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The evolution of e-health technologies presents promising alternatives for health-care excellence. Despite the benefits arising from mobile e-health (m-health) and wearables technologies, the literature stands many contradictories signs regarding how users accept and engage in using these technologies. This study aims to synthesize the estimations about m-health user acceptance technologies.

A meta-analytic structural equation modeling was carried out using the 778 relationships estimated by 100 previous research. The estimations follow the relations and constructs proposed in the UTAUT2 technological acceptance model.

The results indicate the performance expectancy, effort expectancy, social influence and habit constructs are most important for predicting the behavioral intention of use of m-health technologies. The Latin American users of e-health technologies are still underestimated in the literature.

The study presents a guide to understanding the acceptance process of m-health technologies and delivers a general orientation for developing new m-health devices considering their acceptance by users.

This study aims to investigate the intention of adopting a technological service innovation that is a mix-mode consumer shopping service in the retailing context.

A modified extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model is tested by using data on a real example named “Mkolay” which is a new mix-mode consumer shopping service. Data are collected from 217 participants and analyzed by using Partial Least Squares Structural Equation Modeling (PLS-SEM).

Research findings show that the customers' intention to use the new mix-mode consumer shopping service is particularly influenced by performance expectations, effort expectations, social influence and personal innovativeness.

In designing a mix-mode consumer shopping service, keeping the design as simple and easy to use as possible seems to increase the intention of adoption. Additionally, collaborating with reference figures or influencers could be instrumental to spread the new service to a wider audience hence increase the likelihood of adoption. Practitioners should also use new technologies in different ways to attract and surprise customers, especially taking into account the technological profile of potential users.

This study contributes to the literature of technological service innovation adoption by revealing the factors affecting the intention of adoption of mix-mode consumer shopping service in the retailing context with a modified UTAUT2 model.

The share of fast-moving consumer goods (FMCG) in e-commerce has increased, particularly after COVID-19, alongside the growing volume of e-commerce transactions. The increased number of orders necessitates the optimization of delivery operations. This situation has made using different technologies in last-mile delivery processes necessary. Currently, used electric vehicles (EVs) play a significant role in integrating these technologies into delivery operations. Adapting to new technologies emerges as a critical need for small enterprises to survive in the face of advancing technologies. In this context, the main purpose of this paper is to examine and identify attitudes of small businesses toward EVs’ usage in last-mile delivery for FMCG.

The theoretical frameworks commonly used in studies investigating new technologies, namely the theory of planned behavior (TPB) and technology acceptance model (TAM) have been employed. The analysis of the study was conducted using partial least squares-structural equation modeling (PLS-SEM) and the Smart PLS software package.

When examining the results of the study, a significant and positive relationship was found between compatibility and enjoyment with perceived usefulness. Furthermore, a significant and positive relationship was identified between attitude toward use, perceived behavioral control and perceived usefulness. Ultimately, a strong relationship was found between intention, which is a precursor to active usage and attitude. Based on the obtained data, it can be concluded that EVs can be actively used in last-mile delivery in the future.

In FMCG processes in Türkiye, EVs are limitedly used in last-mile delivery operations. However, there are very few studies on the use of EVs in FMCG. Therefore, it is expected that the current research will contribute to the literature by providing information on the factors that influence the acceptance of EV usage and their implications. The TAM and TPB models were used in the current study.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.

This study analyzed the relationship between mobile financial services (MFS) usage and customer satisfaction with MFS in Bangladesh, considering perception, purpose of use and technical challenges as the primary factors influencing customer satisfaction with MFS. The aim is to determine the factors most influencing the use of MFS.

Data were collected from 400 MFS users through a structured web survey using snowball sampling that is consistent with the nature of MFS users who are difficult to identify or locate. Structural equation modeling (SEM) was used to analyze the data and evaluate the reliability and validity of the measurement model.

The results show that customers’ perceptions and satisfaction significantly impact their intention to use MFS. Specifically, customers’ perceptions strongly influence their satisfaction with MFS, and the purpose of use significantly predicts both perception and satisfaction. Technical problems and challenges were found to have no significant impact on satisfaction levels, but other factors were more critical. Furthermore, the integration of innovative technological solutions is crucial for fostering sustainability in MFS, as it enhances reliability and efficiency while minimizing environmental footprints.

The study was conducted in a single country, relied on self-reported data, and used a cross-sectional design, which limits the ability to draw causal inferences. Future research could explore the factors that influence customer satisfaction with MFS in different countries and regions and incorporate additional variables to provide a more comprehensive understanding of the drivers of customer satisfaction with MFS.

This study significantly contributes by extending the technology acceptance model (TAM) framework with the innovation resistance theory, offering a nuanced understanding of MFS adoption. The findings challenge conventional wisdom, highlighting the limited impact of technical problems on satisfaction and emphasizing the central role of user perceptions in shaping satisfaction and intention to use.

The appropriation of artificial intelligence (AI) into everyday workplace practices is becoming pervasive. Some of the industries that have seen the improved appropriation of AI include the automotive industry, food chains and other retail businesses. The media industry in developed countries has also been appropriating AI into newsrooms ecologies. This development has left news media workers, especially in developing countries, questioning the viability of their jobs in the foreseeable future as machines take over and disrupt newsmaking and production processes. The unknowns that come with AI adoption have been met with both utopian and dystopian views in African newsrooms, especially in countries that understand less on how to deploy AI affordances. Premised on the technology acceptance model and the concept of technological appropriation and a qualitative methodology that used structured interviews with community media journalists from the Western Cape province of South Africa, this chapter examined journalists' perceptions of AI deployment in the community media space. The findings of the study show that community media journalists hold both utopian and dystopian views on the utility of AI in the community media space. While most journalists perceive AI as enhancing the efficiency and accuracy of the newsmaking process, there is a significant number of journalists who perceive AI as inimical to the legitimacy and credibility of the profession. Furthermore, AI is also seen as a threat to jobs within the journalism profession. These findings resonate with previous studies that have shown that journalists are divided on the perceived utility of AI in newsrooms.

The study examines how adopting e-paisa technology (a mobile payment system) enhances micro-entrepreneurs' perceived success and subjective well-being during the COVID-19 global pandemic. The study also theorizes the moderating effects of gender through a moderated-mediation model.

The data are collected from 282 micro-entrepreneurs in Pakistan using a two-week time-lagged approach to test the hypothesized framework. The analysis was conducted using the PROCESS macro moderated-mediation method.

The results affirm that adopting e-paisa technology positively relates to perceived entrepreneurial success, ultimately increasing the entrepreneur's subjective well-being. Furthermore, in hypothesized moderated-mediation paths, the moderation effect of gender was stronger for women than men.

From the perspective of diversity, equality and inclusion, adopting a digital mobile payment system can be regarded as a catalyst for the inclusion of women entrepreneurs in marginalized communities, enabling them to continue their micro-businesses in times of global crises. Further, it is also suggested that public–private sector partnerships are essential for promoting a sustainable entrepreneurial ecosystem that subsequently leads to social and financial inclusion in marginalized communities.

The mediating role of perceived entrepreneurial success contributes to understanding the importance of psychological functioning among technology acceptance model (TAM) attributes and entrepreneurs' subjective well-being. In addition, theorizing gender differences in TAM and subjective well-being relationships revealed women's advantages, challenging the prevailing belief that women are less fortunate in entrepreneurial endeavors. This raises the need to reconsider and maybe change the perspective to consider women as a disadvantaged group in entrepreneurship.

The purpose of this study is to investigate the factors affecting behaviour intention (BI) to use and actual usages of investment-related FinTech services among the zoomers (Gen Z) and millennials (Gen M) retail investors of India.

The study explores the predictive relevance of actual adoption behaviour among the two different age categories of Indian retail investors. It uses the Unified Theory of Acceptance and Use of Technology-2 and the prospect theory framework as guiding frameworks. Data has been collected from 294 retail investors, actively engaged in the investment-related FinTech services. The multi-group analysis using variance-based partial least square structured equation modelling has been used to compare the two groups. The invariance between the two groups was achieved through measurement invariance assessment.

The study reveals distinct factors significantly affecting BI to use investment-related FinTech services among Gen Z and Gen M retail investors are performance expectancy (PE) to BI, perceived risk (PR) to BI, price value (PV) to BI and PR to service trust (ST).

This study provides insights for financial providers and policymakers, emphasizing different factors influencing BI to use investment-related FinTech services in both age groups. Notably, habit emerges as a common factor influencing the actual usage of investment-related FinTech services across Gen M and Gen Z retail investors in India.

This study explores the heterogeneous behaviour of the heterogenous population in the domain of technological adoption of investment-related FinTech services in India.

Because the success of m-payment services depends on the enablers and barriers that affect user satisfaction, the present research explores the effects of perceived value and sacrifices on users' satisfaction with m-payment services. The predicted relationships among perceived value, perceived sacrifices, users' satisfaction, continuance intention, word-of-mouth (WOM), shopping effectiveness, quality of life (QOL) and stickiness were established based on the mobile technology acceptance model (MTAM) and the value-based adoption model (VAM).

A representative data sample of 430 Egyptian banking clients was analyzed to test the hypotheses using partial least squares-structural equation modeling (PLS-SEM).

The findings revealed that all perceived value constructs significantly positively affect users' satisfaction. Moreover, all perceived sacrifice constructs significantly negatively affect users' satisfaction. Users' satisfaction, in turn, has a significant positive effect on continuance intention, WOM, shopping effectiveness, QOL and stickiness with m-payment services.

This is the first study to examine several levels of m-payment outcomes, including m-payment, consumer and bank outcomes, based on the integration of MTAM and VAM models.

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.