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Angola’s public sector employees’ adoption of e-governance and its impact on organizational performance was the primary objective of this study. The research employed the UTAUT model to conduct an in-depth study and analyze organizational performance, e-governance, and behavioral intention in detail, formulating four hypotheses. To test the hypotheses, a quantitative research method was used to collect data using online surveys sent by SurveyMonkey. A total of 273 individuals participated in the survey, and each survey took around 45 min to complete. Statistical analysis was performed on the acquired data using the SPSS and AMOS programs. The results of the analysis supported three hypotheses and disproved one. The statistical research that resulted in insignificant results revealed that effort expectancy had no direct effect on the behavioral intention of adopting e-governance or the influence on organizational performance. On the other hand, the accepted hypotheses demonstrated that performance expectation, social influence, and facilitating conditions had direct positive effects on organizational performance and a mediating effect on the behavioral intention to adopt e-governance in the public sector of the Angolan state.

Artificial intelligence (AI)-assisted diagnosis and treatment could expand the medical scenarios and augment work efficiency and accuracy. However, factors influencing healthcare workers’ adoption intention of AI-assisted diagnosis and treatment are not well-understood. This study conducted a cross-sectional study of 343 dental healthcare workers from tertiary hospitals and secondary hospitals in Anhui Province. The obtained data were analyzed using structural equation modeling. The results showed that performance expectancy and effort expectancy were both positively related to healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Social influence and human–computer trust, respectively, mediated the relationship between expectancy (performance expectancy and effort expectancy) and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Furthermore, social influence and human–computer trust played a chain mediation role between expectancy and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Our study provided novel insights into the path mechanism of healthcare workers’ adoption intention of AI-assisted diagnosis and treatment.

PurposeThe purpose of the research is to analyze the factors that determine the intention of small- and medium-sized enterprises (SMEs) to adopt the Metaverse. For this purpose, the analysis of the effort expectancy and performance expectancy of the constructs in relation to business satisfaction is proposed.Design/methodology/approachThe analysis was performed on a sample of 182 Spanish SMEs in the technology sector, using a PLS-SEM approach for development. For the confirmation of the model and its results, an analysis with PLSpredict was performed, obtaining a high predictive capacity of the model.FindingsAfter the analysis of the model proposed in this research, it is recorded that the valuation of the effort to be made and the possible performance expected by the companies does not directly determine the intention to use immersive technology in their strategic behavior. Instead, the results obtained indicate that business satisfaction will involve obtaining information, reducing uncertainty and analyzing the competition necessary for approaching this new virtual environment.Originality/valueThe study represents one of the first approaches to the intention of business behavior in the development of performance strategies within Metaverse systems. So far, the literature has approached immersive systems from perspectives close to consumer behavior, but the study of strategic business behavior has been left aside due to the high degree of experimentalism of this field of study and its scientific approach. The present study aims to contribute to the knowledge of the factors involved in the intention to use the Metaverse by SMEs interested in this field.

PurposeThe emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.Design/methodology/approachAn advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.FindingsBoth EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.Practical implicationsSome practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.Originality/valueThis study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.

Purpose>The purpose of this paper is to examine the impact of key antecedents of unified theory of acceptance and use of technology model 2 on behavioral intention to accept and use mobile payment systems in National Capital Region, India.Design/methodology/approach>A sample of 267 mobile payment system users in National Capital Region was obtained through an online survey. A partial least squares method was used to find out whether key antecedents of UTAUT2 predict behavioral intention to accept mobile payment systems which further predicts use behavior toward mobile payment systems.Findings>The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention.Research limitations/implications>The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention.Originality/value>The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention.

The advent of Internet heralded the rise of scalable educational technology dubbed as massive open online course (MOOC). Easy to use, access, economical as well as flexible, provide students lot of freedom and the advantage of self-paced learning. Despite all these merits, MOOC adoption is low in the higher educational institutions (HEIs) of India. The aim of this study is to explore the factors affecting the behavioural intention to adopt MOOCs among Generation Z (Gen Z) enrolled in the Indian HEIs. The study uses the extended UTAUT2 model with additional constructs of language competency and teacher influence to explore MOOC adoption among the Gen Z. Using online survey, data of 483 students was collected from HEIs of India using stratified random sampling and analysed using partial least square-structure equation modelling (PLS-SEM) technique. The results establish the general applicability of UTAUT2 model in context of MOOC in Indian settings with explanatory power of 69.9% and highlights the positive influence of price value, hedonic motivation, facilitating conditions, performance expectancy and effort expectancy on MOOC adoption. However, the constructs of social influence, habit, language competency, and teacher influence unexpectedly do not have an impact on Behavioural Intention of Gen Z towards MOOC adoption. Based on the research findings, study implications and future directions of the research have been suggested.

PurposeThis article aims at a Unified Theory of Acceptance and Use of Technology (UTAUT) model framework that was used to investigate the impact of a 16-h smartphone training program on the correlations among different constructs of smartphone use in a sample of older adults.Design/methodology/approachA total of 208 participants aged 60–78 (mean: 65.4) years completed a questionnaire that collected information on demographic variables and the frequency and duration of smartphone use as well as the answers to questions on the six UTAUT constructs of performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention and usage behavior. The data were analyzed using partial least squares analysis.FindingsThis study was the first to compare post-training changes in the correlations among UTAUT constructs. The results revealed significant post-training changes in all construct correlations. Behavioral intention and facilitating conditions were shown to significantly impact usage behavior both before and after training and performance expectancy was shown to impact behavioral intention before training. After training, both effort expectancy and social influence were found to impact behavioral intention significantly. Moreover, the impact of facilitating conditions on usage behavior was significantly increased after training.Originality/valueTo date, no study published in the literature has investigated the impact of technological training on the technology-use intentions and behaviors of older adults. The findings of this study suggest that, for older adults, the results of the acceptance and use model for smartphones change significantly and positively between pre-smartphone training and post-smartphone training time points. The findings support that technology training has a positive impact on smartphone use in older adults.