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The unified theory of acceptance and use of technology (UTAUT) is a little over a decade old and has been used extensively in information systems (IS) and other fields, as the large number of citations to the original paper that introduced the theory evidences. In this paper, we review and synthesize the IS literature on UTAUT from September 2003 until December 2014, perform a theoretical analysis of UTAUT and its extensions, and chart an agenda for research going forward. Based on Weber's (2012) framework of theory evaluation, we examined UTAUT and its extensions along two sets of quality dimensions; namely, the parts of a theory and the theory as a whole. While our review identifies many merits to UTAUT, we also found that the progress related to this theory has hampered further theoretical development in research into technology acceptance and use. To chart an agenda for research that will enable significant future work, we analyze the theoretical contributions of UTAUT using Whetten's (2009) notion of cross-context theorizing. Our analysis reveals several limitations that lead us to propose a multi-level framework that can serve as the theoretical foundation for future research. Specifically, this framework integrates the notion of research context and cross-context theorizing with the theory evaluation framework to: 1) synthesize the existing UTAUT extensions across both the dimensions and the levels of the research context and 2) highlight promising research directions. We conclude with recommendations for future UTAUT-related research using the proposed framework.

Designing technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user's journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches. This review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance. We searched the PubMed database for publications indexed under the Medical Subject Headings terms \"Patient Acceptance of Health Care\" and \"Mobile Applications.\" We included publications that (1) contained at least one of the terms \"acceptability,\" \"acceptance,\" \"adoption,\" \"accept,\" or \"adopt\"; and (2) defined the term. The final corpus included 68 relevant studies. Several interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools. Definitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.

Intelligent information technology (IIT) based on AI and intelligent network communication technology is rapidly changing the social structure and the personal lives. However, IIT acceptancefrom various perspectives still requires extensive research. The research question in this paper examines how five factors—psychological, technological, resource, risk perception, and value factors—influence IIT acceptance. Based on an analysis of survey data, it was first found that the acceptance rate of IIT itself was generally very high. Second, in terms of IIT acceptance, among twenty-five predictors, voluntariness (+), positive image of technology (+), performance expectancy (+), relative advantage (+), radical innovation (+), and experience of use (+) were found to have significant effects on the IIT acceptance. Third, in addition to technological factors, psychological factors and risk perception factors also played an important role in individuals’ decisions regarding IIT acceptance.

Background: Leveraging healthcare technology improves human development and well-being. However, adoption is frequently delayed by behavioural and psychological barriers, such as perceived usefulness, trust, and organisational readiness. This review examines the suitability of the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM) frameworks in healthcare settings, focusing on behavioural, educational, and psychological factors that influence technology adoption. Methods: A total of 20 peer-reviewed articles from 2019 to 2024 were examined. Results: The review identified significant organisational and psychological obstacles, including a lack of trust, inadequate training, and organisational support. While the UTAUT provided a more comprehensive viewpoint, it needed to be modified to include context-specific factors, including trust, facilitating circumstances, and educational interventions. Meanwhile, the TAM’s emphasis on perceived usefulness and ease of use was shown to be insufficient for dealing with complex healthcare situations. Conclusions: Interventions targeting stakeholders’ organisational and psychological preparation and educational strategies are essential to overcoming resistance and enhancing trust. Future research should look into integrative frameworks incorporating behavioural, psychological, and instructional tactics to improve the use of technology in healthcare.

The technology readiness (TR) index aims to better understand people’s propensity to embrace and use cutting-edge technologies. The initial TR construct considers four dimensions—innovativeness, optimism, insecurity, and discomfort—that collectively explain technology usage. The present meta-analysis advances understanding of TR by reexamining its dimensionality, and investigating mediating mechanisms and moderating influences in the TR–technology usage relationship. Using data from 193 independent samples extracted from 163 articles reported by 69,263 individuals, we find that TR is best conceptualized as a two-dimensional construct differentiating between motivators (innovativeness, optimism) and inhibitors (insecurity, discomfort). We observe strong indirect effects of these dimensions on technology usage through mediators proposed by the quality–value–satisfaction chain and technology acceptance model. The results suggest stronger relationships for motivators than for inhibitors, but also that these TR dimensions exert influence through different mediators. Further, the moderator results suggest that the strength of TR–technology usage relationships depends on the technology type (hedonic/utilitarian), examined firm characteristics (voluntary/mandatory use; firm support), and country context (gross domestic product; human development). Finally, customer age, education, and experience are related to TR. These findings enhance managers’ understanding of how TR influences technology usage.

Research on information systems has identified a variety of factors across a range of adoption models that determine their acceptance. In this research, the unified theory of acceptance and use of technology (UTAUT), which integrates determinants across eight models, was utilised to analyse students’ intentions to use and their actual usage of Moodle, an e-learning system at Hashemite University, a public university in Jordan, one of developing countries. Four principal determinants of intention and usage were explored: performance expectancy, effort expectancy, social influence, and facilitating conditions. Data were collected from 370 undergraduate students and analysed using structural equation modelling techniques. The results indicated that performance expectancy and effort expectancy affected behavioural intentions to use Moodle whereas social influence did not. In addition, the results confirmed the direct impact of behavioural intentions and facilitating conditions on students’ use of Moodle. UTAUT thus provides a valuable tool that enables university decision makers, faculty members, and designers to understand the factors driving e-learning system acceptance and thus facilitate the adoption of the system by students. The study will help educational institutions prepare e-learning systems, which is especially important during a state of emergency such as that caused by COVID-19.

PurposeIn recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.Design/methodology/approachThis study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.FindingsResults indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.Originality/valueThis study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

Acceptance of, behavioral intention towards, and actual use of AI-based systems or programs has been a topic of growing interest in the field of education. A considerable number of studies has been conducted to investigate the driving factors affecting users’/students’ intentions regarding certain technology or programs. However, few studies have been performed to understand college students’ actual use of AI-based systems. Moreover, the mediating effect of students’ learning motivation was seldom considered. Therefore, the present study was conducted to explain factors contributing to college students’ actual use of AI-based systems, as well as to examine the role of their learning motivations. As a result, perceived usefulness and perceived ease of use of AI-based systems positively impacted students’ attitude, behavioral intentions, and their final, actual use of AI-based systems, while college students’ attitude towards AI-based systems showed an insignificant impact on students’ learning motivations of achieving their goals and subjective norms. Collectively, the findings of the present study could enrich the knowledge of the technology acceptance model (TAM) and the application of the TAM to explain students’ behavior in terms of the adoption of AI-based systems.

To become an information society, it is required that the citizens have access to information and communication technologies (ICT) in appropriate ways. ICT plays a major role to improve inclusion of various parts of the society (such as children, disabled citizens, and elderly) into daily life. According to reports of WHO, the world population is getting older. This urges the need for a systematic investigation of ICT needs of elderly citizens and potential problems being faced during the course of interaction with ICT interfaces. The present study focuses on the use and acceptance of ICT by elderly citizens in comparison to younger adults by providing data from citizens living in Turkey. It reports data collected from 232 elderly participants (60–96 years old) and 235 younger adults (19–40 years old). The findings of the study show that, both elderly and younger adults confirm the technology acceptance model (TAM) in a similar way. This was accompanied by elderly citizens’ need for assistance, encouragement and friendlier interface designs. The present study aims to contribute towards increasing awareness about the needs and expectations of elderly citizens and inspire further research on ICT use of the elderly population.

Qualitative comparative analysis (QCA) allows researchers to study how configurations of conditions lead to outcomes and, thereby, richly explain the dynamics of complex digital phenomena. To advance discussion on QCA in the information systems (IS) discipline, we introduce its fundamental concepts and offer guidelines for authors on how to apply QCA to advance IS research. We also provide checklists for reviewers of QCA papers. We illustrate how to apply our guidelines through two exemplar studies. In the first exemplar study, we focus on IT-business strategic alignment to study the influence that different forms of alignment have on firm performance. In the second exemplar study, we use the perspective of the integrated technology acceptance model to explain an individual’s intention to use a digital assistant. The contrasting results from both studies highlight how to use QCA to derive robust and reproducible results. By doing so, we contribute to encouraging IS scholars to use QCA to develop sophisticated models that accurately depict real-world IS phenomena.