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Privacy has become a crucial issue of the digital age, with significant social, political, and economic ramifications. A growing body of literature has dedicated to the patterns, causes, and consequences of individuals’ privacy concerns, skills, and practices. Advancing a producer’s perspective, this research draws on in-depth interviews with 45 tech entrepreneurs to examine privacy practices of mobile start-ups in the United States. Results reveal (a) factors that contribute to the problematic status of privacy issues and (b) whether and how entrepreneurs leverage privacy management as a competitive advantage. Results show that data are widely seen by entrepreneurs as a potentially profitable asset. Privacy practices are networked and thus pose challenges for privacy management as different parties may have different privacy practices. Fast-moving technologies often leave government regulations behind, making them look outdated or irrelevant to many entrepreneurs. For most start-ups not specialized in identity, privacy, or anonymity service, privacy is neither a core business strategy nor a top concern. Only a few mobile ventures have leveraged privacy management as a competitive advantage and designed their products from the ground up concerned about privacy. Most entrepreneurs adopt a building-the-plane-while-flying-it approach: as business grows, privacy policies and practices would evolve. Many entrepreneurs fail to recognize the significance of privacy policies and practices as they lack the awareness, bandwidth, and capacity. Growth and monetization pressures from investors are perceived as more urgent and important than privacy and security issues. Offering a richer account of the power structure that shapes mobile entrepreneurs’ privacy practices and their challenges of managing privacy in a data-driven digital economy, our work advances the existing literature dominated by stories of the individual consumers.

Few studies in the eHealth literature have paid attention to participatory eHealth behaviors. Addressing this gap, the present study examines how informational and participatory eHealth behaviors are related to eHealth literacy, Internet use and Facebook interaction, as well as user characteristics. Drawing on a sample of college students (N=540), results from Structural Equation Modeling (SEM) analysis show that eHealth literacy has a positive direct effect on informational eHealth behaviors. It also serves as a mediator suppressing the negative relationship between excellent mental health status and eHealth behaviors. While both instrumental Internet use and Facebook interaction are related to participatory eHealth behaviors, only instrumental Internet use is associated with informational eHealth behaviors. There are significant eHealth disparities by health status, gender, and class. Implications for health communication and promotion are discussed.

Studies on the mental health implications of social media have generated mixed results. Drawing on a survey of college students (N=513), this research uses structural equation modeling to assess the relationship between Facebook interaction and psychological distress and two underlying mechanisms: communication overload and self-esteem. It is the first study, to our knowledge, that examines how communication overload mediates the mental health implications of social media. Frequent Facebook interaction is associated with greater distress directly and indirectly via a two-step pathway that increases communication overload and reduces self-esteem. The research sheds light on new directions for understanding psychological well-being in an increasingly mediated social world as users share, like, and comment more and more.

This study employs survey data from the Center for Community College Student Engagement to examine the similarities and differences that exist across student-level domains in terms of student engagement in community colleges. In total, the sample used in the analysis pools data from 663 community colleges and includes more than 320,000 students. Using data-mining techniques to discover a parsimonious number of natural clusters and, in turn, a k-means cluster analysis as a means of revealing a naturally occurring typology of engagement patterns, our findings reveal that support service utilization is the most distinguishing feature of the similarities and dissimilarities across student groups, suggesting areas for further theory development and testing.