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This study aims to understand the learner behaviour of millennials for Massive Open Online Courses (MOOCs) in the post-adoption stage by extending the theory of Unified Theory of Acceptance and User Technology 2 (UTAUT2) with expectancy confirmation model (ECM) along with personal innovativeness as the exogenous, satisfaction as a mediating and continued intention as an endogenous construct.

This study applied a cross-sectional research design by using a survey method to collect primary data with a structured questionnaire. Convenience sampling was used to collect data from millennial MOOC users, and partial least square structural equation modelling method was applied for data analysis.

The results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation influence satisfaction. Similarly, performance expectancy, hedonic motivation, personal innovativeness and satisfaction influence the continued intention for MOOCs.

In terms of limitations, the study applied a cross-sectional research design that could lead to data collection bias. Similarly, the study used convenience sampling as the authors did not have access to the participant list of users from MOOC platforms.

The research highlights various insights to all the stakeholders on improving MOOC satisfaction and enhance the continued intention for millennial learners.

The findings of this research bridge this gap by examining the post-adoption usage behaviour of MOOCs by extending the baseline model of UTAUT2 with personal innovativeness and integrating it with ECM.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

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 study aims to provide a better comprehension of the behavioural intentions that influence the adoption of Islamic financial technology (Fintech) in Malaysia for two kinds of Islamic lending Fintech services, which are crowdfunding and peer-to-peer (P2P) lending.

From May to July 2022 the primary data were collected by using a questionnaire distributed online to survey 437 Islamic Fintech clients in Malaysia. Structural equation modelling has been used to analyse the data based on using the partial least squares approach.

The findings of this paper shows that planned behaviour, acceptance model and technology's use models are positively impacting factors that influence customers' opinions on adapting Islamic Fintech services in lending. The acceptance model was found to exert a negative impact on the intention to adopt Islamic lending P2P Fintech service. In addition, technology's use has a negative impact on the intention to adopt Islamic lending crowdfunding Fintech service.

First, the study is limited to Islamic Fintech customers in Malaysia only, second, the study adopted an online survey but there is no guarantee that the geography area was fully covered. Another limitation is that the study covers only Islamic Fintech services in lending, thus the study did not attend to variables such as religiosity and the authors believe that this will provide useful insights for future research.

Despite the importance of this topic, there has been a lack of empirical evidence until now. In this paper, the authors take stock of the empirical evidence in the literature through the importance of the adoption Fintech. This study provides a broad view of the market potentials for Fintech providers from the demand side on a wide range of Islamic Fintech services rather than focussing only on payment, transfer, etc. as presented in previous studies.

ChatGPT is an advanced artificial intelligence (AI) form that can generate human-like text based on large amounts of data. This paper aims to empirically examine the ChatGPT adoption level among Indian individuals by considering the key factors in determining individuals’ attitudes and intentions toward newly emerged AI tools.

This paper used “partial least square structural equation modeling” (PLS-SEM) to investigate the relation among several latent factors by applying a representative sample of 351 individuals.

This study found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes. Additionally, trialability, performance expectancy, effort expectancy, personal innovativeness and attitude significantly influence behavioral intentions. However, compatibility has an insignificant impact on behavioral intention. Moreover, the research highlights that attitude and behavioral intention directly correlate with actual use. Specifically, the absence of compatibility makes people hesitate to use technology that does not meet their specific needs.

These unique findings provide valuable insights for technology service providers and government entities. They can use this information to shape their policies, deliver timely and relevant updates and enhance their strategies to boost the adoption of ChatGPT.

This paper is one of the pioneering attempts to exhibit the research stream to understand the individual acceptance of ChatGPT in an emerging country. Moreover, it gained significant attention from individuals for delivering a unique experience and promising solutions.

This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.

This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.

The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.

This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.

This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

Despite the extensive benefits of human resource (HR) analytics, the intention to adopt such technology is still a matter of concern in the engineering and construction sectors. This study aims to examine the slow adoption of HR analytics among HR professionals in the engineering and construction sector.

A cross-sectional online survey including 376 HR executives working in Indian-based engineering and construction firms was conducted. Hierarchal regression, structural equation modeling and artificial neural networks (ANN) were applied to evaluate the relative importance of HR analytics predictors.

The results reveal that hedonic motivation (HM), data availability (DA) and performance expectancy (PE) influence the behavioral intention (BI) to use HR analytics, whereas effort expectancy (EE), quantitative self-efficacy (QSE), habit (HA) and social influence (SI) act as barriers to its adoption. Moreover, PE was the most influential predictor of BI.

Based on the findings of this study, engineering and construction industry managers can formulate strategies for the implementation and promotion of HR analytics to enhance organizational performance.

This study draws attention to evidence-based decision-making, emphasizing barriers to the adoption of HR analytics. This study also emphasizes the concept of DA and QSE to enhance adoption among HR professionals, specifically in the engineering and construction industry.

Drawing on the cognitive–affective–conative framework, this study aims to develop a model of service robot acceptance in the hospitality sector by incorporating both cognitive evaluations and affective responses.

A mixed-method approach combining qualitative and quantitative methods was used to develop measurement and test research hypotheses.

The results show that five cognitive evaluations (i.e. cuteness, coolness, courtesy, utility and autonomy) significantly influence consumers’ positive affect, leading to customer acceptance intention. Four cognitive evaluations (cuteness, interactivity, courtesy and utility) significantly influence consumers’ negative affect, which in turn positively affects consumer acceptance intention.

This study provides significant implications for the design and implementation of service robots in the hospitality and tourism sector.

Different from traditional technology acceptance models, this study proposed a model based on the hierarchical relationships of cognition, affect and conation to enhance knowledge about human–robot interactions.

The purpose of this study is to gain empirical insights into whether accounting information systems (AIS) usage matters among Jordanian small and medium-sized enterprises (SMEs) during the period of COVID-19 pandemic.

The suggested research model in the current study is based on the extending technology acceptance model (TAM) to test the antecedents’ factors that impact on AIS usage among SMEs. To test the proposed research model, partial least squares structural equation modeling (PLS-SEM) was used.

The empirical findings revealed all postulated hypotheses were accepted except H3. Contrary to what is expected, the empirical outcomes confirmed that perceived compatibility does not affect the perceived usefulness of AIS, and hence, the related hypothesis was rejected.

The results of the current research could be beneficial to a number of managers (owners) to obtain a better understanding of the benefits of AIS success usage among Jordanian SMEs performance during crises time as the COVID-19 pandemic crisis.