Contributions published at Department of Business Administration (Egon Franck)

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We suggest a novel choice of non-separable basis functions for an approximate linear programming approach to the well-known network revenue management problem. Considering non-separability is particularly important when interdependencies between resources are large. Such a situation can be illustrated for example by a bus line, where different origin-destination pairs have many overlapping segments. Traditional separable approximation approaches tend to ignore the resulting interactions. We suggest to group resources into non-separable subnetworks. For each chosen subnetwork, basis functions either span the whole function space or consist of linear functions. Given this more general choice of basis functions, we extend existing reductions of approximate linear programs. If there is only one subnetwork, for which the basis functions span the whole function space, we prove the equivalence to a compact linear program of polynomial size. For the general case, we suggest an approximate reduction. Numerical examples illustrate our novel upper bounds for the maximum expected revenue and the corresponding competitive policies. In particular, we find that the added benefit of non-separability heavily depends on the network structure and the capacity. Our work helps to better understand the impact of assuming separability in network revenue management. The polynomial sized reductions make it possible to estimate the added average revenue resulting from incorporating interactions between resources. The theory we develop demonstrates how the interpretation of dual variables as state-action probabilities can be applied to reduce exponentially large approximate linear programs via variable aggregation.

This paper examines the role of lifelong learning in counteracting skill depreciation and obsolescence. We differentiate between occupations with more hard skills versus more soft skills and draw on representative job advertisement data that contain machine-learning categorized skill requirements and cover the Swiss job market in great detail across occupations (from 1950 to 2019). We examine lifelong learning effects for “harder” versus “softer” occupations, thereby analyzing the role of training in counteracting skill depreciation in occupations that are differently affected by skill depreciation. Our results reveal novel empirical patterns regarding the benefits of lifelong learning, which are consistent with theoretical explanations based on structurally different skill depreciation rates: In harder occupations, with large shares of fast-depreciating hard skills, the role of lifelong learning is primarily as a hedge against unemployment risks rather than a boost to wages. By contrast, in softer occupations, in which workers build on more value-stable soft-skill foundations, the role of lifelong learning instead lies mostly in acting as a boost for upward career mobility and leads to larger wage gains.

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We examine how education expansions affect the job opportunities for workers with and without the new education. To identify causal effects, we exploit a quasi-random establishment of Universities of Applied Sciences (UASs), bachelor-granting three-year colleges that teach and conduct applied research. By applying machine-learning methods to job advertisement data, we analyze job content before and after the education expansion. We find that, in regions with the newly established UASs, not only job descriptions of the new UAS graduates but also job descriptions of workers without this degree (i.e., middle-skilled workers with vocational training) contain more high-skill job content. This upskilling in job content is driven by an increase in high-skill R&D-related tasks and linked to employment and wage gains. The task spillovers likely occur because UAS graduates with applied research skills build a bridge between middle-skilled workers and traditional university graduates, facilitating the integration of the former into R&D-related tasks.

Purpose: The purpose of this paper is to investigate self-competence—the ability to act responsibly on one's own—and likely nonlinear wage returns across different levels of self-competence as part of training curricula. Design/methodology/approach: The authors identify the teaching of self-competence at the occupational level by applying machine-learning methods to the texts of occupational training curricula. Defining three levels of self-competence (high, medium, and low) and using individual labor market data, the authors examine nonlinearities in wage returns to different levels of self-competence. Findings: The authors find nonlinear returns to teaching self-competence: a medium level of self-competence taught in an occupation has the largest wage returns compared to low or high levels. However, in occupations with a high cognitive requirement profile, a high level of self-competence generates positive wage returns. Originality/value: This paper first adds to research on the importance of teaching noncognitive skills for economic outcomes, which recently—in addition to personality traits research—has primarily focused on social skills by introducing self-competence as another largely unexplored but important noncognitive skill. Second, the paper studies not only average but also nonlinear wage returns, showing that the right level of self-competence is crucial, i.e. neither teaching too little nor too much self-competence provides favorable returns because of trade-offs with other skills (e.g. technical or professional skills). Third, the paper also examines complementarities between cognitive skills and noncognitive skills, again pointing toward nonlinear returns, i.e. only in occupations with a high cognitive requirement profile, high levels of self-competence generate positive wage returns.

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A key element to understand complex systems is the relationship between the spatial scale of investigation and the structure of the interrelation among its elements. When it comes to economic systems, it is now well-known that the country-product bipartite network exhibits a nested structure, which is the foundation of different algorithms that have been used to scientifically investigate countries' development and forecast national economic growth. Changing the subject from countries to companies, a significantly different scenario emerges. Through the analysis of a unique dataset of Italian firms' exports and a worldwide dataset comprising countries' exports, here we find that, while a globally nested structure is observed at the country level, a local, in-block nested structure emerges at the level of firms. This in-block nestedness is statistically significant with respect to suitable null models and the algorithmic partitions of products into blocks correspond well with the UN-COMTRADE product classification. These findings lay a solid foundation for developing a scientific approach based on the physics of complex systems to the analysis of companies, which has been lacking until now.

Workers’ occupational skill sets play a crucial role in successfully handling digital transformation. We investigate whether and how different types of occupational skill sets benefit from digital transformation. We theoretically and empirically analyze wage returns of workers in occupations with more or less specialized skill sets and with more or less social skills when IT increases in their industry. Applying natural language processing methods to the texts of occupational training curricula, we develop measures for occupational specialization and social skills. We use vocational education and training curricula from Switzerland because they cover approx. two-thirds of the working population. Using curricula, industry-level IT data and individual-level administrative wage data, our individual fixed-effects analyses show that IT progress leads to higher wage returns for workers in highly specialized occupations but not for workers in more general occupations. In addition, we find that high levels of social skills cannot make up for this difference when IT advances. However, our results indicate that for workers with high specialization, a combination with high social skills generates additional benefits when IT advances. Overall, our results suggest that, contrary to typical assumptions in educational policy debates, workers with specialized occupational skill sets - possibly in combination with high social skills - appear to be the ones who are particularly well prepared to cope with digital transformation.

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The governance of corruption is increasingly important in a global business environment involving ever more frequent transactions across diverse institutional contexts. Previous scholarship has theorized a fundamental tension between the enforcement of organizational compliance and the achievement of social ends, finding that efforts to remedy policy-practice decoupling in the governance of corruption and other complex global issues can exacerbate means-ends decoupling. However, these studies have tended to apply a rather static lens to a highly evaluative and processual phenomenon, meaning we still lack in-depth understanding of the dynamics underlying the interactive communicative processes of sensemaking and negotiation involved in working out the problems of both means-ends and policy-practice decoupling across different institutional contexts. To address this gap, we present a longitudinal qualitative study of the governance of corruption that identifies the emergence of locally contingent and open-ended sensemaking processes arising from and surrounding problems of decoupling. Specifically, we identify four key sensemaking mechanisms across different contexts and periods that ultimately shifted the focus of the actors away from a compliance-based approach toward a new shared understanding of progress as achievement, i.e., the mechanisms of localized theorizing, leveling, recalibrating, and public criticizing. Based on these findings, we develop a model to explain the role of sensemaking in the governance of corruption and the dynamics of decoupling.

Diversity is a topic that is garnering much attention in society and organizations. We use a paradox lens to suggest a tendency of diversity practices to focus on diversity only, and to essentially neglect unity. We identify two categories of strategies to address diversity: (1) “woke”, and (2) “integrative.” Woke strategies take a single-edged approach to diversity, whereby concerns for diversity are pursued without concern for potentially detrimental effects on unity. In contrast, integrative strategies simultaneously activate the advantages of both diversity and unity, while neutralizing their downsides. In support of this reasoning, we also refer to positive organizational behavior (POB) theory, which would suggest that “woke” strategies run counter to the development of psychological capital on the part of both minority and majority group members. Despite the arguments in this article, it is clear that research is necessary to better understand these alternative approaches to diversity, as well as their effects on individuals, organizations, and society as a whole. Thus, we conclude with a consideration of research needs, as well as practical and policy implications

We analyze the relationship between social gender norms and adolescents' occupational choices by combining regional votes on constitutional amendments on gender equality with job application data from a large job board for apprenticeships. The results show that adolescent males in regions with stronger traditional social gender norms are more likely to apply for typically male occupations. This finding does not hold for females, suggesting that incentivizing men to break the norms and choose gender-atypical occupations (e.g., in healthcare) can be even more effective in accelerating advancement toward gender equality in the labor market than incentivizing women to choose STEM occupations.

Länder mit einer starken Betonung der beruflichen Bildung in ihren Bildungs- und Innovationssystemen, beispielsweise Deutschland und die Schweiz, zählen seit Jahren zu den innovativsten der Welt. Gleichzeitig konstatiert internationale Innovationsforschung, dass ein hoher Akademisierungsgrad (und damit gerade nicht die berufliche Bildung) für starke Innovationsleistungen unabdinglich ist. Wie lässt sich dieser Widerspruch erklären? In diesem Überblicksartikel zeigen Uschi Backes-Gellner und Patrick Lehnert anhand einer Aufarbeitung neuer Forschungserkenntnisse, warum die deutschsprachigen Länder nicht trotz, sondern gerade auch wegen ihres Berufsbildungssystems hochinnovativ sind. Zu den erforderlichen institutionellen Rahmenbedingungen für diesen Effekt gehören dabei die zukunftsorientierte Gestaltung und Aktualisierung von Berufsausbildungscurricula, die Vermittlung von breiten beruflichen (statt engen betriebsspezifischen) Kompetenzen in dualen Berufsausbildungsprogrammen, ein durchlässiges Bildungssystem mit attraktiven Karrierepfaden sowie eine Verknüpfung von tertiärer beruflicher Bildung und angewandter Forschung.

This paper develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. We demonstrate our measure’s usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.

Objective We examined changes in value interrelations during middle childhood. In line with the Personal Values Theory, we expected a value system, with individuals similarly valuing related motivations, and setting priorities between conflicting motivations. We hypothesized this system to develop dynamically during middle childhood as children deepen their understanding of their own values. Method Using unfolding analysis, we estimated intraindividual value structure coherence, that is, the extent to which the interrelations among a child's values are similar to the hypothesized interrelations. Cross-Cultural Study 1 (N = 4615, 6–12-year-old children) included children from 12 countries. Cross-Sequential Study 2 (N = 629, 6–10-year-old children at Time 1) included three annual measurements. Results In Study 1, we found a curvilinear association between age and intraindividual value structure coherence: Children's values were more coherent at ages 9–10 than before or after. Study 2 confirmed this pattern of within-individual development. Conclusions We propose that development in coherence with the theoretical value structure offers insight into children's understanding of values as well as changes in value priorities.