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This study aims to examine young social media users’ differential acceptance of data-driven ad personalization depending on the types of personal data used, and to propose and test the Privacy and Trust Equilibrium (PATE) model, a new conceptual model developed to explain the intertwined nature of the competing influences of platform-related factors (privacy concern, trust, and privacy fatigue) on acceptance of ad personalization.

A survey of 440 Instagram users aged 18–24 in Australia was conducted to examine the relationships between the three factors of the PATE model and acceptance of ad personalization utilizing overt vs covert data collection methods.

This study shows the highest level of acceptance for personalization using overtly collected data and the lowest for covert data. The results also support the PATE model, revealing the competing dynamics of how the platform-related factors shape consumers’ acceptance of data-driven ad personalization. Privacy concern discourages Instagram users from accepting personalized ads, while trust encourages them. When the pushing influence of privacy concern and the pulling influence of trust form equilibrium, generating cognitive dissonance, privacy fatigue seems to play a significant role in resolving the dissonance, leading to increased acceptance.

This study advances the understanding of how concurrent push–pull-resigning factors affect young consumers’ acceptance of data-driven ad personalization practices, expanding the scope of research on data-driven personalized advertising and privacy.

Although a fashion subscription offers significant environmental benefits by transforming physical products into shared services, most customers are reluctant to adopt it. This hesitation, exacerbated by poor communication from brands that primarily emphasize its personal benefits, hinders its sustainable growth. This study aims to examine specifically which concerns increase hesitation, and the role of explicitly informing consumers about the service’s environmental benefits in mitigating the impact of consumer concerns on their hesitation.

Data were collected through an online experiment with more than a thousand U.S. adults nationwide and analyzed using a two-step analysis. First, theory-based causal modeling was conducted to examine the effects of consumer concerns on hesitation, accounting for ambivalence as a mediator and informed environmental benefits as a moderator. Second, machine learning was used to cross-validate the findings.

Results show that certain types of consumer concerns increase hesitation, significantly mediated by ambivalence, and confirm that informed environmental benefits mitigate the effects of some concerns on hesitation.

This study contributes to building on the hierarchy of effects theory by exploring negatively nuanced constructs – concerns, ambivalence and hesitation – beyond the traditional constructs representing the cognitive, affective and conative stages of consumer decision-making. Findings provide strategic guidance to brands on how to communicate the new service to consumers. Leveraging theory-based causal modeling with machine learning-based predictive modeling provides a novel methodological approach to explaining and predicting consumer hesitation toward new services.

In an era of blurred moral boundaries, brands face “moral dilemma crises”, which are distinguished from traditional crises. Based on expectancy violations theory, this study aims to explore how consumers interpret (inferred goodwill) and evaluate (consumer regret) the precrisis committed brands, reinforced by corporate ability and responsibility (expectancies) during moral dilemma crises and whether self-brand connection moderates this relationship.

The main study used a scenario-based online survey with a purchased beauty brand, analyzed via structural equation modeling and PROCESS macro, to examine the effects of corporate ability and responsibility on regret, mediated by inferred goodwill and moderated by self-brand connection. A validation study with a fictitious brand used an online experimental design, analyzed with t-tests and ANCOVAs.

When corporate ability and corporate responsibility are considered together, corporate ability negatively impacts inferred goodwill (boomerang effect), whereas corporate responsibility positively impacts inferred goodwill (buffering effect). Inferred goodwill acts as a mediator that reduces regret. The negative impact of corporate ability on inferred goodwill and regret becomes stronger among consumers with strong self-brand connections. However, self-brand connection does not moderate the relationship between corporate responsibility and inferred goodwill.

This research extends the application of expectancy violations theory to moral dilemma crises, differentiates between the effects of corporate ability and responsibility, highlights the role of inferred goodwill and regret and explores the moderating effects of self-brand connection, providing tailored insights for brand management.

Given its growing economic potential and social impact, this study aims to understand the motivations and concerns regarding metaverse usage. It identifies user needs and risks around the metaverse grounded on uses and gratifications theory and perceived risk theory.

The authors analyzed user reviews and rating data from Roblox, a representative modern metaverse platform. They applied BERTopic modeling to extract topics from reviews, identifying key motivations and risk aspects related to metaverse usage. They further constructed an explanatory model to assess how those affect user satisfaction and changes in these effects over time.

This study discovered that gratifications like entertainment, escapism, social interaction and avatar-based self-expression significantly influence user satisfaction in the metaverse. It also highlighted that users find satisfaction in self-expression and self-actualization through creating virtual spaces, items and video content. However, factors such as identity theft, fraud and child safety were identified as potential detriments to satisfaction. These influences fluctuated over time, indicating the dynamic nature of user needs and risk perceptions.

The novelty of this study lies in its dual application of the uses and gratifications theory and perceived risk theory to the metaverse. It provides a novel perspective on user motivations and concerns, shedding light on the distinct elements driving user satisfaction within the metaverse. This study unravels the metaverse’s unique capacity to assimilate features from established digital media while offering a distinctive user-generated experience. This research offers valuable insights for academics and practitioners in digital media and marketing.

This research pioneers the application of both uses and gratifications and perceived risk theories to understand factors influencing metaverse satisfaction. By establishing a comprehensive framework, it explores the metaverse’s unique value as a user-content creation platform, while encompassing existing digital platform characteristics. This study enriches the academic literature on the metaverse and offers invaluable insights for both metaverse platforms and brand marketers.