Schafheitle Simon, Weibel Antoinette, Rickert Alice (2021), The Bermuda Triangle of Leadership in the AI Era? Emerging Trust Implications From “Two-Leader-Situations” in the Eyes of Employees, in Hawaii International Conference on System Sciences
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Leicht-Deobald Ulrich, Busch Thorsten, Schank Christoph, Weibel Antoinette, Schafheitle Simon, Wildhaber Isabelle, Kasper Gabriel (2019), The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity, in Journal of Business Ethics
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EbertIsabel, WildhaberIsabelle, Adams-PrasslJeremias, Big Data in the Workplace: Privacy Due Diligence as a human rights-based approach to employee privacy protection, in Big Data & Society
Weibel Antoinette, Schafheitle Simon, Ebert Isabel, Goldgräberstimmung im Personalmanagement? Wie Datafizierungs-Technologien die Personalsteuerung verändern, in Zeitschrift für Organisationsentwicklung
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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 retention and performance management (Young & Phillips, 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. At the moment, we have little systematic knowledge of 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, the aim of this project is to understand the impact of big data-based HR control, i.e. HR’s big data-based goal setting, monitoring, feedback and punishment/reward practices, on employees’ trust in their employer. Drawing from literature on HR control practices (Weibel et al., 2015), we expect three main contingencies to shape the association between employees’ perception of big data-based HR control and their trust in the employer: (1) the specific type of implementation of metrics and predictive analytics actually used by HR for controlling purposes, (2) ethical guidelines and processes on what is being measured for what reason and how individuals’ data is dealt with, and (3) the implementation of legal requirements by the employer (employment law and data and privacy protection laws). We will study these influences using a mixed-methods approach of four modules including the following research steps: (1) interviews and discussion techniques with professional experts on big data who will serve as our sounding board for the entire duration of the project, (2) a survey of 1,200 Swiss companies on their big data-based control practices, (3) in-depth case studies of companies applying forms of big data-based HR control, and (4) a factorial survey that will allow us to test causal hypotheses on interaction effects of big data-based control and specific contingencies on employee trust 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-based HR control activities 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-based control, and how HR departments could contribute to an ethical stakeholder dialogue and the implementation of legal regulations. Third, we analyze how ethical guidelines and legal regulations should be adapted to meet both legitimacy and effectiveness criteria.