatesh, Thong and Xu, 2012). In blockchain-supply chain integration, significant improvement is expected in the worker’s performance (Kshetri, 2018). Therefore, we postulate the following:
Fig. 1. Proposed research model.
H5. Performance expectancy has a positive effect on behavioural intention to adopt blockchain. 3. METHODOLOGY 2.6 Effort Expectancy Similar to performance expectancy, effort expectancy is a construct that explores the ease of use of the system. It has been well exploited in previous studies (Venkatesh et al., 2003; Batara et al., 2017). Regarding the blockchain context, it is expected that the complexity of the various activities will drop significantly. Thus, enabling more efficiency across supply chain activities (Aste, Tasca and Di Matteo, 2017; Veuger, 2018). Hence, we hypothesize that: H6. Effort expectancy has a positive effect on behavioural intention to adopt blockchain.
2.7 Mediators Considering the blockchain dynamics, in this study, we identified effort expectancy, facilitating conditions, and performance expectancy as mediator variables in the relationship between social influence and blockchain adoption. In the original UTAUT, these variables predict behavioural intention directly. However, to the best of our knowledge, prior literature concerning blockchain adoption, has not yet proposed these mediations. Thus, we hypothesize the following:
We applied a questionnaire survey in a Brazilian supply chain context and sent the questionnaire to senior supply chain specialists via LinkedIn (Sibona, Cummings and Scott, 2017; Queiroz and Telles, 2018). The items of the survey instrument were adapted from previously validated studies (Venkatesh et al., 2003; Venkatesh, Thong and Xu, 2012). We measured the scales by a seven-point Likert, ranging from “strongly disagree” to “strongly agree”. Of the 500 organizations the questionnaire was sent to, we received 138 useful responses, totalizing a 27.6% response rate. Of 138 respondents, the majority were male (86.96%). In terms of the age range of respondents, majority of the respondents fell between the ages of 42-49 (32.61%), followed by 34-41 (26.09%). In terms of the highest level of education, those with a postgraduate degree (specialization level) were at 47.83%.
3.1 Nonresponse, Common Method Bias and Endogeneity To establish the nonresponse bias between the respondents (early and late), we performed a t-test (Tsou and Hsu, 2015). Based on the fact that we used single respondents, we also assessed common method bias (Podsakoff and Organ, 1986). Moreover, we evaluated the possible problem of endogeneity by Ramsey regression equation error test (Lai, Sun and Ren, 2018) due to the recursivity of the structural model. Thus, none of these possible problems affected our model.
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4. RESULTS AND ANALYSIS
Table 3. Path coefficients results
In order to ensure the reliability of the results, Table 1 shows the reliability indicators. Firstly, the Cronbach's alpha and composite reliability were in line with the 0.70 literature cutoffs (Nunnally, 1978; Hair Jr. et al., 2017), confirming the reliability of the questionnaire. We assessed the convergent validity by factor loadings, in which all values were greater than the threshold 0.70 (Fornell and Lacker, 1981; Hair Jr. et al., 2017). We also used the average variance extracted to analyse the convergent validity. The values obtained were higher than the cut-off 0.50, indicating convergent validity (Fornell and Lacker, 1981). Finally, we performed the Fornell-Lacker criterion to assess the discriminant validity (Table 2). The results highlighted in bold showed that the items from constructs are distinct from each other. Therefore, confirming the discriminant validity.
H P β SD t p H1 SINF -> FCON 0.622 0.057 10.907 0.000 H2 SINF -> PEXP 0.593 0.065 9.077 0.000 H3 SINF -> EEXP 0.526 0.074 7.159 0.000 H4 FCON -> BINT 0.403 0.111 3.618 0.000 H5 PEXP -> BINT 0.005 0.086 0.040 0.968 H6 EEXP -> BINT 0.267 0.097 2.759 0.006 Note: H=Hypothesis; P=Path; β=standardized coefficient; SD=Standard deviation; t=t-statistics; p=p-values.
Table 1. Reliability indicators of the model VARIABLE CA BINT 0.959 EEXP 0.902 FCON 0.863 PEXP 0.903 SINF 0.837 Note: CA=Cronbach’s alpha; CR AVE = Average variance extracted.
CR 0.973 0.932 0.908 0.933 0.902 = composite
AVE 0.924 0.775 0.713 0.778 0.755 reliability;
Also, we performed a mediation test following the extant literature (Baron and Kenny, 1986; Hair Jr. et al., 2017). The results reported in Table 4 show that both, effort expectancy and facilitating conditions are good mediators in the relationship between social influence and blockchain behavioural intention. Following the previous results of the performance expectancy in H5, the result of the mediation also showed the non-significant effect of the performance expectancy on the relation between social influence and blockchain behavioural intention. Table 4. Mediation test analysis H
P
β
SD
t
p
H7 SINF -> EEXP -> BINT 0.143 0.062 2.288 0.022 H8 SINF -> FCON -> BINT 0.254 0.085 2.949 0.003 H9 SINF -> PEXP -> BINT 0.005 0.052 0.039 0.969
Table 2. Discriminant validity results VARIABLE BINT EEXP FCON PEXP SINF
BINT 0.961 0.521 0.570 0.343 0.726
EEXP 0.880 0.630 0.397 0.527
FCON
0.844 0.594 0.622
PEXP
0.882 0.592
SINF
4.2 Regression analysis of the model
0.869
The proposed model based on the UTAUT constructs (Venkatesh et al., 2003), showed good power of prediction. The results reported in Table 5 show that our model explains 35.4% of the variation in blockchain adoption between Brazilian supply chain specialists. This result is in line with others studies on technology adoption (Lancelot and Oliveira, 2013; Hajli et al., 2017). Also, social influence explained 27.3%, 38.2%, and 34.5% in the variance of the effort expectancy, facilitating conditions and performance expectancy, respectively.
4.1 Analysis of the Structural Model We used SmartPLS 3 to assess the hypotheses of the proposed model (Ringle, Christian M., Wende, Sven, & Becker, 2015; Hair Jr. et al., 2017). Table 3 reports the results of the hypotheses. The results of the H1 (β = 0.622, p = 0.000), H2 (β = 0.593, p = 0.000), and H3 (β = 0.526, p = 0.000) strongly support the positive effect of social influence on facilitating conditions, performance expectancy, and effort expectancy, respectively. Also, H4 (β = 0.403, p = 0.000) and H6 (β = 0.267, p = 0.006) were supported, indicating the positive effect on the facilitating conditions and effort expectancy, respectively, on blockchain behavioural intention. Surprisingly, H5 showed a non-significant effect (β = 0.005, p = 0.968) of the performance expectancy on blockchain behavioural intention. Thus, H5 was rejected. This result contrasts a recent study of blockchain adoption in India and the U.S. (Queiroz and Fosso Wamba, 2019a), showing that there are meaningful differences between countries in blockchain adoption.
Table 5. Regression analysis results Dependent Variable
R Square
R Square Adjusted
BINT
0.368
0.354
EEXP
0.278
0.273
FCON
0.387
0.382
PEXP
0.350
0.345
5. DISCUSSION AND CONCLUSIONS 5.1 Theoretical Implications In this study, we proposed and validated a model based on UTAUT constructs (Venkatesh et al., 2003) to understand the
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blockchain behavioural intention in the Brazilian supply chains. The model accounted for 35.4% of the variation in intention to adopt blockchain. Since we modelled the social influence as a predictor of the facilitating conditions, performance expectancy, and effort expectancy, our findings represent an interesting contribution to the theory. Firstly, we showed the strong effect of social influence in facilitating conditions, performance expectancy, and effort expectancy. Secondly, contrary to recent literature (Queiroz and Fosso Wamba, 2019), performance expectancy was found to have a non-significant effect on blockchain intention to adopt in Brazilian supply chains. Thirdly, this was in contrast to results achieved from a similar construct (perceived usefulness) in blockchain adoption in India supply chains (Kamble, Gunasekaran and Arha, 2018). Furthermore, we showed the mediation effect of effort expectancy and facilitating conditions on the relationship between social influence and behavioural intention to adopt blockchain.
5.2 Managerial Implications From a practical lens, our findings suggest valuable implications and insights for managers and practitioners. Firstly, we confirmed the positive effect of the social influence in supply chains toward blockchain adoption. Secondly, we showed that in Brazilian supply chains, the findings from performance expectancy emphasizes that the workers are not sure about the job improvement/productivity increase by blockchain applications. This implies that managers have to be careful and develop strategies to improve the awareness of the blockchain benefits in the SCM. Lastly, managers and practitioners involved in blockchain projects need to consider the particularities of the countries (Kamble, Gunasekaran and Arha, 2018; Queiroz and Fosso Wamba, 2019).
5.3 Limitations and Future Research This study has the following limitations: i) Due to the fact that blockchain adoption literature is scarce, the generalizations of our findings need to be carefully analysed; ii) The respondents awareness of blockchain can be inferred from the quality of the responses; and iii) Although we did not use the moderators variables of the UTAUT, it can help to understand nuances in the sample. These limitations unlock potential future studies regarding blockchain adoption in supply chains and other contexts. For example, it needs more studies to understand the different behaviours in blockchain adoption between countries. Also, the positive effect of social influence in the other UTAUT constructs can be tested in others countries. In the same line of thought, the non-effect of the performance expectancy needs more investigation, same as the proposed mediation relationships.
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