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This study aims to examine the feasibility of adopting blockchain technology in Jordan’s industrial sector and its intangible benefits. It also analyzes the influence of factors like technological, process, cultural and leadership readiness on the willingness of enterprises to adopt blockchain.

To gain insights into the potential adoption of blockchain technology and its intangible benefits for enterprises in the Jordanian industrial sector, this study gathered perspectives from a broad range of professionals, including financial managers, internal control staff, accounting departments, IT department managers and IS-related personnel. This was achieved through the administration of a comprehensive questionnaire designed to capture their opinions.

This study highlights the importance of technological and leadership readiness in adopting blockchain. It also shows that blockchain adoption can yield significant intangible benefits for enterprises. However, the study did not find a significant relationship between process readiness, cultural readiness and the intention to adopt blockchain.

The study’s outcomes underscore the importance of prioritizing technological and leadership readiness for enterprises and policymakers intending to adopt blockchain technology. By doing so, they can increase their willingness to adopt this technology and leverage its benefits.

This pioneering study investigates the adoption of blockchain technology and its intangible benefits for Jordanian businesses. It also examines the influence of factors like technological, process, cultural and leadership readiness on the decision to adopt blockchain in the industrial sector.

This study aims to investigate the factors influencing the adoption of data analytics in performance management. By examining the role of organizational and environmental contexts, this study contributes to the existing literature by proposing a novel and detailed technology-organization-environment (TOE) model for the complex interplay between firm characteristics and the adoption of data analytics. The results offer valuable insights and practical implications for organizations seeking to leverage data analytics for effective performance management.

The research draws upon a data set encompassing over 21,869 companies operating across all European Union member states. A multilevel logistic regression model was developed to evaluate the influence of organizational and environmental factors on the likelihood of adopting performance analytics in organizations.

The findings indicate that the lack of awareness of the benefits of data analytics and its practical application to address specific business challenges is a significant barrier to its adoption. Organizational contexts, such as variable-pay systems, employee training, hierarchical structures and frequency of monetary rewards, also influence the adoption of data analytics.

The study informs managers about the strategic role of data analytics capabilities in performance management for improved business intelligence and driving data culture.

The study helps managers understand the strategic role of data analytics capabilities in performance management, leading to improved business intelligence and fostering a data-driven culture in five key areas: structural alignment, strategic decision-making, resource allocation, performance improvement and change management.

The study advances the TOE theory, making it a more detailed and complete framework, particularly applicable to the adoption of performance analytics. It identifies the main factors of adoption that play a crucial role in this process.

The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

Blockchain technology has created possibilities for environmental supply chain sustainability and climate protection. However, because of its early development stage, users tend to resist the adoption of this new technology. The purpose of this study is to investigate the effects of resistance on blockchain adoption intentions in the context of the foodservice industry. This study further explores if public pressures and climate change awareness could possibly weaken the negative relationships between blockchain resistance and adoption intentions.

Data were collected from managers and full-time employees in the foodservice industry, using an online research panel survey. A structural equation model was developed and tested to examine the hypothesized relationships. Additionally, a multi-group analysis was performed to test the moderating roles of public pressures and climate change awareness.

The findings from this study confirmed that foodservice employees’ characteristics, including traditional barriers, and blockchain technology factors, like perceived risk, are both significant in forming resistance to blockchain. This study also demonstrated the significant roles of internal and external stakeholders in weakening the negative associations between blockchain resistance and adoption intentions.

This study recommends that foodservice companies address how to reduce their employees’ negative perceptions about changes imposed by blockchain adoption. This study also suggests the joint consideration of the pressures from internal and external stakeholders to provide continued insights into developing environmental practices for the foodservice industry.

This study extends the theoretical underpinning of the innovation resistance theory by incorporating the stakeholder theory as a strong foundation for understanding how external pressures and internal awareness may influence foodservice employees’ responses to the implementation of blockchain technology to mitigate climate change.

The purpose of this paper is examining how financial inclusion can be enhanced in rural and remote areas in emerging economies through the adoption of Financial technologies. The study examines the direct influence of technological characteristics, facilitating conditions, trust and perceived risk on adoption of FinTech. Furthermore, the study examines the mediating effects of facilitating conditions and moderating effects of education level in adoption of FinTech.

The study employed quantitative research design. Stratified and simple random sampling were employed to identify areas in which respondents will be collected. Drop and Pick method was further employed to collect 275 valid and reliable responses. Structural equation modeling was used to analyze the data and assess the reliability and validity of the measurement model. Structural model analysis was employed to examine the hypothesized relationship.

The results show that compatibility, complexity agent trust, perceived risk and facilitating have significant influence on adoption of FinTech. Furthermore, the findings show that facilitating conditions mediate the relationship between all technological characteristics and FinTech adoption except the relationship between compatibility and FinTech. The study also has found that education level moderates the relationship between observability and FinTech adoption as well as trialability and FinTech adoption.

This study employed cross-sectional research-based techniques in collecting data for analyzing the hypothesized relationship. However, the drawback of cross-sectional may impact the findings of this study over a long time. Therefore, future studies must consider conducting longitudinal studies which may collect data over some time for analysis of the FinTech adoption. Furthermore, the generalization of these findings should be considered carefully by other countries which are not economically similar to Tanzania and developed countries. This also provides room for future studies to examine the same area in countries with different economic development.

This study contributes to knowledge into two folds: First there is limited knowledge on adoption of FinTech in emerging economies; the study addresses the knowledge gap by providing empirical findings which will help scholar. Furthermore, less studies have examined the moderating effects of facilitating condition in adoption of technology. This study addresses the knowledge gap by examining the moderating influence of moderating influence of facilitating conditions. Also, this study provides empirical findings which will help policymakers to implement strategy that will speed-up financial inclusion in rural and isolated areas in emerging economies.

In recent years, Malaysia has seen a dramatic change in the landscape of financial transactions due to the fast growth of mobile payment systems. This study aims to examine the technological, organisational and environmental (TOE) factors of merchants’ adoption intention to use mobile payment platforms essential for the continuing development and profitability of these cutting-edge payment options.

The research model was developed from the TOE framework and tested with the data collected from 120 merchants in Malaysia. The partial least squares structural equation modelling technique was used in analysing the collected data.

Technology readiness and competitor pressure were directly related to merchants' mobile payment adoption intention and indirectly through perceived strategic value. Also, perceived ease of use and perceived strategic value were significant predictors of the adoption intention of mobile payment.

This model demonstrates the relevance of TOE in explaining merchants' mobile payment adoption intention, with implications for policy and strategy to support the broader adoption of mobile payment platforms in Malaysia.

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.

Building information modelling (BIM) has transformed the traditional practices of the Architecture, Engineering and Construction (AEC) industry. BIM creates a collaborative digital representation of built environment data. Competitive advantage can be achieved with collaborative project delivery and rich information modelling. Despite the abundant benefits, BIM’s adoption in the AEC is susceptible to confrontation. A substantial impediment to BIM adoption often cited is data interoperability. Other facets of interoperability got limited attention. Other academic areas, including information systems, discuss the interoperability construct ahead of data interoperability. These interoperability factors have yet to be surveyed in the AEC industry. This study aims to investigate the effect of interoperability factors on BIM adoption and develop a comprehensive BIM adoption model.

The theoretical foundations of the proposed model are based on the European interoperability framework (EIF) and technology, organization, environment framework (TOE). Quantitative data collection from construction firms is gathered. The model has been thoroughly examined and validated using partial least squares structural equation modelling in SmartPLS software.

The study’s findings indicate that relative advantage, top management support, government support, organizational readiness and regulation support are determinants of BIM adoption. Financial constraints, complexity, lack of technical interoperability, semantic interoperability, organizational interoperability and uncertainty are barriers to BIM adoption. However, compatibility, competitive pressure and legal interoperability do not affect BIM adoption.

Finally, this study provides recommendations containing the essential technological, organizational, environmental and interoperability factors that AEC stakeholders can address to enhance BIM adoption.

To the best of the authors’ knowledge, this paper is one of the first studies to combine TOE and EIF in a single research model. This research provides empirical evidence for using the proposed model as a guide to promoting BIM adoption. As a result, the highlighted determinants can assist organizations in developing and executing successful policies that support BIM adoption in the AEC industry.

AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees.

The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM.

This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”.

This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory.

This study investigates the psychological adoption of technology in relation to employees' mental beliefs about using technology in their workplace, because it is necessary to investigate the direct and indirect effects of information systems (IS) on employees' work-related results that underpin creativity and engagement.

Using a cross-sectional design, data were collected from 153 human resource (HR) employees who used human resource information systems (HRIS) in small- and medium-sized enterprises (SMEs) in Malaysia.

The results show that effective acceptance and adoption of an HRIS enables HR employees and management in SMEs to be creative, balanced and engaged. Facilitating conditions and task-technology fit positively affect the behavioral intention to accept and adopt an HRIS. Additionally, organizational citizenship behavior moderates the relationship between the behavioral intention to accept and adopt an HRIS and employee creativity.

This study significantly advances the fields of human resource management and IS by elucidating the factors influencing employees' adoption of technology. In an effort to address a research gap in existing research, it introduces a unified theory of acceptance and use of technology, which precedes the psychological adoption process by individuals. Furthermore, it offers both empirical and theoretical insights into the interplay between technology adoption factors and their subsequent impact on work-related outcomes.