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During the past few years, social media has faced the challenge of maintaining its user base. Reports show that the social media giants such as Facebook and Twitter experienced a decline in their users. Taking WhatsApp's recent change of its terms of use as the case of this study and using the push-pull-mooring model and a configurational perspective, this study aims to identify pathways for switching intentions.

Data were collected from 624 WhatsApp users recruited from Amazon Mechanical Turk and analyzed using fuzzy set qualitative comparative analysis (fsQCA).

The findings identify seven configurations for high switching intentions and four configurations for low intentions to switch. Firm reputation and critical mass increase intention to switch, while low firm reputation and absence of attractive alternatives hinder switching.

This study extends extant literature on social media migration by identifying configurations that result in high and low switching intention among messaging applications.

The study identifies factors the technology service providers should consider to attract new users and retain existing users.

This study complements the extant literature on switching intention that explains the phenomenon based on a net-effect approach by offering an alternative view that focuses on the existence of multiple pathways to social media switching. It further advances the authors’ understanding of the relevant importance of switching factors.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

Increasing incidents of privacy invasion on social networking sites (SNS) are intensifying the concerns among stakeholders about the misuse of personal data. However, there seems to be limited research on exploring the impact of specific privacy concerns on users’ intention to engage in various privacy protection behaviors. This study aims to examine the role of social privacy concerns, institutional privacy concerns and privacy self-efficacy as antecedents of privacy protection–related control activities intention among young adults active on SNS.

Data collected from 284 young adults active on SNS was analyzed through partial least squares structural equation modeling using Smart PLS.

The results indicate that institutional privacy concerns, social privacy concerns and privacy self-efficacy positively influence the control activities intention of SNS users. The extent of privacy self-efficacy and privacy protection-related control activities intention differs among users based on gender.

This study is limited to a population of young adults in the age group of 18–25 years.

The findings of this study form the basis for specific recommendations addressing the different types of privacy concerns experienced by social media users, promoting responsible privacy control behaviors on online platforms and discouraging the possible misuse of information by third parties.

This study validates a theoretical framework that can contribute to future investigations concerning the use of SNS. The study findings form the basis for a set of practical recommendations for policymakers, SNS platforms and users.

Online advertising becomes an essential tool to reach the target audience. One of the most widely used strategies is re-targeting. Firstly, this study explores the impact of ethics, privacy and ads' perceived benefits (ad effectiveness and ad relevance) on consumers' attitudes toward online advertising. Secondly, the study investigates the mediating effect of attitudes toward re-targeting online advertising on consumers' purchase intentions. Finally, the study investigates the moderating effect of the perceived ethicality of re-targeting online advertising on consumers' purchase intentions.

Participants (n = 307) were recruited through an online survey platform (MTurk) in the USA. The sample consisted of 65% male and 35% female respondents. The majority are aged 25–34 years, followed by 35–44 years (20%), 45–54 years (14%), 18–24 years (8%) and 55 years and older (6%).

The results show that ad effectiveness and ad relevance influenced consumers' attitudes toward re-targeting. This study shows that consumers are willing to trade their privacy for better search quality. Moreover, perceptions toward the ethicality of re-targeting ads moderated the relationship between consumers' attitudes and purchase intentions.

This study will make several contributions. First, the study will extend the consequential theory in the context of online advertising. Second, the study will assist companies in using re-targeting strategies. The results will reveal which factor is the most important factor impacting consumers' attitudes toward re-targeting strategies.

This is one of the first few papers investigating consumers' perceptions of the ethicality of re-targeting online advertising.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

The deployment of artificial intelligence (AI) technologies in travel and tourism has received much attention in the wake of the pandemic. While societal adoption of AI has accelerated, it also raises some trust challenges. Literature on trust in AI is scant, especially regarding the vulnerabilities faced by different stakeholders to inform policy and practice. This work proposes a framework to understand the use of AI technologies from the perspectives of institutional and the self to understand the formation of trust in the mandated use of AI-based technologies in travelers.

An empirical investigation using partial least squares-structural equation modeling was employed on responses from 209 users. This paper considered factors related to the self (perceptions of self-threat, privacy empowerment, trust propensity) and institution (regulatory protection, corporate privacy responsibility) to understand the formation of trust in AI use for travelers.

Results showed that self-threat, trust propensity and regulatory protection influence trust in users on AI use. Privacy empowerment and corporate responsibility do not.

Insights from the past studies on AI in travel and tourism are limited. This study advances current literature on affordance and reactance theories to provide a better understanding of what makes travelers trust the mandated use of AI technologies. This work also demonstrates the paradoxical effects of self and institution on technologies and their relationship to trust. For practice, this study offers insights for enhancing adoption via developing trust.

This study builds on previous theoretical work that considered artificial intelligence (AI) and its potential for creating “teacher-centaurs” whose labor could be accelerated through the use of generative AI (Fassbender, in review). The purpose of this paper is to use empirical methods to study centaur teachers and the division of labor (Durkheim, 1893/2013) that arise from outsourcing teaching tasks to AI.

Multiple case study (Stake, 2006) was used to collect data on two secondary English teachers who were early adopters of generative AI. Data included semi-structured interviews as well as ChatGPT chat logs, which helped in describing how teaching approaches evolved using AI technology.

Results showed that teachers used AI for planning, instruction and assessment. AI-augmented teaching practices allowed teachers to complete tasks with greater speed, which in turn increased stamina and short-term work–life balance. Given the novelty of AI, concerns about data privacy and academic integrity raised ethical questions.

ChatGPT’s rise to popularity in 2023 brought with it significant discussions about education, specifically how students would use AI primarily as a tool for plagiarism. This study takes a different focus, considering how early adoption of AI has begun changing teacher labor, offering implications for the future of the teaching profession.

The primary objective of this study is to explore consumers' non-adoption intentions towards meta-commerce (or metaverse retailing). Utilizing the Innovation Resistance Theory (IRT) as the theoretical foundation, this study investigates the impact of diverse barriers on non-adoption intentions within the meta-commerce context.

A total of 356 responses were gathered to test the proposed hypotheses. Structural Equation Modelling (SEM) with SmartPLS 4 software was used to examine these hypotheses.

The findings of this study show that perceived cyber risk, perceived regulatory uncertainty, perceived switching cost and perceived technical uncertainty are significantly linked to non-adoption intention towards meta-commerce. Furthermore, the study suggests that the moderating influence of technostress on these connections is more pronounced for consumers with high technostress compared to those with low technostress.

This study makes a significant contribution to the current body of literature by providing valuable insights into the fundamental barriers that consumers encounter when contemplating the adoption of meta-commerce. This contribution is particularly noteworthy as it fills a gap in the existing literature, as no prior study has comprehensively examined the primary obstacles that shape consumer intentions towards meta-commerce adoption. This novel perspective offers scholars, businesses and policymakers a foundation for developing strategies to address these barriers effectively.

This paper aims to increase understanding of pertinent exogenous and endogenous antecedents that can reduce data privacy breaches.

A cross-sectional survey was used to source participants' perceptions of relevant exogenous and endogenous antecedents developed from the Antecedents-Privacy Concerns-Outcomes (APCO) model and Social Cognitive Theory. A research model was proposed and tested with empirical data collected from 213 participants based in Canada.

The exogenous factors of external privacy training and external privacy self-assessment tool significantly and positively impact the study's endogenous factors of individual privacy awareness, organizational resources allocated to privacy concerns, and group behavior concerning privacy laws. Further, the proximal determinants of data privacy breaches (dependent construct) are negatively influenced by individual privacy awareness, group behavior related to privacy laws, and organizational resources allocated to privacy concerns. The endogenous factors fully mediated the relationships between the exogenous factors and the dependent construct.

This study contributes to the budding data privacy breach literature by highlighting the impacts of personal and environmental factors in the discourse.

The results offer management insights on mitigating data privacy breach incidents arising from employees' actions. Roles of external privacy training and privacy self-assessment tools are signified.

Antecedents of data privacy breaches have been underexplored. This paper is among the first to elucidate the roles of select exogenous and endogenous antecedents encompassing personal and environmental imperatives on data privacy breaches.