Williams, J. R., Sindermann, C., Yang, H., Montag, C., & Elhai, J. D. (2023). Latent profiles of problematic smartphone use severity are associated with social and generalized anxiety, and fear of missing out, among Chinese high school students.
Cyberpsychology: Journal of Psychosocial Research on Cyberspace,
17(5), Article 5.
https://doi.org/10.5817/CP2023-5-7
Abstract
We explored problematic smartphone use (PSU) using latent profile analysis (LPA) and relationships with anxiety variables, including severity of generalized anxiety disorder (GAD), social anxiety disorder (SAD), and Fear of Missing Out (FoMO) in a non-clinical sample. We conducted a web-based survey (during the COVID-19 pandemic from February to March 2020) with high school students (N = 1,797; 1,164 female; ages 13–19 years) in Tianjin, China, administering the Smartphone Addiction Scale-Short Version (SAS-SV) to assess PSU, Generalized Anxiety Disorder (GAD-7) Scale, Social Interaction Anxiety Scale (SIAS), and Fear of Missing Out (FoMO) Scale. Using Mplus 8.7, we conducted LPA on SAS-SV item responses to uncover latent profiles and relations with anxiety and fear measures. A three-profile PSU model fit the data according to fit indices and likelihood ratio tests. SAS-SV item responses were lowest in profile 1, moderate in profile 2, and most severe in profile 3. Individual PSU profiles modeled by LPA demonstrated significant differences in social and generalized anxiety severity and FoMO. Controlling for age and sex, adolescents with higher levels of anxiety were more likely to be classified as profiles 2 and 3 rather than profile 1. These findings will hopefully inspire future studies and treatments concerning the severity of PSU as it relates to various psychopathology constructs.BibTeX
Abstract
Machine learning (ML) techniques have become one of the most successful scientific tools and changed the everyday life of people around the globe (e.g., search engines). A vast amount of digital data sources on human behaviour has emerged due to the rise of the internet and opened the door for computer scientists to apply ML on social phenomena. In the social sciences, however, the adoption of ML has been less enthusiastic. To investigate the relation of traditional statistics and ML, this paper shows how ML might be used as regression analysis. For that purpose, we illustrate what a typical social science approach might look like and how using ML techniques could contribute additional insights when it comes to estimators (non-linearity) or the assessment of model fit (predictive power). In particular, we reveal how epistemological differences shape the potential usage of ML in the social sciences and discuss the methodological trade-off of applying ML compared to traditional statistics.BibTeX
Ðula, I., Berberena, T., Kepliner, K., & Wirzberger, M. (2023). Hooked on artificial agents: a systems thinking perspective.
Frontiers in Behavioral Economics,
2, 1223281.
https://doi.org/10.3389/frbhe.2023.1223281
Abstract
Following recent technological developments in the artificial intelligence space, artificial agents are increasingly taking over organizational tasks typically reserved for humans. Studies have shown that humans respond differently to this, with some being appreciative of their advice (algorithm appreciation), others being averse toward them (algorithm aversion), and others still fully relinquishing control to artificial agents without adequate oversight (automation bias). Using systems thinking, we analyze the existing literature on these phenomena and develop a conceptual model that provides an underlying structural explanation for their emergence. In doing so, we create a powerful visual tool that can be used to ground discussions about the impact artificial agents have on organizations and humans within them.BibTeX
BibTeX
Zhang, Y., Yao, S., Sindermann, C., Rozgonjuk, D., Zhou, M., Riedl, R., & Montag, C. (2023). Investigating autistic traits, social phobia, fear of COVID-19, and internet use disorder variables in the context of videoconference fatigue.
Telematics and Informatics Reports,
11, 100067.
https://doi.org/10.1016/j.teler.2023.100067
Abstract
In response to the coronavirus disease 2019 (COVID-19) pandemic, many individuals turned to synchronous online video communication technologies as a substitute for real-world face-to-face interactions. Evidence indicates that some users of such technologies show symptoms of exhaustion and fatigue during and after videoconferences (VCs) – this phenomenon is referred to as Videoconference Fatigue (VC fatigue). Research characterizing the possible vulnerability factors for VC fatigue is still scarce and considered to be in its early stage. Contributing to closing this gap in the existing literature is the motivation for the present study. Survey data was collected from 311 German-speaking participants to explore the relationships of VC fatigue with several psychological factors including autistic traits, social phobia, Fear of COVID-19, tendencies towards Internet Use Disorders (IUD tendencies), and Fear of Missing Out (FoMO, trait and state variables). Results showed that VC fatigue was significantly positively correlated with all of these psychological factors except state-FoMO, and corss-sectional mediation analyses provided further evidence for the positive association between autistic traits and VC fatigue. Specifically, the relationship between autistic traits and VC fatigue was mediated by Fear of COVID-19 and IUD tendencies rather than social phobia, with the latter being a preregistered hypothesis. This study adds to the literature by revealing several possible vulnerability factors associated with VC fatigue. In essence, the present work sheds light on the complex association between autistic traits and VC fatigue. We discuss the implications of our study as well as its limitations and potential avenues for future research.BibTeX
Sindermann, C., Scholz, R. W., Löchner, N., & Montag, C. (2023). The revenue model of mainstream social media: advancing discussions on social media based on a European perspective derived from interviews with scientific and practical experts.
International Journal of Human–Computer Interaction.
https://doi.org/10.1080/10447318.2023.2278292
Abstract
Potential benefits and risks related to mainstream social media platforms and their revenue model are vigorously debated. However, a comprehensive framework of performance criteria to evaluate social media platforms and suggestions for transforming them are rare. Employing a transdiscipli nary approach, the present work aimed to close these gaps through semi-structured interviews with experts from academia and industry, coupled with exploratory thematic content/topic analysis.BibTeX