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A wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have hampered efforts to obtain a clear understanding of what truly influences online self-disclosure. To address this gap, this study draws on the antecedent-privacy concern-outcome (APCO) framework in a one-stage meta-analytical structural equation modeling (one-stage MASEM) study to test a nomological online self-disclosure model that assesses the factors affecting online self-disclosure.

Using the one-stage MASEM technique, this study conducts a meta-analysis of online self-disclosure literature that comprises 130 independent samples extracted from 110 articles reported by 53,024 individuals.

The results reveal that trust, privacy concern, privacy risk and privacy benefit are the important antecedents of online self-disclosure. Privacy concern can be influenced by general privacy concern, privacy experience and privacy control. Furthermore, moderator analysis indicates that technology type has moderating effects on the links between online self-disclosure and some of its drivers.

First, with the guidance of the APCO framework, this study provides a comprehensive framework that connects the most relevant antecedents underlying online self-disclosure using one-stage MASEM. Second, this study identifies the contextual factors that influence the effectiveness of the antecedents of online self-disclosure.

This paper determines how travel intentions can be predicted using self-disclosure behaviour, trust and intimacy. This case study focuses on Tinder users who utilised the application's Passport feature which allowed them to travel virtually and interact with other users around the globe amid global travel restrictions.

This quantitative research conveniently sampled 294 Tinder users who used the Passport feature during COVID-19 pandemic lockdowns. Data were analysed using PLS-SEM.

This study revealed that self-disclosure had a significant influence towards future travel intentions. Findings show that the more users self-disclose, the more their intent to travel increase. Trust and intimacy also had significant relationship on travel intentions while intimacy had a mediating effect between self-disclosure and travel intentions.

Tourism-oriented establishments and destination marketers should consider Tinder users as a market segment of future tourists. These users have developed travel intentions through in-app interactions and thus comprise an untapped market of potential tourists seeking for meet-ups and niche experiences in a post-pandemic era.

This study provides novelty in showing the predictive relationship of self-disclosure, trust and intimacy towards travel intentions. A model consisting of these constructs in the context of online interactions was also empirically tested and found adequate to predict travel intentions.

In this study, we drew on the theories of social exchange and social learning and hypothesized that the online social capital (SC) and offline SC of social networking affect the online self-disclosure (OSD) of individuals through social self-efficacy (SSE).

After retrieving 514 valid questionnaires, we used structural equation modeling to analyze the data.

The results indicated that the users’ SSE affected their OSD, and that both online and offline bridging and bonding SC increased their SSE. However, online bonding SC directly affected their OSD, whereas online bridging SC did not considerably affect their OSD. Given these findings, we presented both theoretical and practical implications to elucidate SSE and OSD behavior from the perspective of online and offline bridging and bonding SC.

In this study, we drew on theories of social exchange and social learning to examine the effects of online and offline bridging and bonding SC on users’ SSE and OSD on SNSs. Given the importance of SC and SSE in social relationships and the effects of OSD on SNSs, our goal was to provide SNS marketers with a thorough understanding of how to facilitate SSE and OSD from the perspective of online and offline bridging and bonding SC.

This paper aims to investigate the extent of personal privacy concerns expressed by university students in Vietnam while using TikTok, the influence of peer interactions and social norms on privacy attitudes and behaviors and the strategies used by university students in Vietnam to mitigate privacy risks on TikTok.

A qualitative approach using semi-structured interviews was used to gather data on the following: the degree to which Vietnamese university students express concerns about their personal privacy while using TikTok; how peer interactions and social norms influence privacy attitudes and behaviors; and the strategies these students use to mitigate privacy risks on the platform.

Findings indicate that although university students exhibit a relatively low level of concern regarding their personal privacy on TikTok, peer influences and societal norms significantly shape their attitudes and behaviors on the platform. Moreover, students use several strategies to mitigate privacy risks, such as selective content sharing and refraining from engaging with unknown links.

These insights provide valuable implications for the development of targeted interventions and educational initiatives aimed at fostering a more privacy-conscious TikTok user community among university students in Vietnam.

This research fills a critical gap in the existing literature by focusing on the influence of social norms and peer influences on privacy attitudes and behaviors on social media platforms. While prior studies have explored various factors impacting privacy concerns on social media, the role of social dynamics has been understudied. Moreover, the research specifically addresses the lack of investigation into privacy concerns on TikTok, a platform rapidly gaining popularity among younger demographics.

This study aims to analyze and synthesize literature on consumer privacy-related behavior and intelligent device-to-device interactions within the Internet of Things (IoT).

We conducted a systematic review using Elsevier’s Scopus database, focusing on studies published in English from 2000 to 2023. The review targeted articles within selected social sciences and business disciplines, specifically concerning consumer behavior in IoT contexts.

We categorized the privacy literature into three thematic clusters: legislation and policy, business implications and consumer behavior. Within the consumer behavior cluster, our analysis indicates a shift from general Internet and e-commerce privacy concerns prior to 2016, toward issues related to advertising and policy between 2017 and 2018, and increasingly toward pronounced concerns in technological systems, particularly IoT, from 2019 onwards. We identify eight distinct areas of privacy concern within IoT and propose a framework that links antecedents and privacy concerns to subsequent attitudes and behaviors. This framework highlights varying patterns of information disclosure and bridges theoretical constructs with empirical research in IoT privacy.

Originality lies in enhancing the Antecedents-Privacy Concerns-Outcomes (APCO) macro-model by integrating diverse theoretical perspectives on technological and individual-specific antecedents, alongside privacy concerns and beliefs. This comprehensive integration enriches the framework, enabling it to predict and categorize consumer behavior in IoT environments more effectively. The revised model provides a robust tool for understanding privacy-related behavior within the IoT, significantly enriching its theoretical relevance and practical applicability.

A major portion of our social interaction now occurs online, facilitated by social networking sites (SNSs) that enable people to connect and communicate at will. However, the characteristics of SNS communication can introduce problematic outcomes on otherwise healthy processes, one of which is social comparison. In this work, we investigate whether compulsive SNS use could be driven by two phenomena related to social comparison: the fear of missing out (FoMO) and envy.

Using the stimulus-organism-response framework, we developed a model that was tested with data from a sample of SNS users (N = 330) based in the United States. The analysis was done through partial least squares structural equation modeling.

Our findings show FoMO's association with the two forms of dispositional experienced envy, benign and malicious, as well as expected envy of others (expected envy). Interestingly, benign and expected envy were associated with SNS stalking and self-disclosure, but malicious envy had non-significant associations. Finally, both SNS stalking and self-disclosure were linked to compulsive SNS use.

We study the nuanced ways in which the two forms of experienced envy and expected envy can be triggered by FoMO and result in users' engagement with problematic SNS use. Our research provides evidence that, in addition to benign envy being an antecedent of compulsive SNS use, the wish to invoke envious feelings in others can also significantly drive compulsive use.

The COVID-19 pandemic has had far-reaching effects on society and will continue to be a subject of study for researchers in the years to come. Businesses have implemented technologies that reduce reliance on physical currencies, such as e-commerce sites and contactless payments. This study aims to examine the users’ attitudes and behaviors toward mobile payments. The focus is on identifying the most effective techniques and approaches that businesses can use to encourage user adoption of mobile payments.

This study uses survey data from 396 active mobile payment users across the mid-west region of the USA to test the proposed hypothesis. The snowball sampling approach is used to sample the participants for the data collection. This study uses partial least squares structural equation modeling to test the ten hypotheses proposed in this study.

This study finds that organizational commitment and privacy customization can significantly overcome users’ protective attitudes toward mobile payments during the pandemic. In addition, providing users with privacy customization options can significantly encourage self-disclosure, which is crucial for transaction authentication and fraud detection.

Envisioned in the backdrop of the COVID pandemic, this is one of the earliest studies investigating the role of privacy customization, self-disclosure and organizational commitment on mobile payment adoption.

From a consumer–brand relationship (CBR) perspective, this study proposes a serial multiple mediation model to explore the pathways (e.g. cognitive, affective and hybrid) to increase online celebrity brand equity (OCBE) and follower's hotel booking intentions (FBI).

Paper-based and online surveys were used to collect data from 443 respondents who had been using TikTok and had followed at least one online celebrity on TikTok, while that online celebrity had reviewed one or more types of accommodation. A serial multiple mediation model was evaluated through covariance-based structural equation modeling.

The results confirmed the hybrid cognitive–affective and cognitive–affective–conative pathways among antecedents and components of OCBE and FBI.

From a CBR perspective, this study provides a serial multiple mediation model to increase OCBE via hybrid cognitive–affective pathways and to drive FBI via cognitive–affective–conative pathways. These sequential relationships contribute to the human branding literature by defining a mechanism of how online celebrities can efficiently attract followers, thus driving online celebrity brand loyalty and hotel booking intentions.

The results should be validated in other cultural contexts to generalize findings and broaden the range of target respondents to include international followers and those within other nations.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Amidst the COVID-19 pandemic, education has shifted to online teaching and learning. Interactivity is a crucial tool used to make online education effective. This study empirically examines the role of interactivity in higher education and its influence on students' behavioral outcomes, specifically focusing on soft skills and personality upgradation.

A quasi-experimental research design was carried out for post-graduate students undergoing a business communication course from four major institutions. For analysis, t-test, confirmatory factor analysis (CFA) and partial least squares structural equation modeling (PLS-SEM) have been employed. Experimental research has established the causal relationship between interactivity, personality and soft skill upgradation (SSU).

It was found that the theoretical structural model has a rational model-fit validity. Resultantly, practitioners may use prior knowledge of virtual community (VC) members to enhance web interactivity, thereby increasing social identity and social bonds in a group for more meaningful and effective delivery of online courses.

The major limitations lie in its context-dependent nature, predominantly influenced by the pandemic-induced mandatory online learning. The study's cross-sectional design also inhibits its ability to assess goal-directed behaviors over time, necessitating further longitudinal research.

The study is one of the pioneering pieces of research that examines the role of pre-defined grouping and enhanced web interactivity in VCs in the context of online learning, especially during the COVID-19 pandemic. Integrating theories of web interactivity, social bond theory (SBT) and social identity theory (SIT) provides a novel understanding of cognitive and social influences that drive meaningful online discussions and their impacts on knowledge enhancement and personality development. Its findings have implications for the design of effective online learning environments and e-learning pedagogy, contributing to the growing domain of information and communication technology (ICT)-enabled education.