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Problems such as information asymmetry and a lack of trust among construction practitioners damage the quality and progress of construction projects. The decentralization, transparency, traceability and temper-proof nature of blockchain technology (BCT) can provide solutions and facilitate multiparty cooperation. However, BCT acceptance in the construction industry is relatively low, and there are few pilot projects adopting BCT. Most relevant literature focuses on BCT acceptance at the industry and organizational levels, but the impact of non-managerial practitioners executing BCT or the traditional approach in day-to-day work tends to be disregarded. This study aims to establish a theoretical model of BCT acceptance, identify key influencing factors and paths of behavioral intention to adopt BCT and promote strategies to enhance BCT adoption.

A new BCT acceptance model for construction practitioners was proposed. A survey was performed with 203 construction practitioners in Shenzhen, China and post-survey interviews were conducted with four BCT experts for validation. Covariance-based structural equation modeling was used to examine the influence paths and moderating effect analysis was performed to check practitioners’ differential perceptions.

Performance expectancy, social influence, facilitating conditions and perceived behavioral control significantly and positively influence behavioral intention to accept BCT, while impacts from effort performance and risk are negative. Overcoming obstacles related to the effort required for BCT adoption and effective risk management will be essential to unlocking BCT’s transformative potential. Then, the moderating effects of respondents’ gender, degree and BCT knowledge as well as the project type involved were analyzed. Continued adoption of BCT in the construction industry has the potential to revolutionize project management, transparency and trust among stakeholders.

The findings of this research can help practitioners and government agencies understand crucial influencing factors and pathways of BCT acceptance. Targeted measures, such as increasing practitioners’ benefits and sense of BCT usefulness, conducting pilot projects and increasing publicity, were proposed for project leadership teams to enhance BCT adoption. This may lead to increased efficiency, reduced disputes and more streamlined and secure construction processes, ultimately enhancing the industry’s overall performance.

Few studies have explored BCT acceptance from the perspective of non-managerial construction practitioners. The BCT acceptance model proposed in this study is a novel adaptation of previous technology acceptance models, with new factors (risk and perceived behavioral control) and moderating variables (degree, BCT knowledge and project type) added for better understanding of non-managerial practitioners’ perceptions and differences.

This paper aims to examine the factors that influence bank customers’ acceptance towards FinTech using the unified theory of acceptance and use of technology (UTAUT) model.

A survey was carried out, and a judgemental sampling method was used. Moreover, over 200 responses were gathered from individuals that were bank account holders. Besides, the literature on the factors that influence an individual acceptance towards FinTech using the UTAUT model was also reviewed as an effort to contribute towards the factors that are significant in predicting the acceptance of FinTech services.

The results of the study revealed that performance expectancy and effort expectancy are the main factors in determining the acceptance of FinTech services. Moreover, consumer innovativeness plays an important role in the acceptance of FinTech by bank users. In contrast, social influence and facilitating conditions were not the determinants of FinTech acceptance in Pakistan.

This study not only contributed to the theoretical extensions but also to practical implications, which would benefit the community of FinTech service providers in Pakistan.

The limitations of this study were two but are not limited to, firstly, the respondents which were taken among bank customers in Pakistan, confining its contribution to the narrowed perspective of Pakistan. Besides, this study also considers its contributions towards the use of the UTAUT in which the factors examined were specific, which may elucidate that the generalization of the findings only includes FinTech, where the UTAUT comes into play. This study is an eye-opener for the merging perspective of both the UTAUT and FinTech.

UTAUT is extended in the context of FinTech. Consumer innovativeness is incorporated in the context of the UTAUT model. Consumer innovativeness is yet to be tested and for that this study is a useful reference for academicians, policymakers and future researchers.

In view of the promising growth of FinTech in the Arab region, specifically in Egypt, this study identifies the significant factors likely to influence consumers’ acceptance intention toward e-payment products and services (mobile wallets, virtual-cards, mobile/online banking etc.).

Quantitative deductive research was used to investigate the extension of the unified theory of acceptance and use of technology (UTAUT) on consumers' e-payment acceptance intentions. Questionnaires were electronically administered through convenience sampling. Three hundred and ninety-nine questionnaires were analyzed through multiple regression to test the proposed hypotheses.

Performance expectance, facilitating condition, effort expectancy and social influence impact consumers' e-payment acceptance intentions. These factors contribute to the growing number of individuals using e-payments to the extent that e-payment eventually becomes a preferred medium for economic transactions. Hedonic motivation, price value and habit are insignificant in encouraging consumers' e-payment acceptance.

The study benefits to governments, policymakers, banking institutions, businesses involved in online transactions and software developers. They can utilize the findings to develop strategies aimed at increasing e-payment usage and overcoming cultural-resistance to changing traditional financial transaction methods. These methods align with the government’s goal of achieving a digital society.

This study presents information on the current state of e-payment acceptance. Its potential contribution lies in identifying the factors that influence the intention to e-payment among Arab consumers, thereby highlighting important considerations for the adoption of e-payment. Furthermore, this study supports UTAUT over UTAUT2 in the FinTech context. It suggests that individuals exhibit similar behavior and inspiration when using e-payment technology as they do when using technology in an organizational context.

The study aims to validate a mobile learning readiness scale through the technology readiness and acceptance model (TRAM), thereby assessing students' readiness to adopt m-learning in teaching and learning, including its acceptance.

A structured questionnaire was administered to open and distance learning (ODL) students in Odisha, India, to assess their readiness and acceptance of m-learning. 665 valid responses were collected, and collected data was analysed using statistical packages for social sciences (SPSS) and SmartPLS.

The findings of the study reveal that optimism contributes positively to perceived ease of use (PEOU) and perceived usefulness (PU) of m-learning (β = 7.921, p < 0.001; β = 2.123, p < 0.05), whereas innovativeness positively contributes to PEOU of m-learning (β = 2.227, p < 0.05), but not PU of m-learning. ODL student's optimism improves his/her PEOU and PU of m-learning, but innovativeness improves only his/her PEOU. Further, the impact of innovativeness is higher than that of optimism in the TRAM and innovativeness is the strong predictor to adopt m-learning. It also shows that the PU of m-learning positively influences behavioural intention to use m-learning (β = 4.757, p < 0.001). Integrating technology readiness (TR) with technology acceptance model (TAM) to predict students' acceptance of m-learning is very useful.

The paper will help decision-makers to adopt and use m-learning in higher educational institutions.

This paper is the first to explore the readiness and acceptance of m-learning in higher education in India.

Deepfake information poses more ethical risks than traditional disinformation in terms of fraud, slander, rumors and other malicious uses. However, owing to its high entertainment value, deepfake information with ethical risks has become popular. This study aims to understand the role of ethics and entertainment in the acceptance and regulation of deepfake information.

Mixed methods were used to qualitatively identify ethical concerns and quantitatively evaluate the influence of ethical concerns and perceived enjoyment on the ethical acceptability and social acceptance of deepfake information.

The authors confirmed that informed consent, privacy protection, traceability and non-deception had a significantly positive impact on ethical acceptability and indirectly influenced social acceptance, with privacy protection being the most sensitive. Perceived enjoyment impacts the social acceptance of deepfake information and significantly weakens the effect of ethical acceptability on social acceptance.

The ethical concerns affecting acceptance behavior identified in this study provide an entry point for the ethical regulation of deepfake information. The weakening effect of perceived enjoyment on ethics serves as a wake-up call for regulators to guard against pan-entertainment deepfake information.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

The public's trust in the authorities has a great impact on people's perception and cognition on development of different types of urban transport infrastructure projects (UTIPs). Given the importance of public acceptance for the efficient construction and operation of UTIPs, this study aims at investigating the personal and environmental factors that influence public acceptance behavior from the perspective of stakeholder management.

Based on social cognitive theory (SCT), this study explores the multiple dimensions of social trust on public acceptance in the development of UTIPs by a comparative case study. Two types of UTIPs, a metro railway and a bridge in the Wuhan City, China, were selected as cases, with a questionnaire distributed among the public to collect their sense of trust towards the development of these projects. The data were analyzed through structural equation modeling (SEM).

This study reveals that social trust positively influences public acceptance, directly or indirectly through perceived benefit and -risks and self-efficacy. However, the emphasis on social trust about competence and integrity of the authorities varies with the types of projects. Self-efficacy worked as the “mirror of trust” reflecting people's attitude towards social trust in the authorities on their ability and morality.

The value of the paper lies in discussing social trust from multiple dimensions in the field of urban infrastructures, which provides new insights into specific mechanisms for shaping public acceptance in project management towards the development of UTIPs.

The commonly used e-procurement systems still show unsatisfactory implementation outcomes because many organisations are still unable to effectively interpret the initial adoption decision. The e-procurement systems are generally developed at organisational level, but their usage is at individual level, by the employees particularly. This paper examined technology acceptance model’s (TAM) key antecedents, involving e-procurement systems usage by employees in their daily activities. This study aims to examine the impact of factors affecting e-procurement acceptance among users through the mediating role of users’ attitude. The commonly used e-procurement systems still show.

TAM was applied and expanded in this study, in exploring the factors impacting the employees’ e-procurement acceptance. This study used quantitative method, and questionnaires were distributed to 200 users in Jordanian public shareholding firms. The collected data were quantitatively analysed using PLS modelling.

Significant TAM relationships involving e-procurement were affirmed. The expanded TAM in the scrutiny of antecedents showed that content, processing and usability affected perceived usefulness, while perceived convenience did not affect the usefulness factor. Furthermore, it was noticed that perceived ease of use was affected by usability and training, while perceived connectedness was not affected by usability and training.

The results demonstrated the necessity of e-procurement training. Furthermore, at the start of the implementation stage, effective design on system navigation and system usability, and consistent support, could increase use effectiveness and acceptance. Also, expedient information and buyer–supplier product flows should be made available.

Most organizations invest a lot of time and money in installing e-procurement systems to deliver their goods at the right time and at the right price. However, many of these e-procurement systems have failed due to low acceptance among users. Thus, to the best of the authors’ knowledge, this is the first study that used TAM and theory of planned behaviour in examining the effects of perceived convenience, perceived connectedness, content, training, processing and usability factors, in Jordanian firms. Lastly, the focus of this study was on the individual employee’s acceptance, rather than on the organisational-level adoption, as the unit of analysis, to provide insight on how organisations can achieve maximally from e-procurement investments and from other comparable technologies of e-supply chain management.

Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

Blockchain can track the material from the manufacturer to the end customers. Therefore, it can ensure the product's authenticity, transparency and trust in the retail supply chain (SC). There is a need to trace and track the retail products before it reaches the customers to check the quality of the products so that expired products can be recycled and reused, which in turn will help gain customers' trust. This research aims to investigate retail employees' behavioural intention to adopt blockchain in the retail SC.

To examine the behavioural intention of employees in the retail SC, the research uses three theories – the technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour. The technology acceptance model measures the employee's acceptance of blockchain in the retail SC. The unified theory of acceptance is used in this research to measure how blockchain adoption will improve the performance of the employees. The theory of planned behaviour is used in this research to measure whether the employees intend to adopt blockchain. A survey was carried out in the retail stores of India. Exploratory factor analysis and structural equation modelling were used for data analysis.

This study found that the employees of the retail stores have a positive intention and attitude to adopt blockchain technology. Further, it was found that perceived behavioural control and effort expectancy was not promoting blockchain adoption in the retail sector.

This study will help the retail stores' employees understand the blockchain in their operations and will motivate the top management of the retail companies to adopt this technology. The study is limited to the retail SC in India only.

This study uses three theories technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour, which were not used in earlier studies of blockchain adoption in the retail SC.