Factors Affecting the Intention to Use Blockchain: A Technology Acceptance Perspective

Abstract

This study investigates the specific factors affecting blockchain or the usage intention of distributed ledger technology (DLT), specifically availability, diversity, and economic value, from the perspective of a unified theory of technology acceptance. Users of DLT in public and private sectors were surveyed. Using a structural equation model, the results indicate that availability and economic value affect performance expectancy, while availability, diversity, and economic value have an influence on effort expectancy. Performance expectancy and transparency have a positive effect on the intention to use DLT, which in turn exerts a positive effect on usage behavior. This study provides implications for researchers in that it attempts to investigate the factors directly (like performance expectancy and transparency) or indirectly (like availability and economic value) affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, IT, financial, service medical, and logistics sectors.

Keywords

blockchain distributed ledger technology (DLT) (→“(DLT)” should be added or not?)unified theory of acceptance and use of technology performance expectancy transparency behavioral intention to use usage behavior

Introduction

Blockchain or distributed ledger technology (DLT) employs a decentralized and distributed platform that ensures the safe sharing and utilization of data by allowing appropriate access rights, as well as transparent, secure, and reliable transaction processes not depending on a third party such as a government agency, reducing costs while expanding applications in digital assets, public facility, architecture, and real estate management. DLT, which is also represented as blockchain technology, supports real-time autonomous cooperation in inter-object telecommunication (IoT) based on smart contracts. It provides a means to ensure security in digital networking and IoT. The international market for DLT will be $94.0 billion in 2027 from $7.4 billion in 2022, projecting a CAGR of around 66.2% from 2022 to 2027 (Markets and Markets, 2024).

As financial processes based on IoT applications combined with mobile communications technology develop, “Fintech” is becoming increasingly sophisticated, demanding more secure and reliability technologies such as DLT, which provides strong protection against system hacking and abuse (Queiroz & Wamba, 2019). Other approaches have attempted to improve storage through smart-contract transmission and execution strategies in DLT (Spataru et al., 2021). The importance for DLT for the usage of big data analysis and distance education also is growing (Abuhassna & Alnawajha, 2023a, 2023b; Samsul et al., 2023).

This study is motivated by the following factors. First, it is necessary to investigate the determinants of the intention to use DLT based on a unified theory of acceptance and the use of technology using a structural equation modeling approach as a central method for validating research variables and testing the research model. Intention to use DLT represents the inclination or willingness of individuals to use it. It is a crucial factor in understanding customer behavior and predicting their future actions. Previous studies have posited challenges and opportunities regarding the usage process of DLT platforms (Berdik et al., 2021; Chen et al., 2020; Dabbagh et al., 2021). For instance, the influence of technological, organizational, and environmental factors with regard to DLT adoption has been investigated based on an innovation theory perspective (Clohessy & Acton, 2019; Drummer & Neumann, 2020). In order to offer practical implications for businesses and policymakers, it is necessary to provide an empirical analysis that compares employees of both public and private sectors.

Owing to the increasing importance of DLT based on recent publications on related technologies, it is becoming increasingly necessary to investigate factors that affect its acceptance, and a number of such applications have been implemented in various areas related to business processes, such as supply chain processes (Chang et al., 2019; S. Chen et al., 2020; Hong & Hales, 2021; Kshetri, 2018; Queiroz & Wamba, 2019; Wamba & Queiroz, 2020), personnel management (J. Chen et al., 2019), and healthcare (Balasubramanian et al., 2021). There is also a growing need to use blockchain technology in finance due to its secure transaction processing mechanism (Queiroz & Wamba, 2019).

Second, there is a lack of empirical studies across industries that use DLT, such as the public, IT, financial, service, medical, and logistics sectors. Given its early stage of diffusion, few empirical studies targeting various industries’ platforms or services based on DLT have been reported. Recent DLT studies focus on specific applications and not on a broad spectrum of areas, including the public and private sectors and associations related to DLT, due to the lack of a workforce to develop these platforms and services in the early stages of the maturity of this technology. Our study is intended to bridge this gap by encompassing the public, IT, financial, service, medical, and logistics sectors in the sample of industries adopting DLT.

Literature Review

Unified Theory of Acceptance and Use of Technology

Studies on the factors affecting block have received considerable attention in previous blockchain literature regarding such aspects as the differences and commonalities in blockchain adoption (Marengo & Pagano, 2023), priority evaluation factors for blockchain application services (Lee et al., 2023), and factors for innovation through DLT (Sciarelli et al., 2022). DLT applications encompasses various business contexts such as energy (C. Zhang et al., 2023), supply chain (Uddin et al., 2023), cyber secure supply chain (Etemadi et al., 2021), and public sectors (Lee et al., 2023).

In order to investigate the factors affecting DLT for various contexts, our study employs an established theory, that is, the unified theory of the acceptance and use of technology (UTAUT), in order to explain factors affecting the intention to use DLT. UTAUT refers to combining a technology acceptance model (TAM) with behavior intention. It has been utilized to explain factors affecting technology acceptance (Queiroz & Wamba, 2019). UTAUT posits that attitudes toward technology are the most important factor affecting usage behavior. UTAUT has added factors to overcome the lack of an explanation of individual-related or external factors (Venkatesh et al., 2012; Wang et al., 2018), applying performance expectations, efforts expectations, social impacts, and facilitating conditions, and adjusting them to fit into general consumption environments. Price values, hedonic motivation, and habits have been added to the initial UTAUT, greatly improving its explanatory power of usage intention and behavior (Venkatesh et al., 2012). UTAUT adjusts and integrates theories of planned behavior, theories of rational behavior, TAM, social cognitive theories, innovation diffusion theories, and motivation theories.

Attitudes toward technology are a crucial factor directly affecting usage intentions of technology. UTAUT excludes attitudes toward technology due to their inconsistent mediating effects on technology usage. It uses gender, age, experiences, and voluntariness of use as moderators between four independent variables (performance expectancy, effort expectancy, social impact, and facilitating conditions) and behavioral intention. UTAUT is expected to improve explanatory power capabilities, especially for newly developed technologies, by overcoming the limitations of explaining technology acceptance compared to previous theories and models related to social psychology and technology acceptance, from 17%–53% to 70% (Queiroz & Wamba, 2019). Recently, it was applied to explain the acceptance of technologies such as networking webs (Chua et al., 2018), cloud service utilization (Dwivedi et al., 2017; Wang et al., 2018), DLT (Batara et al., 2017; Kshetri, 2018; Lu & Xu, 2017), mobile financial applications, and government systems used by government employees (Batara et al., 2017).

In an extended UTAUT, hedonic motivation, price value, and habits were added as independent factors of behavioral intention to use technology to consider individual desires and expectations as well as individual perceptions, with existing independent variables redefined in general consumer environments in order to overcome the limitations of explaining non-technology-related and individual consumer factors. The extended UTAUT shows improved explanatory power compared to the original UTAUT, from 56% to 74% for behavioral intention and from 40% to 52% for usage behavior (Queiroz & Wamba, 2019). Further, the explanatory power of the extended UTAUT is improved by linking facilitating conditions with both behavioral intention and usage behavior (Venkatesh et al., 2012). The extended UTAUT has been applied to public or security applications (Batara et al., 2017; Kshetri, 2018; Li et al., 2019; Zou et al., 2018) and logistics (Aste et al., 2017; Viryasitavat et al., 2020).

DLT is posited to be safer and transparent, as well as verifiable for the entire process. It is also more efficient than any other form of transaction (Batara et al., 2017; Kshetri, 2018; Lu & Xu., 2017; Zou et al., 2018). DLT improves collaborative efforts among network participants while also reducing the cost and improving the efficiency along supply chains (Aste et al., 2017). It can support the tracking of supply chain flows, thus increasing reliability (Chang et al., 2019). The tracking mechanism in DLT can help find fake products and prevent fraudulent activities (Batara et al., 2017; Lu & Xu, 2017; Queiroz & Wamba, 2019).

Distributed Ledger Technology

DLT is based on consensus algorithms such as the practical Byzantine fault tolerance (BFT), proof of work (PoW), and proof of stake (PoS) algorithms and is developed as an algorithm to coordinate distributed ledgers owned by all network participants as well as smart contracts among network participants to prevent changes not agreed upon and/or alterations of contracts. DLT appears in the name of Bitcoin in the encryption technology community Gmane (Schwab, 2018). Nakamoto (2008) suggests three principles of DLT. First, DLT provides a solution for double payments in peer-to-peer (P2P) networks without third-party intervention. Second, it replaces a central trustful authority with a cryptography proof system. Third, it provides a means by which to trust a network without the intervention of a central trustful authority.

DLT is described as distributed ledger-network-based technology with self-executable programs through no existing server, where participants share cyber-transaction information pertaining to validation, storage, and management issues after recording each transaction in the distributed ledger across online P2P networks (Queiroz & Wamba, 2019); included is a backend database that provides open access to the distributed ledger, with P2P network transfers of individual transactions, valuables, and assets, as well as the methods to replace a traditional trust certification agency by providing transaction certification and validation using verifiable specially designed digital information with no risk of a double payment (Kshetri, 2018). DLT stores data in blocks eternally without the risk of data deletion, with the linking, updating, and storing pf blocks. It provides a distributed computing technology that enables the transfer of valuable assets by individuals and groups with reliability, stability, efficiency, and security as well as economic efficiency due to the use of a distributed computing network without a central control server and also lower maintenance costs and transparency to ensure the prevention of unauthorized data alterations. DLT is in its early stages in terms of technology maturity (Markets and Markets, 2024) and was included as a top-10 promising future technologies in 2017, 2018, and 2019. The acceptance of DLT is thus expanding across public and private sectors and financial organizations. DLT is also considered a core technology in the fourth industrial revolution, combining and integrating artificial intelligence, the IoT, and cloud computing. This study defines blockchain technology as a non-server-based and distributed infrastructure to ensure transparency, reliability, security, and integrity while also providing strong prevention against hacking and deliberate data alterations.

The functions of DLT can be described as follows. First, when transaction information is initially recorded, it is stored through blocks distributed among participants, and the blocks are updated after new transactions occur and are transferred in an encrypted form. A newly created block has a header hash computed from existing stored information, pointing to an existing block. After new blocks are validated using existing blocks, they are linked to existing blocks. Transaction information such as that pertaining to a funds transfer from A to B is disclosed to every network participant, and in order to change transaction information deliberately, it is necessary to hack the computers of all participants. If users with malicious intent intend to create blocks, the creation of such blocks is prevented. Participant validation is conducted through codes embedded in the running DLT smart contracts.

It is possible to compensate participants based on economic activities and not indirect costs or broadcasting fees, and by using a distributed ledger, DLT ensures transparency by providing auditability for all DLT processes (Kshetri, 2018; Schwab, 2018; Zou et al., 2018) as well as traceability and accountability for the monitoring of the overall logistics process, preventing fraudulent activities and the appearance of fake products (Batara et al., 2017; Lu & Xu, 2017; Queiroz & Wamba, 2019). Further, a consensus among network participants increases cost reductions and operational efficiency among supply chain processes (Aste et al., 2017).

Types of DLT include public, private, and consortium DLT depending on the rights and role of the network participants, and permission and non-permission DLT according to the necessity of permission for network participation. Non-permission DLT allows any form of participation in managing the distributed ledger, and transaction details are open and distributed. Coordinating algorithms for creating blocks are based on PoW and are slowly expected to be replaced with the PoS approach.

In terms of usage types, DLT includes those intended for transfers of value, that is, cryptocurrencies such as Bitcoin, Litecoin, Dash, ZCash, and Monero for payments. Others include platform-based forms implemented in smart contracts, such as Ethereum, NEO, and Qtum. The number of decentralized applications (DApp) using Ethereum amounted to 1,000 in 2015 and 2,000 in 2019, with more based on PoS than PoW.

Non-permission DLT has several technical limitations, including scaling issues with expanding users, economic inefficiencies such as slow processing speeds, and questionable information security for open information. In order to increase the processing efficiency, block sizes are increasing, and lightening networks, with their high processing speeds and low fees, are crucial to run distributed applications efficiently. The block-creation time has been reduced and the coordinating procedures have been changed.

PoW increases energy usage and related expenses by creating competition among network participants in order to form the longest DLT. PoS creates blocks using votes based on cryptocurrency stakes, without requiring much processing or increasing the application processing speed, with lower expenses as well. Thus, in terms of efficiency, PoS is preferred over PoW. Delegated proof of stake (DPoS; e.g., EOS, ICON) is another consensus algorithm that allows consensus rights to a few chosen participants.

Several technologies have been developed to enhance security and privacy in the non-permission type of DLT. Using the concept of zero knowledge proof, all transaction details are open to all network participants. Zero knowledge proof, developed by Zcash in 2016, allows no disclosure of any information other than whether it is true or false for any statement to ensure secure processes.

Permission DLT includes private and consortium DLT types. Private DLT allows one entity to control all blocks with established rights, and entities who have consensus or individuals allowed by the entities can participate in the networks. Only authorized individuals can create transactions and blocks. Permission DLT was developed in order to solve the issues of scaling (low performance) and security (privacy) of non-permission DLT and for better network control.

Consortium DLT is the mixed type of public and private DLT. N entities manage one node and transactions are possible only through a consensus among entities. Only organizations participating in a consortium that are in a strong trustful relationship have rights regarding transactions and blocks. For example, financial organizations such as banks and LGCNS form a R3CEV consortium. Samsung SDS, Coinplug, the Korea Stock Exchange, SK Telecom, Bloco, and The Roof participate in Hyperledger or EEA. It is faster to create transaction and blocks and easier to gain a consensus for system updates. DLT platforms have been launched by dozens of blockchain consortiums, such as R3CEV, the Hyperledger Project, and the Enterprise Ethereum Alliance, which intend to improve DLT and its standardization (Table 1).

Table 1.

Types of DLT.

Type	Non-permission	Permission
Class	Pubic DLT	Private DLT	Consortium DLT
Concept	Anybody can participate in DLT network and consensus	Permission required to participate in DLT network	Permission required to participate in DLT network
Features	It is hard to change the determined rules	It is easy to change rules according to decision making of central agency	It is possible to change rules through agreements of consortium participants
Operator	All transaction participants (decentralized)	Central agency (special operator)	Organizations participating in consortium
Read rights	All participants in DLT	Users who are chosen to be allowed to have read rights including operators	Organizations who are chosen to be allowed to have read rights
Rights of creating transaction	All participants in DLT	Users who are chosen to be allowed to have creating transaction rights	Organizations who are chosen to be allowed to have creating transaction rights
Rights of creating blocks	All participants in DLT	DLT operator	Organizations participating in consortium
Consensus algorithm	PoW, PoS	PoS, BFT, PoA	PoS
Transaction speed	Low	High (network expansion is very easy)	High (network expansion is very easy)
Example	Bitcoin	Linq (Nasdaq unlisted stock exchange platform	Corda (distributed ledger record system for financial institution)

Industries adopting DLT are expanding and include those other than financial applications, with retailing, logistics, medicine, and public sectors now present, using an initial coin offering as a means to create funds. Amazon, MS, and IBM intend to apply DLT in various industries and compete fiercely to become the leader in the BaaS market (DLT as a service), which is a distributed network service for creating a DLT service development environment.

Korean DLT is directed toward improving Ethereum in terms of data processing, capacity, and smart contracts. Samsung SDS, LG CNS, Naver, and Kakao have attempted to control the DLT market by developing separate main DLT networks. DLT has been applied to public security areas, such as digital assets and public facilities management, construction, real estate management, auto leases, and ownership certification. Cryptocurrency is the representative application of DLT for asset management and payments, and new forms of cryptocurrency are appearing. Further, in order to ensure the efficiency and security of IoT, DLT is being utilized in the areas of production, retailing, and shared economies as digital networks and IoT are expanding and requiring safe transactions and intellectual asset protection. DLT can fundamentally prevent double payments and facilitate payments of small amounts, representing a highly appropriate payment method for financial applications.

Research Model

The research model is suggested based on TAM, UTAUT, and the extended UTAUT in order to suggest factors affecting behavioral intention and usage behavior in the context of DLT. This study adopts security, availability, trust, diversity, and economic efficiency as external factors representing the characteristics of DLT. Transparency and trust are added to the original set of endogenous variables, that is, performance expectancy, effort expectation, social impact, and facilitating conditions, based on previous studies of DLT (e.g., Queiroz & Wamba, 2019). We exclude moderators such as gender, age, and experience because DLT is in the early stage of diffusion and there are too few samples to investigate differences in the effects of independent variables on dependent variables (Figure 1).

Figure 1.

Research model.

The availability of DLT is described as when the use of DLT is always possible without malfunctions across all network nodes. Availability is especially important as network participants share blocks, and a malfunction of any network node can have a detrimental influence on other nodes. The availability of DLT has effects on performance and effort expectancy, which is related to behavioral intention (Queiroz & Wamba, 2019). Blockchain applications have created high expectations in terms of improvements in business (Kshetri, 2018), which is possible as a result of ensuring availability of talent and funds (Mishra et al., 2023). It is important that blockchain brings resilience by reducing single points of failure and delivers sustainability to operations (Singh et al., 2023). Furthermore, if the availability of DLT increases, users willingly exert efforts to be accustomed to using technology in its early stage of diffusion. Thus, the availability of DLT can be considered to be a factor that positively affects performance and effort expectancy.

H1-1: The availability of DLT positively influences performance expectancy.

H1-2: The availability of DLT positively influences effort expectancy.

The diversity of DLT refers to the extent to which DLT can be utilized in diverse area to ensure its usefulness. The diversity of DLT has an influence on performance and efforts expectancy, which affects behavioral intention (Queiroz & Wamba, 2019). Blockchain applications have achieved remarkable performance expectations (Kshetri, 2018), which are established through obtaining diversity of its applications from innovation and growth. The development of timely delivery capabilities in the system, reliable suppliers, and ordering systems should first be prepared to ensure growth and innovation (Goodarzian et al., 2023). In addition, if diversity is established as a facilitating condition for DLT, users will intend to make larger efforts for using the technology for greater business performance. As blockchain can be established in diverse applications such as supply chain processes (Hong & Hales, 2021), finance based on secure transaction processing mechanism (Queiroz & Wamba, 2019), and healthcare (Balasubramanian et al., 2021), it can bring out greater expectations for subsequent business performance.

Thus, the diversity of DLT can be hypothesized as a factor that positively affects performance and effort expectancy.

H2-1: The diversity of DLT positively influences performance expectancy.

H2-2: The diversity of DLT positively influences effort expectancy.

The economic value of DLT is described as the extent of added utility and benefits compensating for the costs paid by consumers. Economic value is considered as price value and added utility using functions for smart contracts and tokens for currency. The economic value of DLT influences performance and efforts expectancy (Queiroz & Wamba, 2019). Blockchain technologies have increased much expectations in business innovations (Kshetri, 2018), which is established through obtaining economic value of its applications. These economic value can be realized because blockchain can utilize its decentralized status (less need for central intermediary for transaction validation) to decrease process complexity and uncertainty, especially through operations based on smart contracts (Kim & Laskowski, 2017). Reduced cost and increased efficiency, which directly result in economic value, can contribute to perceived benefits, which in turn affect perceived usefulness and the attitude toward the blockchain adoption (Sciarelli et al., 2022). Karamchandani et al. (2020) have shown that perceived benefits positively influence the PU of blockchain. Therefore, it is reasonable to infer that perceived benefits based on economic value positively influence PU and users’ attitude and intention to adopt blockchain technology. The main advantages of blockchain are linked to the reduction of costs and the improvement of the efficiency Further, when economic value or profitability is expected from the usage of DLT, users are more likely to intend to overcome hurdles or difficulty from using the technology for greater business outcomes. Thus, the economic value of DLT can be considered as a factor that positively affects performance and effort expectancy.

H3-1: The economic value of DLT positively influences performance expectancy.

H3-2: The economic value of DLT positively influences effort expectancy.

Performance expectancy represents the extent of the belief that the use of new technology improves task performance (Venkatesh et al., 2003). A smart-contract-based task, a characteristic of a distributed ledger of DLT, can minimize the complexity and uncertainty of related processes. The intention of individuals to use and adopt a technology depends significantly on performance expectancy (Alalwan et al., 2017). In the context of blockchain technology, performance expectancy has an effect of behavioral intention to use (Queiroz & Wamba, 2019). Performance expectance based on perceived benefits represents the perception of the positive consequences that are caused by a specific action. Further, it has been shown that perceived benefits positively influence the perceived usefulness of blockchain (Karamchandani et al. 2020), and the behavioral intention to use blockchain is affected by perceived usefulness (Knauer & Mann, 2019; Nuryyev et al., 2020). Thus, the performance expectancy of DLT can be considered as a factor that positively affects behavioral intention to use DLT.

H4: The performance expectancy of DLT positively influences behavioral intention to use.

Effort expectancy represents the extent to which a new technology can be easily used by anyone (Venkatesh et al., 2003). Difficulties in using new technology include education and costs related to adjustments and can create a barrier to the acceptance of new technology despite its contributing role to the accomplishment of individual objectives (Venkatesh et al., 2003). While effort expectancy is not suggested as an influential factor for behavioral intention to use DLT in comparison to performance expectancy (Queiroz & Wamba, 2019), the effort expectancy of DLT, which is in the early stage of its adoption, can influence behavioral intention to use it as effort expectancy is likely to be more salient in the early stages of a new experience or behavior, when process issues show obstacles to be overcome, and later become overshadowed by its technological importance. In the context of blockchain, perceived ease of use has a positive effect on perceived usefulness of technology adoption (Kamble et al., 2018; Kamble, 2021). When users perceive that technology does not require much effort to learn, they are more likely to use it. Further, it has been suggested that perceived ease of use positively affects the attitude toward adopting blockchain (Nuryyev et al., 2020; Sciarelli et al., 2022). Thus, the effort expectancy of DLT can be considered as a factor that negatively affects behavioral intention to use.

H5: The effort expectancy of DLT negatively influences behavioral intention to use.

Social impact is described as the recognition of important related people or organizations who should use a new technology (Venkatesh et al., 2003). It is also the influence from the opinions and behaviors of peers, colleagues, or friends (Queiroz & Wamba, 2019). A person or organization should consider the opinions of peers and related organizations when deciding whether to use a new technology. For instance, social influence exerts a crucial role in the adoption of Internet-based banking (Zhang, Weng, and & Zhu, 2018) and mobile apps (Lee & Kim, 2020, 2021). The integration of blockchain into supply chains demands a collaboration between supply chain members because the existing relationships exerts a significant influence on whether to adopt blockchain across the network. Thus, the social impact of DLT can be considered as a factor that positively affects behavioral intention to use.

H6: The social impact of DLT positively influences behavioral intention to use.

The transparency of information technology represents how information is not distorted or altered while being delivered to stakeholders to demonstrate publicity, communicativeness, and verifiability. Our study describes the transparency of technology as a type of communication among organizational members and related to the task visibility of related networks. Queiroz and Wamba (2019) asserted that the transparency of logistics represents the verifiability of the locations and statuses of logistic flows. Previous studies have shown that the transparency of DLT in relation to logistics exerts an influence on the transparency and accountability of logistics (Batara et al., 2017; Kshetri, 2018; Lu & Xu, 2017).

The main advantages of blockchain are linked to the enhanced security of business processes (Sciarelli et al., 2022) and these perceived benefits through security positively influence the perceived usefulness of blockchain. Therefore, it is reasonable to infer that increased transparency from security enhancement affects the intention to adopt blockchain technology (Karamchandani et al., 2020).

The transparency of DLT increases cooperation among network members in a supply chain and results in large changes in large social groups in an industry (Aste et al., 2017). Previous studies have found that the transparency of DLT has an effect on behavioral intention to use it (Queiroz & Wamba, 2019). Thus, the transparency of DLT can be considered as a factor that positively affects behavioral intention to use.

H7: The transparency of DLT positively influences behavioral intention to use.

Behavioral intention to use represents the tendency of a new technology to be used by consumers (Venkatash et al., 2003); it is considered to be a crucial factor in the determination of the usage behavior related to a technology, especially when applying UTAUT.

Behavioral intention to use has an influence on the use of technologies (Weerakkody et al., 2013). Our study thus suggests that behavioral intention can predict usage behavior, the latter being related to the probability to perform a particular behavior associated with the use of blockchain in the future. In the context of blockchain, there can exist an association between behavioral intention and usage behavior (Queiroz & Wamba, 2019). Thus, the behavioral intention to use DLT can be considered as a factor that positively affects usage behavior.

H8: The behavioral intention to use DLT positively influences usage behavior.

Methods

The operational definitions and the measurement items for research variables in this study are given in Tables 2 and 3, respectively. Each item is measured using a five-point Likert-type scale. These items are adapted from previous related research, which is respectively indicated in the last column of Table 3. The major sources include literature regarding UTAUT (Venkatesh et al., 2003) and blockchain (Karamchandani et al., 2020; Li et al., 2019; Mishra et al., 2023; Queiroz & Wamba, 2019; Sciarelli et al., 2022). Further, the sources for items include the studies suggesting system factors such as availability, diversity, and economic value, (Karamchandani et al., 2020; Lee & Kim, 2017; Mishra et al., 2023).

Table 2.

The Operational Definitions of Research Variables.

Variables	Operational definitions
Availability	The extent of possible use of system with stability and without system errors, and with the convenient use of data
Diversity	The extent of belief that the scope of service is wide enough to be provided in terms diverse functions, areas, and purposes
Economic value	The extent of belief that investment cost is not large compared to other IT, and operation costs can be reduced and economic profit can be increased
Performance expectancy	The extent of expectation that the opportunities of organizational development and new businesses increase, and competitiveness improves, the company grows, and performance improves
Effort expectancy	The extent of belief that learning how to use technology is easy, using technology is convenient, and its learning time is short
Social influence	The extent of belief that people around users are considering usage or use the technology, and the technology will be widely used in the future
Transparency	The extent of belief that progress can be checked and visibility of work progress and related information is improved, and cooperation is occurred
Behavioral intention to use	The intention to use service and accept the technology
Usage behavior	The extent of usage learning time for the system

Table 3.

Measurement Items for Research Variables.

Variables	Items	Sources
Availability	A blockchain technology based system is always a candidate to be used due to its stability	Mishra et al. (2023)
	A blockchain technology based system has no problems in performing functions despite the malfunction of one subsystem
	A blockchain technology based system is always possible to be functioning because of distributed structure
	A blockchain technology based system enables the convenient use of data because they are stored in a distributed system
	A blockchain technology based system enables the continuous processing of tasks using the distributed structure of P2P networks despite network failures
Diversity	A blockchain technology based system can be utilized for various functions	Lee and Kim (2017)
	A blockchain technology based system can be utilized for various areas
	A blockchain technology based system can be utilized for various purposes
	A blockchain technology based system can be possible for diverse service due to technology development
	A blockchain technology based system can be considered to be wide in the areas where it can be utilized
Economic value	A blockchain technology based system does not require greater investment cost than other technologies	Karamchandani et al. (2020), Li et al. (2019)
	A blockchain technology based system can reduce operational cost than other technologies
	A blockchain technology based system is economically beneficial than other technologies
	A blockchain technology based system is increasing profits more than the case for other technologies
	A blockchain technology based system can reduce maintenance cost than other technologies
Performance expectation	A blockchain technology based system can increase the opportunities of development	Queiroz and Wamba (2019), Venkatesh et al. (2003)
	A blockchain technology based system can provide new business opportunities
	A blockchain technology based system can improve competitiveness
	A blockchain technology based system can make companies grow
	A blockchain technology based system can increase company’s performance
Efforts expectation	A blockchain technology based system is easy to use	Sciarelli et al. (2022), Venkatesh et al. (2003)
	A blockchain technology based system is easy to understand for beginners
	A blockchain technology based system is expected to provide less difficulty in learning technology
	A blockchain technology based system is expected to require short time in learning to use
Social impact	A blockchain technology based system is expected to be preferred	Queiroz and Wamba (2019), Venkatesh et al. (2003)
	A blockchain technology based system is considered to be used
	A blockchain technology based system is expected to be used by most people
	A blockchain technology based system is expected to be commonly used in the future
	A blockchain technology and services should be developed to have some attention from people
Blockchain transparency	A blockchain technology based system is expected to enable checking progress of processes	Batara et al. (2017), Kshetri (2018), Lu and Xu (2017), Queiroz and Wamba (2019)
	A blockchain technology based system is expected to improve visibility of processes.
	A blockchain technology based system can greatly increase the transparency of related information
	A blockchain technology based system is expected to increase collaboration
	A blockchain technology based system is expected to increase accountability of processes in all levels
Behavioral intention to use	I need the tasks for blockchain technology based system	Queiroz and Wamba (2019), Venkatesh et al. (2003)
	I agree to adopt blockchain technology based system
	I have the intention to use blockchain technology based services
	I think that more blockchain technology based systems should be released
	I like to perform related tasks using blockchain technology based system
	I am eager to utilize blockchain technology for my business
	I am proud that I understand blockchain technology more than any others
	I am eager to participate in related seminars for blockchain technology
Usage behavior	I have experiences of searching information in order to use blockchain technology based service	Queiroz and Wamba (2019), Venkatesh et al. (2003)
	I spend considerable time using blockchain technology based service
	I spend considerable time studying related literature in order to use blockchain technology based service
	I greatly use blockchain technology platform
	I have performed tasks for developing blockchain technology based service
	I request a large amount of data for using blockchain technology based service

Our rationale for the selection of a sample is the usage extent of DLT in the corresponding industry. Thus our study focuses on public sectors and private industries that heavily utilized DLT. First, the target respondents for our sample are individuals who work at public organizations, such as central or local governments in administrative departments, at public research institutes related to DLT development, and for Korean DLT associations and educational institution related to DLT. Second, the target respondents include private developers of DLT-based systems in the financial, medical/health, logistics/retail, and IT sectors (including LG CNS and Samsung SDS). The respondents in these organizations are developing DLT-based services, platforms, and/or are establishing policies or working at applying a DLT-based service or platform to their tasks. Participation by survey is persuaded through phone calls with staff members who are knowledgeable about DLT. Respondents participated in online and offline surveys from February to April of 2020. The online survey is based on social network sites, Messenger, Google-based survey, and email. The final sample includes 362 responses.

The industries of the respondents are largely public (23.2%) and IT (33.4%), as our study focuses on targeting central or local government administrative departments, and two major IT service companies that have more invested in the potential of DLT than other smaller private companies. The majority of the respondents (62.4%) have participated in DLT for less than a year, a finding related to the initial stage of the diffusion of DLT.

Based on the collected sample, this paper uses a structural equation modeling approach as a central method for validating research variables and testing the research model.

Our study limited the risk of harm to the research participants as it was survey study in which such potential harmful situations were not possible and henceforth the potential benefits of the research to society and to the study participants outweighs the risk of harm to the study participants and we obtained informed consent from the study participants (Table 4).

Table 4.

Demographic Characteristics of the Respondents (N = 362).

Class	Frequency	Percentage
Gender
Male	297	82.0
Female	65	18.0
Age
Less than 20	1	.3
20s	75	20.7
30s	81	22.4
40s	107	29.6
More than 50	98	27.1
Education (final degree)
High school graduates	12	3.3
Community college graduates	7	1.9
Bachelor degree (or students)	214	59.1
Higher than master degree	129	35.6
Industry
Public	84	23.2
Financial	27	7.5
Medical or health	18	5.0
IT	121	33.4
Logistics or retail	8	2.2
Service	29	8.0
Manufacturing	17	4.7
Others	58	16.0
Position
Employee	71	19.6
Assistant manager	25	6.9
Manager	56	15.5
Assistant executive manager	39	10.8
Executive manager	76	21.0
Senior executive	74	20.4
CEO	21	5.8
DLT usage period
Less than 1 year	226	62.4
1–2 years	85	23.5
2–3 years	21	5.8
3–4 years	9	2.5
4–5 years	6	1.7
More than 5 years	15	4.1

Results and Discussion

Before testing research hypotheses using the structural equation modeling approach, our study employs an exploratory and confirmatory factor analysis to assess the measurement properties of variables. Our study tests the validity and reliability of the measures above by examining whether the items measuring each construct converge to measure the same construct or are differentiated from the items measuring other constructs (Tables 5 and 6).

Table 5.

Test of Reliability and Validity of the Measures.

Class	Variables	Items	Factor loading	Cronbach alpha
DLT technology variables	Availability	AVA1	.731	.632
		AVA3	.711
		AVA4	.731
	Diversity	DIV1	.897	.918
		DIV2	.905
		DIV3	.854
		DIV4	.833
	Economic value	ECON1	.670	.749
		ECON2	.774
		ECON3	.716
		ECON5	.704
UTAUT variables	Performance expectation	PEXP1	.834	.823
		PEXP2	.741
		PEXP5	.829
	Efforts expectation	EEXP1	.770	.894
		EEXP2	.810
		EEXP3	.885
		EEXP4	.866
	Social impact	SIMP2	.634	.866
		SIMP4	.869
		SIMP5	.792
		SIMP6	.717
	Transparency	TRAS1	.749	.885
		TRAS2	.813
		TRAS3	.785
		TRAS5	.820
Behavioral intention to use		BINT1	.529	.883
		BINT2	.588
		BINT4	.563
		BINT5	.537
		BINT6	.728
Usage behavior		UB1	.863	.940
		UB2	.769
		UB3	.888
		UB4	.871
		UB5	.842
		UB6	.725
		UB7	.847

Table 6.

Test of Reliability and Convergent Validity.

Variables	Items	Factor loading	Composite construct reliability	Average variance extracted
Availability	AVA1	.724	.726	.870
	AVA3	.782
Diversity	DIV1	.895	.866	.963
	DIV2	.902
	DIV3	.842
	DIV4	.799
Economic value	ECON2	.725	.777	.873
	ECON5	.858
Performance expectation	PEXP1	.741	.825	.903
	PEXP5	.891
Efforts expectation	EEXP1	.651	.820	.930
	EEXP3	.914
	EEXP4	.92
Transparency	TRAS1	.885	.881	.937
	TRAS2	.89
Social impact	SIMP2	.904	.736	.841
	SIMP6	.56
Behavioral intention to use	BINT4	.8	.704	.826
	BINT5	.811
Usage behavior	UB1	.843	.738	.919
	UB2	.841
	UB4	.906
	UB7	.933

An exploratory factor analysis was conducted in order to exclude items with low factor loading values (.5) or those that do not converge with the intended constructs.

The composite construct reliability and average variance extracted for all variables are greater than .7 and .5, respectively. The factor loading values for all items are greater than .5, demonstrating that reliability and convergent validity are established for all variables.

In Table 7, the diagonal values represent the average variance extracted, and they are greater than the squared values of the correlations among the variables, which establishes the discriminant validity of variables. Further, the range within the correlation plus or minus two times the standard deviation of the correlation does not include one, which verifies that the correlation is significantly different from one and indicates that discriminant validity of the variables is established.

Table 7.

Discriminant Validity of the Variables. Values in Diagonals Represent Average Variance Extracted.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Availability (1)	.726
Diversity (2)	.012	.866
Economic value (3)	.027	.000	.777
Performance expectation (4)	.078	.033	.234	.825
Efforts expectation (5)	.163	.104	.022	.002	.820
Transparency (6)	.000	.080	.106	.118	.071	.736
Social impact (7)	.013	.096	.000	.075	.004	.526	.881
Behavioral intention to use (8)	.000	.019	.009	.100	.015	.501	.484	.704
Usage behavior (9)	.008	.036	.042	.001	.016	.081	.202	.511	.738

Goodness of Fit Index (GFI) = .832, Root Mean-squared Residual (RMR) = .037, Root Mean Squared Error of Approximation (RMSEA) = .077, Normed Fit Index (NFI) = .852, Comparative Fit Index (CFI) = .8815.2. Test of Hypotheses.

Structural equation modeling is used to test research hypotheses using an estimated structural model. Figure 2 and Table 8 show the structural model and hypotheses testing results of this study. Availability significantly affects performance and effort expectancy. This is consistent with the interview feedback from the survey participants and indicates that the more available the technology is, the greater the perceived performance and easiness of the technology is. The significant negative effect of diversity on performance expectancy indicates that diverse applications are not by themselves related to performance expectation; rather, a few focused applications can be associated with an expected performance improvement. This is reflecting the current state of DLT diffusion which is largely focusing a few applications where maximizing their performance can be obtained.

Figure 2.

Structural model.

Table 8.

Test of the Structural Model.

Hypothesis	Path	Estimate	SE	p-Value
H1-1	Availability → performance expectation	.119*	.101	.035
H1-2	Availability → efforts expectation	.322***	.104	.000
H2-1	Diversity → performance expectation	–.161**	.058	.005
H2-2	Diversity → efforts expectation	.293***	.051	.000
H3-1	Economic value → performance expectation	.415***	.069	.000
H3-2	Economic value → efforts expectation	.123*	.05	.029
H4	Performance expectation → behavioral intention to use	.141*	.084	.013
H5	Efforts expectation → behavioral intention to use	–.189***	.085	.000
H6	Social impact → behavioral intention to use	–.041	.084	.635
H7	DLT transparency → behavioral intention to use	.722***	.106	.000
H8	Behavioral intention to use → usage behavior	.753***	.075	.000

p < .05. **p < .01. ***p < .001

Because DLT is associated with diverse applications and services, the significant effects of availability and diversity on effort expectancy indicate that DLT can be utilized in a wide range of other applications and services in the public and private sectors. With a greater extent of diversity in the applications of DLT, more users perceive the technology as easy to learn and understand. This shows when the diversity is expected from the usage of DLT, users are more likely to intend to overcome hurdles or difficulty from using the technology for greater business outcomes.

The significant effect of economic value on performance and effort expectancy shows that one of the most important conditions for perceived performance impact and ease of its application is the degree of economic benefits that DLT can bring to its adopters. The economic benefits of DLT are perceived to be directly related to the return on investment in IT and organizational competitiveness. The economic effects of DLT on effort expectancy show that if economic benefits are provided, the technology can be learned and can become familiar despite its complexity. Further, if profitability is established as a facilitating condition for DLT, users more willingly intend to make efforts for using the technology for greater business performance.

The effect of performance expectancy on behavioral intention is significant, a finding in line with Sciarelli et al. (2022), who showed the relation between perceived usefulness and attitude toward blockchain, and Queiroz and Wamba (2019), who found that in India and the US, the expectation of a performance improvement after adopting DLT is crucial to create a behavioral intention to use DLT. As long as DLT is expected to lead to better organizational performance, it will likely be adopted to enhance organizational innovation and growth.

The significant negative effect of effort expectancy on behavioral intention indicates that DLT is perceived as a complicated technology, which alone is not a hurdle to its adoption. On the contrary, participants perceived DLT as a necessary system despite the complexity of facilitating the adoption of DLT. The interview feedback from the survey participants indicates that manpower with expertise in DLT is still weak, which contributes to the negative effect of effort expectancy on behavioral intention to use.

The effect of a social impact on behavioral intention is insignificant. This is consistent with Queiroz and Wamba (2019), who found that in US, the social influence related to the adoption of DLT does not importantly affect behavioral intention to use DLT. The interview feedback confirms that businesses in the public and private sectors adopt blockchain technologies to enhance their competiveness and performance, but not from social pressure or any influence to adopt the technology.

The significant effect of DLT transparency on behavioral intention indicates that clear monitoring across production or retail business processes as enabled by DLT to ensure visibility of the processes is crucial to create a behavioral intention to use DLT. This supports the importance of security issues for perceived usefulness of blockchain (Sciarelli et al., 2022) as DLT transparency is greatly dependent upon security assurance.

Finally, the effect of behavioral intention on usage behavior is significant. This is in line with Queiroz and Wamba (2019), who showed the relation between behavioral intention and behavioral expectation, indicating that having an intention to use DLT is a precondition to explain its usage behavior.

Conclusions and Implications

This study attempts to examine the factors affecting the usage intention of DLT based on the perspective of the unified theory of the acceptance and use of technology. Those in the study sample who provided the responses are adopters of DLT in the public and private sectors. Economic value is positively related to performance expectancy, while availability, diversity, and economic value affect effort expectancy. Performance expectancy and transparency have a positive influence on the intention to use DLT, which exerts a positive effect on usage behavior.

Given the lack of studies on investigating the determinants of the intention to use DLT based on a unified theory of acceptance, and based on a variety of samples encompassing the public, IT, financial, service, medical, and logistics sectors, this study provides implications for researchers in that it attempts to investigate the factors directly (like performance expectancy and transparency) or indirectly (like availability and economic value) affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, IT, financial, service medical, and logistics sectors.

Implications for Researchers

This study provides implications for researchers in that it attempts to investigate the factors affecting the usage intention of DLT based on the perspective of the extended unified theory of the acceptance and use of technology as it pertains to DLT features. The participants here were adopters of DLT in various industries in the public and private sectors.

Due to the availability and diverse services of DLT, the technology is easy to learn and the leaning time is not very long. Because operational and maintenance costs are relatively lower than those in other IT areas, the expectations of development opportunities and performance improvements are increased. DLT enables visibility of processes, transparent information, collaboration among participants, and network accountability, all of which increase behavioral intention to use. Ease of use and the short learning time facilitate user participation in DLT, resulting in increased levels of user intention to use.

This study has both limitations and future research opportunities. First, the sample of the study can be expanded as the number of adopters of DLT increases such that diverse industries can be encompassed, such as those related to public, financial, retail, health, and logistics areas, which will enable comparison studies among different industries. Second, our study examines the perspectives of employees of organizations that are adopting DLT; the perspectives and responses of general customers who adopt DLT can also be investigated in future studies. Third, in the future study, other interacting relations among the variables in this paper can be considered in testing. For instance, effort expectancy may shape performance expectancy, which impacts behavioral intention to use. Further, other factors to be considered for the implementation of DLT such as technical complexity, regulatory constraints, organizational culture, respondents’ personal demographic profiles, respondents’ prior exposure to blockchain, facilitating conditions, and user adoption challenges such as privacy and security concerns are worth being examined. These factors will provide more enriched understanding of practical challenges in DLT adoption. Fourth, there exist limitations of self-reported data as this study was based on respondents’ subjective perceptions and self-reported intentions rather than actual blockchain usage. In the future, more objective measures of system usage will be able to overcome these drawbacks of social desirability bias, where respondents may overstate their willingness to adopt blockchain technology. Sixth, the future studies can incorporate other alternative frameworks, such as Technology Acceptance Model (TAM), Technology-Organization-Environment (TOE) framework or Diffusion of Innovation (DOI) theory and compare these models, which could provide a more holistic perspective.

Implications for Practitioners

The study results also have implications for practice. Blockchain managers can have insights regarding the factors affecting behavioral intention to use. Factors that are perceived by business practitioners as affecting performance expectancy levels for companies in the public and private sectors are availability, which ensures continuous operation, and economic value, which represents cost reductions and competitiveness compared to those factors in other IT areas. Further, availability, diversity, and economic value have an influence on effort expectancy, and this shows to managers that employees willingly intend to use DLT given that it brings availability, diversity, and economic value.

Blockchain managers can also have insights on the extent of behavioral intention or actual usage. The expectation of a greater organizational performance improvement through the adoption of DLT compared to other types of IT and the capability to monitor data processing and business processes at any time to enhance transparent progress all affect the behavioral intention to use DLT.

A feedback interview with business employees found that the current education system for experts in DLT is not sufficiently meeting industry demand and that there are too few experts with knowledge and expertise. Moreover, employees consider DLT as difficult and complicated, but they continue to attempt to adopt it given its performance expectations and the transparent control of processes it offers. DLT education to produce more experts is necessary to break down the entry barrier to develop DLT.

The lack of a positive social impact indicates that a social consensus has not yet formed to enhance the diffusion of DLT. Moreover, this gap between state-of-the-art technology and social recognition can even negatively influence the adoption of the technology. Social recognition toward DLT must be improved, as some in Korean society still consider it as a means for speculative actions on business or investments. Thus, to continue the start-up businesses initiated by major IT service companies, associations, and local governments, policies and infrastructure regarding such issues as standardization of its core technologies should be established. Our study results provide directions and insights for practitioners of DLT to facilitate its efficient application and diffusion in the organizational infrastructure of public, financial, retail, logistics, and health businesses.

Footnotes

Acknowledgements

This study has consented Sage’s Guidelines for studies involving humans and those ethics guidelines.

ORCID iD

Sangjae Lee

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

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