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

Ullah, Nazir; Al-Rahmi, Waleed Mugahed; Alkhalifah, Ali

doi:10.1080/21693277.2021.1976963

Cited by 13 publications

(5 citation statements)

References 106 publications

(169 reference statements)

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“…The determination of students and educators is an important factor in accepting or refusing M-learning [6,7]. While the delivery of learning facilities is now predictable for M-learning, many of the universities' initiatives have been a key step in M-learning [8][9][10]. However, technical and non-technical hurdles remain, especially for students' utilization and adoption of M-learning [11].…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model

Alyoussef

2021

Electronics

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The goal of this study was to develop a new model and conduct confirmatory factor analysis to learn more about how students use M-learning in higher education. The study is theoretically based on the unified theory of acceptance and use of technology (UTAUT) theory and the technology acceptance model (TAM). Theoretically, the factors related to the adoption of M-learning in higher education, identified as contributory to perceived ease of use, perceived usefulness, and attitudes towards M-learning and actual use of M-learning, were analyzed. A questionnaire survey was distributed to 362 university students who were randomly selected. Structural Equation Modeling (SEM)-AMOS was used for data analysis. Based on the findings, M-learning appears to be one of the most promising educational technologies for development in educational environments. Perceived facilitating conditions, performance expectancy, effort expectancy, social influence, and perceived enjoyment have a significant positive effect on the perceived ease of use and perceived usefulness, while performance expectancy has a negative effect on the perceived ease of use. Perceived ease of use and perceived usefulness have a positive and significant effect on attitudes towards using M-learning and actual use of M-learning. Therefore, we recommend lecturers encourage students to utilize M-learning for educational purposes in higher education.

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Section: Introductionmentioning

confidence: 99%

Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model

Alyoussef

2021

Electronics

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“…Generally, most of the recent literature evidenced that Attitude is the main factor towards the intention to adopt cryptocurrency usage while Subjective Norm is not the influencing factor (Ramachandran & Stella, 2022;Zamzami, 2020). In addition, other researchers report similar findings which indicated that Subjective Norm did not affect significantly on the behavioral intention towards Cryptocurrency usage as evidenced by Ullah, Al-Rahmi and Alkhalifah's (2021) study in Pakistan and Mazambani and Mutambara's (2019) study in South Africa. Contrariwise, findings by Almajali, Masa'Deh and Dahalin (2022) showed that Subjective Norm was positively affected cryptocurrency's intention to use in Jordan.…”

Section: Intention To Adopt Cryptocurrencymentioning

confidence: 89%

Exploring Cryptocurrency Readiness Among University Students

Yusof¹,

Munir²,

Zolkaply³

et al. 2023

Proceedings of the 10th International Conference on Business, Accounting, Finance and Economics (BAFE 2022)

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Digital currency or cryptocurrency, the latest development in investment is a virtual medium to purchase and sell goods and services, plays the role of a diversification tool, and features an attractive, alternative store of value. Recently, the Malaysian regulators were urged to legalise cryptocurrency as it is very attractive to young investors. Despite the overwhelming interests among young investors, they have held their interests over cryptocurrency due to the fear of the unknown and a few of its vague areas. This research is undertaken to explore the cryptocurrency interest and awareness among undergraduate university students as potential future cryptocurrency investors. As of to date, 116 students have participated in data collection and at this stage, the preliminary findings will be reported. About 78% of the young respondents admitted that they had little or no knowledge of the virtual currency. This research contributed in exploring cryptocurrency's value and risk from users' perspectives in a developing country setting such as Malaysia. This might shed new lights in raising awareness among the youth while managing cryptocurrency developments by regulators. More future research should be undertaken to assess the behavioural traits of the cryptocurrency providers, players as well as the societal acceptance of it.

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“…Similarly, Walton and Johnston (2018) discovered that people rather invest in Bitcoin if their social group of family, friends, and peers have a positive attitude towards cryptocurrency and invest in it, which was confirmed by several studies (Boxer & Thompson, 2020;Gazali et al, 2019;Jankeeparsad & Tewari, 2018;Kim, 2021;Schaupp & Festa, 2018). Still, Zamzami (2020) has not found subjective norm as an influencing factor in Indonesia, nor Mazambani and Mutambara (2019) in South Africa, Ullah et al (2021) found its negligible impact in Pakistan, and Arias-Oliva et al ( 2021) discovered it as an enabling factor with a positive influence on intention to use cryptocurrency in Spain.…”

Section: Introduction To Cryptocurrencymentioning

confidence: 84%

“…Some studies also found a significant positive impact of perceived usefulness and ease of use (Albayati et al, 2020;Arias-Oliva et al, 2019;Nadeem et al, 2021;Nuryyev et al, 2018), and other authors only found their indirect effect (Shahzad et al, 2018;Walton & Johnston, 2018), or that it fluctuates within various consumer categories (Janssen et al, 2015). Similarly, while several studies found personal innovativeness as a good predictor of intention to use cryptocurrency (Abbasi et al, 2021;Sohaib et al, 2019;Sun et al, 2020), others found it had a negligible impact (Ullah et al, 2021). Previous studies have also found the impact of perceived benefits (Gazali et al, 2019;Yoo et al, 2020), performance expectancy (Arias-Oliva et al, 2019), effort expectancy and facilitating condition (Jankeeparsad & Tewari, 2018;Ter Ji-Xi et al, 2021), perceived enjoyment (Nadeem et al, 2021), and other factors.…”

Section: Introduction To Cryptocurrencymentioning

confidence: 94%

“…However, these models cannot fully explain the acceptance of new technology involved with financial transactions such as cryptocurrency (Won-Jun, 2018) since it, besides the novelty, includes various financial, security, and human risks that can be better explained with attitudinal models such as the TRA and TPB (Al-Amri et al, 2019). Therefore, the best approach to research this topic comprehensively is to combine the factors from technology adoption and attitudinal models, as some previous studies have already done (Boxer & Thompson, 2020;Ullah et al, 2021;Yoo et al, 2020).…”

Section: Theory Of Reasoned Action (Tra)mentioning

confidence: 99%

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The Extended TRA Model for the Assessment of Factors Driving Individuals’ Behavioral Intention to Use Cryptocurrency

Alaklabi

Kang

2022

IJIKM

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Aim/Purpose: The aim of this study was to explore the factors driving individuals’ behavioral intention to use cryptocurrency in Saudi Arabia using the extended TRA model. Background: Despite the great potential of cryptocurrencies and the exponential growth of cryptocurrency use throughout the world, scholarly research on this topic remained scarce. Whereas prior studies are mostly done in developed countries or specific cultural contexts, limiting the generalizability of their results, they mainly used technology adoption models that cannot fully explain the acceptance of new technology involved with financial transactions such as cryptocurrency and provided contradictory evidence. Entire regions have been excluded from the research on this topic, including Saudi Arabia which has a high potential to increase the volume of cryptocurrency use. Methodology: This study extends the theory of reasoned action (TRA) with the factors from technology adoption models that proved relevant for this topic, namely perceived usefulness, perceived enjoyment, perceived innovativeness, and perceived risk with three sub-factors: security, financial, and privacy risk. Data are collected using a quantitative research methodology from 181 respondents residing in Saudi Arabia and then analyzed by several methods, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). Contribution: This study contributes to the scientific knowledge by extending the TRA model with a range of factors from the technology adoption field, thus enabling the analysis of this topic from human, financial, and technology perspectives and providing additional empirical evidence on the factors that previously either provided contradictory evidence or were not explored in this field. This research also provides the first empirical data on this topic in Saudi Arabia and enables further research on the topic and a comparison of the results. The study also contributes to practice by enhancing the actual understanding of the phenomena and providing valuable information and recommendations for governments, investors, merchants, developers, and the general population. Findings: The study found attitude, subjective norm, perceived usefulness, perceived enjoyment, personal innovativeness, privacy risk, and financial risk as significant predictors of the intention to use cryptocurrencies, whereas the influence of security risk was not found to be significant in Saudi Arabia. Recommendations for Practitioners: Using this study’s results, governments can create appropriate legal frameworks, developers can design fewer complex platforms, and merchants may create appropriate campaigns that emphasize the benefits of cryptocurrency use and transpire trust in cryptocurrency transactions by enhancing the factors with a positive impact, such as usefulness, enjoyment, and personal innovativeness while reducing concerns of potential users regarding the risky factors. By promoting a positive user experience, they can also improve attitudes and social norms towards cryptocurrencies, thus further stimulating the interest in their use. Recommendation for Researchers: As this study validated the influence of factors from technology, financial, and human-related fields, researchers may follow this approach to ensure a comprehensive analysis of this complex topic, especially as privacy risk was never examined in this context, while personal innovativeness, perceived enjoyment, financial, and security risk were explored in just a few studies. It is also recommended that researchers explore the impact of each part of subjective norms: social media, friends, and family, as well as how information on the benefits of cryptocurrencies affects the perception of the factors included. Impact on Society: Understanding the factors affecting cryptocurrency use can help utilize the full potential of cryptocurrencies, especially their benefits for developing countries reflected in safe, speedy, and low-cost financial transactions with no need for an intermediary. The research model of this study could also be used to investigate this topic in other contexts to discover similarities and differences, as well as to investigate other information systems. Future Research: Future studies should test this research model in similar and different contexts to determine whether its validity and study results depend on cultural and contextual factors. They can also include different or additional variables, or use mixed methods, as interviews would augment the comprehension of this topic. Future studies may also explore whether the impact of variables would remain the same if circumstances changed or use cases expanded, and how the preferences of the target population would change within a longitudinal time frame.

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

Cited by 13 publications

References 106 publications

Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model

Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model

Exploring Cryptocurrency Readiness Among University Students

The Extended TRA Model for the Assessment of Factors Driving Individuals’ Behavioral Intention to Use Cryptocurrency

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