Does working in a gender-atypical occupation reduce individuals’ likelihood of finding a different-sex romantic partner, and do such occupational partnership penalties contribute to occupational gender segregation? To answer this question, we theorized partnership penalties for working in gender-atypical occupations by drawing on insights from evolutionary psychology, social constructivism, and rational choice theory and exploited the stability of occupational pathways in Germany. In Study 1, we analyzed observational data from a national probability sample (N= 1,634,944) to assess whether individuals in gender-atypical occupations were less likely to be partnered than individuals who worked in gender typical occupations. To assess whether the observed partnership gaps found in Study 1 were causally related to the gender typicality of men’s and women’s occupations, we conducted a field experiment on a dating app (N = 6,778). Because the findings from Study 2 suggested that young women and men indeed experienced penalties for working in a gender-atypical occupation (at least when they were not highly attractive), we employed a choice-experimental design in Study 3 (N = 1,250) to assess whether women and men were aware of occupational partnership penalties and showed that anticipating occupational partnership penalties may keep young and highly educated women from working in gender-atypical occupations. Our main conclusion therefore is that that observed penalties and their anticipation seem to be driven by unconscious rather than conscious processes.

Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.

Content website providers have two main goals: They seek to attract consumers and to keep them on their websites as long as possible. To reach potential consumers, they can utilize several online channels, such as paid search results or advertisements on social media, all of which usually require a substantial marketing budget. However, with rising user numbers of online communication tools, website providers increasingly integrate social sharing buttons on their websites to encourage existing consumers to facilitate referrals to their social networks. While little is known about this social form of guiding consumers to a content website, the study proposes that the way in which consumers reach a website is related to their stickiness to the website and their propensity to refer content to others. By using a unique clickstream data set of a video-on-demand website, the study compares consumers referred by their social network to those consumers arriving at the website via organic search or social media advertisements in terms of stickiness to the website (e.g., visit length, number of page views, video starts) and referral likelihood. The results show that consumers referred through social referrals spend more time on the website, view more pages, and start more videos than consumers who respond to social media advertisements, but less than those coming through organic search. Concerning referral propensity, the results indicate that consumers attracted to a website through social referrals are more likely to refer content to others than those who came through organic search or social media advertisements. The study offers direct insights to managers and recommends an increase in their efforts to promote social referrals on their websites.

We propose a conceptual model of acceptance of contact tracing apps based on the privacy calculus perspective. Moving beyond the duality of personal benefits and privacy risks, we theorize that users hold social considerations (i.e., social benefits and risks) that underlie their acceptance decisions. To test our propositions, we chose the context of COVID-19 contact tracing apps and conducted a qualitative pre-study and longitudinal quantitative main study with 589 participants from Germany and Switzerland. Our findings confirm the prominence of individual privacy calculus in explaining intention to use and actual behavior. While privacy risks are a significant determinant of intention to use, social risks (operationalized as fear of mass surveillance) have a notably stronger impact. Our mediation analysis suggests that social risks represent the underlying mechanism behind the observed negative link between individual privacy risks and contact tracing apps' acceptance. Furthermore, we find a substantial intention–behavior gap.

Business incubators hatch start-ups, helping them to survive their early stage and to create a solid foundation for sustainable growth by providing services and access to knowledge. The great practical relevance led to a strong interest of researchers and a high output of scholarly publications, which made the field complex and scattered. To organize the research on incubators and provide a systematic overview of the field, we conducted bibliometric performance analyses and science mappings. The performance analyses depict the temporal development of the number of incubator publications and their citations, the most cited and most productive journals, countries, and authors, and the 20 most cited articles. The author keyword co-occurrence analysis distinguishes six, and the bibliographic coupling seven research themes. Based on a content analysis of the science mappings, we propose a research framework for future research on business incubators.