Contributions published at Department of Business Administration (Egon Franck)

Objectives Patient mindsets influence health outcomes; yet trainings focused on care teams’ understanding, recognizing, and shaping patient mindsets do not exist. This paper aims to describe and evaluate initial reception of the “Medicine Plus Mindset” training program. Methods Clinicians and staff at five primary care clinics (N = 186) in the San Francisco Bay Area received the Medicine Plus Mindset Training. The Medicine Plus Mindset training consists of a two-hour training program plus a one-hour follow-up session including: (a) evidence to help care teams understand patients’ mindsets’ influence on treatment; (b) a framework to support care teams in identifying specific patient mindsets; and (c) strategies to shape patient mindsets. Results We used a common model (Kirkpatrick) to evaluate the training based on participants’ reaction, learnings, and behavior. Reaction: Participants rated the training as highly useful and enjoyable. Learnings: The training increased the perceived importance of mindsets in healthcare and improved self-reported efficacy of using mindsets in practice. Behavior: The training increased reported frequency of shaping patient mindsets. Conclusions Development of this training and the study’s results introduce a promising and feasible approach for integrating mindset into clinical practice. Practice Implications Mindset training can add a valuable dimension to clinical care and should be integrated into training and clinical practice.

Scientific and technological progress is largely driven by firms in many domains, including artificial intelligence and vaccine development. The early identification of the future performance of innovation players is a relevant goal for policymakers and practitioners. In this work, we investigate how the future trajectory of a firm can be predicted by the economic or technological value of its early patents. By inspecting the patenting life cycles of 7440 publicly listed firms, we find that the economic value of a firm’s early patents is an accurate predictor of various dimensions of a firm’s future research success. At the same time, a smaller set of future top-performers do not generate early patents of high economic value, but they are detectable via the technological value of their early patents. Importantly, the observed heterogeneity of the firms’ temporal success patterns markedly differs from the patterns previously observed for individuals’ research careers.

Two increasingly popular domains of research have made great strides explaining leadership via an identity lens (Haslam et al., 2022). These domains focus either on a leader’s own identity or on a leader’s influence in representing and altering the identities of others. Our paper contributes to these areas by highlighting dominant assumptions underlying the literatures and generating counterfactual assumptions in need of systematic exploration. It is important to acknowledge and evaluate assumptions because of the role they play in what we study and how we interpret data. As such, our paper brings existing assumptions to light and generates counterfactuals that are in need of more sustained empirical work. Our work thus sets out to a) expose existing assumptions at the intersection of leadership and identity, b) generate theoretically plausible counterfactual assumptions and c) identify themes tying our counterfactual assumptions together. Together, this paper supports, challenges, and promotes the extension of research applying an identity lens to leadership.

While today it is universally acknowledged that COVID-19 has generated immense challenges for businesses and societies worldwide, public perceptions varied significantly at the time of the pandemic’s initial appearance, even among democratic societies with comparable media systems. The growing scholarship on grand societal challenges in management and organization studies, however, tends to neglect the initial social construction of issues as complex, uncertain, evaluative, and widespread. We address this shortcoming by exploring the initial communicative enactment of COVID-19 in the media-based public discourse in Switzerland, Germany, and the United Kingdom. By applying a social problem work lens, we identify three mechanisms that explain the maturation of COVID-19 into a grand challenge, further showing how these are contextually dependent on differences in discourse quality. We add to research on grand challenges, issue maturation, and framing dynamics by theorizing how issues become constructed and acknowledged as grand challenges in the first place.

In many workplaces, managers provide some employees with unique privileges that support their professional development and stimulate productivity and creativity. Yet with some employees more deserving of a privileged status than others, co-workers feeling left out of the inner circle may begin to exhibit feelings of envy. With workplace envy and intergroup conflicts going hand in hand, the question arises whether co-worker acceptance of employee privileges—where conflict can be constrained through an affirmative re-evaluation of co-workers’ privileged status—may lower the envy experienced by employees. Using virtual reality technology, 112 employees participated in a virtual employee meeting at a virtual organization where they were exposed to a new workforce differentiation practice. We show through our experiment that co-worker acceptance of employee privileges negatively influences workplace envy, which was partially mediated by the anticipated ostracism of employees. Moreover, we show that this effect is only found for employees with privileges, who worry more about being ostracized than their non-privileged co-workers. We anticipate that our findings will enable managers to conscientiously differentiate between their employees, using virtual reality simulations to steer employees’ thoughts and feelings in a direction that benefits both employees and organizations.

Identifying the rank of species in a complex ecosystem is a difficult task, since the rank of each species invariably depends on the interactions stipulated with other species through the adjacency matrix of the network. A common ranking method in economic and ecological networks is to sort the nodes such that the layout of the reordered adjacency matrix looks maximally nested with all nonzero entries packed in the upper left corner, called Nestedness Maximization Problem (NMP). Here we solve this problem by defining a suitable cost-energy function for the NMP which reveals the equivalence between the NMP and the Quadratic Assignment Problem, one of the most important combinatorial optimization problems, and use statistical physics techniques to derive a set of self-consistent equationswhose fixed point represents the optimal nodes’ rankings in an arbitrary bipartite mutualistic network. Concurrently, we present an efficient algorithm to solve the NMP that outperforms state-ofthe- art network-based metrics and genetic algorithms. Eventually, our theoretical framework may be easily generalized to study the relationship between ranking and network structure beyond pairwise interactions, e.g. in higher-order networks.

We investigate how interdependence among investors' beliefs affects the reliance on market prices as a performance measure and how this in turn affects the firm's preference for financial reporting quality. When investors want to align their values more with other investors' beliefs, optimal contracts become more reliant on the accounting report and less on the market price, emphasizing the stewardship role of accounting in a herding market. If the baseline accounting quality required by a reporting standard is high enough, the firm prefers to increase its accounting quality for the sake of contracting efficiency. However, if the baseline quality is low, the firm further lowers accounting quality for the same reason. The benchmark level that determines whether the firm prefers to increase accounting quality increases with the interdependence of investors' beliefs, implying that it is difficult to align the information and stewardship roles of accounting in a herding market.

Temperament is claimed to be the basis for personality; therefore, discovering the temperamental underpinnings of borderline personality disorder and its facets is crucial for understanding this personality disorder. In this article, we explore these underpinnings by using a new model of temperament, based on the Regulative Theory of Temperament, the Big Two of temperament, and the Circumplex of Personality Metatraits. Two studies were conducted on adults—the first was in a general population sample (N = 315) and the second was in a clinical sample (N = 113) in people with a diagnosis of borderline personality disorder. The following measurements were used: The Screening Instrument for Borderline Personality Disorder (SI‐Bord), the Five‐Factor Borderline Inventory‐Short Form (FFBI‐SF), and the Temperament Metadimensions Questionnaire (TMQ). General borderline was explained by Reactivity (high Sensitivity) and Activity (high Dynamism). At the facet level, the Borderline Internalizing Facet was mainly explained by Reactivity (high Sensitivity), while the Borderline Externalizing Facet was explained by Activity (high Dynamism) in addition to Reactivity (high Sensitivity). The results of our study revealed specific temperamental underpinnings of borderline and its facets. Reactivity underlies all borderline facets, while Activity differentiates between the Borderline Externalizing Facet and Borderline Internalizing Facet.

Conference calls provide opportunities for CEOs to inform market participants (i.e., financial analysts and investors) about their companies’ prospects. Much research has focused on how CEOs speak about business-related topics in these calls, yet surprisingly the literature has not considered how statements that go beyond financial information affect market participants. When we explored archival data of how CEOs of publicly traded U.S.-based companies from the Russell 3000 Index spoke about COVID-19 in conference calls as the pandemic began in 2020, we noticed that about half of CEOs made human care statements that expressed a concern for people, with seemingly little direct financial relevance. However, although these statements were largely generic, vague expressions rather than clear plans, we discovered that the more such statements CEOs made, the better their companies fared on the stock market when stock prices tumbled globally. Follow-up explorations unveiled a negative association between CEO human care statements and stock volatility, meaning that market participants discounted these companies’ future earnings less. Our explorations suggest that it pays off for CEOs to go beyond mere financial information and show some humanity, with implications for downstream theorizing about CEO impression management.

Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To this end, most existing studies from diverse research domains focus on the disproportionate role played by highly-connected “hub” individuals. However, we demonstrate here that information superspreaders in online social media are best understood and predicted by simultaneously considering two individual-level behavioral traits: influence and susceptibility. Specifically, we derive a nonlinear network-based algorithm to quantify individuals’ influence and susceptibility from multiple spreading event data. By applying the algorithm to large-scale data from Twitter and Weibo, we demonstrate that individuals’ estimated influence and susceptibility scores enable predictions of future superspreaders above and beyond network centrality, and reveal new insights on the network position of the superspreaders.

Artificial intelligence (AI)-enabled voice assistants (VAs) are transforming firm-customer interactions but often come across as lacking empathy. This challenge may cause business managers to question the overall effectiveness of VAs in shopping contexts. Recognizing empathy as a core design element in the next generation of VAs and the limits of scenario-based studies in voice commerce, this article investigates how empathy exhibited by an existing AI agent (Alexa) may alter consumer shopping responses. AI empathy moderates the original structural model bridging functional, relational, and social-emotional dimensions. Findings of an individual-session online experiment show higher intentions to delegate tasks, seek decision assistance, and trust recommendations from AI agents perceived as empathic. In contrast to individual shoppers, families respond better to functional VA attributes such as ease of use when AI empathy is present. The results contribute to the literature on AI empathy and conversational commerce while informing managerial AI design decisions.

We uncover the connection between the Fitness-Complexity algorithm, developed in the economic complexity field, and the Sinkhorn-Knopp algorithm, widely used in diverse domains ranging from computer science and mathematics to economics. Despite minor formal differences between the two methods, both converge to the same fixed-point solution up to normalization. The discovered connection allows us to derive a rigorous interpretation of the Fitness and the Complexity metrics as the potentials of a suitable energy function. Under this interpretation, high-energy products are unfeasible for low-fitness countries, which explains why the algorithm is effective at displaying nested patterns in bipartite networks. We also show that the proposed interpretation reveals the scale invariance of the Fitness-Complexity algorithm, which has practical implications for the algorithm's implementation in different datasets. Further, analysis of empirical trade data under the new perspective reveals three categories of countries that might benefit from different development strategies.

This study addresses a class of linear mixed-integer programming (MILP) problems that involve uncertainty in the objective function parameters. The parameters are assumed to form a random vector, whose probability distribution can only be observed through a finite training data set. Unlike most of the related studies in the literature, we also consider uncertainty in the underlying data set. The data uncertainty is described by a set of linear constraints for each random sample, and the uncertainty in the distribution (for a fixed realization of data) is defined using a type-1 Wasserstein ball centered at the empirical distribution of the data. The overall problem is formulated as a three-level distributionally robust optimization (DRO) problem. First, we prove that the three-level problem admits a single-level MILP reformulation, if the class of loss functions is restricted to biaffine functions. Secondly, it turns out that for several particular forms of data uncertainty, the outlined problem can be solved reasonably fast by leveraging the nominal MILP problem. Finally, we conduct a computational study, where the out-of-sample performance of our model and computational complexity of the proposed MILP reformulation are explored numerically for several application domains.

Digitalization allows retailers to target customers with personalized promotions when they enter the store. Although traditional promotional retailer objectives, such as store visit, become obsolete once the shopper is already in the store, retailers still tend to target customers based on indicators that drive store visit, such as recency, frequency, and monetary value (RFM). In order to improve promotional efficiency, the authors propose targeting shoppers based on information derived from regularity patterns in individual interpurchase times at the point of sale. When compared to RFM-based targeting, the proposed live targeting approach translates into higher redemption rates (+ 10.5 percentage points), revenues (+ 42.3 percentage points), and purchase frequencies (+ 44.2 percentage points). The findings emphasize the importance of promotional timing and of considering customers’ outside potential for dynamic in-store targeting.

Corporate decision-makers (DMs) are increasingly being challenged to adopt emerging technologies with undefined market potential while being susceptible to biases. Failure to achieve the expected benefits may affect collective and individual-level performance. Fear of missing out (FOMO) influences the ability to make rational decisions. Although FOMO can lead DMs to prioritize popular but immature technologies, there remains a limited understanding of the notion in organizational settings. Drawing on semi-structured interviews and archival data corroborated by insights from key stakeholders, our research investigates the role of FOMO when adopting emerging technology. Findings reveal that FOMO (i) is experienced by DMs experience in one of three performance levels (firm, team, employee), each differentiated by specific targets and responses, and (ii) influences the decision process both directly and via inflated expected outcomes. The mere presence of FOMO does not constitute a bias in the decision. Further, we suggest how to regulate FOMO in organizations.

Die FER-Fachkommission hat am 5. Dezember 2023 der Veröffentlichung eines Diskussionspapiers über die Nachhaltigkeit in der FER zugestimmt. Dieses Papier enthält einen Leitfaden, der KMU in sieben Schritten darin unterstützt, Nachhaltigkeit in der Organisation zu verankern und transparent darüber zu berichten. Die Öffentlichkeit ist bis am 14. April 2024 zur Kommentierung eingeladen.

This article presents a portfolio construction approach that combines the hierarchical clustering of a large asset universe with the stock price momentum. On one hand, investing in high-momentum stocks enhances returns by capturing the momentum premium. On the other hand, hierarchical clustering of a high-dimensional asset universe ensures sparse diversification, stabilizes the portfolio across economic regimes, and mitigates the problem of increased drawdowns typically present in momentum portfolios. Moreover, the proposed portfolio construction approach avoids the covariance matrix inversion. An out-of-sample backtest on a non-survivorship-biased dataset of international stocks shows that, compared to the model-based and model-free benchmarks, hierarchical momentum portfolios achieve improved cumulative and risk-adjusted portfolio returns as well as decreased portfolio drawdowns net of transaction costs. The study further suggests that the unique characteristics of the hierarchical momentum portfolios arise because of both dimensionality reduction via clustering and momentum-based stock selection.

In an environment of accelerating technological change and increasing digitalization, firms need to adopt new technologies faster than ever before to stay competitive. This paper examines whether updates of education curricula help to bring new technologies faster into firms’ workplaces. We study technology changes and curriculum updates from an early wave of digitalization (i.e., computer-numerically controlled machinery, computer-aided design, and desktop publishing software). We take a text-as-data approach and tap into two novel data sources to measure change in educational content and the use of technology at the workplace: first, vocational education curricula and, second, firms’ job advertisements. To examine the causal effects of adding new technology skills to curricula on the diffusion of these technologies in firms’ workplaces (measured by job advertisements), we use an event study design. Our results show that curriculum updates substantially shorten the time it takes for new technologies to arrive in firms’ workplaces, especially for mainstream firms.

Whether one looks at revenue, investment or coverage, men’s sports do better than women’s. Many assume that absolute differences in quality of athletic performance are the driving force. However, the existence of stereotypes should alert us to another possibility: gender information might influence perceived quality. We perform an experiment in which 613 participants viewed clips of elite female and male soccer players. In the control group, participants evaluated unmodified videos where the gender of the players is clear to see. In the treatment group, participants evaluated the same videos but with gender obscured by blurring. Using a regression analysis, we find that participants rate men’s videos higher – but only when they know they are watching men. When blurring obscures the gender, ratings for female and male athletes do not differ. We discuss implications for research and the sports industry.

We study whether outcome bias persists in markets with actors who are financially incentivized to make optimal decisions. We test whether inherently noisy match outcomes from European football are correctly incorporated into prices from a betting exchange market. We find that market prices overestimate (underestimate) the winning probability of teams that previously overperformed (underperformed) in terms of match outcomes compared to their performance based on “expected goals”. This pattern is mirrored in negative (positive) betting returns on overperforming (underperforming) teams. These results suggest that even competitive market mechanisms fail to completely erase outcome bias.