Predictors for distributed ledger technology adoption: integrating three traditional adoption theories for manufacturing and service operations

ABSTRACT

It is expected that blockchain technology will bring a disruptive paradigm shift in the manner in which transactions are conducted in the manufacturing and service enterprises. By eliminating the drawbacks of trust-related issues in a business chain, the distributed database of blockchain can bring transparency with pseudonymity and irreversibility of records. In this paper, we advance the limited literature on DLT and its adoption in the manufacturing and service enterprises. The proposed model is based on the integration of three traditional adoption theories namely Technology Acceptance Model (TAM), Technology Readiness Index (TRI) and Theory of Planned Behavior (TPB). Based on a survey of 211 experts of Pakistan, the proposed model was tested using structural equation modelling. The study result confirms that Theory of Planned Behavior and TAM play a key role in the disruptive technology implementation. It is one of the early studies on blockchain technology adoption in the manufacturing and service enterprises and the study results indicate that more manufacturing and service industries are transforming to intelligent operations. Smart manufacturing system through blockchain applications has become the focus of attention of businesses.

KEYWORDS:

• Blockchain
• adoption theories
• distributed ledger technology
• DLT
• disruptive technology
• supply chain

1. Introduction

The connection between technological innovation and business advance development has included academic talk for quite a long time (Schumpeter, 1939). By the expanding size of the businesses, differentiating item portfolio, and various geographic areas to be aided, the supply chain has gotten increasingly perplexing. Producers are thinking that it’s difficult to offer profoundly altered items to contend in market segments, although the wholesales are controlling position of huge requests as a result of the usage of lean selling rehearses. Subsequently foreseeing demands, arranging generation and synchronizing order have become a noteworthy task in the unbalanced environment (Pereira, 2009). To overcome such issues, digitalization of inventory network has been a basic improvement over the most recent few years moving towards an information-based budget where a move from customary assembling to a time of worldwide, shrewd and economical manufacturing is observed (Michel, 2017). The manufacturing businesses are searching for the new technologies to improve their products performance with viable inventory network joint effort and coordination (Farooq & O’Brien, 2012). For the manufacturing and service enterprises, blockchain is a disruptive innovation that provides better traceability and transparency for the supply chains (Francisco & Swanson, 2018). It stores the data in a chronological order which is shared to all the member entities (Yuan & Wang, 2016). The distributed ledger technology irreversibility of records design brings better traceability and can possibly resolve trust-related issues in the organizations (Samaniego & Deters, 2016).

However in its nascent stage, blockchain technology has few challenges to overcome as the organizations are inadequate with regards to a sorted-out environment and stages for scaling up the applications of distributed ledger technology. These issues are mainly related to the security and data governance. In the developing states most of the organizations are doubtful about disruptive technology implementation because of its high cost. Moreover, the viability and advantages in the non-financial segments are also doubtful (Koteska et al., 2017). To gain a competitive advantage the manager of each organization needs to study its potential benefits and risks. There exists a chance to make various sharing applications, for example, digital rights and cultural heritage management, peer to peer payments mechanisms (R. R. Xu et al., 2017). Due to the current risks in using blockchain technology, it is found that the logistic firms are less likely to adopt DLT as compared to the other firms (Sadouskaya, 2017). The study of Mthethwa (Mthethwa, 2016) reports that still people don’t know about disruptive technology so its adoption is slow in the developing countries. The empirical study on the adoption of DLT is limited, most of the existing literature is mainly based on conceptual expositions (Ying et al., 2018). Thusly, it is necessary to empirically study the distributed ledger technology adoption for the manufacturing and service enterprises.

To analyze the behavior of consumers across several information technology services and products, adoption models such as Technology Acceptance Model (Davis, 1989); Theory of Planned Behavior (Icek Ajzen, 1985, 1987; Icek Ajzen, 1991); Diffusion of Innovation Theory (Rogers, 1995); Technology Readiness Index by (Parasuraman, 2000)) are being used in the previous studies. The major studies includes Enterprise Resource Planning (Calisir et al., 2009); RFID (M. S. Lee, 2009), virtual reality (Shih et al., 2012; Sternad & Bobek, 2013); IoT (Gao & Bai, 2014); bitcoin (Folkinshteyn & Lennon, 2016) and others. A survey of the previous literature uncovers that there has been little attention on examining the DLT applications more specifically in the developing countries. The current study expects to address the research gap by understanding the distributed ledger technology adoption in the manufacturing and service industries-Pakistan context. The finding of this study will assist the specialists with identifying the several factors that are affecting the distributed ledger technology adoption and ultimately helps the organization to design strategies for its implementation.

Many scholars have studied the adoption of distributed ledger technology in the supply chain. Some of the technology adoption models that were employed include TAM (Francisco & Swanson, 2018), Unified Theory of Acceptance Model (Queiroz & Wamba, 2019), diffusion of innovation theory (Sun et al., 2018), Technology Organization Environment (Clohessy et al., 2019). In order to supplement these past studies, based on a survey of 211 supply chain experts working in Pakistan, the present study integrates three traditional adoption theories namely TAM (Davis, 1989), Technology readiness Index (Parasuraman, 2000) and Theory of planned behavior (Icek Ajzen, 1985). Consequently, the main purpose of this study is to address the following research questions.

RQ01. What are the factors that drive the intention of Pakistani firms to adopt distributed ledger technology ?

RQ02. Among the factors, which has a greater association with the adoption intention?

Our study is structured as follows: section two enlightens prior literature on blockchain and technology adoption models, section three presents hypotheses development and research model, section four shows the methodology for DLT adoption, section five clarifies the results and section six provides a summary.

2. Related work

2.1. Blockchain technology

In the developing countries, the traditional bookkeeping system of double entry is widely used in the manufacturing and service industries. Blockchain technology evacuates all the trust-related problems related with the traditional system and improves the traceability in the transaction process (Davidson et al., 2016). Distributed ledger technology can work as a single database and incorporate all the necessary functions of inventory network (Korpela et al., 2017). Blockchain technology helps the businesses to supervise assets viably and decrease stock conveying costs due to its capacity to make records of all the operations. This aids the supply chains in risk mitigations at lesser cost contrasted with the conventional stock chain where more supplies of stock, abundance limit, and outsider reinforcement sources are created fully expecting interruptions (Ivanov et al., 2019). Blockchain technology applications will aid to upgrade the scale and extent of the tracking system (Hofmann et al., 2017). For instance, ADEPT created by IBM and Samsung could be utilized to give a protected, minimal cost approach to start the smart contract-based order to secure the item and later pay likewise (Cohn et al., 2017). Hyperledger composer is a free tool set designed to build applications for blockchain. The Hyperledger composer supports the Hyperledger Fabric architecture and enables faster business network modeling, deployment of applications and integration with existing systems (Cachin, 2016). Aside from customary assembling, recent innovative disruptions, for example, Industry 4.0 and utilization of robotics related to manage rule-based intelligence would be much increasingly focus on big data analytics (Jeschke et al., 2017). Quality documentation can likewise be institutionalized utilizing distributed ledger and could be dispersed to all participating node to help well decision making (Apte & Petrovsky, 2016). There are reports that certain organizations have begun with the coordination of blockchain technology idea into their assembling rehearses (L. D. L. D. Xu et al., 2018). Wipro’s committed distributed ledger technology solutions are pointed towards assembling organizations and could be custom-made to the customer needs. Utilizing a special ID, Wipro plans to approve manufacturing processes by dispensing with the plausibility of fake things entering the inventory network. The things would be filtered at each purpose of the assembling procedure. Such a transparency will give advantages to quality administration and verification (Wipro, 2017). To understand the previous literature on distributed ledger technology, for supply chain finance fraud issues a recent study has been conducted by Du et al.,= (2020), blockchain base trust management system for supply chain has been presented by Malik et al., (2019), traditional adoption theories for blockchain (Kamble et al., 2019), smart contracts (S. S. Wang et al., 2019), blockchain-based business process management (Viriyasitavat et al., 2018), supply chain management objectives (Kshetri, 2018), supply chain transparency (Francisco & Swanson, 2018), blockchain applications (Dobrovnik et al., 2018), Supply chain Provenance (Kim & Laskowski, 2018), Fintech operations (Lou & Li, 2017), supply chain finance (Omran et al., 2017), supply chain traceability (Tian, 2017), information sharing for supply chain management (Nakasumi, 2017), digital supply chain (Korpela et al., 2017), Product ownership management (Toyoda et al., 2017), fraud detection for online business (Cai & Zhu, 2016), manufacturing (Abeyratne & Monfared, 2016b), protection of personal data using blockchain (Zyskind & Nathan, 2015). The recent literature review is presented in Table 01.

Table 01. Prior literature

Author	Major Findings
(Yoon et al., 2020)	This study introduces an analytical model that reflect the adoption of distributed ledger technology in international transactions, to evaluate whether the blockchain advances an exporting companies performance under demand volatility risk.
(Queiroz et al., 2019)	This paper shed light on blockchain technology applications in Supply chain management and what are challenges on implementation of distributed ledgers in Supply chain domain.
(Francisco & Swanson, 2018)	By using the Unified Theory of Acceptance and use of Technology (UTAUT), the finding indicates the concept of technological innovation as a basic structure for the traceability of the Supply chain. The proposed model is being developed, and the work culminates with blockchain Supply chain implications inspired by analysis of theory and literature.
(Fosso Wamba et al., 2018)	The study aims at bridging the knowledge gap in the current Bitcoin, distributed ledger technology and Fintech literature. The results indicate that these innovations are emerging, and for competitive advantage they are being adopted by organizations.
(Choi et al., 2020)	The paper proposed a stylized duopoly model to evaluate product information disclosure game Nash between two rental service platforms whose rent to rent goods are substantiable. Moreover, they extract the equilibrium degree of disclosure of product information and define conditions under which the platforms chose to disclose or not disclose information that corresponds to different sorts of supply chain.
(Chang et al., 2020)	The study provides a comprehensive and detailed analysis of the state of the art threats, vulnerabilities and opportunities for both the public and private agencies in the global supply chain and trade operations by synthesizing a wide range of information from industry leaders and academic papers.
(Bai & Sarkis, 2020)	The paper introduced distributed ledger technology performance actions incorporating several sustainable supply chain transparency and technical attributes. The study proposed a new hybrid group decision method for the evaluation and selection of disruptive technology by Integrating hesitant fuzzy set and regret theory.
(Babich & Hilary, 2020)	This study identified the five main strength for understanding the blockchain technology namely visibility, aggregation, validation, automation and resiliency. Moreover, several question of industrial organization with respect to OM are discussed.
(Du et al., 2020)	The study proposed a blockchain-based model for solving the fraud problems in the supply chain finance.
(Malik et al., 2019)	The paper proposed a Trust chain as a three-layer trust management system that used a blockchain consortium to monitor interactions between supply chain participants and dynamically allocate trust and reputation scores based on these interactions.
(Kamble et al., 2019)	The results suggest that integration of theory of planned behavior and TAM plays a pivotal role in the blockchain technology adoption. However, technology readiness index has no significant impact during disruptive technology implementation in the Supply chain.
(S. S. Wang et al., 2019)	The study presents a systematic and detailed overview of smart contracts enabled by blockchain, with the goal of stimulating further research into this emerging field of study.
(Viriyasitavat et al., 2018)	The study proposed a Business process management framework to demonstrate how to incorporate distributed ledger technology to enable efficient and cost-effective assessment and transition of quality service in the composition and management of workflows.
(Kshetri, 2018)	By using an innovative diffusion theory, the findings suggest that ventures would increase as the pioneer in blockchain appropriation, for example, oil trading will various provider layers. Using the rancher’s case, the study predicted that a blockchain endorsement by one entity would apply standardizing pressure on various supply network elements.
(Francisco & Swanson, 2018)	The conceptual model was based on UTAUT for supply chain traceability. A theoretical approach is created and the research finishes with supply chain ramifications of blockchain that are roused by prior literature and theory.
(Dobrovnik et al., 2018)	Using academic and practitioner literature, the study defines potential applications for implementation and provide a structure for identifying opportunities for blockchain in the logistic industry.
(Kim & Laskowski, 2018)	The paper discusses the effect of DLT along with its application in ontological engineering, on supply chain provenance. The research focuses on possible effect and idea evidence for the demonstration of technique based on formal and informal ontology.
(Lou & Li, 2017)	The study proposed a model based on the TAM with innovative diffusion theory to understand the applications of distributed ledger technology in the Fintech operations.
(Omran et al., 2017)	This study suggested a proposed model for blockchain driven supply chain finance. Transparency, automatic reconciliation and quality have been described as the supply chain prime value drivers. The distributed ledger technology is designed to achieve the paradigms set out.
(Tian, 2017)	The study proposes a blockchain-based traceability system for food tracing based on hazard analysis and critical control points. In addition, the author also introduced a new model BigchainDB to meet the gap in the decentralized system at scale.
(Nakasumi, 2017)	The author proposed a blockchain-based system to tackle the supply chain issues like information asymmetry & double marginalization.
(Korpela et al., 2017)	The study proposed a cloud-based system to address the difference between business readiness and present functionalities. In addition, the findings suggested that smart contracts are the most important functionalities for transforming the digital supply chain through incorporation into blockchain.
(Toyoda et al., 2017)	The study introduced a distributed ledger technology-based product ownership management system of RFID for anti-counterfeits goods.
(Cai & Zhu, 2016)	The paper indicates that disruptive technology-based reputation systems are more robust against bad-mouthing than ballot-stuffing fraud.
(Abeyratne & Monfared, 2016b)	The study claims that distributed ledger technology and Internet of things will profoundly affect next-generation manufacturing.
(Zyskind & Nathan, 2015)	The study proposed a blockchain based decentralized model for the personal data management ensuring users own and control their data. They implement a protocol that transforms distributed ledgers into an automated access-control manager that needs no third-party trust.

2.2. Technology adoption models

Technological advancements consistently assume an essential job in the current business environment. Technological development encourages scattering of information too. But, until and unless, it is accepted or used, technology is of little use (Oye et al., 2012). While acceptance is implemented at the discrete level, technology adoption will prompt dispersion (Sharma & Mishra, 2014). Accordingly, understanding innovation adoption is of most extreme significance. Carr, (1999) has characterized innovation adoption as the phase of choosing an innovation for individual and association use. Technology selection can additionally be characterized as eagerness inside a gathering of users to utilize innovation for their advantage (Samaradiwakara & Gunawardena, 2014). Some research have uncovered that innovation adoption is not identified with the parts of innovation alone still has developed as a considerably more muddled procedure including measurements of customer character (Venkatesh et al., 2012), trust (Gefen et al., 2003a) and many facilitating situations (Thompson et al., 1991). Moreover, to determine the dynamics that affect organizations decision to implement IT, several conceptual models through adoption theories have been created (Imran & Gregor, 2007). These proposed models have increased a lot of prevalence in the literature (Venkatesh & Davis, 2000) because of its accomplishment in deciding the implementation of IT are: Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Though, these theories have been created and tested for the most part with regards to western nations and scarcely any investigations have been done with regards to developing nations (Imran & Gregor, 2007), what’s more specifically it has not been studied in the context of Pakistan.

To review the recent studies on the technology adoption models by using traditional adoption theories, for municipal solid waste source-separated collection behavior a recent study has been conducted by Ma et al., (2018), e-government adoption (Xie et al., 2017a), organic farming adoption (Issa & Hamm, 2017), social media site use (Howell, 2016), mobile banking adoption (Alalwan et al., 2016), bitcoin (Folkinshteyn & Lennon, 2016), and e-commerce adoption (Awa et al., 2015). The recent study on the technology adoption models is presented in Table 02.

Table 02. Technology adoption models

Author	Findings
(Ma et al., 2018)	The study disclosed that by integrating the theory of Planned behavior (TPB) with TAM, the factors influencing municipal solid waste sources were analyzed. The proposed model findings explained how householder’s subjective norms, attitude and perceived behavior control affect their overall behavior.
(Xie et al., 2017a)	In this paper, the integrated construct of TAM, TPB is used with Trust & Perceived risk. The findings proposed that trust have positive impact on subjective norms for e-government adoption while perceived risk shows negative impact.
(Issa & Hamm, 2017)	The study revealed that theory of planned behavior framework plays pivotal role for the adoption of organic farming.
(Howell, 2016)	The study result indicates that trust with TAM have a significant impact on intention to use social media sites.
(Alalwan et al., 2016)	The results suggested that TAM have significant impact on the behavioral intention for the consumer adoption of mobile banking.
(Folkinshteyn & Lennon, 2016)	The study suggested the TAM model to establish similarities and discrepancies in how different stakeholders embrace the bitcoin adoption.
(Awa et al., 2015)	The study suggested that the constructs implemented in the integrated system (company purpose, trust and service quality) introduced socio-technical systems and strengthened the theoretical base of e-commerce adoption by SMEs.

3. Hypotheses development

3.1. Technology Acceptance Model (TAM)

In this paper, consequently the theoretical grounding originates from the traditional adoption theories. As indicated TAM, regardless of whether end-user is inclined to acknowledge and use information system, is dependent on two particular beliefs: Perceived ease of use (PEOU), Perceived usefulness (PU) and Attitude (ATT) (Davis, 1989); PEOU is the degree to which an individual affirms that using a specific system would be free of physical and mental efforts while PU is the degree to which an person recognizes the use of a system to improve one’s output (Davis, 1989). Both convictions impact one’s attitude towards information system use, which impacts individual social expectation to utilize information system which thus decides real system utilization as depicted in Figure 01. The fundamental connections have been endorsed empirically in numerous investigations of individual acceptance in various situations: for instance, for mobile computing a study has been conducted by Wu et al., (2007), to examine the acceptance of online shopping (Gefen et al., 2003b) and e-government services (Carter & Bélanger, 2005). Moreover, attitude is the degree to which an person has a great or unfavorable assessment of the conduct being referred to H.-d. Yang & Yoo, (2004). Attitude with regard to user acceptance of IT is characterized as a person’s general productive response (loving, delight, happiness, and joy) to utilize technology (Davis, 1989). The results from the previous studies proposed that perceived ease of use has significant impact on perceived usefulness (Davis et al., 1989; Kleijnen et al., 2004; Wamba et al., 2020). Moreover, perceived ease of use positively affect attitude (Chen & Chen, 2008; Queiroz & Wamba, 2019; Y. S. Y. S. Wang et al., 2003). Perceived usefulness positively impact attitude (Karamchandani et al., 2019; Venkatesh et al., 2003). Attitude have positive impact on behavioral intention (I Ajzen & Fishbein, 2005; Davis, 1989; Folkinshteyn & Lennon, 2016; Guriting & n.d.ubisi, 2006; Taylor & Todd, 1995). Perceived usefulness have positive and significant impact on behavioral intention (Agarwal & Prasad, 1998; Gefen et al., 2003a; Kamble et al., 2019). So, we formulate the following hypotheses.

Figure 01. Conceptual model

H₁-Perceived ease of use positively affect perceived usefulness of blockchain technology

H_2- Perceived ease of use positively affect attitude towards using the blockchain technology

H_3- Perceived usefulness positively affect attitude towards using the blockchain technology

H_4- Attitude positively affect behavioral Intention to use the blockchain technology

H_5- Perceived usefulness positively affect behavioral Intention to use the blockchain technology

3.2. Technology Readiness Index (TRI)

Parasuraman (Parasuraman, 2000; Parasuraman & Colby, 2015) defines TRI as the construct that can be seen as a common perspective coming about because of a gestalt of psychological empowering agents and inhibitors that by and large decide a person preference for using new technology. TRI refers to a human’s ability to use new technology for achieving goals. TRI measures discrete general innovation convictions and comprises four sub-measurements: optimism (OPTIM), innovativeness (INNOV), discomfort (DISCOM) and insecurity (INSEC). Optimism is termed as a positive view of technology and a belief that it provides increased flexibility, adaptability and ability in individuals’ lives (Parasuraman & Colby, 2007). Innovativeness is characterized as a desire to be a leader and pioneer of a technology. Positive thinking can be seen as a guide to a positive innovation outlook and fills in as a belief that it can build capabilities, improve flexibility and adaptability. Discomfort is stated as a perceived lack of control over creativity and a feeling of being overwhelmed by it (Parasuraman & Colby, 2007). Discomfort portrays the sentiment of absence of control and a feeling of overpowering while at the same time utilizing the innovation. Consequently, insecurity identifies with stresses or doubt for the technology and doubt towards its ability (Parasuraman & Colby, 2007). However, optimism and innovativeness are studied as the sparks of the technology while discomfort and insecurity fill in as inhibitors. Technology Readiness Index has been utilized in mix with Technology acceptance model to anticipate the technology selection before (Pattansheti et al., 2016). The previous finding indicate that Optimism and Innovativeness have positive and significant impact on perceived usefulness (Walczuch et al., 2007; Yi et al., 2003). Moreover, Optimism and Innovativeness have positive impact on perceived ease of use (Kuo et al., 2013; Sun et al., 2018; Yoon et al., 2020). However, the previous studies indicate that discomfort and insecurity have negative impact on perceived usefulness (Tsikriktsis, 2004; Z. Yang et al., 2015). Furthermore, discomfort and Insecurity have negative impact on perceived ease of use (Walczuch et al., 2007; Pires et al., 2011). Therefore, we postulate the following hypotheses.

H_6- Optimism positively affect perceived usefulness of blockchain technology

H_7- Optimism positively affect perceived ease of use of blockchain technology

H_8- Innovativeness positively affect perceived usefulness of blockchain technology

H_9- Innovativeness positively affect perceived ease of use of blockchain technology

H_10-Discomfort negatively affect perceived usefulness of blockchain technology

H_11-Discomfort negatively affect perceived ease of use of blockchain technology

H_12-Insecurity negatively affect perceived usefulness of blockchain technology

H_13- Insecurity negatively affect perceived ease of use of blockchain technology

3.3. Theory of Planned Behavior (TPB)

Theory of Planned Behavior, proposed by Icek (Icek Ajzen, 1985), is an extension of the theory of reasoned action (TRA). The TRA depended on a reconciliation of different theories of attitude, for example, learning, consistency and attribution theory. The TRA indicates that individuals are bound to develop motivation in the event that they have an uplifting frame of mind towards a subject and their peers. The theory of planned behavior tends to circumstances where the people don’t have unlimited authority over their conduct. The several constructs included in theory of planned behaviors are subjective norms (SUBJ), perceived behavioral control (PBC) and behavioral intention to use (BI). Subjective norms are the person’s recognition that the enormous majority who are imperative to him figure he ought to or ought not play out the behavior in question. Choi et al., (2008) examined that (SUBJ) had the most notable influence on the behavioral intention. Perceived behavioral control refers to people’s understanding of their capacity to perform a given action to the degree that it is a precise reflection that perceived behavior control may be used to predict actions along with behavioral intention. The previous finding indicates that theory of planned behavior constructs play a noteworthy impact on behavioral intention (Kamble et al., 2019). More specifically, Subjective norms indicates a substantial influence on perceived usefulness (Choi et al., 2008; Y. Lee et al., 2006). In addition, subjective norms have positive and significant impact on behavioral intention (Icek Ajzen, 1985; Choi et al., 2008). Moreover, perceived behavior control also shows a positive and significant impact on behavioral intention to use the Information System (Chai & Pavlou, 2004; George, 2004). Therefore, we assume the following hypotheses.

H_14- Subjective norms positively affects the perceived usefulness of blockchain technology

H_15- Subjective norms positively affect behavioral intention to use the blockchain technology

H_16- Perceived behavioral control positively affect behavioral intention to use the blockchain technology

In the present study, the Technology Readiness Index model gives the theoretical premise to estimating the perceived risks and opportunity that go about as the hindering variables during Blockchain technology adoption in the manufacturing and service industries of Pakistan. TAM model should estimate how the attitude towards the social intention is generated and what role Perceived ease of use and Perceived usefulness is played. Theory of Planned Behavior assesses the impact of the adoption process on subjective standards and perceived regulation of behavior. The proposed model is shown in Figure 01.

4. Analysis and Methodology

4.1. Data Collection

To analyze the relationship between the constructs proposed in the conceptual model, survey method was developed. In 2019, a sample of 211 experts working in the Pakistan major cities (Abbottabad, Charsadda, Kohat, Mardan, Peshawar, Swabi) were considered for this study. The pilot testing process and five likert scale was used (Allen & Seaman, 2007; Croasmun & Ostrom, 2011). The respondents in our target population were professionals working in the manufacturing, logistic, finance and Information Technology department of Pakistan. Every respondent was given an envelope with a questionnaire, a letter welcoming respondent to take an interest, describing the reason for the research and the classification of their feedbacks. Out of the 350 distributed questionnaires, complete 211 responses were received with a response rate of 60.28 percent. The sample of 211 respondents fulfils the minimum requirement of five observations per parameter (Bollen, 1989; Willis et al., 2016). Moreover, we select 27 parameter for SEM analysis, with a minimum requirement for a sample size of 165 respondents. Hence, Wolf et al., (2013) suggested small samples as appropriate for structural equation model analysis. The sample of 211 experts is considered suitable for performing the SEM study, taking into account practical constraints in collecting data on an exploring topic such as distributed ledger technology adoption. The respondent’s demographic information is presented in the Table 03.

Table 03. Respondents profile

Profile		Frequency
Jobs	Inventory Manager	77
	Operations Manager	43
	Finance Manager	36
	Research and Development Manager	21
	Testing Officers	16
	Others (Senior Account, Production Supervisor, sales staff etc.)	18
Employees	>500	76
	500 ≤ 1000	93
	>1000	42
Experience	≤5 years	55
	>5 ≤ 10 years	85
	>10 years	71
Industries	Textile	63
	Sugar	44
	Food	26
	Mineral Products	21
	Fuel & Energy	17
	Electrical Machinery and Apparatus	12
	Cement	11
	Coke & Refined Petroleum Products	07
	Motor Vehicles	06
	Transport Services	03
	Paper, Paperboard and Products	01
Operations	Manufacturing	126
	Services	85

N = 211

4.2. Structural equation modeling

In this study, partial least square structural equation modeling (PLS-SEM) is used. It is a multivariate data analysis technique which enables linear and additive models and widely used in operations management research (Shah & Goldstein, 2006). It has been used increasingly in various researches, because of its appropriate and robust methods that enable it to examine composite models in the exploratory research (Chin, 1998). The first-generation techniques were opting out because of their limited capability with regards to casual and complex modeling (Lowry & Gaskin, 2014), among the second-generation analysis technique PLS-SEM was applied instead of Covariance-Based because of the complex nature of structural models, i.e. many constructs with several indicators. The study of Hwang et al. (Hwang et al., 2010) encourages using PLS-SEM. SmartPLS is widely adopted and accepted method in term of studying technology adoption models (J. F. Hair et al., 2011). The details of the measurement items is presented in Table 04.

Table 04. Construct measurements

Construct	Items	Measurement	References
Optimism	OPTIM1	Blockchain technology will make you more efficient in your job	Parasuraman, 2000; Shirahada et al., 2019; Spenkelink, 2014)
	OPTIM2	Blockchain will give you more control on your daily business life
	OPTIM3	You like the idea of blockchain technology because you are not restricted to systematic business hours
	OPTIM4	Blockchain will give you more flexibility
Innovativeness	INNOV5	You like the task of figuring out high-tech devices	(Parasuraman, 2000; Shirahada et al., 2019; Spenkelink, 2014)
	INNOV6	Developers/service providers come to your firm for suggestion of blockchain technology adoption
	INNOV7	You keep up with the high-tech developments
	INNOV8	Your firm have less problems than others in adopting new technology
Discomfort	DISCOM9	It’s difficult to understand the blockchain technology	Kamble et al., 2019; Parasuraman, 2000; Shirahada et al., 2019)
	DISCOM10	You think blockchain is only for the use of complex supply chain firms
	DISCOM11	You think blockchain service providers may take the advantage of your supply chains
	DISCOM12	Technology seems mostly to fail at the worst
Insecurity	INSEC13	You consider, it is not secure for your firm to adopt blockchain technology	(Kamble et al., 2019; Parasuraman, 2000; Shirahada et al., 2019)
	INSEC14	You are worried, blockchain technology adoption will disturb your firm security in the future
	INSEC15	You do not feel confident and reliable on adopting blockchain technology
	INSEC16	Online transactions will disturb your privacy
Perceived usefulness	PU17	Using distributed ledger technology can help your firm for fast and speedy transactions	(Davis, 1989; Queiroz et al., 2019; Rehouma & Hofmann, 2018; Spenkelink, 2014)
	PU18	Using distributed ledger technology can improve your firm transparency
	PU19	Using distributed ledger technology can improve your firm’s traceability
	PU20	Using distributed technology can improve your firm Supply chain effectiveness, productivity & performance
Perceived ease of use	PEOU21	You feel distributed ledger technology features is easy to use	(Davis, 1989; Rehouma & Hofmann, 2018; Spenkelink, 2014)
	PEOU22	You feel distributed ledger technology is understandable and faultless
	PEOU23	You feel it is easy to perform multi task on the blockchain technology
	PEOU24	You think distributed ledger is easier to use than normal supply chain management activities
Attitude	ATT25	You will be happy if your firm adopt blockchain technology	(Davis, 1989; Kamble et al., 2019; Shirahada et al., 2019; Spenkelink, 2014)
	ATT26	In your opinion, it is right for your firm to use the Blockchain technology
	ATT27	You think, it is good for the future of Pakistan firms to use the blockchain technology
	ATT28	Overall, yours attitude towards adoption of distributed ledger technology is favorable
Subjective norms	SUBJ29	Most of your firm employee believe adoption of blockchain is a good decision	(Icek Ajzen, 1985; Baker et al., 2007; Kamble et al., 2019)
	SUBJ30	Most of your firm partners expect your firm will adopt disruptive technology
	SUBJ31	You prefer opinion of employee regarding adoption of peer to peer transmission technology
	SUBJ32	Competitors can get advantage if they adopt blockchain earlier than you
Perceived behavioral control	PBC33	Your firm has the capability and resources to adopt the blockchain technology	(Icek Ajzen, 1985; Baker et al., 2007; Kamble et al., 2019; Madden et al., 1992)
	PBC34	Your firm will use Blockchain in the near future
Behavioral intention	BI35	your organization would be able to use Blockchain technology very well	(Icek Ajzen, 1985; Baker et al., 2007; Kamble et al., 2019)
	BI36	You expect your firm will use distributed ledger technology in manufacturing domain to measure anti-counter feits and improve transparency in the supply chain

4.3. Common method bias

The use of a single instrument to assess exogenous and endogenous structures usually raises questions about common method bias issues (Tan et al., 2018). Therefore, both methodological and statistical methods were used to prevent the common method bias problems. The statistical solution was applied using the Harmon’s single factor test. The findings showed that the data variation was recorded by the first factor by 42.915 percent. Since the outcome is below the 50 percent, it can be assumed that there was no common method bias problem (Wong et al., 2015). In addition, the variance inflation factor (VIF) was tested before inspecting the structural model to detect the existence of highly correlated constructs. The findings showed that the highest VIF values of all constructs were (2.724) below the standard cut-off threshold of 3 (Chuah et al., 2017). The VIF is presented in Table 05. Endogeneity can be generated by the structural model recursively. Thus, we applied a Ramsey regression equation error test and found no endogeneity problem (Babatunde et al., 2014; Guide & Ketokivi, 2015). The results indicate that this research does not pose a significant multicollinearity problem and suitable for the structural model analysis.

Table 05. VIF Values

	VIF
OPTM1	2.409
OPTIM2	2.724
OPTIM3	1.111
OPTIM4	2.012
INNOV5	1.703
INNOV6	1.484
INNOV7	1.711
INNOV8	1.495
DISCOM9	1.570
DISCOM10	1.719
DISCOM11	1.497
DISCOM12	1.508
INSEC13	1.699
INSEC14	1.972
INSEC15	1.568
INSEC16	1.284
PU17	1.717
PU18	2.071
PU19	2.162
PU20	2.005
PEOU21	1.799
PEOU22	1.940
PEOU23	2.137
PEOU24	1.874
ATT25	1.917
ATT26	2.019
ATT27	2.114
ATT28	2.004
SUBJ29	1.482
SUBJ30	1.858
SUBJ31	1.794
SUBJ32	1.391
PBC33	1.538
PBC34	1.538
BI35	1.845
BI36	1.845

5. Analysis and results

In this paper, the conceptual model was tested using a two-step method. In the first step the reliability and validity were checked for the evaluation of the measurement items and the structural equation model was analyzed in the next step.

5.1. Measurement model

Sanders et al. (Sanders et al., 2009) define validity as the extent within which data information methods calculate precisely what they intended to calculate. In this study the following reliability and validity tests were applied.

5.1.1. Convergent validity

Convergent validity test applied, when the hypothetical construct developed for the analysis is closely correlated with the items used to measure it, a high proportion of variance shared by the indicators of a given construct must be present. In the present study, ten constructs were evaluated by following the guidelines for finding their convergent validity.

• For the reflective model, the factor loadings of all the measurement items shall be checked for the significance level, the value of all the items must be above 0.70 (Jr, J. Hair et al., 2016).

• After checking the loadings, the composite reliability test have to be applied on all the constructs, the value of each construct must be above 0.70 (Jr, J. Hair et al., 2016).

• Average Variance Extracted of each construct must be tested after checking the reliability, the standard for each construct is 0.50 and above (Fornell & Larcker, 1981).

• The Discriminant validity test to examine the extent within which measurement constructs in a conceptual model are different from each other (Fornell & Larcker, 1981).

By using the SmartPLS 3.2.8, all the tests were conducted for the study. The loading values for the measurement item OPTIM3 (λ = 0.44) and INSEC 16 (λ = 0.48) were found to be less than standard (0.50) indicating internal consistency issues. So, OPTIM3 and INSEC 16 both the items were dropped from the study. The measurement model loading of each construct item is shown in Figure 02. The composite reliability and the AVE after loadings were tested. The results were above the standard value (0.7) and (0.5), representing a strong indicator reliability of the measurement constructs as presented in Table 06. The square root of average variance extracted in each latent variable were tested to check the discriminant validity. Based on the tests, the square root of AVE is considered to be greater than the correlation between the constructs reflecting that all constructs satisfied the validity and can be used for the testing the structural equation model. The discriminant validity is presented in Table 07.

Table 06. Construct reliability and validity

Attitude	0.848	0.848	0.898	0.687
Behaviour intention	0.807	0.807	0.912	0.838
Discomfort	0.784	0.795	0.859	0.605
Innovativeness	0.793	0.798	0.865	0.617
Insecurity	0.761	0.774	0.862	0.677
Optimism	0.866	0.869	0.918	0.789
Perceived Behavioural control	0.743	0.765	0.885	0.794
Perceived Usefulness	0.848	0.850	0.898	0.687
Perceived ease of use	0.841	0.841	0.893	0.677
Subject Norms	0.785	0.789	0.861	0.609

Table 07. Discriminant Validity

	Attitude	Behaviour intention	Discomfort	Innovativeness	Insecurity	Optimism	Perceived Behavioural control	Perceived Usefulness	Perceived ease of use	Subject Norms
Attitude	0.829
Behaviour intention	0.750	0.916
Discomfort	−0.434	−0.412	0.778
Innovativeness	0.700	0.618	−0.375	0.785
Insecurity	−0.479	−0.489	0.562	−0.475	0.823
Optimism	0.670	0.587	−0.494	0.780	−0.558	0.888
Perceived Behavioural control	0.739	0.683	−0.359	0.628	−0.448	0.617	0.891
Perceived Usefulness	0.783	0.701	−0.387	0.555	−0.366	0.547	0.653	0.829
Perceived ease of use	0.792	0.725	−0.434	0.621	−0.423	0.594	0.684	0.789	0.823
Subject Norms	0.743	0.653	−0.371	0.625	−0.355	0.576	0.703	0.663	0.728	0.781

Figure 02. Measurement model

5.2. Structural model

In the second step to evaluate the structural equation modeling, bootstrapping was applied for the structural path’s significance testing. In the bootstrapping procedure, a large number of subsamples (5000) were tested from the original sample with replacement to check standard bootstrap errors, which in turn directed the approximate T-values for significance testing of the structural model. The bootstrapping process result approximates data normality for a structural model, as shown in Figure 03. The final decision about the hypothesis development is presented in Table 08.

Table 08. Hypothesis test result

Number	Hypothesis Statement	T Statistics	P Values	Decision
H1	Perceived ease of use -> Perceived Usefulness	10.090	0.000	Supported
H2	Perceived ease of use -> Attitude	5.734	0.000	Supported
H3	Perceived Usefulness -> Attitude	5.355	0.000	Supported
H4	Attitude -> Behavior intention	3.716	0.000	Supported
H5	Perceived Usefulness -> Behavior intention	3.003	0.003	Supported
H6	Optimism -> Perceived Usefulness	1.020	0.308	Rejected
H7	Optimism -> Perceived ease of use	1.761	0.079	Rejected
H8	Innovativeness -> Perceived Usefulness	0.137	0.891	Rejected
H9	Innovativeness -> Perceived ease of use	3.806	0.000	Supported
H10	Discomfort -> Perceived Usefulness	0.454	0.650	Rejected
H11	Discomfort -> Perceived ease of use	2.759	0.006	Supported
H12	Insecurity -> Perceived Usefulness	0.237	0.813	Rejected
H13	Insecurity -> Perceived ease of use	0.521	0.603	Rejected
H14	Subject Norms -> Perceived Usefulness	2.383	0.018	Supported
H15	Subject Norms -> Behavior intention	1.502	0.134	Rejected
H16	Perceived Behavioral control -> Behavior intention	3.157	0.002	Supported

Figure 03. Structural model

6. Discussion and conclusion

6.1. Major findings

Based on the final decision about the hypotheses development in the Table 08, it is considered that nine of them were found statistically significant from the total sixteen tested hypothesis relationships in the structural equation model. The finding further indicate that the latent constructs explained 62.6% of the variance in the behavioral Intention (R² = 0.626). Consequently, the current study findings indicate that Theory of Planned Behavior with Technology Acceptance Model plays a pivotal role in the adoption of the distribution ledger technology. The Technology acceptance model constructs- perceived ease of use, perceived usefulness, attitude and Theory of planned behavior construct-perceived behavior control shows a significant impact during the behavioral intention of disruptive technology in the manufacturing and service industries of Pakistan. However, Technology readiness constructs-optimism and insecurity shows insignificant impact with regards to adopting complex technologies. Both the constructs neither influence technology acceptance model construct-perceived ease of use and perceived usefulness. Therefore, rejecting hypothesis (H6, H7, H12, H13) and the result is supported by the finding of the other studies of (Walczuch et al., 2007),(Kamble et al., 2019).

The study results indicate that technology acceptance model construct-perceived ease of use influence the perceived usefulness and supported by the finding of other major studies of (Amoako-Gyampah & Salam, 2004) (M. S. Lee, 2009), (Shih et al., 2012) (Gao & Bai, 2014) (Kumpajaya & Dhewanto, 2015) (Rajan & Baral, 2015) (Bröhl et al., 2016; Kamble et al., 2019). Therefore, accepting Hypothesis (H1).

The study findings show that the technology acceptance model construct-perceived ease of use has a substantial effect on the attitude of the supply chain experts, so accepting Hypothesis (H2) and the finding is supported by the other studies of (Amoako-Gyampah & Salam, 2004) (M. S. Lee, 2009), (Shih et al., 2012), (Pattansheti et al., 2016).

The study results show that construct-perceived ease of use of technology acceptance model has a significant impact on the behavioral intension to use the distributed ledger technology. So accepting Hypothesis (H3) and the findings are accompanied by other relevant studies carried out for technology adoption (M. S. Lee, 2009), (Gamal Aboelmaged, 2010), (Shih et al., 2012), (Pattansheti et al., 2016), (Kamble et al., 2019).

The findings also indicate that the technology acceptance model construct-attitude has a significant impact on the behavioral intension to use the blockchain technology so accepting Hypotheses (H4). The outcome conforms to the other research of (Amoako-Gyampah & Salam, 2004), (M. S. Lee, 2009),(Gamal Aboelmaged, 2010), (Shih et al., 2012), (Moons & De Pelsmacker, 2015), (Safa et al., 2015), (Pattansheti et al., 2016), (Kamble et al., 2019).

Based on the findings, the current study further confirms that technology acceptance model construct-perceived usefulness has a significant effect on the behavior strength, so accepting Hypothesis (H5) and the findings are backed by the major earlier studies of (Gamal Aboelmaged, 2010), (Gao & Bai, 2014), (Kumpajaya & Dhewanto, 2015), (Bröhl et al., 2016; Rajan & Baral, 2015). The overall constructs indicate that technology acceptance model is essential for the adoption of distributed ledger technology. The results further suggest that more manufacturing and service sectors are turning into smart operations. smart manufacturing system through blockchain applications have become the focus of attention of businesses (Qu et al., 2019),(Kumar et al., 2020)

The analysis indicates that technology readiness index construct-innovativeness shows negligible effect on technology acceptance model construct- perceived usefulness, thus denying Hypothesis (H8) and supported by the finding of the previous studies of (Walczuch et al., 2007; Yi et al., 2003), (Godoe & Johansen, 2012), (Kamble et al., 2019). However, the findings of the study further indicate that innovativeness shows a significant impact on the perceived of use, So accepting Hypothesis (H9) and supported by the study of (Walczuch et al., 2007), (Pires et al., 2011), (Godoe & Johansen, 2012).

The result shows that technology readiness index construct-discomfort shows an insignificant effect on the technology acceptance model construct-perceived usefulness, thus rejecting the Hypothesis (H10) and supported by the findings of other studies (Walczuch et al., 2007), (Pires et al., 2011), (Kamble et al., 2019). The findings also consider that discomfort have a significant effect on perceived ease of use, therefore accepting hypothesis (H11) and supported by the previous study of (Walczuch et al., 2007).

This study was find more interesting when results show that Theory of planned behavior construct-subjective norms shows a significant impact on the technology acceptance model construct-perceived usefulness, so accepting Hypothesis (H14) and is supported by the findings of the other interesting studies of (Gumussoy et al., 2007), (Kamble et al., 2019). This findings consider that adoption of a distributive ledger technology can play a key role in improving traceability and transparency in the supply chains (Abeyratne, 2016a; Francisco & Swanson, 2018) and additive manufacturing for improving the anti-counterfeiting measures (J Battistini, 2016; Kennedy et al., 2017). However, the subjective norms shows negligible impact on the behavioral intension to use the blockchain so rejecting Hypothesis (H15) and backed by the prior studies of (Sentosa & Mat, 2012), (Moons & De Pelsmacker, 2015), (Safa et al., 2015). Moreover, the theory of planned behavior construct- perceived behavior also show a significant impact on the behavioral intension to use the blockchain technology by the manufacturing and service enterprises of Pakistan, and supported by the previous studies of (Baker et al., 2007; Issa & Hamm, 2017; Kamble et al., 2019; Xie et al., 2017b), so accepting final Hypotheses (H16).

6.2. Theoretical implications

There has been increasing attention in the research of Blockchain technology which is indicated by the large number of calls for manuscripts on several aspects of Blockchain technology from reputable journals like International Journal of Production Economics, Harvard Business Review, International Journal of Production Research and others. The current study proposed a model to better understand the adoption of distributed ledger technology in the Supply chain-Pakistan context. It is one of the primary studies by using the three traditional adoption theories namely TAM with technology readiness Index and theory of planned behavior. Previous literature on distributed ledger technology has been conducted from the TOE perspective (Clohessy et al., 2019), UTAUT (Queiroz & Wamba, 2019), TAM perspective (Francisco & Swanson, 2018). The present study thus expands the literature on innovation adoption by testing a unified traditional adoption theory, and hence has far enough resolved the gap in the existing literature with respect to the adoption of distributed ledger technology in Information System and Supply chain management. In addition, few studies have been done in the developing nations for the blockchain adoption, and it has not been studied in the Pakistan context. This study can be act as a starting point for the other Information System researchers to deeply analyze the blockchain implementation. The results of the proposed model offer significant insights which could aid supply chain experts to better understand blockchain technology implementation in the supply chain management.

6.3. Managerial implications

This study shows that the conceptual model has a strong explanatory power (R² = 0.626 and R² adjusted = 0.618), reflecting a 62.6 percent variance of the intention to use blockchain technology. Moreover, a variation of (R² = 0.643 and R² adjusted = 0.633) was shown by the perceived usefulness towards disruptive technology. Subsequently, a variation of (R² = 0.447 and R² = 0.436) was shown by the perceived ease of use. Finally, the highest variation was shown by attitude representing (R² = 0.693 and R² adjusted = 0.690). Accordingly, the major findings also have important strategic implications and insights that could enable organizations to better manage and organize the effective adoption of distributed ledger technology. For the organizations implementing distributed ledger technology, the finding suggests that their marketing endeavors need not concentrate only on awareness about distributive ledger technology, however they are required to create intrigue and drive the professionals towards its effective usage through viable buyer and supplier dyads. For the blockchain enterprises, the finding indicates that their marketing strategies need to concentrate on influencing the decision makers to purchase blockchain applications. The using of the blockchain applications will help the retailers to track and control stock in the real time, thus reduce product misplacement, stock level, and labor costs. Moreover, in the manufacturing domain, the blockchain applications will help the organizations for the anticounter measures (Mohamed & Al-Jaroodi, 2019).

6.4. Limitation and conclusion

This study also has some limitations as in other studies. Some of the limitations are due to the inherent features of disruptive technology. Blockchain technology is comparatively a new idea in Pakistan with few organizations planning to implement in their enterprises. So, future studies have to answer the following issues. For example: Does blockchain adoption by end users affect the form of product of service? Since, would blockchain impact more on industries such as medicine and aviation, where goods have to follow very strict standards? Would the component parts and services like nails, grains and lawnmowers also be less impactful or demanded?

In the current study we didn’t examine the blockchain integration with other technologies for security and privacy. Therefore, it is suggested that the future studies should consider incorporating other technologies like IoT that support distributed ledger technology. For instance: How to integrate the proliferation of the Internet of things, a technology that can deliver information inputs, and blockchain? Maybe IoT will provide more inputs and blockchain will produce more production through application such as smart contracts. Such a model of integration necessitates less emphasis on human involvement.

In the present study we didn’t examine the government regulations that supports blockchain technology adoption. So, the future studies will be required to conduct a comparative study between the states where the government rules and regulation support the disruptive technology adoption and the states where the government regulation is skeptical. The outcome of these analyses will be interesting.

In conclusion, this study has provided an overview of potential factors for consideration from a holistic view via the three traditional adoption theories framework. In response to RQ1, perceived ease of use, perceived usefulness, attitude and perceived behavioral control shows a significant relationship with the intention to adopt blockchain technology. However, Optimism and Insecurity shows insignificant influence during disruptive technology adoption. On the other hand, Innovativeness and discomfort shows a significant impact on perceived ease of use while insignificant influence on perceived usefulness. Consequently, subjective norms show significant impact on perceived usefulness while insignificant influence on intention to use blockchain technology. Pertaining to RQ2, perceived ease of use matters most in adoption of distributed ledger technology. While the present study may not be comprehensive, in future some other important factors like compatibility, complexity, relative advantage, observability and trialability have to be tested for Pakistan- service and manufacturing operations. Hence, this study may offer a reference to academics as well as experts. For blockchain technology implementing experts, the research offers valuable insights for developing disruptive technology solutions, it allow organizations to get the customer information quickly and thereby contribute to the competitive advantage of their businesses.

Disclosure statement

No potential conflict of interest was reported by the author(s).