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The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to test whether network externality, gamification and media richness as environmental feature antecedents to learners' learning engagement (LE) can affect their continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking the gamified MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 315 usable questionnaires were analyzed using structural equation modeling in this study.

This study verified that learners' perceived network externality, gamification and media richness in MOOCs positively influenced their behavioral LE, emotional LE and social LE elicited by MOOCs, which collectively caused their continuance intention of MOOCs. The results support all proposed hypotheses, and the research model accounts for 75.6% of the variance in learners' continuance intention of MOOCs.

This study uses the S-O-R model as a theoretical groundwork to construct learners' continuance intention of MOOCs as a series of the internal process, which is influenced by network externality, gamification and media richness. Noteworthily, three psychological constructs, behavioral LE, emotional LE and social LE, are employed to represent learners' organisms of MOOCs usage. To date, the concepts of network externality, gamification and media richness are rarely together adopted as environmental stimuli, and psychological constructs as organisms have received lesser attention in prior MOOCs studies using the S-O-R model. Hence, this study's contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners' continuance intention of MOOCs is well documented.

Artificial intelligence (AI) allows the brand to co-create value with young customers through mobile apps. However, as many brands claim that their mobile apps are using the most updated AI technology, young customers face app fatigue and start questioning the authenticity of this touchpoint. This paper aims to study the mediating effect of authenticity for the value co-creation of AI-powered branded applications.

Drawing from regulatory engagement theory, this study conceptualize authenticity as the key construct in customers’ value experience process, which triggers customer value co-creation. Two scenario-based online experiments are conducted to collect data from 444 young customers. Data analysis is performed using ANOVA and Process Hayes.

The results reveal that perceived authenticity is an important mediator between media richness (chatbot vs AI text vs augmented reality) and value co-creation. There is no interaction effect of co-brand fit (high vs low) and source endorsement (doctor vs government) on the relationship between media richness and perceived authenticity, whereas injunctive norms (high vs low) strengthen this relationship.

The finding provides insights for marketing managers on engaging young customers suffering from app fatigue. Authenticity holds the key to young customers’ technological perceptions.

This research highlights the importance of perceived authenticity in encouraging young customers to co-create value. Young customers consider authenticity as a motivational force experience that involves customers through the app’s attributes (e.g. media richness) and social standards (e.g. norms), rather than brand factors (e.g. co-brand fit, source endorsement).

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether media richness and social interaction as environmental feature antecedents to nurses’ learning engagement (LE) can affect their continuance intention of massive open online courses (MOOCs) and task performance.

Sample data for this study were collected from nurses at five university-/medical university-affiliated hospitals in Taiwan. A total of 500 questionnaires were distributed, and 303 (60.6%) usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that nurses’ perceived media richness and social interaction in MOOCs positively influenced their behavioral LE and psychological LE elicited by MOOCs, which jointly caused their continuance intention of MOOCs and, in turn, enhance their task performance. The results support all proposed hypotheses and the research model, respectively, explains 84.3% and 63.7% of the variance in nurses’ continuance intention of MOOCs and task performance.

This study uses the S-O-R model as a theoretical base to frame nurses’ continuance intention of MOOCs and task performance as a series of the internal process, which is affected by environmental stimuli (i.e. media richness and social interaction) and organismic states. Noteworthily, while the S-O-R model has been extensively used in prior literature, little research uses this paradigm to expound nurses’ continuance intention of MOOCs in the work settings. Besides, there is a dearth of evidence on the antecedents of nurses’ task performance in the context of MOOCs. Hence, this study’s empirical evidence contributes significantly to the existing literature on bridging the gap of limited evaluation for the research on the impact of nurses’ MOOCs learning on their task performance in the work settings, which is very scarce in the S-O-R view.

This study aims to investigate the effect of deepfake video advertisements on hotel booking intention by applying the media richness theory (MRT) and information manipulation theory (IMT).

A quantitative survey was conducted using a structured questionnaire to understand the effect of deepfake hotel video advertisements on booking intention. A large cross-section of 1,240 tourists was surveyed and data were analyzed with partial least squares structural equation modeling (PLS-SEM).

The outcome of this research provides the factors affecting the booking intention due to deepfake hotel video advertisements. These factors are media richness (MR), information manipulation (IM) tactics, perceived value (PV) and perceived trust (PT). Cognitive load and perceived deception (DC) negatively influence the hotel booking intention.

The distinctive model that emerged is insightful for senior executives and managers in the hospitality sector to understand the influence of deepfake video advertisements. This research provides the factors of hotel booking intention due to deepfake video advertisements, which are helpful for designers, developers, marketing managers and other stakeholders in the hotel industry.

MR and IMT are integrated with variables such as PT and PV to explore the tourists' hotel booking intention after watching deepfake video advertisements. It is the first step toward deepfake video advertisements and hotel booking intentions for tourists. It provides an empirically tested and validated robust theoretical model to understand the effect of deepfake video advertisements on hotel booking intention.

This paper explores the similarities and differences in privacy attitudes, trust and risk beliefs between younger and older adults on social networking sites. The objective of the article is to ascertain whether any notable differences exist between younger (18–25 years old) and older (55+ years old) adults in how trust and risk are influenced by privacy concerns upon personal information disclosure on social media.

A Likert scale instrument validated in previous research was employed to gather the responses of 148 younger and 152 older adults. The scale was distributed through Amazon Mechanical Turk. Data were analyzed through partial least squares structural equation modeling.

No significant differences were found between younger and older adults in how social media privacy concerns related to trust and risk beliefs. Two privacy concern dimensions were found to have a significant influence on perceptions of risk for both populations: collection and control. Predictability and a sense of control are proposed as two conceptual approaches that can explain these findings.

This article is the first one to explore age differences in privacy concerns, trust and risk on social media employing conceptual developments and an instrument specifically tailored to the social media environment. Based on the findings, several strategies are suggested to keep privacy concerns on social media at a minimum, reduce risk perceptions and increase users' trust.

This paper aims to evaluate the impact of various preview modes on tourist attitudes and intentions to visit a destination based on consumers’ level of involvement in travel decision-making.

The study was conducted as a between-subjects one-factor [preview mode: static images vs 360-degree tour vs virtual reality (VR) mode] in a laboratory experiment setup to examine how consumers with different levels of involvement in travel decision-making respond to destination marketing toward three different preview modes.

The findings indicated that VR preview mode highly influences tourist attitudes and visit intentions toward a destination compared to static images and 360-degree tours. This effect is more significant among participants with higher levels of customer involvement. Finally, the results from the study offer empirical evidence of the effectiveness of VR in shaping user behavior compared to traditional preview modes.

The limitations are using a non-probability sampling method, a small sample size and affordable mobile-compatible VR headsets.

This study offers empirical evidence on the effectiveness of VR in shaping tourist behavior compared to traditional preview modes. It helps destination marketers develop appropriate strategies for promoting tourist destinations.

The novelty of this paper lies in understanding the effectiveness of VR in shaping tourist behavior with different levels of customer involvement in travel decision-making.

This article aims to understand users' information needs in the metaverse communities and to analyze the similarities and differences between their information needs and those of users in Internet communities.

This study conducted semi-structured interviews with users in the metaverse communities to gather raw data. Grounded theory research methods were employed to code and analyze the collected interview data, resulting in the extraction of 40 initial concepts, 15 subcategories and 5 main categories. Based on Maslow’s hierarchy of needs theory, this paper constructs the hierarchical model of users' information needs in the metaverse communities. It compares the differences between users' information needs in the metaverse and Internet fields.

The user’s information needs in the metaverse communities are divided into two types: deficiency needs and growth needs. Deficiency needs have two levels. The first level is the demand for basic information resources. The second level is the users demand for information assistance. Growth needs have three levels. The first level is the need for information interactions. The second level is the need for community rules. The ownership information in the community rules can provide proof of user status, assets and so on. The third level is the need for users to contribute and share their own created information content.

This article presents the latest research data from in-depth interviews with users in the metaverse communities. It aims to help builders and managers of metaverse communities understand users' information needs and improve the design of virtual communities.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether media richness (MR), human-system interaction (HSI) and human-human interaction (HHI) as technological feature antecedents to medical professionals’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from medical professionals at six university-/medical university-affiliated hospitals in Taiwan. A total of 600 questionnaires were distributed, and 309 (51.5%) usable questionnaires were analyzed using structural equation modeling in this study.

This study certified that medical professionals’ perceived MR, HSI and HHI in MOOCs positively affected their emotional LE, cognitive LE and social LE elicited by MOOCs, which together explained their LP in MOOCs. The results support all proposed hypotheses and the research model accounts for 84.1% of the variance in medical professionals’ LP in MOOCs.

This study uses the S-O-R model as a theoretical base to construct medical professionals’ LP in MOOCs as a series of the psychological process, which is affected by MR and interaction (i.e. HSI and HHI). Noteworthily, three psychological constructs, emotional LE, cognitive LE and social LE, are adopted to represent medical professionals’ organisms of MOOCs adoption. To date, hedonic/utilitarian concepts are more commonly adopted as organisms in prior studies using the S-O-R model and psychological constructs have received lesser attention. Hence, this study enriches the S-O-R model into an invaluable context, and this study’s contribution on the application of capturing psychological constructs for completely explaining three types of technological features as external stimuli to medical professionals’ LP in MOOCs is well-documented.

In light of the stimulus-organism-response (SOR) theoretical paradigm, this paper explores how information relevance and media richness affect social network exhaustion and, moreover, how social network exhaustion ultimately leads to health anxiety and COVID-19-related stress.

The conceptual model is explicitly analyzed and estimated by using data from 309 individuals of different ages in mainland China. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were utilized to validate the proposed hypotheses through the use of online data.

The findings suggest that information relevance is negatively associated with social network exhaustion. In addition, social network exhaustion is a significant predictor of health anxiety and stress. Furthermore, information relevance and media richness can indirectly influence health anxiety and stress through the mediating effect of social network exhaustion.

Theoretically, this paper verifies the causes and consequences of social network exhaustion during COVID-19, thus making a significant contribution to the theoretical construction and refinement of this emerging research area. Practically, the conceptual research model in this paper may provide inspiration for more investigators and scholars who are inclined to further explore the different dimensions of social network exhaustion by utilizing other variables.

Although social network exhaustion and its adverse consequences have become prevalent, relatively few empirical studies have addressed the deleterious effects of social network exhaustion on mobile social media users’ psychosocial well-being and mental health during the prolonged COVID-19. These findings have important theoretical and practical implications for the rational development and construction of mobile social technologies to cultivate proper health awareness and mindset during the ongoing worldwide COVID-19 epidemic.

Asynchronous Video Interviews (AVIs) incorporating Artificial Intelligence (AI)-assisted assessment has become popular as a pre-employment screening method. The extent to which applicants engage in deceptive impression management (IM) behaviors during these interviews remains uncertain. Furthermore, the accuracy of human detection in identifying such deceptive IM behaviors is limited. This study seeks to explore differences in deceptive IM behaviors by applicants across video interview modes (AVIs vs Synchronous Video Interviews (SVIs)) and the use of AI-assisted assessment (AI vs non-AI). The study also investigates if video interview modes affect human interviewers' ability to detect deceptive IM behaviors.

The authors conducted a field study with four conditions based on two critical factors: the synchrony of video interviews (AVI vs SVI) and the presence of AI-assisted assessment (AI vs Non-AI): Non-AI-assisted AVIs, AI-assisted AVIs, Non-AI-assisted SVIs and AI-assisted SVIs. The study involved 144 pairs of interviewees and interviewers/assessors. To assess applicants' deceptive IM behaviors, the authors employed a combination of interviewee self-reports and interviewer perceptions.

The results indicate that AVIs elicited fewer instances of deceptive IM behaviors across all dimensions when compared to SVIs. Furthermore, using AI-assisted assessment in both video interview modes resulted in less extensive image creation than non-AI settings. However, the study revealed that human interviewers had difficulties detecting deceptive IM behaviors regardless of the mode used, except for extensive faking in AVIs.

The study is the first to address the call for research on the impact of video interview modes and AI on interviewee faking and interviewer accuracy. This research enhances the authors’ understanding of the practical implications associated with the use of different video interview modes and AI algorithms in the pre-employment screening process. The study contributes to the existing literature by refining the theoretical model of faking likelihood in employment interviews according to media richness theory and the model of volitional rating behavior based on expectancy theory in the context of AVIs and AI-assisted assessment.