Effort Expectancy Research Articles

Factors influencing meta-banking adoption: an empirical study

Purpose This study aims to examine the main factors influencing customer meta-banking (metaverse banking) adoption intention based on the unified theory of acceptance and use of technology (UTAUT) model. Design/methodology/approach In this study, the PLS-SEM modelling technique was used to analyse the quantitative data collected from a total of 407 participants who were purposefully chosen. Findings The findings revealed that performance expectation, effort expectancy, facilitating conditions and hedonic motivation have significant positive effects on customers’ perception of the value associated with adopting the metaverse. However, the effect of social influence was found to be statistically insignificant. The results further emphasised that technostress is an obstacle that negatively influences the perceived value. Lastly, perceived value is found to be a key facilitator of adoption intention as it shows a positive effect on it. Originality/value This study stands at the forefront of research on banking customers’ adoption intention of meta-banking. Going beyond the investigation of adoption intention, the research also critically assesses the applicability of UTAUT in the context of meta-banking. The results demonstrate that UTAUT is a viable theoretical framework for examining the dynamics of meta-banking. In particular, the findings indicate that UTAUT model provides a reliable lens for comprehending the intricacies of meta-banking adoption.

Design characteristics and service experience with e-government services: A public value perspective

We propose a model that explains how e-government service design characteristics influence citizens’ service experience. Drawing on the public value perspective, we define four categories of e-government impacts and identify their corresponding design characteristics: (1) capability—accuracy and completeness; (2) interactions—accessibility and self-service capability; (3) orientation—user support and personalization capability; and (4) value distribution—privacy protection. We propose that citizens’ perceptions of the design characteristics affect four core technology beliefs—i.e., performance expectancy, effort expectancy, facilitating conditions, and trust—that in turn affect two service experience outcomes—i.e., perceived service quality and continuance intention. We tested our model in a two-stage survey of 2623 users of two e-government services—i.e., an e-government portal and an online appointment booking service. The results showed that citizens’ perceptions of the design characteristics predict the core technology beliefs that in turn predict perceived service quality and eventually continuance intention. Further mediation analyses showed that the core technology beliefs significantly mediate the effects of design characteristics on perceived service quality, but with different patterns of full and partial mediation across the two services. These findings contribute to understanding the underlying mechanisms through which service design characteristics influence service experience. We discuss the theoretical and practical implications on e-government service design and delivery.

Factors influencing the use of big data within healthcare services: a systematic review.

Background: The emergence of big data holds the promise of aiding healthcare providers by identifying patterns and converting vast quantities of data into actionable insights facilitating the provision of precision medicine and decision-making. Objective: This study aimed to investigate the factors influencing use of big data within healthcare services to facilitate their use. Method: A systematic review was conducted in February 2024, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Database searches for articles published between 01 January 2020 and 18 February 2024 and included PubMed, Scopus, ProQuest and Cochrane Library. The Authority, Accuracy, Coverage, Objectivity, Date, Significance ( AACODS) checklist was used to evaluate the quality of the included articles. Subsequently, a thematic analysis was conducted on the findings of the review, using the Boyatzis approach. Results: A final selection of 46 studies were included in this systematic review. A significant proportion of these studies demonstrated acceptable quality, and the level of bias was deemed satisfactory. Thematic analysis identified seven major themes that influenced the use of big data in healthcare services. These themes were grouped into four primary categories: performance expectancy, effort expectancy, social influence, and facilitating conditions. Factors associated with "effort expectancy" were the most highly cited in the included studies (67%), while those related to "social influence" received the fewest citations (15%). Conclusion: This study underscored the critical role of "effort expectancy" factors, particularly those under the theme of "data complexity and management," in the process of using big data in healthcare services. Implications: Results of this study provide groundwork for future research to explore facilitators and barriers to using big data in health care, particularly in relation to data complexity and the efficient and effective management of big data, with significant implications for healthcare administrators and policymakers.

EXAMINING CRITICAL FACTORS INFLUENCING GENERATION Z’S ACCEPTANCE OF MOBILE PAYMENT SYSTEMS IN BANGLADESH: A UTAUT MODEL ANALYSIS

Mobile payment systems have become an integral part of the digital ecosystem, simplifying financial transactions among individuals, businesses, and even the government in some cases (C2G, G2C) using handheld mobile gadgets. The current academic literature concentrates on the acceptance of mobile payment systems (MPS), including generational diversity, emphasizing the necessity for more concentration on the factors behind MPS adoption trends based on specific economic aspects concerning marketers’, marketing professionals', and practitioners' interests. Therefore, this study synthesizes the existing insights to address the current knowledge gap regarding MPS adoption among Generation Z citizens in developing economies, particularly focusing on Bangladesh. However, grounded in the UTAUT model, this study utilizes structural equation modeling (SEM). IBM SPSS 27 and SmartPLS 3.2.9 have been used for data analysis. The results of this study reveal that Gen Z citizens have positive perspectives toward MPS, and the important factors affecting their acceptance are effort expectancy, enjoyment, performance expectancy, and trust. Finally, the study offers significant insights for further studies in terms of empirical and theoretical contributions.

Online shopping behaviour on social media platforms from the perspective of trust and flow experience: a SEM-Neural Network Modeling

This study aims to examine online shopping behaviour on social media platforms. This study formulates a research model integrating trust with the flow theory and some basic constructs of the UTAUT. To analyze the data from an online survey involving 305 participants actively making online purchases through social media platforms. This study applied the Structural Equation Modeling-Artificial Neural Networks (SEM-ANN) technique. Incorporating statistically significant SEM findings, the ANN model was used to analyze linear and nonlinear interactions among proposed variables. The research findings demonstrate that flow emerges as the most significant determinant, succeeded by effort expectancy, social influence, and performance expectancy, in defining the concept of trust. However, the sensitivity analysis using ANN indicates that effort expectancy is the most important factor in establishing trust, followed by flow, social influence, and performance expectancy. The predictive power of intention to use is noteworthy in determining actual use behaviour, with trust and flow emerging as influential factors favourably impacting this intention.

Factors Affecting The Adoption Of Mobile Learning In Vocational High Schools And High Schools Using Extended UTAUT

M-Learning is a learning process that uses technology or mobile devices such as smartphones, tablets or wearable devices to support the learning process. This is still being done because there are many different theoretical models proposed. However, there is no model that can be generally accepted as an established theoretical model in the application of M-learning in vocational and high school education environments in Sidoarjo. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determining factors that influence M-learning adoption using the Unified Theory of Acceptance and Use of The Technology (UTAUT). To collect data, researchers distributed questionnaires to respondents using Google Form. The data used were 444 M-learning users. Theoretical model research was carried out using Structural Equation Modeling (SEM) analysis, then SPSS and Amos as analysis support. There are seven factors that determine the results of acceptance of M-Learning adoption in this research, namely Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. The six factors that show a positive and significant relationship are Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. Perceived Convenience has the first strongest positive and significant value, and Performance Expectancy has the second strongest value. Each factor has a moderate influence on Intention to Use. This factor is the most influential in implementing M-Learning in vocational and high schools in the Sidoarjo area.

Community Acceptance of SIKESAL: An UTAUT Model Approach in E-Government Services in Jambi City

Purpose: This research examines the factors influencing public acceptance of e-government services, particularly the Sikesal system in Jambi City. The urgency of this research lies in its potential to improve the quality of public services, increase community engagement, and support digital transformation goals in Jambi City. This study contributes to understanding how local communities perceive and utilize e-government services, providing insights to improve service design and Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, we evaluated variables including Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. The study utilized a quantitative approach, collecting data through questionnaires distributed to 100 respondents, selected using the Slovin formula. Result: Results indicate that Effort Expectancy significantly impacts the use of e-government services (T value of 18.339, P value of 0.000), highlighting the importance of user-friendliness. However, performance expectancy, social influence, and facilitating conditions did not significantly impact. Novelty: The novelty of this study lies in its localized examination of e-government acceptance, providing insights for targeted improvements in service design and implementation.

IT professionals’ post-pandemic online learning: UTAUT model study

PurposeThis paper aims to examine employees’ online learning continuation intentions (OLCI), exploring factors such as performance expectancy (PE), effort expectancy (EE), social influence (SI), perceived benefits (PB) and management support (MS) influencing their commitment to online learning engagement.Design/methodology/approachThe Unified Theory of Acceptance and Use of Technology (UTAUT) model was expanded to include PB and MS constructs. This study used a quantitative research approach using purposive sampling techniques. Three hundred and eighty-six responses from Indian information technology (IT) professionals at various levels were analysed using Statistical Package for the Social Sciences-Analysis of Moments Structures tool.FindingsThis study found a strong positive influence of PE, EE, PB and MS on OLCI in the context of post-pandemic. Workplace learning rapidly generates outcomes if employees associate it with their career growth. However, the authors found that SI does not significantly affect OLCI.Originality/valueThis research is unique work in the area of workplace learning by evaluating the OLCI of IT professionals using the extended UTAUT model in a new normal. Moreover, this study contributes to online learning literature with a combined study of technology usage, continuance intention and organization learning and development.

Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT)

The use of artificial intelligence (AI) tools, such as chatbots, has significantly increased in academia and research. The present study seeks to determine the key factors influencing chatbot adoption, as well as attempts to validate the unified theory of acceptance and use of technology (UTAUT) in the context of AI chatbots adoption among research scholars. The data for this study were collected through purposive sampling using a cross-sectional survey. The population of the study comprised research scholars enrolled in three public sector universities in Pakistan. The eight-factor proposed measurement model was estimated using confirmatory factor analysis (CFA) based on 30 valid items. The goodness of fit indices suggest a favourable fit for the model χ2 = 1.710, DF = 381; p = 0.000; IFI = .902; TLI = 0.886, CFI = 0.900, RMSEA = 0.056. Our research affirms that social influence, trust, and facilitating conditions play pivotal roles as primary predictors of behavioural intentions for AI chatbots adoption among scholars. The study suggests that the perceived risks associated with using AI chatbots due to their potential misuse can be minimized by effectively implementing AI user guidelines, and developing AI literacy among scholars. Information professionals and ethical libraries can play an important role in “building the bridge” between cutting-edge technology capabilities and information users’ needs and rights. The proposed eight-factor AI chatbots adoption model holds substantial potential in understanding the influence of performance expectancy, effort expectancy, social influence, trust, perceived risk, and facilitating conditions on behavioural intention to AI chatbots adoption. This study contributes to the limited body of research investigating the factors influencing AI chatbot adoption among research scholars using the UTAUT model with additional constructs for trust and perceived risk.

Perceptions of cognitive workload measurement using multimodal sensors in surgery

BackgroundThe study of cognitive load management in surgery has led to identification of objective cognitive workload (CWL) correlates offering the potential to improve patient safety, enhance surgeon performance and their long-term well-being. Sensors have been used in isolation within surgery to measure physiological changes of the surgeon to infer CWL. More recently however, the use of multimodal sensors (MMS) has been explored to improve the reliability of CWL measurement. Despite the emergence of this technology, the behavioural intention of future users of MMS is not well understood. The aim of this study is to explore the perceptions of CWL measurement using MMS in surgery and secondly, to develop a model of acceptance amongst a cohort of future users.MethodsA cross-sectional survey of medical students in the United Kingdom was undertaken by means of an online questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, using performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) and behavioural intention (BI) as latent constructs. A purposive sampling method was undertaken over a period of nine months. Results were analysed using structural equation modelling.ResultsThere were 232 responses with 138 fully completed responses used for final data analysis. Weighted averages of all item responses demonstrated positive responses to all questionnaire statements. Students strongly agreed to ‘Technology used to measure my mental workload would be useful to me’ (42.8%), ‘I would use the system if it was comfortable to wear’ (53.6%) and ‘I would like to know that the technology is reliable before using it’ (68.8%). Pathway co-efficients were 0.444 for PE → BI (p = < 0.001), 0.221 for EE → BI (p = < 0.001), 0.096 for SI → BI (p = 0.186) and 0.142 for FC → BI (p = 0.094).ConclusionThis study demonstrates an overall positive perception of CWL measurement using MMS. Although social influences and facilitating conditions demonstrate a positive influence on the behavioural intention of students, performance and effort expectancy are significant constructs to consider, namely the comfort and reliability of multimodal sensors. Findings from this study can be used to guide future development of emerging MMS systems that can be used to reduce human error, improve training and patient safety.

Digital transformation in financial industry: antecedents of fintech adoption, financial literacy and quality of life

Purpose This paper aims to investigate the variables that could contribute to facilitating or hindering FinTech adoption in Jordan and how that will affect human well-being (quality of life [QoL]). Design/methodology/approach A conceptual framework was formulated through the integration of “the unified theory of acceptance and use of technology” (UTAUT), “task-technology fit” (TTF) model and two additional factors, namely, “financial literacy” (FL) and “quality of life” (QoL). A cross-sectional online survey was used to obtain data from 378 FinTech users employing a quantitative method. AMOS 26.0 was utilized to analyse the data based on “structural equation modelling” (SEM). Findings The analysis of the structural path found that UTAUT constructs including “performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI)”, and TTF were significant determinants of FinTech adoption. Only technology characteristics (TECH) was a significant predictor of TTF. Also, the analysis of empirical data revealed a significant mediating impact of FinTech adoption on the association between FL and QoL, underlining the important role of digital FL in digitalizing societies. Likewise, FL affected the QoL directly. Practical implications This research will be beneficial for “FinTech service providers” (FSPs) and policymakers to offer thorough insights regarding the current relatively low acceptance rates of FinTech, contributing to strategies’ formulation that could promote FinTech usage by Jordanian customers, where FinTech is still considered an innovative technology. In addition, FL needs to integrate digital literacy to utilize state-of-the-art technologies for more effective financial management. This is with being able to make decisions facilitating the management of life outcomes which could result in better QoL. Originality/value To the best of the author’s knowledge, this research is the first research paper that integrates the UTAUT and TTF models and also adds two additional constructs, namely, FL and QoL, to investigate the FinTech in the Jordanian setting. This study could contribute to the literature on IT adoption by considering FinTech usage and incorporation into individuals’ life in Jordan.

The adoption of electric vehicle in Thailand with the moderating role of charging infrastructure: an extension of a UTAUT

ABSTRACT Electric vehicles (EVs) have the potential to greatly reduce the amount of gasoline consumed and carbon emissions produced during transportation. Consequently, the Thai government actively advocates for the widespread use of electric vehicles (EVs). This study has examined the factors that influence the adoption of electric vehicles in Thailand, with the moderation of charging infrastructure. The research builds upon the UTAUT2 model and incorporates new elements such as environmental concerns, satisfaction with incentive policies, trust, and the availability of charging infrastructure. Environmental concerns, price value, and hedonic motivation significantly affect use behaviour through trust and behaviour intentions; however, facilitating conditions do not affect them. Habits positively affect use behaviour through behaviour intention, though factors such as effort expectancy and social influence satisfaction with policies showed insignificant results when affecting use behaviour. The moderating effect of charging infrastructure showed an insignificant result.

Beyond Brick and Mortar: Unveiling the Willingness to Adopt E-Banking Services

The banking sector is one of the most essential sectors for the Palestinian economy. Therefore, many researchers are examining recent developments in banking services and clients' attitudes toward accepting or rejecting technological developments in these services and their transformation from traditional services to electronic banking services. This research explores the relationship between performance expectancy, effort expectancy, social influence, perceived risk, and consumer attitudes toward accepting and adopting electronic banking services. It also examined the mediating effect of consumers' attitudes on the relationship between the sum of independent and dependent variables. This study extends the UTAUT framework by implementing it with two additional factors, which are perceived risk as an independent variable and consumer attitude as a mediating variable. We constructed a conceptual model and employed Smart-Pls 3 to conduct structural equation modeling analyses. We implemented a survey-based methodology. A convenience sample technique was followed, and 306 bank customers in Palestine were surveyed using structured questionnaires. The results indicate that Palestinian consumers' acceptance of using electronic banking services is significantly influenced by their attitude toward these services, their expectations, and the availability of facilitating conditions. Furthermore, consumer attitudes are approved to have a positive mediating effect in these relationships.

A Study on Retailers’ Behavior Intention to Accept and Use the Omni channel Commerce Platforms

Research on retailers’ behavioral intention to accept and use the omnichannel commerce platforms (OCPs) in the Vietnam context used the Unified Theory of Acceptance and Use of Technology (UTAUT2) model and supplemented the other factors such as omnichannel capability, omnichannel integration. The results of this study show Effort Expectation, Social Influence, Hedonic Motive, Professional Capacity, Seamlessly Linking have a positive impact on retailers' behavior intention to accept and use OCPs. Behavioral Intention and Favorable Conditions have a positive impact on use behavior. Meanwhile, Habits and Omnichannel Integration have a negative impact on the use behavior of OCPs in Vietnam. The findings of this model help managers and OCPs providers identify factors that influence OCPs' behavioral intention and use behavior of retailers in Vietnam.

Attitudes of support people: a key element when implementing technologies for people with intellectual and visual disabilities

Aim The degree to which people with intellectual and visual disabilities can use technology relies on the level of support they receive. However, there is a lack of knowledge about the relationship between the constructs effort expectancy, attitudes, and behavioural intentions of support people (i.e. relatives and healthcare professionals) regarding the use of such technologies for people with intellectual and visual disabilities. The aim of this study was to gain insight into how these constructs are connected and to explore their relationship with support person’s characteristics and the support need levels of people with intellectual and visual disabilities. Methods In total, 186 support people from a Dutch healthcare organisation focusing on people with intellectual and visual disabilities participated in an online questionnaire. We used a regression analysis to explore how the constructs, the characteristics, and the level of support needs were related. Results Both effort expectancy (β = .35; t(185) = 5.04; p < .001) and attitude (β = .75; t(185) = 15.55; p < .001) of support people were related to the intention to use technologies. The effect of effort expectancy (β = .04; t(177) = .74; p = .462) on the intention to use technologies was mediated through attitude (β = .74; t(177) = 13.28; p < .001). Younger support people scored higher on attitude than older support people. Conclusions Support people’s effort expectancy and attitude play a significant role in their intention to use technologies when supporting people with intellectual and visual disabilities, with attitude emerging as pivotal factor.

Analisis Penerimaan dan Kesuksesan Aplikasi M-health pada Lansia menggunakan Model UTAUT dan Delone &amp; McLean

M-health plays a crucial role in providing medical services through features like online doctor appointments. While it offers convenience, there are challenges in its adoption among the elderly. The success of M-health depends on user acceptance and continued use. Therefore, an evaluation of information technology focused on the elderly in Indonesia is necessary. Technology accepted by users is not necessarily successful, and vice versa. This study aims to identify factors influencing the acceptance and success of M-health applications among the elderly. It combines the UTAUT and Delone &amp; McLean models to investigate acceptance and success factors. The variables used are performance expectancy, effort expectancy, information quality, system quality, service quality, user satisfaction, and continuance intention. The PLS-SEM method is used to process respondent data. Analysis result shows that 61.6% of elderly users' satisfaction with M-health is influenced by information quality, service quality, performance expectancy, and effort expectancy. Meanwhile, 59.4% of the continuance intention is influenced by user satisfaction. This indicates that the application is well received and successful because it provides a satisfying experience. This study confirms that the combination of the UTAUT and Delone &amp; McLean models is adequate.

User perceptions of intelligent offloading diabetic footwear.

Adherence to therapeutic footwear is vital for effective diabetic foot ulcer prevention and treatment. Understanding the key adherence factors and potential barriers is important for footwear design and implementation. Our team is creating intelligent offloading footwear to prevent lower extremity amputations in people living with diabetes (PLwD). This exploratory study assessed the ability of the established Unified Theory of Acceptance and Use of Technology (UTAUT) model to predict behavioral intention to use or recommend this intelligent offloading footwear by PLwD, caregivers of PLwD, or medical professionals treating PLwD. Online and paper questionnaires were implemented to assess the impact of the UTAUT model factors (performance expectancy, effort expectancy, social influence, facilitating conditions) and psychosocial factors (attitude, anxiety, self-efficacy) on the overall behavioral intention to use the footwear. Furthermore, factors influencing potential acceptance and rejection of the footwear were explored. Patients (4.0/5) and medical professionals (4.1/5) showed a behavioral intention to "agree" to use or recommend the footwear when it becomes available. Structural equation modeling showed that the UTAUT constructed model may not be the best indicator for behavioral intention here based on a lack of statistical significance. However, the logistic regression modeling showed that the social influence for PLwD (p=0.004) and the attitude toward the footwear for medical professionals (p=0.001) may be the most important when designing and implementing the footwear, though several other factors (performance expectancy, effort expectancy, facilitating conditions, and self-efficacy) were also important for one or both of these populations. Additionally, cost and clinician support were shown to be important factors influencing potential acceptance of the footwear. The study found promising intention to use the intelligent footwear in the future. This highlights the need to continue future development and implementation of the footwear to incorporate these results, thus improving the likelihood of high adherence of the footwear.

Enhancing the effectiveness of digital transformation on teaching in higher education in Kuwait

IntroductionThis study investigates the digital citizenship behavior (DCB) and behavioral intention (BI) of faculty members at the Public Authority for Applied Education and Training (PAAET) colleges in Kuwait. The research aims to evaluate the effectiveness of technology in teaching and provide a case study of higher educational institutions in Kuwait. The study’s theoretical framework focuses on four key factors: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), examining their impact on faculty readiness (RED) through the mediation of both BI and DCB. A total of 28 hypotheses were tested, exploring direct and indirect relationships among these variables.MethodsA survey was conducted among 122 faculty members from PAAET colleges in Kuwait. The questionnaire measured the participants’ perceptions of PE, EE, SI, and FC, along with their BI and DCB. The study employed statistical methods to analyze the data and test the 28 hypotheses, aiming to identify significant paths of relationships that contribute to faculty readiness for technology use in education.ResultsThe findings confirmed eight significant paths of relationships, highlighting the importance of inputs such as effort and social influence and their impact on performance as an output in determining faculty readiness. However, the study raised empirical doubts about the remaining 20 hypotheses, providing new evidence that challenges the currently established relationships between technology acceptance and digital citizenship.DiscussionThe study offers valuable insights into the factors influencing the effective use of technology in higher education institutions in Kuwait. It suggests that while some pathways, such as effort and social influence, significantly contribute to faculty readiness, other expected relationships may not hold as previously thought. This new evidence calls for a reevaluation of the existing models of technology acceptance and digital citizenship. The results have important implications for policy and practice, offering guidance on enhancing technology use among faculty members in educational settings. The study contributes to the broader discussion on digital transformation in education, providing a comprehensive understanding of the key factors that affect faculty readiness for adopting new technologies.

Understanding the academic use of KM-driven Metaverse technology: insights from medical colleges

Purpose This study delves into the behavioural intentions of educators within medical colleges at Mosul Universities concerning the adoption of Knowledge Management-Driven Metaverse technology (KM-D-MT). Rooted in an adapted Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, the research aims to enrich the understanding of Metaverse adoption factors, exploring correlations among key constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value, hedonic motivation and interaction. Furthermore, the study investigates the mediating roles of knowledge generation and knowledge sharing in the relationship between interaction and behavioural intention. Design/methodology/approach The research employs a quantitative approach, gathering 278 responses from educators in medical colleges. Structural Equation Modelling-Partial Least Squares (SEM-PLS) is used to analyse the data, rigorously examining the reliability and validity of research instruments. The investigation involves an extensive evaluation of various factors influencing educators’ intentions to adopt KM-D-MT, using a cross-sectional design. Findings The study reveals significant positive impacts of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value and hedonic motivation on behavioural intention to adopt KM-D-MT. Interaction is identified as a key factor positively influencing knowledge sharing and knowledge generation. Furthermore, knowledge sharing and knowledge generation exhibit positive correlations with behavioural intention. Interaction indirectly impacts behavioural intention through the mediating roles of knowledge generation and knowledge sharing, highlighting the transformative potential of Metaverse technology in reshaping knowledge processes. Practical implications The findings of this study hold practical implications for educators, institutions and policymakers. The adoption of KM-D-MT can enhance educational experiences, facilitate global collaboration and contribute to the continuous professional development of educators in medical colleges. Institutions are encouraged to strengthen technological and organisational infrastructure to support effective Metaverse implementation. Furthermore, promoting positive social norms, providing technical support and offering training programs can contribute to overcoming barriers and fostering a conducive environment for Metaverse adoption in medical education. Originality/value This research significantly contributes to theoretical perspectives by advancing Metaverse research and addressing the call for extensive studies covering theoretical, conceptual and empirical elements. It extends current UTAUT2 frameworks, exploring correlations in the context of medical education and contributes to knowledge management paradigms. The study’s originality lies in its exploration of Metaverse acceptance in higher education institutions, specifically in medical colleges in Iraq, providing valuable insights for further research and practical applications globally.

Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.

Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. This meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence. The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs. The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy. This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.