Technology Acceptance Research Articles

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States

This article aims to investigate the factors that affect behavioural intention (BI) and user behaviour (UB) among Arabian users of generative artificial intelligence (GenAI) applications in the context of media content creation. The study’s theoretical framework is grounded in the unified theory of acceptance and use of technology (UTAUT2). A sample of 496 users was analysed using the partial least squares structural equation modelling technique (PLS-SEM). The results revealed that BI is significantly influenced by performance expectancy, effort expectancy, social influence, hedonic motivation, habit, and user trust, with hedonic motivation having the greatest impact. In terms of UB, facilitation conditions, habit, user trust, and BI were all found to have a positive and significant impact. This study contributes to the existing theory on the utilisation of GenAI applications by organising findings pertaining to the use of AI technology for media content creation.

The Impact of Information and Communication Technologies on the Competitiveness of Smes in Morocco: An Approach Based on the Technology Acceptance Model

This article examines the impact of Information and Communication Technologies (ICT) on economic competitiveness in Morocco, with a focus on ICT adoption among small and medium-sized enterprises (SMEs). Through an analysis based on the Technology Acceptance Model (TAM), the study identifies key challenges faced by companies in integrating ICT and highlights opportunities presented by government initiatives such as the "Digital Morocco Plan 2020." The findings indicate that the acceptance and increased use of ICT are essential for fostering innovation and enhancing the performance of Moroccan businesses, thereby contributing to sustainable economic growth.

Virtual reality in medical education: Effectiveness of Immersive Virtual Anatomy Laboratory (IVAL) compared to traditional learning approaches

Immersive Virtual Anatomy Laboratory (IVAL) is an innovative learning tool that combines virtual reality and serious games elements to enhance anatomy education. This experimental study compares IVAL with traditional learning methods in terms of educational effectiveness and user acceptance. An experimental design was implemented with 120 undergraduate health-science students, randomly assigned to two groups: an experimental group using IVAL, and a control group following traditional learning methods. Data collection focused on quantitative measures such as pretest and posttest vocabulary assessment scores and task completion times, alongside qualitative measures obtained through a user experience questionnaire. This study utilizes the Technology Acceptance Model (TAM), incorporating variables such as Perceived Usefulness and Perceived Ease of Use. Results revealed significant improvements in the experimental group, with a 55.95% increase in vocabulary scores and an 18.75% reduction in task completion times compared to the control group. Qualitative data indicated that IVAL users reported greater Perceived Usefulness of the technology, improved Perceived Ease of Use, a more positive Attitude Towards Using IVAL, and stronger Behavioral Intention to continue using IVAL for anatomy learning. This study demonstrates that the integration of immersive virtual reality in the IVAL approach offers a promising method to enhance anatomy education. The findings provide insights into the effectiveness of immersive learning environments in improving learning outcomes and user acceptance. While further research is needed to explore long-term effects, this innovative approach not only enhances the effectiveness and enjoyment of anatomy learning but also provides valuable data on optimizing educational technology for improved learning outcomes.

Role of Technological Advancements on Customer Satisfaction in Japan’s Hospitality

Purpose: The aim of the study was to analyze the role of technological advancements on customer satisfaction in Japan’s hospitality. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Technological advancements have significantly enhanced customer satisfaction in Japan’s hospitality industry by improving service efficiency, personalization, and overall guest experience. Innovations such as AI-driven customer service robots, automated check-in systems, and smart hotel rooms equipped with IoT devices offer convenience and seamless interactions for guests. These technologies reduce waiting times, streamline operations, and allow for personalized services, such as customized room settings and tailored recommendations, increasing guest satisfaction. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), service quality model (SERVQUAL) & expectancy-confirmation theory (ECT) may be used to anchor future studies on the role of technological advancements on customer satisfaction in Japan’s hospitality. Hospitality businesses should adopt integrated management systems that combine customer relationship management (CRM), property management systems (PMS), and online booking platforms. Industry regulators should promote standards for technology integration in the hospitality sector.

A focus group study of students’ expectations of digital onboarding tools in higher education

In higher education, the array of roles performed by digital tools has been increasing rapidly. While some areas of higher education, such as teaching, already employ digital tools, the use of such tools in student onboarding has not yet been thoroughly explored. This study sought to develop a better understanding of student perspectives regarding digital onboarding tools through an investigation that built on the technology acceptance model and expectancy–value theory. The goal was to identify what students expect from digital onboarding tools concerning, for example, content and design. To this end, we examined the data from five focus groups using qualitative content analysis. The results revealed that students expect the design of digital onboarding tools to feature websites that are usable on mobile devices and that such a tool should be readily available for their use independently of time and place. Our study also found that digital onboarding tools can influence students’ motivation to study. The learnings for higher education institutions are that digital onboarding tools should enhance easy information gain and provide opportunities that foster early socialization with fellow students.

Latent class approach to classify e-scooter non-users: A comparative study of Helsinki and Tokyo

Recent years have seen a rapid deployment of shared electric scooters (e-scooters), across hundreds of cities worldwide. Despite the global diffusion, most urban residents are still not using e-scooters, and they often bear the negative consequences of e- scooter deployment. This comparative study investigates the attitudes and characteristics of the e-scooter non-users in Helsinki, Finland and Tokyo, Japan using latent class clustering analysis. Our findings reveal five classes of e-scooter non-users, ranging from very negative perspectives to the least negative perspectives towards e-scooter deployment. Non-users also have a diverse set of socio-demographics background, including variation across age, gender, and income. In addition, non-users mostly do not feel the necessity of using an e-scooter as well as lack competences and have safety concerns. Safety concerns are dominant among those with more negative perceptions, while classes with the least negative perceptions and low income tend to not use e-scooters due to high rental costs. Understanding the different categories and perspectives of non-users, alongside with understanding users, has clear smart mobility policy and management implications. On the one hand, it should help with understanding the effectiveness of different interventions stemming from e-scooter operators and public policy actors. On the other hand, this study’s findings contribute to understanding public acceptability of e-scooter technology and associated policy and management actions. The study also opens further pathways for both comparative studies and for understanding different groups of users and non-users of smart mobility technologies.

Exploring the paradoxical use of ChatGPT in education: Analyzing benefits, risks, and coping strategies through integrated UTAUT and PMT theories using a hybrid approach of SEM and fsQCA

ChatGPT's impact on education is both significant and inevitable, presenting a paradox of threats and benefits. While studies have explored ChatGPT's usage, intention, and motivation, few have addressed its paradoxical use from a benefit-risk-coping perspective, leaving gaps in practical and theoretical solutions. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Protection Motivation Theory (PMT) to elucidate ChatGPT's paradoxical use in education. Using Structural Equation Modeling (SEM) and fuzzy sets Qualitative Comparative Analysis (fsQCA), it aims to provide insights and solutions for managing the benefits, risks, and coping mechanisms associated with ChatGPT in education. Hypotheses and propositions were tested on Taiwanese higher education users (N = 351). Findings from SEM confirm that the perceived threat of ChatGPT's severity decreases the intention to use it, while coping strategies such as self-efficacy and response efficacy strongly predict the intention to use ChatGPT. Additionally, the benefits of performance expectancy and task efficiency significantly increase the intention to use ChatGPT, which in turn significantly increases actual usage behavior. Findings from fsQCA reveal three configurations for both the usage and disusage of ChatGPT in education. The study identifies three constructs with necessary conditions. This research makes a significant theoretical contribution by integrating UTAUT and PMT into a unified framework to elucidate the paradoxical aspects of benefit-risk-coping in the use of ChatGPT. Practical implications for higher educational institutions and their scholars (e.g., students, lecturers, researchers) are also provided through confirmed solutions from the QCA model. These solutions offer strategic insights to leverage the benefits, identify the risks, and cope with the threats associated with using ChatGPT in education.

Predicting EFL University Students' Acceptance of Mobile Assisted Language Learning through the UTAUT2 Model

This study used the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to examine the factors influencing the acceptance of MALL by the Moroccan EFL students of Sidi Mohamed Ben Abdellah University. For this purpose, 150 undergraduate EFL students participated in the research. The survey questionnaire adapted from (UTAUT2) model showed the influence of the following factors on students' behavioral intention to use MALL: performance expectancy, effort expectancy, social impact, facilitating conditions, hedonic motivation, and habit. The findings highlighted the importance of perceived usefulness and ease of use, the need for institutional support, engaging learning experiences, and promoting MALL use as a habit. This study provides valuable insights for educators and policymakers seeking to optimize MALL adoption and improve language learning outcomes.

Investigating the Drivers of E-Commerce Continuance Intentions Among Older Consumers in Latin America

This study investigates the drivers of e-commerce continuance intentions among older consumers in Latin America. It employs the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) to develop and test a conceptual model of e-commerce adoption for older adults in Chile. Data were collected through an online survey involving 202 participants aged 65 and over. The findings reveal that attitude toward e-commerce and three UTAUT2 predictors—performance expectancy, effort expectancy, and social influence—are significantly related to e-commerce continuance intentions. Consumer innovativeness also has an indirect relationship through its impact on attitudes toward e-commerce. Additionally, facilitating conditions and habit are indirectly and significantly related to e-commerce continuance intentions through their influence on effort expectancy. However, hedonic motivation, perceived privacy risk, and health condition do not serve as significant predictors of e-commerce continuance intentions. This study enhances the understanding of older consumers’ intentions to continue adopting e-commerce in a Latin American country.

The adoption of fast payment systems (FPS) in Latin America: the role of financial literacy and cash-related beliefs

PurposeIn this paper, we ask: how do individual factors affect individuals’ intentions of adopting fast payment systems (FPS)? We examine the role of financial literacy and beliefs about cash as individual-related variables that complement the technology acceptance model (TAM) in explaining the individuals’ intention to adopt financial innovations.Design/methodology/approachWe surveyed consumers in 11 Latin American countries to measure relevant variables. By applying structural equation modeling, we extended the TAM. We demonstrated that financial literacy and beliefs about cash contribute to developing a fine-grained understanding of what stimulates individuals’ adoption of financial innovations like FPS.FindingsOur results support the claim that individuals with higher levels of financial literacy exhibit a higher intention to adopt FPS through the mediation role of beliefs about cash, perceived usefulness, perceived ease of use and attitude.Practical implicationsTo improve FPS’s acceptance probability, designers must add highly usable platforms. However, it could be more important to increase the perceived usefulness of FPS as the system competes directly with traditional payment methods such as cash. If individuals exhibit strong positive beliefs about cash, their attitudes and intentions of adopting FPS will decrease. In addition, policymakers and designers may modify beliefs about cash individuals exhibit by identifying and intervening in their determinants (e.g. financial literacy).Originality/valueBesides technology-related factors, we demonstrate that beliefs about alternative technologies (e.g. cash) influence the intention to adopt financial innovations like FPS.

Old habits die hard: Technology acceptance of teachers and police officers

Old habits die hard: Technology acceptance of teachers and police officers

Student Comfort and Well-being Emerge as Influencers of Virtual Classroom Attendance

Student acceptance and voluntary use of virtual classrooms such as Zoom is influenced by various latent factors which can be managed to improve student engagement, and their effects measured by technology acceptance models (TAMs). This study sought to identify factors not currently accounted for in educational TAM research relating to virtual classroom use. To achieve this, we invited 742 students at a major Australian university to respond to four open questions on their attitudes towards using Zoom for learning. Thematic analysis was conducted on 169 valid responses. Themes were mostly aligned with known factors from a published taxonomy of important factors. However, health and well-being, and social comfort, emerged as two new factors affecting student intentions to use Zoom for learning. The findings suggest that these two new constructs influence student voluntary use of virtual classrooms such as Zoom.

Adoption of quick response codes as a digital microlearning tool among clinical nurses: a quasi-experimental study.

The rapid growth of technology forced enormous changes in the provision of healthcare services. Different strategies are used to keep nurses up to date with rapid changes in health systems. Microlearning is one of the new methods of teaching professional skills to health workers. This method is effective in care settings that have many limitations in terms of time and place. However, it is very important to test the acceptability of this method among nurses before practical measures are taken for its widespread use. This study aimed to determine nurses' acceptance rate of quick response code as a microlearning tool in workplace. This is a cross-sectional study was conducted in medical and surgical wards in hospitals affiliated to Guilan University of Medical Sciences. 185 nurses participated in the study. A number of selected medical devices were labeled with quick response codes containing educational content. The eligible nurses were instructed how to use the QR codes. After two months, they were asked to complete a questionnaire adapted from the technology acceptance model 3. SPSS 21 software was used to analyze the data. 166 nurses and 19 head nurses with mean age of 34.26 ± 8.17 years and mean work experience of 10.46 ± 7.64 years completed the questionnaires. Most participants were female, married, with a bachelor's degree, worked on rotating shifts, in medical wards. The findings showed that the acceptance of the quick response code as a learning tool was at a moderate level (M = 66.1, SD = 16.6). Statistically, there was no significant relationship between nurses' demographic characteristics and the total acceptance rate (P > 0.05). However, the analyses at the multivariate level, using multiple linear regression, showed a significant superiority of the total acceptance score in head nurses compared to nurses (b = 7.97, P = 0.047) and in nurses who had previous experience of using quick response codes, compared to colleagues without such experience (b = 5.18, P = 0.036). Based on the coefficient of determination, only 6.1% of the changes in the total acceptance score of quick response codes of nurses are explained by their personal-occupational characteristics (R2 = 0.061). The provision of QR code requirements such as necessary infrastructure and training by the health authorities could increase the acceptance of this tool as a microlearning measure.

Managers’ Perception of Financial Innovations in Turkey

Innovation is often considered a key contributor to firms’ competitiveness. However, its success largely depends on manager’s willingness to accept an innovation. The purpose of this study is to examine managers’ perceptions on financial innovation and the factors that influence their adoption decisions. Using the Technology Acceptance Model (TAM), the study analyzes data collected from a sample of 500 firms in Turkey. The findings show that managers are more likely to adopt financial innovations that are perceived to be easy to use and accessible. Gender, age and level of education are observed to have significant impact on managers’ perception. For example, female managers are likely to be risk averse when making financial innovation decisions than male managers. Younger managers tend to test the innovation before fully implementing it and are more concerned about accountability for the success or failure of the innovation. The results also indicate that accessibility, ease of use, risk and accountability are crucial factors influencing managers' perception on financial innovations. Finally, this research contributes to the literature on financial innovation and provides valuable insights for managers in Turkey to make informed financial innovation decisions. Received: 10 August 2024 / Accepted: 31 October 2024 / Published: 05 November 2024

Factors Influencing Community’s Acceptance of Twitter (X) on Improving Information Access in Tanzania’s Police Force

This study was conducted to investigate the acceptance of Twitter (X) for improving information access in Tanzania’s Police Force. The specific objectives of the study were to assess the community’s perceived usefulness of Twitter (X) in improving information access and to examine the community’s perceived ease of use of Twitter (X) for enhancing information access in Tanzania. The Technology Acceptance Model (TAM) guided this research. The study employed a cross-sectional research design using a quantitative research approach, while a stratified sampling technique was utilized to select 154 respondents. The population comprised 250 Tanzania Police officials, from which a sample size of 154 respondents was determined using Yamane’s formula. Out of the 154 questionnaires issued, 100 were returned, resulting in a response rate of 64 percent. Data were collected through a questionnaire and analysed using descriptive analysis and inferential analysis through multiple linear regression. The study findings indicated that the community’s perceived usefulness and perceived ease of use of Twitter (X) play significant roles in improving information access in Tanzania. The findings enhance the contribution of the Technology Acceptance Model (TAM) by unveiling the importance of its major constructs—perceived usefulness and perceived ease of use—in influencing information access within the police community. The research concluded that the Tanzania Police Force’s access to Twitter (X) is significantly influenced by perceived ease of use. Law enforcement officials use the platform more frequently as it becomes easier to navigate and more intuitive. Therefore, this study recommends that digital platforms should be utilized as long as they are user-friendly and provide value to individuals.

Changes in Technology Acceptance of Smart Care Beds Among Long-Term Care Workers in Korea.

This study investigates the changing perceptions of Korean care workers regarding a robotic bed designed to assist with repositioning and prevent pressure ulcers. With the primary aim of assessing the technology acceptance among care workers using the robotic bed to solve the problem of a shortage of care workers in Korea, we sought to examine the possibility of applying care robots in the field. A total of 20 long-term care workers participated in the experiment, and their attitudes were measured before and after using the robot. Frequency analysis and paired t-tests were conducted using Stata 17 to analyze the data. The results show significant changes in the perceived ease of use (PEOU), facilitating conditions (FCs), and gerontology anxiety (GA), with the PEOU increasing by 19.87%, FC increasing by 20.63%, and GA decreasing by 17.2%. However, there was no significant change in the perceived usefulness (PU) and intention to use (IU). The results showing that the perception of technology acceptance changed significantly mean that the use of the care robot means that there is a high possibility of positive perceptions in Korean care settings when care robots are distributed in the field in the future, considering that the experimental environment was limited due to the early stage of development of care robots. This study highlights the need for practical demonstrations and thorough training to improve technology acceptance among care workers before the application of care technology in the long-term care environment in South Korea.

Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model

This study addresses the academic gap in the adoption of generative AI systems by investigating the factors influencing technology acceptance and usage behavior in Korean firms. Although recent advancements in AI are accelerating digital transformation and innovation, empirical research on the adoption of these systems remains scarce. To fill this gap, this study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) model, surveying 300 employees from both large and small enterprises in South Korea. The findings reveal that effort expectancy and social influence significantly influence employees’ behavioral intention to use generative AI systems. Specifically, effort expectancy plays a critical role in the early stages of adoption, while social influence, including support from supervisors and peers, strongly drives the adoption process. In contrast, performance expectancy and facilitating conditions show no significant impact. The study also highlights the differential effects of age and work experience on behavioral intention and usage behavior. For older employees, social support is a key factor in technology acceptance, whereas employees with more experience exhibit a more positive attitude toward adopting new technologies. Conversely, facilitating conditions are more critical for younger employees. This study contributes to the understanding of the interaction between various factors in AI technology adoption and offers strategic insights for the successful implementation of AI systems in Korean companies.

Using AR to Enhance the Learning of Chirality

This study investigates the use of Augmented Reality (AR) to teach the concept of chirality—a fundamental topic in organic chemistry. Building on Cognitive Load Theory (CLT) and previous research on AR’s educational benefits, we designed an AR learning environment to facilitate students’ understanding of chirality by allowing them to interact with and superimpose virtual and physical models of chiral molecules. An initial pilot study involving 11th-grade students revealed positive student attitudes towards AR, with participants reporting enhanced comprehension of chirality and a preference for AR-based learning over traditional methods. The follow-up study refined the AR lesson based on pilot feedback, extending its scope to introduce, rather than review, the concept of chirality. Results indicated significant learning gains, low extraneous cognitive load, and high acceptance of AR technology among students. These findings underscore the potential of AR to support complex spatial learning in chemistry, though further research, such as value-added studies, is recommended to explore the generalizability and long-term impacts of AR on different student populations.

Assessment of Factors Influencing Healthcare Provider’s Acceptance of Electronic Health Record in Jordanian Hospitals

Acceptance of electronic health record systems is considered an essential factor for effective implementation among healthcare providers. In an attempt to understand healthcare providers’ perceptions of electronic health record systems implementation and evaluate the factors influencing healthcare providers’ acceptance of electronic health record systems, the current research examines the effects of individual (user) context factors and organisational context factors using the technology acceptance model. A quantitative cross-sectional survey design was used, in which 319 healthcare providers from five public hospitals participated in the present study. Data were collected using a self-administered questionnaire, which was based on the technology acceptance model. Jordanian healthcare providers demonstrated positive perceptions of the usefulness and ease of use of the electronic health record systems and subsequently accepted the technology. The results indicated that they had a significant effect on the perceived usefulness and perceived ease of use of the electronic health record, which in turn was related to positive attitudes towards the electronic health record systems as well as the intention to use them. User attributes, organisational competency, management support and training and education are essential variables in predicting healthcare providers’ acceptance towards electronic health records. These findings should be considered by healthcare organisations’ administration to introduce an effective system to other healthcare organisations.