The rapid evolution of digital marketing underscores a critical tension between personalization and privacy, exacerbated by advances in data technologies and artificial intelligence. This study delves into the personalization‐privacy paradox, emphasizing the dichotomy of consumer behavior—desiring customized interactions while guarding personal data. We explore how happiness with the internet (HWI) influences users' willingness to disclose personal information on social media, employing social exchange theory as our conceptual framework. Our research develops and tests a conceptual model that investigates the psychological mechanisms driving information‐sharing behaviors on social media, including the moderating roles of trust beliefs and information collection concerns. By examining the mediating effect of posting frequency on the relationship between HWI and information disclosure for personalization, our findings contribute to understanding the complex interplay between happiness, trust and privacy concerns, coined as transformative privacy calculus. Our study enriches social exchange and privacy calculus theories, providing valuable implications for marketers aiming to navigate the balance between personalization and privacy, suggesting strategies to enhance user engagement without compromising privacy standards.

Autonomous driving technology has made its way into the market at various levels, yet fully autonomous vehicles remain unavailable. The psychological barriers that must be overcome before fully automated vehicles (AVs) become mainstream are numerous. In addition to technological advancements, persuading consumers to transition from the traditional human-driven model to AVs poses a significant challenge. According to the Moral Machine Experiment, Latin American countries form distinct sub-clusters and exhibit the highest preference for action in moral decision-making. To foster user acceptance of AVs in these countries, it is imperative to comprehend cognitive, affective, and ethical factors. To this end, we conducted experiments with respondents from Colombia to examine how varying levels of automation influence algorithm aversion and user acceptance. Algorithm aversion is explored from two perspectives: ethical judgment and behavior, and emergency evaluation and performance. Our findings reveal two key insights. Firstly, higher levels of automation negatively impact people’s assessment of the emergency evaluation capabilities of AVs, partially contributing to algorithm aversion. Secondly, the intention to use AVs is adversely affected by algorithm aversion, encompassing both ethical considerations and emergency performance-related aspects. Furthermore, mediation analysis demonstrates that perceived hedonism elucidates the inverse relationship between algorithm aversion and the intention to use AVs.