Home-based remote work becomes increasingly popular. The facets of remote work, especially working from home, are multifaceted and can become stressors that affect a person’s health. At the same time, self-efficacy is an important personal resource to deal with health-related stressors. The objective of this study is therefore to explore the relationship between self-efficacy (SE), work-related stress (WRS), health outcomes (health and anxiety), contributing factors (autonomy and experience) and work-family conflict (WFC) in a remote work setting. Using a PLS-model (partial least square) with a sample of n = 5163 responses, we found that SE significantly reduces WRS (β = −0.164; p < 0.001). Moreover, WFC increases WRS and anxiety, while SE reduces WFC and mediates health outcomes (anxiety: β = −0.065; p < 0.001; health: β = −0.048; p < 0.001). At the same time, autonomy (β = 0.260; p < 0.001) and experience (β = 0.215; p < 0.001) increase SE. Our results have high practical implications for employers and employees, underlining the importance of self-efficacy as a personal resource to buffer WRS and WFC while promoting overall health at the same time.

Background: Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people’s health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated. Objective: The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) What are the study characteristics of studies on AI algorithms and technologies in the context of WHPP? (2) What specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies? (3) What kind of interventions lead to which outcomes? Methods: A systematic scoping literature review (PRISMA-ScR [Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews]) was conducted in the 3 academic databases PubMed, Institute of Electrical and Electronics Engineers, and Association for Computing Machinery in July 2023, searching for papers published between January 2000 and December 2023. Studies needed to be (1) peer-reviewed, (2) written in English, and (3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the patient or population, intervention, comparison, and outcomes framework were extracted blindly with Rayyan and summarized. Results: A total of 10 studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Further, 4 studies focused on mental health. Most AI algorithms were machine learning-based, and 3 studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone apps (eg, in the form of a chatbot) or used the smartphone as a data source (eg, Global Positioning System). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Additionally, 3 studies evaluated the robustness and accuracy of an AI model or framework. Conclusions: Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines. Trial Registration: OSF Registries osf.io/bfswp; https://osf.io/bfswp

Distance learners are often referred to as ‘lone wolves' who, as a result of the geographical distance and other system-specific characteristics of distance learning programs, place little emphasis on communicating with the university and, in particular, their fellow students. This article, following a demonstration of the developments in the field of distance learning, focuses on the communication patterns of distance learners. As part of a quantitative study, the students of a German distance university answered a survey on this topic. A significant result was the division of the students into three groups according to their tendency to communicate: Power communicators, regular communicators, and the lone wolves. Moreover, a shift within the student cohorts can be identified: students in later semesters prefer synchronous communication as opposed to the asynchronous use of emails. Learning analytics and a shift in the role of academic support towards mentoring can support an increase in the tendency to communicate and, ultimately, in student motivation and performance.

The use of perceptual and objective measures in information systems is an ongoing discussion in IS research, primarily due to different conceptions about objective measures. This research, therefore, empirically tests truly objective measures along with perceptual measures to gain a more comprehensive understanding of the role of perceptual and objective independent variables as predictors of user satisfaction and continuance intention. This paper developed an integrated research model that examines perceptual and objective measures as predictors of satisfaction and continuance intention. The approach was then empirically tested with survey-based perceptual variables and corresponding objective variables in the context of a mobile network operator (n = 998). Specifically, we used serial mediation analysis with PROCESS to test our hypotheses. The results showed that the different objective variables show insignificance as predictors of satisfaction and continuance intention, contrasting with previous research calling for objective measures. Our results contribute to IS literature by indicating that perceptual measures are superior to objective measures in predictive relevance. Hence the “silent acceptance” of potential conceptual weaknesses is indeed justified by the results of this research.