Dr. Ulrich Leicht-Deobald

• Leicht-Deobald, U., Huettermann, H., Bruch, H., Lawrence, B.S., Organizational Demographic Faultlines: Their Impact on Collective Organizational Identification, Firm Performance, and Firm Innovation, Journal of Management Studies, 58, (8), 2021, p2240-2274Journal Article, 2021, DOI
• Leicht-Deobald, U., Recognizing People at Work in Their Full Humanity - A Commentary on Bal (2020), Zeitschrift fur Arbeits- und Organisationspsychologie, 64, (3), 2020, p200-202Journal Article, 2020, DOI
• Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., Kasper, G., The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity, Journal of Business Ethics, 160, (2), 2019, p377-392Journal Article, 2019, DOI
• Ambos, B., Leicht-Deobald, U., Leinemann, A., Understanding the formation of psychic distance perceptions: Are country-level or individual-level factors more important?, International Business Review, 28, (4), 2019, p660-671Journal Article, 2019, DOI
• Ambos, B., Kunisch, S., Leicht-Deobald, U., Schulte Steinberg, A., Unravelling agency relations inside the MNC: The roles of socialization, goal conflicts and second principals in headquarters-subsidiary relationships, Journal of World Business, 54, (2), 2019, p67-81Journal Article, 2019, DOI
• Leicht-Deobald, U., Bruch, H., Bönke, L., Stevense, A., Fan, Y., Bajbouj, M., Grimm, S., Work-related social support modulates effects of early life stress on limbic reactivity during stress, Brain Imaging and Behavior, 12, (5), 2018, p1405-1418Journal Article, 2018, DOI
• Leicht-Deobald, U., Lam, C.F., Bruch, H., Kunze, F., Wu, W., Team boundary work and team workload demands: Their interactive effect on team vigor and team effectiveness, Human Resource Management, 61, (4), 2022, p465-488Journal Article, 2022, DOI
• Giermindl, L.M., Strich, F., Christ, O., Leicht-Deobald, U., Redzepi, A., The dark sides of people analytics: reviewing the perils for organisations and employees, European Journal of Information Systems, 31, (3), 2022, p410-435Journal Article, 2022, DOI
• Håkanson, L., Ambos, B., Schuster, A., Leicht-Deobald, U., The psychology of psychic distance: Antecedents of asymmetric perceptions, Journal of World Business, 51, (2), 2016, p308-318Journal Article, 2016, DOI
• Leicht-Deobald, U., Lam, C.F., A Moderated mediation model of team boundary activities, team emotional energy, and team innovation, 76th Annual Meeting of the Academy of Management, AOM 2016, 2016, p231-236Journal Article, 2016, DOI
• Cameron, L., Lamers, L., Leicht-Deobald, U., Lutz, C., Meijerink, J., & Möhlmann, M., Algorithmic Management: Its Implications for Information Systems Research, Communications of the Association for Information Systems, 52, 2023, p518 - 537, p19Journal Article, 2023
• Leicht-Deobald, U., Backmann, J., de Vries, T. A., Weiss, M., Hohmann, S., Walter, F., van der Vegt, G. S., Hoegl, M., A Contingency Framework for the Performance Consequences of Team Boundary Management: A Meta-analysis of 30 Years of Research, Journal of Management, 2023, p1 - 44, p44Journal Article, 2023, URL , TARA - Full Text
• Schafheitle, S., D., Weibel, A., Ebert, I., Kasper, G., Schank, C., Leicht-Deobald, U., No Stone Left Unturned? Towards a Framework on the Impact of Datafication Technologies on Organizational Control, Academy of Management Discoveries, 6, (3), 2020, p455 - 487Journal Article, 2020, URL

• Title

  Socially Acceptable AI and Fairness Trade-offs in Predictive Analytics
  Summary

  The use of artificial intelligence (AI), for example in the context of decisions pertaining to personnel in companies, can lead to social injustice. The aim of our interdisciplinary project is to develop a methodology for the designing of fair AI applications. This methodology will help stakeholders configure artificial intelligence for specific purposes in a socially acceptable way, and will make it possible to train software developers in ethical topics. The project combines philosophical, technical and social science issues: What does fairness mean? How is fairness perceived? How can fairness be implemented in AI? In doing so, the project connects the ethical discourse on AI with the technological implementation of AI.
  Funding Agency

  Swiss National Science Foundation (SNSF)
  Date From

  01.04.2020
  Date To

  30.09.2024
• Title

  Big Data or Big Brother? " Big Data HR Control Practices and Employee Trust
  Summary

  The advent of big data holds the promise that organizational decision-making may change from more intuitive types of reasoning toward more deliberate kinds of choices (George, Haas, & Pentland, 2014). Particularly, in the field of Human Resource (HR) Management, big data techniques offer the potential to improve many HR functions, such as recruitment, retention, and performance management (The Conference Board, 2015). Despite this potential, HR practitioners have been reluctant to implement more refined analytical approaches. One major obstacle for the more widespread use of big data in HR is the expected skeptical reaction of the workforce. As of now, we have little systematic knowledge on how employees will perceive their employers" big data-enhanced monitoring and measurement activities, but drawing from research in management fields with a more mature big data literature (such as marketing), it seems likely that employee trust in their employer will play a key role in whether organizations can effectively apply big data techniques in their HR management. Thus, this project aims to understand the impact of a big data-driven workforce analytics on employees" trust in their employer. Drawing from the literature on HR control practices (Weibel et al., 2015), we expect that three main contingencies will shape the association between employees" perception of the use of big data-driven HR analytics and their trust in the employer: (1) the bundle of metrics and predictive analytics used by HR; (2) the implementation of legal requirements by the employer (particularly data and privacy protection laws); and (3) ethical guidelines on what is being measured for what reason, and how individuals" data are dealt with. We will study these influences using four modules including the following steps: (1) Interviews with experts on big data from both academia and practice who will serve as a `trust in big data" sounding board for the entire duration of the project; (2) a quantitative survey of 1,200 Swiss companies on their big data practices; (3) in-depth case studies of leading companies in the field; and (4) a factorial survey that will allow us to test causal hypotheses derived from modules 1-3. Our research project will generate systematic and relevant knowledge in three areas: First, we contribute to trust and human resources management theory by testing how and under which conditions big data-driven HR analytics influence employees" trust in their employer. Second, we contribute to HR management practice by describing the role HR departments could be playing in the effective use of big data-driven analytics, and how HR departments could contribute to the implementation of legal regulations and ethical stakeholder dialogue. Third, we analyze how legal regulations and ethical guidelines should be adapted to meet both legitimacy as well as effectiveness criteria.
  Funding Agency

  Swiss National Science Foundation (SNSF)
  Date From

  01.03.2017
  Date To

  31.02.2020

Psychology and Behavioural Sciences, Economics, Business & Management,

• Finalist for Best Paper at Interdisciplinary Network for Group Research (INGroup) Conference 2019
• Invited as Young Scholar to the Lindau Nobel Laureate Meeting 2014
• Finalist for Best Oral Presentation at European Association of Work and Organizational Psychology (EAWOP) 2015
• Dissertation paper nominated for the Interdisciplinary Network for Group Research (INGroup) Best Graduate Student Paper Award 2014
• Best Paper of European Academy of Management (EURAM) Organizational Behavior (OB) Team Performance Management Track 2022
• Best Paper Proceeding of the Academy of Management (AOM) - Organizational Behavior (OB) Division 2016
• Best Conference Paper Proposal Award at the Academy of Management (AOM) Big Data and Managing in a Digital Economy Conference 2018

• Interdisciplinary Network for Group Research (INGroup) today
• Academy of Management (AOM) today
• German Psychological Association (DGPs) today
• German Academic Association of Business Research (VHB) today

• Academy of Management (AOM): Organizational Behavior (OB) Division: Making Connections Committee (2021-present)
• German Psychological Society (Deutsche Gesellschaft für Psychologie, DGPs): Young Scholar Representative of the Division Work, Organizational, and Business Psychology (AOW) (2018-2020)
• Academy of Management (AOM): Organizational Behavior (OB) Division: Global Committee (2020-present)