What Can Mental Health Teach Us About Social Media Screen Time Misestimation?

Mobile platform providers have provided the ability to measure the time consumers spend on each app. This provides the opportunity to measure a consumer’s misestimation of their screen time which is a concept relevant to several mental health attributes such as depression, anxiety, and addiction. We provide additional evidence about the effect of objective screen time on mental health, but add a unique perspective on how screen time misestimation is determined by various mental health attributes. We collected data from a student sample (n=1005) who are from the demographic who most commonly use social media apps (18-29 yr olds). We measured our model across several of the most common platforms including Facebook, Instagram, Twitter, and YouTube to maximize the practical implications. The results indicate that mental health attributes can indeed be reflected by misestimations of screen time. However, this effect varies by social media platform

STUDENT RETENTION IN INFORMATION SYSTEMS MAJORS: THE ROLE OF CREATIVE SELF-EFFICACY

At times the Information Systems (IS) major has suffered from declining enrollment. Also, STEM fields such as IS are known to suffer from a lack of gender diversity. This research focuses on why students drop out from IS programs and how to provide actionable feedback to improve student retention, particularly among female students. We use creative self-efficacy (CreaSE) as a theoretical lens to explain student retainment. In particular, as students have more confidence in their ability to solve business problems with IS solutions, they are more likely remain in IS courses. Students who sought help from their instructor and StackOverflow.com developed greater CreaSE. However, women were less likely to seek help in general, which creates unique opportunities for future research

Development and Validation of the Information Systems Creative-Self-Efficacy Scale

High-performing information systems (IS) professionals harness creativity as they build systems to solve new and unstructured business problems. Psychology has developed useful scales and techniques for measuring creativity. However, being creative is not sufficient. IS professionals must also have confidence in their creative ability to succeed. The belief in one’s ability to be creative is termed creative self-efficacy (CreaSE). CreaSE is defined in the general business context, but scales are not thoroughly developed or refined. CreaSE has also never been studied in the IS context. We detail steps to develop and validate a theoretically-based measure of CreaSE as related to IS. Our process includes six datasets collected during refinement. Participants include business and IS students, online respondents, university professors, IS executives, and IS professionals. The validated instrument is a second-order formative measure with reflective first-order sub-constructs based on belief in cognitive ability, affect, domain knowledge, skills, and understanding of people

Augmenting Information Systems Creative Self-Efficacy through Video Gaming

Creativity is a critical trait of Information Systems (IS) employees. Organizations are keen to hire and develop employees who can solve unstructured business problems with IS solutions. However, self-efficacy research has shown that an employee’s confidence in their ability to solve creative problems may be more important than their actual capability. To this end, IS professionals and students seek opportunities for experience in creative problem solving to improve their confidence. However, failure in performing tasks can lower confidence. Problem-solving based video games (psbVG) may be an alternative and risk-free proxy to direct experience. PsbVG require very creative thinking to problem solving and may help to generate confidence. Based on a theoretical model centered on self-efficacy theory and enhanced with flow theory, we performed a laboratory experiment designed to explore whether psbVG (and to what degree) may be a proxy for direct experience in developing greater self-efficacy

Improving Mental Health Outcomes through Machine Learning Feedback of Social Media Behavior

With mental health issues on the rise, the need for early diagnosis is especially pivotal to reduce the risk of suicide and disease. Both social media usage and social media content can act as indicators of a user’s mental health status. We posit that through using machine learning feedback, we can assist users in early self-diagnoses and monitor how that feedback affects their social media behavior and their mental health. By providing continuous feedback about users’ mental health, we can encourage users to change their social media habits and seek help from a mental health professional