Contributions published at Department of Business Administration (Egon Franck)

We use vocational training curricula to investigate how IT skills are trained within broader skills packages and how these relate to labour market outcomes. Skills packages are the typical combinations of IT skills (e.g., CNC) and technical or nontechnical skills (e.g., material sciences or work safety) that are jointly required in the real world and occur in training curricula. This broadened perspective of teaching IT skills offers new insights into how digital skills can be successfully integrated into future education and training programmes. We use legally binding vocational education and training (VET) curricula of dual apprenticeship training in Switzerland. We apply natural language processing methods to analyse the extensive curriculum texts, which meticulously define the skills that have to be taught. We identify four typical skills packages, each of which are centred around one of four different types of IT skill (CNC/CAD, control technologies, system technologies, IT-applications). Our empirical analyses show that VET graduates trained in these skills packages receive positive labour market outcomes compared to VET graduates without these skills packages. Moreover, we find that the positive outcomes are not just driven by differences in cognitive skill requirements of the respective occupations.

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Exploiting a quasi-experimental field setting, we examine whether people are outcome biased when self-evaluating their past decisions. Using data from Swiss driving license exams, we find that candidates who narrowly passed the theoretical driving exam are significantly less likely to pass the subsequent practical driving exam – which is taken several months after the theoretical exam – than those who narrowly failed. Those candidates who passed the theoretical exam on their first attempt receive more objections regarding their momentary, on-the-spot decisions in the practical exam, consistent with the idea that the underlying behavioral difference is worse preparation.

Addressing the demands of our aging society, technologies such as smart home systems enhance older adults' independence and enable them to live longer at home. Yet, older adults tend to avoid, delegate, or defer decisions, and smart home systems are rarely used by individuals over the age of 65. Drawing on research on choice overload and age labels, we investigate across four studies how the presentation of smart home systems can influence the decision-making process of older adults ages 65+ by mitigating choice complexity and, consequently, choice avoidance. We argue that age-framing of complex systems makes the choice options easier to process for older adults than does price-framing, the current market standard. We find that age-framing positively affects the rate of choice, and we identify reduced choice complexity as the underlying process. Our research further demonstrates that family members, as important co-deciders, evaluate decisions made on age-framed alternatives more positively, thus making it easier for older adults to justify their decisions. We thus contribute to research on the decision-making of older adults in general and on age labels in particular, and we identify important practical implications for providers of high investment products for older adults.

Delegation is an important part of organizational success and can be used to overcome personal shortcomings and draw upon the expertise and abilities of others. However, delegation comes with risks and uncertainties, as it entails a transfer of power and loss of control. Indeed, research has documented that people tend to under-delegate to other humans, often leading to poor decisions and ultimately negative economic consequences. Today, however, people are faced with a new delegation choice: Artificial Intelligence (AI). Fueled by Big Data, AI is rapidly becoming more intelligent and frequently outperforming human forecasters and decision-makers. Given this evolution of computational autonomy, researchers need to revisit the hows and whys of decision delegation and clarify not only whether people are willing to cede control to AI agents but also whether AI can reduce the under-delegation that is especially pronounced when people are faced with decisions that spur a high desire for control. By linking research on decision delegation, social risk, and control premium to the emerging field of trust in AI, we propose and find that people prefer to delegate decisions to AI as compared to human agents, especially when decisions entail losses (Studies 1–3). Results further illuminate the underlying psychological process involved (Study 1 and 2) and show that process transparency increases delegation to humans but not to AI (Study 3). These findings have important implications for research on trust in AI and the applicability of autonomous AI systems for managers and decision makers.

Objectives: This paper presents results of a study on the Polish adaptation of the Personality Inventory for ICD-11 (PiCD), which was developed to measure pathological traits under a new, dimensional model of personality disorders proposed in ICD-11. Methods: The study involved a non-clinical sample of N = 597 adults (51.4% female; M = 30.24 years, SD = 12.07 years). For convergent and divergent validity, Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012) and Big Five Inventory-2 (BFI-2; Soto & John, 2017) were used. Results: The results showed the Polish adaptation of the PiCD to be reliable and valid. Cronbach's alpha coefficient for PiCD scale scores ranged from 0.77 to 0.87 (M⍺ = 0.82). The four-factor structure of PiCD items with the three unipolar factors, Negative Affectivity, Detachment, and Dissociality, and one bipolar Anankastia vs. Disinhibition factor was conformed. All PiCD traits are related to PID-5 pathological traits and BFI-2 normal traits in an expected way both in correlational and factor analyses. Conclusions: Obtained data demonstrate satisfactory internal consistency, factorial validity, and convergent-discriminant validity of the Polish adaptation of PiCD in a non-clinical sample.

Approximately one-third of all food produced for human consumption is either lost or wasted. Given the central position of retailers in the supply chain, they have the potential to effectively reduce consumer food waste by implementing targeted interventions. To do so, however, they should target distinct consumer groups. In this research, we use a unique data set comprising the grocery shopping data of customers who use loyalty cards, complemented with food waste reports, to derive three distinct target groups: traditionals, time-constrained, and convenience lovers. Based on the general behavioral change literature, we discuss diverse target group-specific interventions that retailers can implement to reduce consumer food waste. Overall, we pave a research path to examine how retailers and marketing can effectively shift consumer behavior toward more sustainable food and shopping practices and assume responsibility within the food supply chain.

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