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

Figures & data

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.

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.

Figure 01. Conceptual model

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

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

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

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

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

Yoon, J., Talluri, S., Yildiz, H., & Sheu, C. (2020). The value of Blockchain technology implementation in international trades under demand volatility risk. International Journal of Production Research, 58(7), 2163–2183. https://doi.org/https://doi.org/10.1080/00207543.2019.1693651

 Web of Science ®Google Scholar

Queiroz, M. M., Telles, R., & Bonilla, S. H. (2019). Blockchain and supply chain management integration: A systematic review of the literature. Supply Chain Management: An International Journal, 25(2), 241–254. https://doi.org/https://doi.org/10.1108/SCM-03-2018-0143

 Google Scholar

Francisco, K., & Swanson, D. (2018). The supply chain has no clothes: Technology adoption of blockchain for supply chain transparency. Logistics, 2(1), 2. https://doi.org/https://doi.org/10.3390/logistics2010002

 Google Scholar

Fosso Wamba, S., Kala Kamdjoug, J. R., Bawack, R., & Keogh, J., G. (2018). Bitcoin, Blockchain, and FinTech: A systematic review and case studies in the supply chain. In Production Planning and Control, Forthcoming.

 Google Scholar

Choi, T.-M., Feng, L., & Li, R. (2020). Information disclosure structure in supply chains with rental service platforms in the blockchain technology era. International Journal of Production Economics, 221, 107473. https://doi.org/https://doi.org/10.1016/j.ijpe.2019.08.008

 Web of Science ®Google Scholar

Chang, Y., Iakovou, E., & Shi, W. (2020). Blockchain in global supply chains and cross border trade: A critical synthesis of the state-of-the-art, challenges and opportunities. International Journal of Production Research, 58(7), 2082–2099. https://doi.org/https://doi.org/10.1080/00207543.2019.1651946

 Web of Science ®Google Scholar

Bai, C., & Sarkis, J. (2020). A supply chain transparency and sustainability technology appraisal model for blockchain technology. International Journal of Production Research, 58(7), 2142–2162. https://doi.org/https://doi.org/10.1080/00207543.2019.1708989

 Web of Science ®Google Scholar

Babich, V., & Hilary, G. (2020). OM Forum—Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management, 22(2), 223–240. https://doi.org/https://doi.org/10.1287/msom.2018.0752

 Web of Science ®Google Scholar

Du, M., Chen, Q., Xiao, J., Yang, H., & Ma, X. (2020). Supply chain finance innovation using blockchain. IEEE Transactions on Engineering Management, 67(4), 1045–1058. https://doi.org/https://doi.org/10.1109/TEM.2020.2971858

 Google Scholar

Malik, S., Dedeoglu, V., Kanhere, S. S., & Jurdak, R. (2019). TrustChain: Trust management in blockchain and IoT supported Supply Chains. Paper presented at the 2019 IEEE International Conference on Blockchain (Blockchain).

 Google Scholar

Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009–2033. https://doi.org/https://doi.org/10.1080/00207543.2018.1518610

 Web of Science ®Google Scholar

Wang, S., Ouyang, L., Yuan, Y., Ni, X., Han, X., & Wang, F.-Y. (2019). Blockchain-enabled smart contracts: Architecture, applications, and future trends. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(11), 2266–2277. https://doi.org/https://doi.org/10.1109/TSMC.2019.2895123

 Web of Science ®Google Scholar

Viriyasitavat, W., Da Xu, L., Bi, Z., & Sapsomboon, A. (2018). Blockchain-based business process management (BPM) framework for service composition in industry 4.0. Journal of Intelligent Manufacturing, 1–12.

 Web of Science ®Google Scholar

Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2017.12.005

 Web of Science ®Google Scholar

Dobrovnik, M., Herold, D. M., Fürst, E., & Kummer, S. (2018). Blockchain for and in logistics: What to adopt and where to start. Logistics, 2(3), 18. https://doi.org/https://doi.org/10.3390/logistics2030018

 Google Scholar

Kim, H. M., & Laskowski, M. (2018). Toward an ontology‐driven blockchain design for supply‐chain provenance. Intelligent Systems in Accounting, Finance and Management, 25(1), 18–27. https://doi.org/https://doi.org/10.1002/isaf.1424

 Web of Science ®Google Scholar

Lou, A. T., & Li, E. Y. (2017). Integrating innovation diffusion theory and the technology acceptance model: The adoption of blockchain technology from business managers’ perspective. Paper presented at the International Conference on Electronic Business.

 Google Scholar

Omran, Y., Henke, M., Heines, R., & Hofmann, E. (2017). Blockchain-driven supply chain finance: Towards a conceptual framework from a buyer perspective.

 Google Scholar

Tian, F. (2017). A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things. Paper presented at the 2017 International Conference on Service Systems and Service Management.

 Google Scholar

Nakasumi, M. (2017). Information sharing for supply chain management based on block chain technology. Paper presented at the 2017 IEEE 19th Conference on Business Informatics (CBI).

 Google Scholar

Korpela, K., Hallikas, J., & Dahlberg, T. (2017). Digital supply chain transformation toward blockchain integration. Paper presented at the proceedings of the 50th Hawaii international conference on system sciences.

 Google Scholar

Toyoda, K., Mathiopoulos, P. T., Sasase, I., & Ohtsuki, T. (2017). A novel blockchain-based product ownership management system (POMS) for anti-counterfeits in the post supply chain. IEEE Access, 5, 17465–17477. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2720760

 Web of Science ®Google Scholar

Cai, Y., & Zhu, D. (2016). Fraud detections for online businesses: A perspective from blockchain technology. Financial Innovation, 2(1), 20. https://doi.org/https://doi.org/10.1186/s40854-016-0039-4

 Web of Science ®Google Scholar

Abeyratne, S. A., & Monfared, R. P. (2016b). Blockchain ready manufacturing supply chain using distributed ledger.

 Google Scholar

Zyskind, G., & Nathan, O. (2015). Decentralizing privacy: Using blockchain to protect personal data. Paper presented at the 2015 IEEE Security and Privacy Workshops.

 Google Scholar

Ma, J., Hipel, K. W., Hanson, M. L., Cai, X., & Liu, Y. (2018). An analysis of influencing factors on municipal solid waste source-separated collection behavior in Guilin, China by using the theory of planned behavior. Sustainable Cities and Society, 37, 336–343. https://doi.org/https://doi.org/10.1016/j.scs.2017.11.037

 Web of Science ®Google Scholar

Xie, Q., Song, W., Peng, X., & Shabbir, M. (2017a). Predictors for e-government adoption: Integrating TAM, TPB, trust and perceived risk. The Electronic Library, 35(1), 2–20. https://doi.org/https://doi.org/10.1108/EL-08-2015-0141

 Web of Science ®Google Scholar

Issa, I., & Hamm, U. (2017). Adoption of organic farming as an opportunity for Syrian farmers of fresh fruit and vegetables: An application of the theory of planned behaviour and structural equation modelling. Sustainability, 9(11), 2024. https://doi.org/https://doi.org/10.3390/su9112024

 Web of Science ®Google Scholar

Howell, D. W. (2016). Social media site use and the technology acceptance model: Social media sites and organization success. Capella University.

 Google Scholar

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 29(1), 118–139. https://doi.org/https://doi.org/10.1108/JEIM-04-2015-0035

 Web of Science ®Google Scholar

Folkinshteyn, D., & Lennon, M. (2016). Braving Bitcoin: A technology acceptance model (TAM) analysis. Journal of Information Technology Case and Application Research, 18(4), 220–249. https://doi.org/https://doi.org/10.1080/15228053.2016.1275242

 Google Scholar

Awa, H. O., Ojiabo, O. U., & Emecheta, B. C. (2015). Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science & Technology Policy Management, 6(1), 76–94. https://doi.org/https://doi.org/10.1108/JSTPM-04-2014-0012

 Google Scholar

Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/https://doi.org/10.1177/109467050024001

 Google Scholar

Shirahada, K., Ho, B. Q., & Wilson, A. (2019). Online public services usage and the elderly: Assessing determinants of technology readiness in Japan and the UK. Technology in Society, 58, 101115. https://doi.org/https://doi.org/10.1016/j.techsoc.2019.02.001

 Web of Science ®Google Scholar

Spenkelink, H. (2014). The adoption process of cryptocurrencies-Identifying factors that influence the adoption of cryptocurrencies from a multiple stakeholder perspective. University of Twente.

 Google Scholar

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. In MIS quarterly (pp. 319–340).

 Google Scholar

Rehouma, M. B., & Hofmann, S. (2018). Government employees’ adoption of information technology: A literature review. Paper presented at the Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age.

 Google Scholar

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11–39). Springer.

 Google Scholar

Baker, E. W., Al‐Gahtani, S. S., & Hubona, G. S. (2007). The effects of gender and age on new technology implementation in a developing country. In Information Technology & People.

 Google Scholar

Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18(1), 3–9. https://doi.org/https://doi.org/10.1177/0146167292181001

 Web of Science ®Google Scholar