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What Drives the Adoption of the Blockchain Technology? A Fit-Viability Perspective Ting-Peng Lianga, Rajiv Kohlib, Hang-Chang Huanga, and Zong-Lin Lic

aElectronic Commerce Research Center, National Sun Yat-Sen University, Kaohsiung, Taiwan; bRaymond A. Mason School of Business, William & Mary, Williamsburg, VA, USA; cMoxa Technologies, New Taipei City, Taiwan

ABSTRACT Blockchain technology has the promise of transforming security and trust in digital transactions. However, concerns about technical com- plexity and the benefits of deployment have blunted its adoption. We examine factors that influence managerial intention to adopt block- chain technology. We extend the fit-viability model (FVM) and develop a value-based technology adoption model through an empirical study of 242 managers mostly in medical and financial industries. Managers in such organizations are likely to consider fit and viability in adopting blockchain technology to store and protect data. Drawing upon Fit- Viability and Task-Technology Fit models, and the Unified Theory of Acceptance and Use of Technology (UTAUT), we test a model with Partial Least Squares (PLS) to assess managers’ intention to adopt blockchain technology. Our findings indicate that functional and sym- bolic benefits have positive impact on managers’ assessment of task- technology fit. Furthermore, viability is an important criterion in adopt- ing blockchain technology.

KEYWORDS Blockchain; task-technology fit; environment-technology fit; fit-viability models; functional/symbolic benefits; blockchain adoption; technology adoption

Introduction

As an emerging technology, blockchain has the potential to change the way digital transac- tions are stored, retrieved, and shared. Blockchain refers to a secure decentralized ledger of serial, peer-to-peer transactions without third-party intermediaries [2]. The potentially significant advantages from blockchain technology are anonymity, transparency, security, traceability, and efficiency of transactions [30]. Although transparency and anonymity appear to be contradictory, transparency refers to an indelible record of transactions that can be viewed by anyone. Anonymity refers to the non-identifiability of the sending and receiving parties to the transaction because blocks of transactions that are saved and stored in nodes are coded as a set of English letters mixed with numbers as traders’ names and are known only to the parties to the transactions. Some industries have applied blockchain technology, yet others remain on the sidelines because of uncertainty of business value [15] and have resisted adopting blockchain [67]. Therefore, blockchain adoption among man- agers, particularly in finance and medical industries, remains an important hurdle that needs further exploration.

CONTACT Ting-Peng Liang [email protected] Electronic Commerce Research Center, National Sun Yat-Sen University, Kaohsiung, Taiwan

JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 2021, VOL. 38, NO. 2, 314–337 https://doi.org/10.1080/07421222.2021.1912915

© 2021 Taylor & Francis Group, LLC

The information systems (IS) discipline has a rich tradition of theoretical and empirical research to assess the intention to adopt a new information technology (IT). Previous research has explored various facets of how technologies fit within the organization and its environment, for example, using the technology-organization-environment model (TOE) [36], technology acceptance model (TAM) [13], unified theory of acceptance and use of technology (UTAUT) [69], task-technology fit (TTF) [73], and value-based model (VBM) [66]. Previous adoption models measure factors relating to the context of technol- ogy, organizations, and the environment. However, we must also consider how the relation- ship among these three contexts affects the adoption of an IT because without a deeper understanding of the mechanism we may not be able to accurately interpret the varying effects of these factors that managers consider important when adopting new IT. For example, the relationship between technology and organization can generate insights into whether a technology’s functional utility is suitable within the organization or the industry ecosystem vis-à-vis characteristics of task performance, business operations, and cost. Moreover, environmental factors, such as the competitive and regulatory pressures, may have a direct and profound effect on the adoption of IT. Therefore, it is essential to identify and incorporate these factors from a managerial perspective and form a robust conceptual framework for adopting blockchain technology in a strategic context. Understanding the managerial perspective will be helpful for researchers to construct a framework to expand the value of IT investments and for practitioners to make the business case for IT invest- ment and to implement mechanisms to extract strategic value.

When adopting blockchain technology, managers should place high priority on the benefits and value created from IT. A key objective is the utility of the technology and whether it can facilitate the process of performing business tasks; for example, whether the adoption of a technology for cost reduction can redesign the operations and lower costs. Second, does the adoption of IT build business reputation and self-worth? For example, Wang [71] reported that firms associated with IT fashions had better reputation.1 Similarly, herd behavior in IT adoption [59] and the fashion waves [6] are shown to be salient in adoption of IT. From this perspective, a firm’s adoption of blockchain technology and its potential benefits are by no means unidimensional. Hence, further examination by devel- oping with a research model that addresses the complexity of technology adoption is needed if we are to provide meaningful guidance to scholars and practitioners.

Among the theories for interpreting a firm’s IT adoption, TTF focuses on whether the technology provides adequate functions to enhance the performance of the target task. Another approach is the feasibility/readiness model that focuses on whether the adoption of the technology is cost-beneficial and whether the organization has adequate resources to support the successful implementation of the adopted technology. The third model com- bines the fit and feasibility dimensions to propose a fit-viability model (FVM) [40]. This model is useful for adopting a new technology as fit and viability are combined to form an efficient framework to evaluate and examine factors of technology, organization, and environment in the adoption of IT. Moreover, top management in an organization pri- marily considers the benefits when considering adoption of new IT. In this study, we propose an expanded conceptual model, based on FVM that also incorporates benefits perceived by managers.

There are three reasons why we deploy an expanded FVM for analyzing blockchain adoption. First, blockchain is a new and groundbreaking technology but not yet widely

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used. Although there are several successful use cases, such as Maersk in shipping and Walmart in supply chain management, managers continue to seek ways to integrate blockchain-enabled capabilities with business strategy. Therefore, blockchain adoption can significantly impact the existing IT infrastructure, applications, and new services within organizations and across industries and managers must assess whether this new technology can better fit with their organization’s information processing needs. Second, organizations and industry sectors must incur higher investments and bear greater risks when a revolutionary new technology is introduced. Therefore, sellers and adopters of blockchain must consider the cost-effectiveness and potential impact on the industry. Finally, block- chain technology is a complex technology for which most organizations have minimal prior exposure. Hence, whether the organizations have adequate IT capability and expertise is an important criterion in the adoption decision. The decision to adopt IT by outsourcing can also be important to pursue firm competitiveness (i.e., cost reduction and IT competence). We find that the FVM provides a more encompassing framework to study adoption than TTF and feasibility/readiness models alone.

Our purpose in this study is three-fold. First, by extending FVM to blockchain technol- ogy, our new model will examine managers’ intention to adopt blockchain. Second, we investigate critical factors within organizations that may lead to the willingness to adopt blockchain. Third, we focus on medical and financial sectors because these organizations generate massive amounts of data from transactions, such data and business transactions need to be protected and preserved. By examining these two industry sectors, we explore to what extent, if any, industry type influences the intention to adopt blockchain technology.

The rest of the paper is organized as follows. Initially, in the literature review section, we discuss literature related to the theoretical arguments that shed light on technology adop- tion in organizations. These include the TOE model, FVM, TTF, VBM, innovation diffusion theory (IDT), and UTAUT. Next, we propose a research model and compose our hypoth- eses. Then, we describe our data collection, measurement, and analysis, followed by our findings from the analysis. Finally, we summarize our contributions and the practical implications, a brief description of limitations and future research, and conclusion.

Literature Review

Blockchain Technology

Blockchain is an emerging technology that underlies a series of peer-to-peer transactions to protect the transaction records, called blocks [77]. For protecting each block, blockchain technology utilizes time stamps, cryptographic hash, and data structures to prevent data or information from being tampered [45]. With such design features, blockchain technology provides several advantages, such as decentralization (function without intermediary), immutability (unchangeable and indestructible), anonymity (hidden real-world identity), traceability (irrefutable record), and security (smart contract) [82]. Such advantages can help address nagging problems that expose organizations across industries to unwanted risk. For example, anonymity and security can help prevent negative effects of deceitful buyers/sellers, cybercriminals, and detect financial fraud in electronic markets [60]. Therefore, blockchain technology has the potential to create value in a number of industries.

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However, the technical disadvantages for blockchain have been identified such as throughput (inefficiency of transactions per second), latency (more time to complete transactions), size and bandwidth (large quantity of storage needed), security (risk of 51 percent attack, i.e., anyone having over half of the hashing power can control the chain), wasted resources (high energy consumption), usability (slow process with too many users), and versioning, hard forks, and multiple chains [61].

Among the disadvantages, throughput limitations can be significant. For example, transaction networks throughput for VISA is 2000 tps (transactions per second) and for Twitter is 5000 tps. However, throughput in a Bitcoin network reaches only 7 tps, indicating low throughput in database management [75]. Second, in terms of latency, VISA only took a few seconds to complete a transaction, whereas blockchain needs approximately 10 minutes to add a block [61], an indicator of inefficient transaction processing. Third, as blockchain size increases, larger amount of storage will be needed. For example, the Bitcoin blockchain consumes over 170GB on each network node [19], indicating large consumption of data storage. Such limitations need to be overcome to offer better solutions in the future.

The technical limitations notwithstanding, blockchain technology has garnered interest from managers across many industries. In the healthcare field, there is a need to store and share secure medical data. Blockchain can serve as the underlying technology within integrated systems to verify authenticity of drugs flowing through the pharmaceutical supply chain, monitor medical history exchanges among physicians and hospitals, and to pass health data directly to Content Management System (CMS) [47] for personalization of patient treatment. In banking and finance, financial transactions require features that are secure, distributed, consensus-driven, and trust [21]. Therefore, blockchain technology adoption can fulfil the needs for security in information exchange between firms in financial and medical fields.

Recent blockchain research has identified the importance of its distinctive features and positive impact on the computer network and IS. For example, development of blockchain technology related to finance includes blockchain-based fair payment in the cyber physical system [80], Bitcoin, Ethereum, and other cryptocurrencies [77], cloud storage systems in data sharing [72], smart contracts for financial assets [76], and bilateral double taxation for international transactions [29]. Similarly, healthcare firms demonstrate the significant opportunities in using features of blockchain technology, for instance, in medical insurance storage system [81], medical data such as diagnosis certificates and medical records [38] medical database [32], electronic health records [12], and patient-owned medical apps [78].

Despite the potential applications of blockchain technologies across industries and the benefits reported in the literature, especially in cryptocurrencies such as Bitcoins, Ethereum, and Hyperledger, the potential of blockchain is yet to be exploited. Managers seek assurance about the maturity of the blockchain technology and the scalability needed to support reliable business processes. Cullell [11] outlined the obstacles that businesses face in adopting blockchain technology. These include limited scalability to support the volume of transactions even for cryptocurrencies, network congestion, and low efficiency in proces- sing blocks that make real time transactions difficult to execute, and the lack of data transparency, especially among private blockchains. Hence, managers view blockchain technology as having high potential but are cautious to adopt until they perceive a fit with the business goals.

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Technology-Organization-Environment (TOE) Model

TOE model refers to three contexts that enterprises consider in the adoption of technology: technological, organizational and environmental [63]. The technological context refers to actual and potential technology competence to benefit the firm [4] and includes compat- ibility, complexity, and trialability [49]. The organizational context is defined as formal and informal resources that reinforce adoption of technology, such as top management support, organizational readiness, and financial backing [55]. Environmental context refers to the external environment that presents pressures and/or support for adoption of technology, such as industry competition, government support, and consumer readiness [49]. The TOE model provides a useful framework and a theoretical basis to investigate factors that influence managerial decisions regarding adopting technological innovation.

Fit-Viability model (FVM)

FVM originated from Tjan [62] and later was modified to evaluate how, and in what conditions, an organization adopts a new electronic technology [40]. Fit refers to the extent to which technology competence can match or be suitable to perform tasks in an organiza- tion [39], adding value to network applications, and human resources. Fit proposed the significance of the relationship between IT and the task to which it is applied within an organization. Liang and Wei [40] proposed that the combination of technology and task had become a vital factor for enhancing organizational productivity and profitability.

After the technology has fulfilled the requirement of task performance, the organization should consider the viability within the organizational environment. Viability refers to measuring costs, benefits, and readiness to use within the organizational environment if it were to adopt a new technology [37, 40]. This includes measuring whether adopting a new technology needs essential support of economic feasibility, technical equipment, and social readiness within an organization [65]. Given that the aforementioned factors to adopt a technology in an organization are of significant importance, we draw upon the FVM for our study.

Fit and viability are two core dimensions of the model that yield four categories in a matrix when high/low levels of fit and viability are mapped. These four categories generate four types of strategies for adoption of IT, labeled as good target (high on fit, high on viability), organizational restructuring (low on fit, high on viability), finding alternative technology (high on fit, low on viability), and forget it (low on fit, low on viability) [40] Hence, the preferred strategy for adoption is when the technology under consideration is high on both fit and viability.

Task-Technology Fit (TTF)

Similar to the tenets of the FVM previously discussed, TTF is important for IS adoption as the technology must match the task within an organization and, therefore, generate performance impact(s) [23] such as carrying out tasks easier, faster, and more efficiently. Task and technology fit can be categorized into task and technology characteristics. Task characteristics refer to challenging activities that an individual using IT may encounter. Technology characteristics are machinery or equipment designed for successful completion

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of various tasks [3]. Fit focuses mainly on performance only due to linkage between functions of technology and requirements of tasks [23].

Value-Based Model (VBM)

VBM refers to the evaluation of importance, usefulness, and the worth of a product in comparison with acquisition and detriment [34]. Assessment of overall value of a product can be classified into two core parts: perceived benefit and perceived sacrifices [79]. Perceived benefits refer to anticipated advantages brought from product performance while perceived sacrifices refer to cost and effort an individual spends to acquire a product [54]. Both perceived benefits and perceived sacrifices are central to form an overall value according to perceived quality and monetary price [34]. Benefits play an important role in the intention to adopt IS and therefore motivate us to adopt VBM perspective in our study.

Functional/Symbolic Benefits

Business value of IT refers to the benefits that emerge from the combination of IT with organizational resources that create and sustain competitive advantage for a firm [8]. Business value generated through adoption of IT is categorized as functional and symbolic [42]. We refer to this as the dual value model (DVM). Functional benefit refers to improved organizational performance through adoption of new IT, such as productivity, profitability, and efficiency [5]. In the marketing context, functional benefits reflect product-related attributes that generate the product’s utility to users [42]. Functional benefit represents a good fit between technology and organizational tasks [24]. Fit, in this context, represents how well the new technology matches the core capability, value and culture of the organization.

Symbolic benefit refers to positive effect brought by adoption of new technology, (e.g., brand image, organizational innovation and company reputation) [24]. Such positive effects can be witnessed by market response, which is a signal of technological innovation for the firm [58] and impact on the stock price [24]. Symbolic benefits can cause herd behavior when a firm follows others’ lead and adopts a new technology, even though it introduces uncertainty [59]. Symbolic benefits represent a fit between technology and the organiza- tional environment [24].

Both functional and symbolic benefits form a matrix to illustrate the four alternatives for adoption of IT, with functional benefit as horizontal axis and symbolic benefit as the vertical axis [24]. Four strategies are identified as image builder, strategic transformer, reactive defender, and performance enhancer [24]. For example, when both functional and symbolic benefits are high, IT helps generate value by creating a positive image for the firm. If both are low, the firm should adopt defensive mechanisms rather than pursue IT performance.

Innovation Diffusion Theory (IDT)

IDT explains how new ideas, things, and methods are introduced to individuals, how and when individuals react or interact with such innovation spread across a social system [1]. IDT emphasizes two crucial parts: the speed of diffusion of the innovation and factors that

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result in the adoption of innovation. For the speed of diffusion, the process of diffusing an innovation begins slowly and later accelerates to necessity due to the declining degree of uncertainty from the innovation [53]. As for when to adopt the innovation, five types of individuals influence time to adopt an innovation: innovators, early adopters, early major- ity, late majority, and laggards [68]. Moreover, factors that show the willingness of adopting an innovation are relative advantage, compatibility, complexity, trialability, and observa- bility [68]. This literature emphasizes the timing and willingness to adopt an innovation, two important aspects of blockchain adoption that prompted us to include this theory into our study. In this theory, technology adoption is influenced by two factors: advantages of the technology and perception of potential users.

Unified Theory of Acceptance and Use of Technology (UTAUT)

UTAUT is an integrated model with four core determinants and four moderators in intention-usage relationship [69]. Designed specifically to theorize the adoption of a technology, UTAUT proposes that self-benefit considerations closely connect with the four key constructs such as performance expectancy (gain in job performance), effort expectancy (ease of usage), social influence (advantage for social image), and facilitating conditions (available resource support) [70]. To make UTAUT more adoptable in con- sumer context, Venkatesh et al. [70] modified UTAUT by adding cost considerations, one motivation factor, and one behavior factor in the model, including price value (cost), hedonic motivation (pleasure), and habit (prior behavior) [70].Therefore, UTAUT can explain a wide array of user intentions and behaviors regarding information system adoption.

Among the moderating factors, the four determinants with intention-usage relationship is the previous experience with inferior technology which restrains individuals from usage of new technology [74]. Furthermore, social influence in mandatory contexts becomes significant in early adoption of new technology and eventually insignificant over time [69].

Table 1. Summary characteristics of relevant theories. Theory Theme Study

Objective Limitations

Technology-Organization- Environment (TOE)

Direct effect of technological, organizational, and environmental factors

Direct effect No consideration of factor interaction

Task-Technology Fit (TTF) Fit between task characteristics and technological features

Alignment Lack of environmental factors and organizational readiness

Fit-viability model (FVM) Combination of TTF and feasibility

Hybrid Lack of Environmental factors

Value-based Model (VBM) Rational choice of cost benefits Economic Weak in TTF and environmental factors

Dual Value Model (DVM) Differentiation of functional and symbolic values

Value Classification

Weak in TTF and environmental factors

Innovation Diffusion Theory (IDT)

Technology features and the diffusion process

Process view Lack of interaction effects

Unified Theory of Acceptance and Use of Technology

Benefit and cost considerations and individual differences

Marketing Weak in organizational contexts

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In Table 1, we summarize the core concepts drawn from theories previously cited and how each informs our framing of a model for analyzing the blockchain technology adop- tion. We find that each model has its advantages and limitations. In particular, the column “Theme” indicates that each theory base addresses different aspect(s) of the adoption phenomenon and that integration among these models is needed.

In practice, the decision makers must take into consideration technological, organiza- tional, and environmental factors, along with various types of opportunities and benefits that the new technology can generate that were not feasible without the technology. Hence, it is important to propose and test a model that integrates previous models and examines the factors and values that managers expect from adoption of a new technology, such as blockchain.

Hypothesis

TTF refers to the capacity of new technology to match with task demands, which can make task completion faster, more efficient, and, therefore, reduce cost. Recent findings indicate that high TTF can greatly improve task performance in learning management systems [44] and speed, efficiency, and cost are key factors of functional benefits [7]. A combination of both IT and organizational capabilities can be the foundation for achieving business value with digital technologies or “Digital Business Value” [51]. Therefore, we hypothesize that TTF will enhance functional benefits from blockchain technology.

Hypothesis 1 (1): Task-technology fit is positively related to functional benefits for using blockchain technology.

Environment-technology fit (ETF) refers to factors from the environment that can influence businesses to adopt a new technology [46]. When competitors use a new technol- ogy (e.g., blockchain technology), others will experience competitive pressure and adopt the new technology. To adopt a new technology, businesses must maintain competitiveness [41], which brings extrinsic advantages such as innovation, social influence, and positive image for businesses. Such advantages are key concepts of symbolic benefits. Demlehner and Laumer [14] also found that context is important in digital transformation of manu- facturing industry. Therefore, we hypothesize that ETF will increase symbolic benefits for blockchain technology.

Hypothesis 2 (H2): Environment-technology is positively related to symbolic benefits for using blockchain technology.

Functional benefits refer to product-related benefits that meet firms’ needs after con- suming a product or service [33]. A new technology (i.e., blockchain technology) can contain technology-related benefits, which attract a business to adopt it, such as anonymity, transparency, security, traceability, efficiency, and speed [30]. When businesses know and understand such practical advantages, they assess the effects of these advantages, prompting the business to use the new technology. Therefore, functional benefits are positively related to intention to use blockchain technology. We thus hypothesize the following:

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Hypothesis 3 (H3): Functional benefits are positively related to the intention to adopt blockchain technology.

Symbolic benefits refer to non-product-related benefits that firms perceive after con- suming a product or service [33]. Non-product-related benefits include perception of self- realization such as self-esteem, social approval, social image, and self-concept [31]. Applying this concept to blockchain technology, the use of blockchain technology can benefit by enhancing the social image and social approval for businesses, so they will consider and acquire the new technology. We thus hypothesize that:

Hypothesis 4 (H4): Symbolic benefits are positively related to intention to adopt blockchain technology.

Viability refers to the degree to which a plan, suggestion, or idea that is possible and likely to be done or be successful [65]. A plan, suggestion or idea to adopt a new technology (i.e., blockchain technology) needed support of financial resources, essential equipment and machinery to operate, and top management support [39]. When the support is strong, businesses show interest and think the plan, suggestion, or idea is achievable. Therefore, businesses may demonstrate intention to adopt blockchain technology when they view the technology as viable. We thus hypothesize H5 as follows:

Hypothesis 5 (H5): Viability is positively related to intention to adopt blockchain technology.

In Figure 1 we graphically represent our framework and the hypotheses.

Figure 1. Research model.

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Methods

Sample and Data Collection Processes

Our data collection involved an online survey from May 2018 to June 2018. The survey was completed by 251 informants. The invitations to the informants were posted on online forums related to financial technology, the stock market, and medical institutes. Participants were instructed to rate items in our survey based upon their perception. Nine responses were determined to be invalid because of repeated participation and/or indiscriminate responses, leaving 242 responses as valid and suitable for further analysis. Our sample covers informants from a broad range of professional expertise, including IT, medical, financial, and others, indicating a good representation of decision-making groups that adopt new technology. Table 2 shows the profile of the respondents in our sample.

Table 2. Respondent profiles. Respondents Numbers in Sample Percentage (%)

Industry Finance 76 31.4 Medical 88 36.4 Manufacturing 22 9.1 Information 18 7.4 Technology 13 5.4 Public service 9 3.7 Public utility 16 6.6 Others 242 100.0 Total Job Title Doctor 47 19.4 Nurse 29 12.0 Engineer 49 20.2 Manager 16 6.6 Supervisor 36 14.9 Middle-ranked officials 10 4.1 Senior executive 8 3.3 Others 47 19.4 Total 242 100.0 Tenure Below 1 (year) 49 20.2 1-5 (under) 98 40.5 5-10 (under) 35 14.5 10-15 (under) 20 8.3 15-20 (under) 18 7.4 Above 20 22 9.1 Total 242 100.0 Department Information Technology 41 16.9 Medical 80 33.1 Research & Development 19 7.9 Financial 28 11.6 Human Resource 6 2.5 Planning 7 2.9 Management 22 9.1 Sales 13 5.4 Others 26 10.6 Total 242 100.0.

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Table 3. Constructs and items. Construct Item

Task-technology Fit Data Security Data Processing Needs Compatibility

Aljukhadar et al. (2014) [3]; Goodhue (1998) [22], and Soliman and Janz (2004) [57]

● Security of blockchain technology can satisfy the need of information management.

● Security of blockchain technology can satisfy the need of privacy on information management.

● Speed of blockchain technology to information processing can satisfy the need of efficiency.

● Speed of blockchain technology can satisfy the need of updating informa- tion processing.

● Tamper-proof of blockchain technology can satisfy the need of informa- tion correctness.

● Tamper-proof of blockchain technology can satisfy the need of not being forgery.

● Preventing any third party involvement for blockchain technology can satisfy the need of improving information delivery.

● Sharing for blockchain technology can satisfy the need of information integration.

● Sharing for blockchain technology can satisfy the need of authorizing to other organizations.

Environment-Technology Fit Competitive Pressure Coercive Pressure

Gholami et al. (2013) [20]; Liang et al. (2007) [39]; Premkumar and Ramaurthy (1999) [48]

● Blockchain technology can satisfy the need of competitive market strategies.

● Many competitors have adopted blockchain technology ● Using blockchain technology can satisfy the need of customers. ● Using blockchain technology can satisfy the need of government

regulations. ● Using blockchain technology can satisfy the need of official association. ● Using blockchain technology can satisfy the need of partners.

Functional Benefits Doll and Torkzadeh (1998) [16]; Ravichandran and Lertwongsatien (2005) [50] ● Application of blockchain technology can enhance the quality of

management ● Application of blockchain technology save time of management. ● Application of blockchain can enhance the productiveness. ● Application of blockchain technology can lower the cost of operation. ● Application of blockchain technology can improve financial performance.

Symbolic Benefits Hsu et al. (2007) [27]; King and Teo (1996) [35] ● Application of blockchain technology can enhance reputation ● Application of blockchain technology can improve fame. ● Application of blockchain technology can increase influence among peers. ● Application of blockchain technology can boost prestige. ● Application of blockchain technology can enhance image of innovation. ● Application of blockchain technology can promote leadership among

peers. ● Application of blockchain can acquire recognition among peers, partners,

shareholders, and government. Viability

Financial Resources Kuan and Chau (2001) [36]; Liang et al. (2007) [39]; and Lin (2014) [41] ● It is likely to Investing funds to adopt blockchain technology. ● It is likely to investing funds to operate and maintain blockchain

technology. ● It is likely to investing funds to the cost of job training on blockchain

technology. IT Infrastructure ● Essential hardware can be operating with blockchain technology.

● Essential software can be operating with blockchain technology. ● Data base can be compatible with blockchain technology. ● IS personnel can maintain and develop blockchain technology. ● Systems can integrate with blockchain technology.

(Continued)

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Measurement

The items measuring TTF were drawn from Aljukhadar et al. [3]; Goodhue [22]; and Soliman and Janz [57]. Items measuring ETF were drawn from Gholami et al. [20]; Liang et al. [39]; and Premkumar and Ramaurthy [48]. Six items to measure functional benefit were drawn from Doll and Torkzadeh [16] and Ravichandran and Lertwongsatien [50], while seven items of symbolic benefits originated from Hsu et al. [27] and King and Teo [35]. Items measuring viability were adopted from Kuan and Chau [36]; Liang et al. [39]; and Lin [41]. Items measuring intention to adopt blockchain technology were drawn from Tsai et al. [64]. The survey adopted a Likert scale from 1 (strongly agree) to 5 (strongly disagree), and all scores were averaged to express the level of the construct. Table 3 shows the construct and items in this study.

Analysis Results

Reliability and Validity Testing

The measurement model uses partial least squares (PLS) to assess reliability, convergent validity, and discriminant validity to measure the fitness of this model. To test reliability, we adopted composite reliability and Cronbach’s alpha as the standard of measurement [25]. Table 3 shows that item assessing each construct had Cronbach’s alpha > .753 and composite reliability > .885, indicating acceptable reliability. Moreover, an average variance extracted (AVE) > .573, showing sufficient reliability. Table 4 shows the results.

In the analytical results of validity, all factor loadings were above .07 and AVE was between .57 and .88, indicating adequate convergent validity [18]. The square root of AVE for each variable had a value greater than correlated variables in each column and row, indicating acceptable discriminant validity. Furthermore, the value of correlation coeffi- cients was lower than .85 in each dimension. In a cross-loading matrix, factor loadings for each item in each dimension are larger than other factor loadings in other dimensions, suggesting sufficient discriminant validity [9]. Tables 5 and 6 show the correlation and cross-loading results.

As our model includes formative constructs and the sample size is relatively small, PLS- SEM is considered appropriate to analyze the data [52]. We used SmartPLS to analyze model validity, including coefficient of determination (R2), path coefficients, effect size (f2),

Table 3. (Continued). Construct Item

Top Management ● Top management is interested in adopting blockchain technology. ● Top management has adequate financial resource to adopt blockchain

technology. ● Top management is aware of benefits of adopting blockchain technology. ● Top management will consider adopting blockchain technology to form

a project team. Intention to Adopt Blockchain Technology Tsai et al. (2013) [64]

● Intention to adopt blockchain technology is the first stage for adopting the blockchain technology.

● Willingness to use blockchain technology is the stage before full adoption. ● High intention to adopt blockchain technology

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and predictive relevance (Q2). The value of R2 for functional benefits (FB), symbolic benefits (SB), and intention to adopt blockchain technology (AI) are .56, .58, and .73, respectively, which can explain a large amount of variance and such values are considered as between moderate and substantial [9]. Path coefficients between variables were also analyzed. The results show TTF →FB (.75), ETF→ SB (.76), FB→ AI (.17), SB→ AI (.19), and VI→ AI (.60) [28]. In terms of effect size, all three independent latent variables have an impact on a dependent latent variable. The value of f2 are FB→ AI (f2 = .046), SB→ AI (f2 = .064), suggesting low effect whereas VI→ AI (f2 = .668) indicates large effect in the structural model [10]. Q2 refers to a measure of the predictive relevance of manifest variables. The results of our analysis show that FB→ AI (Q2 = .297), SB→ AI (Q2 = .370), and VI→ AI (Q2

= .604), suggesting Q2 > 0 and meeting the proposed threshold value [17]. Table 7 shows the results.

Path Analysis

We used PLS to analyze the proposed model and hypotheses, which generate the parameters of measurement model and path coefficients. Figure 2 summarizes the results of boot- strapping to test for each hypothesis and t value. In the results of bootstrapping, data security are negative and insignificant (t = .428, p = .669). The reason may be that the

Table 5. Construct correlations. 　 AI CO DN DS FB IT CP OP FR SB TM

AI 0.940 　 　 　 　 　 　 　 　 　 　 CO 0.385 0.860 　 　 　 　 　 　 　 　 　 DN 0.533 0.668 0.757 　 　 　 　 　 　 　 　 DS 0.356 0.591 0.550 0.922 　 　 　 　 　 　 　 FB 0.695 0.616 0.737 0.442 0.816 　 　 　 　 　 　 IT 0.732 0.259 0.440 0.268 0.576 0.918 　 　 　 　 　 CP 0.651 0.604 0.696 0.536 0.794 0.497 0.822 　 　 　 　 CO 0.603 0.611 0.670 0.574 0.767 0.529 0.807 0.848 　 　 　 FR 0.638 0.374 0.490 0.390 0.530 0.633 0.458 0.471 0.936 　 　 SB 0.647 0.538 0.637 0.541 0.696 0.477 0.710 0.738 0.475 0.842 　 TM 0.791 0.373 0.544 0.306 0.642 0.764 0.597 0.597 0.694 0.565 0.883

Notes: AI, intention to adopt blockchain technology; CO, compatibility; DN, data processing needs; DS, data security; FB, functional benefits; IT, information technology infrastructure; CP, competitive pressure; OP, coercive pressure; FR, financial resources; SB, symbolic benefits; TM, top management.

Table 4. Results of composite reliability, Cronbach’s alpha, AVE. Construct Dimension Items Composite Reliability Cronbach’s Alpha AVE

FR 3 0.955 0.929 0.876 Viability TM 4 0.934 0.905 0.779

IT 5 0.964 0.953 0.842 Symbolic Benefits 7 0.945 0.932 0.709 Intention to Adopt Blockchain Tech 6 0.958 0.934 0.884 Functional Benefits 3 0.923 0.900 0.666 Environment- Technology Fit OP 3 0.885 0.804 0.720

CP 3 0.861 0.759 0.675 DS 2 0.919 0.823 0.850

Task-Technology Fit DN 4 0.841 0.753 0.573 CO 3 0.894 0.823 0.739

Notes: AVE, average variance extracted; FR, financial resources; TM, top management; IT, information technology infrastruc- ture; OP, coercive pressure; CP, competitive pressure; DS, data security; DN, data processing needs; CO, compatibility.

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Table 6. Cross loadings. 　 AI CO DN DS FB IT CP OP FR SB TM

AI1 0.953 0.372 0.498 0.37 0.652 0.675 0.627 0.572 0.609 0.629 0.747 AI2 0.938 0.348 0.5 0.299 0.662 0.663 0.595 0.53 0.578 0.575 0.721 AI3 0.929 0.365 0.505 0.334 0.646 0.725 0.613 0.597 0.61 0.621 0.762 CO1 0.34 0.855 0.627 0.533 0.556 0.227 0.551 0.567 0.294 0.551 0.331 CO2 0.339 0.907 0.601 0.521 0.573 0.229 0.561 0.56 0.347 0.457 0.327 CO3 0.312 0.814 0.482 0.467 0.446 0.212 0.433 0.436 0.326 0.363 0.302 DN1 0.466 0.535 0.843 0.411 0.645 0.425 0.552 0.506 0.38 0.407 0.471 DN2 0.468 0.585 0.835 0.376 0.645 0.429 0.574 0.526 0.391 0.42 0.47 DN3 0.353 0.44 0.675 0.414 0.453 0.22 0.498 0.486 0.342 0.578 0.336 DN4 0.297 0.451 0.653 0.512 0.45 0.198 0.486 0.537 0.384 0.613 0.348 DS1 0.355 0.556 0.53 0.929 0.425 0.232 0.499 0.525 0.368 0.522 0.29 DS2 0.299 0.533 0.482 0.914 0.388 0.265 0.49 0.534 0.349 0.473 0.272 FB1 0.58 0.37 0.487 0.278 0.798 0.537 0.639 0.583 0.393 0.497 0.53 FB2 0.583 0.415 0.553 0.33 0.829 0.494 0.676 0.636 0.409 0.601 0.506 FB3 0.654 0.448 0.562 0.297 0.851 0.59 0.656 0.626 0.442 0.547 0.6 FB4 0.528 0.582 0.649 0.428 0.808 0.373 0.687 0.651 0.439 0.611 0.478 FB5 0.508 0.597 0.702 0.38 0.81 0.363 0.62 0.622 0.389 0.536 0.452 FB6 0.554 0.579 0.639 0.438 0.802 0.476 0.611 0.636 0.519 0.609 0.581 IT1 0.653 0.182 0.378 0.214 0.481 0.911 0.387 0.429 0.577 0.423 0.683 IT2 0.7 0.219 0.423 0.252 0.509 0.929 0.46 0.489 0.606 0.464 0.707 IT3 0.67 0.21 0.396 0.268 0.533 0.91 0.45 0.476 0.593 0.435 0.692 IT4 0.646 0.261 0.366 0.228 0.514 0.919 0.456 0.492 0.541 0.402 0.68 IT5 0.687 0.315 0.45 0.267 0.603 0.919 0.523 0.537 0.585 0.462 0.741 CP1 0.46 0.581 0.613 0.56 0.656 0.334 0.825 0.78 0.374 0.597 0.459 CP2 0.563 0.549 0.622 0.465 0.68 0.379 0.885 0.653 0.399 0.65 0.479 CP3 0.599 0.334 0.466 0.271 0.624 0.545 0.75 0.548 0.358 0.489 0.554 OP1 0.501 0.494 0.53 0.516 0.61 0.451 0.665 0.807 0.447 0.573 0.493 OP2 0.523 0.495 0.582 0.461 0.681 0.486 0.696 0.904 0.402 0.659 0.534 OP3 0.512 0.566 0.591 0.489 0.658 0.409 0.693 0.831 0.355 0.642 0.493 FR1 0.561 0.285 0.43 0.328 0.453 0.57 0.388 0.384 0.926 0.408 0.608 FR2 0.621 0.372 0.45 0.361 0.492 0.603 0.422 0.452 0.957 0.43 0.647 FR3 0.606 0.387 0.496 0.403 0.54 0.603 0.474 0.482 0.925 0.493 0.692 SB1 0.56 0.443 0.564 0.486 0.576 0.398 0.59 0.623 0.352 0.827 0.435 SB2 0.515 0.358 0.436 0.407 0.466 0.368 0.527 0.565 0.295 0.833 0.383 SB3 0.528 0.476 0.534 0.44 0.657 0.4 0.606 0.639 0.401 0.861 0.468 SB4 0.547 0.448 0.538 0.471 0.619 0.423 0.622 0.646 0.405 0.88 0.478 SB5 0.406 0.491 0.528 0.515 0.48 0.225 0.513 0.532 0.338 0.826 0.381 SB6 0.576 0.447 0.554 0.407 0.603 0.462 0.602 0.617 0.449 0.861 0.522 SB7 0.639 0.498 0.582 0.467 0.657 0.484 0.686 0.697 0.519 0.805 0.616 TM1 0.689 0.271 0.429 0.211 0.539 0.696 0.458 0.499 0.577 0.457 0.875 TM2 0.733 0.304 0.457 0.287 0.556 0.73 0.54 0.517 0.651 0.455 0.919 TM3 0.709 0.378 0.521 0.318 0.615 0.642 0.601 0.56 0.598 0.517 0.893 TM4 0.661 0.364 0.518 0.26 0.558 0.626 0.506 0.535 0.626 0.573 0.841

Notes: AI, intention to adopt blockchain technology; CO, compatibility; DN, data processing needs; DS, data security; FB, functional benefits; IT, information technology infrastructure; CP, competitive pressure; OP, coercive pressure; FR, financial resources; SB, symbolic benefits; TM, top management.

Table 7. Model validity. Construct Path Co. f2 R2 Q2

TTF→FB .75*** ETF→SB .76*** FB→AI .17*** .046 .56(FB) .297 SB→AI .19*** .064 .58(SB) .370 VI→AI .60*** .668 .73(AI) .604

Notes: TTF, task-technology fit; FB, functional benefits; ETF, environment- technology fit; SB, symbolic benefits; AI, intention to adopt blockchain technol- ogy; VI, Viability.

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respondents are less concerned about security since the existing IS already meet their needs of information security. As indicated, all five hypotheses are supported.

To examine the potential problems of collinearity, we employ suggestions from Hair et al. [26] by calculating the value of variance inflation factors (VIF). A VIF of greater than five represents existence of collinearity [26]. We calculated VIF in SPSS 24 which yielded a value 3.76, below the criterion of Hair et al. [26], indicating that collinearity was not an issue.

To formally examine the existence of common method variance (CMV), we used Harman’s one-factor test to construct the analysis [56]. All items were loaded in one common factor and the analysis showed that the total variance for a single factor was less than 46.92 percent, suggesting that CMV does not affect the data.

Additional Analysis

To examine whether adoption of blockchain technology in different situations may have different positive or negative impacts, we separate the response into two groups: one group of individuals who work in the finance sector with 113 responses, and the second group comprising respondents in the medical sector with 129 responses.

The results of this extended analysis for individuals in the finance sector show that TTF is positively related to functional benefits. EFT is positively related to symbolic benefits. Functional benefits are positively related to intention to adopt blockchain technology. Symbolic benefits are positively related to intention to adopt blockchain technology. Viability is positively related to the intention to adopt blockchain technology. The results of bootstrapping show the data security→ TTF link is non-significant (t = .428, p = .887). It

Figure 2. Results of path analysis. Notes: *p < .05; **p < .01; ***p < .001.

328 LIANG ET AL.

is likely that financial sector respondents are concerned primarily about data processing and compatibility, perhaps because their existing security system is good enough and optimized for financial operations. We present our results of financial sector respondents in Figure 3.

For respondents in the medical sector, the results of the analysis show that TTF is positively related to functional benefits. Environment-task fit is positively related to sym- bolic benefits. Viability is positively related to the intention to adopt blockchain technology. However, functional benefits and symbolic benefits are not positively related to the inten- tion to adopt blockchain technology. The reason may be that in comparison with the finance sector, individuals in medical sector overlook the benefits from blockchain technol- ogy and its impact on self-worth and are more attentive to whether data transmission complies with standards of data privacy, immediacy and accuracy. When these key stan- dards can be achieved, they may feel that there is a minimal reason to be concerned. Therefore, we do not find support for H3 and H4. Figure 4 shows the results.

Discussion

Findings and Contributions

Our findings contribute to the literature in several ways. First, we find that TTF is positively related to functional benefits of blockchain technology. This provides a link between the fit with the tasks at hand and how they relate to the benefits that firms are expected to extract. As the ETF is positively related to symbolic benefits, our findings support the notion that managers are also cognizant of how the adoption of technology is viewed as reputation enhancing by internal stakeholders. Moreover, our findings further the idea that managers

Figure 3. Results from the managers in finance sector. Notes: *p < .05; **p < .01; ***p < .001.

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see viability of blockchain technology and are more likely adopt it when it garners func- tional and symbolic benefits.

To our knowledge, our study is the first attempt to deploy TTF, ETF, and FVM to construct and test a model of intention to adopt blockchain technology. Moreover, our findings further clarify the emergent benefits as functional and symbolic. We also find that viability, measured through organizational factors, is an antecedent to strengthening the intention to adopt blockchain technology. This clarification emphasizes a deeper under- standing of how external use of new technology factors (i.e., TTF and ETF) influence adoption to blockchain technology. More importantly, our use of FVM indicates that internal factors in an organization increase the likelihood of blockchain technology adoption.

Theoretical Implications

With our findings previously described, we make several theoretical contributions to the literature. First, the results show that environmental factors can explain intention to adopt blockchain technology. In this study, ETF, measured by competitive and coercive pressures, emerges as one of the sources that facilitates the adoption of IT. The reason that both pressures play an important role is due to interaction between organization and environ- ment. With the influence of the environment, organizations are pushed to adopt advanced technologies in order to compete. For example, when Walmart announced that it was going to use RFID technology, its suppliers, and those who hoped to become suppliers, rushed to adopt and experiment with RFID.2 Indications are that Walmart suppliers are facing similar pressure to adopt blockchain.3 While this may appear irrational, it is likely to hasten

Figure 4. Result from managers in healthcare sector. Notes: *p < .05; **p < .01; ***p < .001.

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adoption of blockchain resulting in the network effects that will streamline transactions for Walmart as well as its suppliers.

Second, TTF and ETF can enhance intention to adopt blockchain technology. The result is consistent with previous findings. Lu and Yang [43] found that task, social, and technol- ogy characteristics influence intention to use Social Networking Sites (SNS). In our study, blockchain technology represents a tool for improving the quality of transactions. Transactions with security, speed, and efficiency can better help organizations exchange digital information more frequently and conveniently. Therefore, both TTF and ETF are important factors for intention to adopt an IT.

Third, we found that viability leads to intention to adopt blockchain technology. Viability is measured by financial resources, IT infrastructure, and top management sup- port. These dimensions create a new theoretical basis for investigating intention to adopt IT within an organization and open a new path for future research. Such organizational factors are a key in promoting adoption of new IT.

Implications for Blockchain Technology Providers

Our findings contribute new knowledge and important implications for organizations. An understanding of the factors that influence managers’ perception also increases the like- lihood of blockchain technology adoption among organizations. Specifically, we found that functional benefits are positively related to the intention to adopt blockchain technology. Therefore, blockchain technology providers should emphasize functional utility of block- chain to customers. For example, the functional utility of blockchain technology includes greater transparency, enhanced security, improved traceability, and increased efficiency and speed [30]. By promoting such functional utility, the adoption and use of blockchain technology can increase.

We found that symbolic benefits are positively related to the intention to adopt block- chain technology. This finding could foster the development of strategies to promote advantages of self-worth in using blockchain technology. For example, blockchain technol- ogy providers could remind customers that using blockchain technology can enhance social approval, industry leading status, and the reputation of using advanced technology, parti- cularly when their business partners have already adopted blockchain. By promoting extrinsic advantages, the intention to adopt blockchain technology can be enhanced.

Finally, our finding that viability is positively related to the intention to adopt blockchain technology suggests that managers must seek the support of senior management, secure financial resources, and essential equipment. Hence, blockchain technology suppliers could gauge company’s current status and the level of support for blockchain technology, and then fill the gap in order to meet the anticipated demands of the business to ensure a successful adoption.

Research Limitations and Future Research Directions

We tested and found support for the relationship between functional and symbolic benefits and intention to adopt blockchain technology, specifically in the financial and medical sectors. However, using blockchain technology involves a bilateral transaction with two or more groups or businesses, sometimes between industry sectors. Therefore, future studies

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could explore the relationship by investigating social factors such as cooperation and interdependence across industries for adopting blockchain technology.

Competitiveness can be another criterion to test the intention to adopt a new technology. From the perspective of competitive advantage, adopting a new technology can help businesses to gain an advantage over competitors. However, future research may explore whether the decision to adopt a technology is related to company size (large, medium, and small) with varying preference of focus on functional and symbolic benefits.

Our research invited members of online forums to participate in a questionnaire. Despite carefully pilot-testing and improving the questionnaire instrument, potential bias such as data collection, honest responses and validity are inherent survey concerns that also relate to off-line research. Future studies may compare whether adopting conventional off-line research for identifying the critical factors affecting intention to adopt blockchain technol- ogy yield similar findings.

Our research explored how managers’ perception of functional benefits, symbolic ben- efits, and viability influence their intention to adopt blockchain technology. The scope of this study did not include how they perceive blockchain security for data management and privacy. Blockchain technology has a specific architecture and components that include various layers, such as data, network, consensus, incentive, contract, and application [77]. In this study, we explored managers’ intention to adopt blockchain technology which is targeted to the application layer. We focus mainly on how and why existing organizations adopt blockchain technology. Indeed, blockchain is a revolutionary new technology that can fundamentally change how organizations, industries, and markets function and also gen- erate new business strategies. Future studies may consider how the disruption factors of blockchain (i.e., decentralization) impact new ventures and existing centralized organiza- tions (i.e., banks and other financial businesses).

Conclusion

We integrated concepts from TTF, ETF, and FVM to explain the managers’ intention to adopt blockchain technology in organizations. As managers act as gatekeepers of new information technologies, it is important to understand what motivates them to adopt new technologies such as blockchain. We adopted perspectives of functional benefits and symbolic benefits to assess the intention to adopt blockchain technology. Our findings provide evidence that viability and fit, among other symbolic factors such as reputation, enhance the intention to adopt blockchain technology. These findings offer blockchain technology providers valuable insights into how to replace or supplant old processes with new and efficient technologies. Our study will serve as a foundation to incorporate social impacts into TTF, ETF, and FVM to explore intention and attitude to adopting a new technology.

Notes

1. Although successful cases of adopting IT exist, wrongdoing with IT use is also noteworthy. Numerous examples of the negative and fraudulent use of IT are reported such as Silk Road, the DAO, and Quadriga.

332 LIANG ET AL.

2. Sliwa and Brewin (2004) describe how WalMart tested its suppliers’ RFID technology readiness https://www.computerworld.com/article/2563877/rfid-tests-wal-mart-suppliers.html.

3. Rosencrance (2018) describes Walmart’s effort to seek transparency in food supply chain is pushing its suppliers to adopt blockchain technologies. https://www.computerworld.com/ article/2563877/rfid-tests-wal-mart-suppliers.html

References

1. Agag, G.; and El-Masry, A.A. Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM: An integration of innovation diffusion theory and TAM with trust. Computers in Human Behavior, 60(2016), 97–111.

2. Alabi, K. Digital blockchain networks appear to be following Metcalfe’s Law. Electronic Commerce Research and Applications, 24(2017), 23–29.

3. Aljukhadar, M.; Senecal, S.; and Nantel, J. Is more always better? Investigating the task-technology fit theory in an online user context. Information & Management, 51, 4 (2014), 391–397.

4. Awa, H.O.; Ojiabo, O.U.; and Orokor, L.E. Integrated technology-organization-environment (TOE) taxonomies for technology adoption. Journal of Enterprise Information Management, 30, 6 (2017), 893–921.

5. Barney, J.B. Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view. Journal of Management, 27, 6 (2001), 643–650.

6. Baskerville, R.L.; and Myers, M.D. Fashion waves in information systems research and practice. MIS Quarterly, 33, 4 (2009), 647–662.

7. Candi, M.; and Kahn, K.B. Functional, emotional, and social benefits of new B2B services. Industrial Marketing Management, 57(2016), 177–184.

8. Chau, P.Y.; Kuan, K.K.; and Liang, T.P. Research on IT value: What we have done in Asia and Europe. European Journal of Information Systems, 16(2007), 196–201.

9. Chin, W.W. The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295, 2 (1998), 295–336.

10. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. Hillsdale: Lawrence Erlbaum, 1988.

11. Cullell, L.M. 7 Real World Challenges & Barriers to Adoption of Blockchain Tech and Cryptocurrencies, 2020. https://hackernoon.com/transforming-the-flawed-models-of- international-aid-and-development-with-blockchain-lz8c2glc (accessed on July 8, 2020).

12. Dagher, G.G.; Mohler, J.; Milojkovic, M.; and Marella, P.B. Ancile: Privacy-preserving frame- work for access control and interoperability of electronic health records using blockchain technology. Sustainable Cities and Society, 39(2018), 283–297.

13. Davis, F.D.; Bagozzi, R.P.; and Warshaw, P.R. User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 8 (1989), 982–1003.

14. Demlehner, Q.; and Laumer, S. Why context matters: Explaining the digital transformation of the manufacturing industry and the role of the industry’s characteristics in it. Pacific Asia Journal of the Association for Information Systems, 12, 3 (2020), 57–81.

15. Di Vaio, A.; and Varriale, L. Blockchain technology in supply chain management for sustain- able performance: Evidence from the airport industry. International Journal of Information Management, 52 (2020), 102014.

16. Doll, W.J., and Torkzadeh, G. Developing a multidimensional measure of system-use in an organizational context. Information & Management, 33, 4 (1998), 171–185.

17. Fornell, C.; and Cha, J. Partial Least Squares. Oxford: Basil Blackwell, 1994. 18. Fornell, C.; and Larcker, D.F. Evaluating structural equation models with unobservable vari-

ables and measurement error. Journal of Marketing Research, 18, 1 (1981), 39–50.

JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 333

19. Gatteschi, V.; Lamberti, F.; Demartini, C.; Pranteda, C.; and Santamaría, V. Blockchain and smart contracts for insurance: Is the technology mature enough? Future Internet, 10, 2 (2018), 20.

20. Gholami, R.; Sulaiman, A.B.; Ramayah, T.; and Molla, A. Senior managers’ perception on green information systems (IS) adoption and environmental performance: Results from a field survey. Information & Management, 50, 7 (2013), 431–438.

21. Giungato, P.; Rana, R.; Tarabella, A.; and Tricase, C. Current trends in sustainability of bitcoins and related blockchain technology. Sustainability, 9, 12 (2017), 2214.

22. Goodhue, D.L. Development and measurement validity of a task-technology fit instrument for user evaluations of information system. Decision Sciences, 29, 1 (1998), 105–138.

23. Goodhue, D.L.; and Thompson, R.L. Task-technology fit and individual performance. MIS Quarterly, 19, 2 (1995), 213–236.

24. Grover, V.; Chiang, R.H.; Liang, T.P.; and Zhang, D. Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35, 2 (2018), 388–423.

25. Hair, J.F.Jr.; Anderson, R.E.; Tatham, R.L.; and Black, W.C. Multivariate Data Analysis (5th ed.). New Jersey: Prentice-Hall International, 1998.

26. Hair, J.F.; Sarstedt, M.; Ringle, C.M.; and Mena, J.A. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 3 (2012), 414–433.

27. Hsu, C.L.; Lu, H.P.; and Hsu, H.H. Adoption of the mobile Internet: An empirical study of multimedia message service (MMS). Omega, 35, 6 (2007), 715–726.

28. Huber, F.; Herrmann, A.; Meyer, F.; Vogel, J.; and Vollhardt, K. Kausalmodellierung mit Partial Least Squares: Eine Anwendungsorientierte Einführung. Berlin: Gabler Verlag, 2008.

29. Hyvärinen, H.; Risius, M.; and Friis, G. A blockchain-based approach towards overcoming financial fraud in public sector services. Business & Information Systems Engineering, 59, 6 (2017), 441–456.

30. IBM.com. Top Five Blockchain Benefits Transforming Your Industry, 2018. https://www.ibm. com/blogs/blockchain/2018/02/top-five-blockchain-benefits-transforming-your-industry /(accessed on July 7, 2018).

31. Kang, M.; and Shin, D.-H. The effect of customers’ perceived benefits on virtual brand community loyalty. Online Information Review, 40, 3 (2016), 298–315.

32. Kaur, H.; Alam, M.A.; Jameel, R.; Mourya, A.K.; and Chang, V. A proposed solution and future direction for blockchain-based heterogeneous medicare data in cloud environment. Journal of Medical Systems, 42, 8 (2018), 156.

33. Keller, K.L. Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57, 1 (1993), 1–22.

34. Kim, H.W.; Chan, H.C.; and Gupta, S. Value-based adoption of mobile internet: An empirical investigation. Decision support systems, 43, 1 (2007), 111–126.

35. King, W.R.; and Teo, T.S. Key dimensions of facilitators and inhibitors for the strategic use of information technology. Journal of Management Information Systems, 12, 4 (1996), 35–53.

36. Kuan, K.K.; and Chau, P.Y. A perception-based model for EDI adoption in small businesses using a technology–organization–environment framework. Information & Management, 38, 8 (2001), 507–521.

37. Larosiliere, G.D.; and Carter, L.D. Using a fit-viability approach to explore the determinants of e-government maturity. Journal of Computer Information Systems, 56, 4 (2016), 271–279.

38. Li, H.; Zhu, L.; Shen, M.; Gao, F.; Tao, X.; and Liu, S. Blockchain-based data preservation system for medical data. Journal of Medical Systems, 42, 8 (2018), 141.

39. Liang, T.P.; Huang, C.W.; Yeh, Y.H.; and Lin, B. Adoption of mobile technology in business: A fit-viability model. Industrial Management & Data Systems, 107, 8 (2007) 1154–1169.

40. Liang, T.P.; and Wei, C.P. Introduction to the special issue: Mobile commerce applications. International Journal of Electronic Commerce, 8, 3 (2004), 7–17.

334 LIANG ET AL.

41. Lin, H.F. Understanding the determinants of electronic supply chain management system adoption: Using the technology-organization-environment framework. Technological Forecasting and Social Change, 86(2014), 80–92.

42. Liu, C.; Chai, K.K.; Zhang, X.; Lau, E.T.; and Chen, Y. Adaptive blockchain-based electric vehicle participation scheme in smart grid platform. IEEE Access, 6(2018), 25657–25665.

43. Lu, H.P.; and Yang, Y.W. Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34(2014), 323–332.

44. McGill, T.J.; and Klobas, J.E. A task–technology fit view of learning management system impact. Computers & Education, 52, 2 (2009), 496–508.

45. Narayanan, A.; Bonneau, J.; Felten, E.; Miller, A.; and Goldfeder, S. Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton: Princeton University Press, 2016.

46. Oliveira, T.; and Martins, M.F. Understanding e-business adoption across industries in European countries. Industrial Management & Data Systems, 110, 9 (2010), 1337–1354.

47. Pirtle, C.; and Ehrenfeld, J. Blockchain for healthcare: The next generation of medical records? Journal of Medical Systems, 42, 9 (2018), 172.

48. Premkumar, G.; and Roberts, M. Adoption of new information technologies in rural small businesses. Omega, 27, 4 (1999), 467–484.

49. Ramdani, B.; Chevers, D.; and Williams, D.A. SMEs’ adoption of enterprise applications: A technology-organisation-environment model. Journal of Small Business and Enterprise Development, 20, 4 (2013), 735–753.

50. Ravichandran, T.; Lertwongsatien, C.; and Lertwongsatien, C. Effect of information systems resources and capabilities on firm performance: A resource-based perspective. Journal of Management Information Systems, 21, 4 (2005), 237–276.

51. Riera, C.; and Iijima, J. The role of IT and organizational capabilities on digital business value. Pacific Asia Journal of the Association for Information Systems, 11, 2 (2019), 67–95.

52. Ringle, C.M.; Sarstedt, M.; and Straub, D.W. Editor’s comments: A critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Quarterly, 36, 1 (2012), iii–xiv.

53. Rogers, E.M. Diffusion of Innovations. New York: Simon and Schuster, 2010. 54. Roostika, R. Mobile internet acceptance among university students: A value-based adop-

tion model. International Journal of Research in Management & Technology, 2, 1 (2012), 21–28.

55. San-Martín, S.; Jiménez, N.; and López-Catalán, B. The firms benefits of mobile CRM from the relationship marketing approach and the TOE model. Spanish Journal of Marketing-ESIC, 20, 1 (2016), 18–29.

56. Sharma, R.; Yetton, P.; and Crawford, J. Estimating the effect of common method variance: The method—method pair technique with an illustration from TAM Research. MIS Quarterly, 33, 3 (2009), 473–490.

57. Soliman, K.S.; and Janz, B.D. An exploratory study to identify the critical factors affecting the decision to establish Internet-based interorganizational information systems. Information & Management, 41, 6 (2004), 697–706.

58. Spence, M. Job market signaling. The Quarterly Journal of Economics, 87, 3 (1973), 355–374. 59. Sun, H. A longitudinal study of herd behavior in the adoption and continued use of technology.

MIS Quarterly, 37, 4 (2013), 1013–1041. 60. Sun Yin, H.H.; Langenheldt, K.; Harlev, M.; Mukkamala, R.R.; and Vatrapu, R. Regulating

cryptocurrencies: A supervised machine learning approach to de-anonymizing the bitcoin blockchain. Journal of Management Information Systems, 36, 1 (2019), 37–73.

61. Swan, M. Blockchain: Blueprint for a New Economy. Sebastopol: O’Reilly Media, 2015. 62. Tjan, A.K. Finally, a way to put your Internet portfolio in order. Harvard Business Review, 79, 2

(2001), 76–85, 156. 63. Tornatzky, L.G.; Fleischer, M.; and Chakrabarti, A.K. The Processes of Technological

Innovation. Lexington: Lexington, 1990. 64. Tsai, M.C.; Lai, K.H.; and Hsu, W.C. A study of the institutional forces influencing the

adoption intention of RFID by suppliers. Information & Management, 50, 1 (2013), 59–65.

JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 335

65. Turban, E.; Bolloju, N.; and Liang, T.P. Enterprise social networking: Opportunities, adoption, and risk mitigation. Journal of Organizational Computing and Electronic Commerce, 21, 3 (2011), 202–220.

66. Vallejo, M.C. Is the culture of family firms really different? A value-based model for its survival through generations. Journal of Business Ethics, 81, 2 (2008), 261–279.

67. van Hoek, R. Unblocking the chain–findings from an executive workshop on blockchain in the supply chain. Supply Chain Management: An International Journal, 25, 2 (2020), 255–261.

68. Vargo, S.L.; Akaka, M.A.; and Wieland, H. Rethinking the process of diffusion in innovation: A service-ecosystems and institutional perspective. Journal of Business Research, 116(2020), 526–534.

69. Venkatesh, V.; Morris, M.G.; Davis, G.B.; and Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 3 (2003), 425–478.

70. Venkatesh, V.; Thong, J.Y.; and Xu, X. Consumer acceptance and use of information technol- ogy: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 1 (2012), 157–178.

71. Wang, P. Chasing the hottest IT: Effects of information technology fashion on organizations. MIS Quarterly, 34, 1 (2010), 63–85.

72. Wang, S.P.; Zhang, Y.L.; and Zhang, Y.L. A blockchain-based framework for data sharing with fine-grained access control in decentralized storage systems. IEEE Access, 6(2018), 38437–38450.

73. Wu, B.; and Chen, X.H. Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67(2017), 221–232.

74. Xu, X.; Thong, J.Y.L.; and Tam, K.Y. Winning back technology disadopters: Testing a technology readoption model in the context of mobile internet services. Journal of Management Information Systems, 34, 1 (2017), 102–140.

75. Yli-Huumo, J.; Ko, D.; Choi, S.; Park, S.; and Smolander, K. Where is current research on blockchain technology?—a systematic review. PloS one, 11,10 (2016), e0163477.

76. Yuan, R.; Xia, Y.B.; Chen, H.B.; Zang, B.Y.; and Xie, J. ShadowEth: Private smart contract on public blockchain. Journal of Computer Science and Technology, 33, 3 (2018), 542–556.

77. Yuan, Y.; and Wang, F.Y. Blockchain and Cryptocurrencies: Mode techniques, and applications. IEEE Transactions on Systems Man Cybernetics-Systems, 48, 9 (2018), 1421–1428.

78. Yue, X.; Wang, H.J.; Jin, D.W.; Li, M.Q.; and Jiang, W. Healthcare data gateways: Found healthcare intelligence on blockchain with novel privacy risk control. Journal of Medical Systems, 40, 10 (2016), 218.

79. Zeithaml, V.A. Consumer perceeptions of price, quality, and value- A means-end model and synthesis of evidence. Journal of Marketing, 52, 3 (1988), 2–22.

80. Zhao, Y.; Li, Y.; Mu, Q.; Yang, B.; and Yu, Y. Secure pub-sub: Blockchain-based fair payment with reputation for reliable cyber physical systems. IEEE Access, 6(2018), 12295–12303.

81. Zhou, L.J.; Wang, L.C.; and Sun, Y.R. MIStore: A blockchain-based medical insurance storage system. Journal of Medical Systems, 42, 8, (2018), 149.

82. Ziolkowski, R.; Miscione, G.; and Schwabe, G. Decision problems in blockchain governance: Old wine in new bottles or walking in someone else’s shoes? Journal of Management Information Systems, 37, 2 (2020), 316–348.

About the Authors

Ting-Peng Liang ([email protected]) is Director of the Electronic Commerce Research Center of National Sun Yat-Sen University, Taiwan, ROC. He received a Ph.D. degree from the Wharton School of the University of Pennsylvania and is a Fellow, Leo Award Recipient, and Past President of the Association for Information Systems. Dr. Liang’s research interests include electronic commerce, data analytics, information systems strategy, and neural information systems. His papers have appeared in top journals such as MIS Quarterly, Journal of Management Information Systems,

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Journal of the AIS, Management Science, Operations Research, Decision Support Systems, Information & Management, International Journal of Electronic Commerce, among others.

Rajiv Kohli ([email protected]) is John N. Dalton Memorial Professor of Business at the Raymond A. Mason School of Business, William & Mary. Prior to joining academia, he was a Project Leader in Decision Support Services at Trinity Health. Dr. Kohli’s research has been published in Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, MIS Quarterly Executive and Journal of Operations Management, among other journals. He is a coauthor of the book The IT Payoff: Measuring Business Value of Information Technology Investment, published by Financial Times Prentice-Hall. He serves as a Senior Editor of Information Systems Research.

Hang-Chang Huang is a postdoctoral research fellow at the Electronic Commerce Research Center of National Sun Yat-Sen University. He received a Ph.D. degree from Chang Gung University in Taoyuan, Taiwan, and has published a number of papers in such journals as Computers in Human Behavior, International Journal of Information Management, Journal of Electronic Commerce Research, among others.

Zong-Lin Li is a Software Engineer at the Marketing Application System Development of Moxa Technologies, Taiwan, that specializes in intelligent manufacturing and industrial Internet of Things. He received a master’s degree in MIS from National Sun Yat-Sen University and was a graduate research assistant at the Electronic Commerce Research Center of National Sun Yat-Sen University in Kaohsiung, Taiwan. His expertise includes intelligent systems development and blockchain technology.

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