People Analytics

Author(s): 

Pilar Garcia-Gomez, Ernst Maug, Stefan Obernberger

Cooperation Partners:

Chair of Corporate Finance

Description:

We examine the role of employee health in Private Equity buyouts using employee-level data on employment, wages, and medical prescriptions. We conduct matched-sample difference-in-differences estimations including more than 55,000 buyout employees. Employees with a lower health status before the buyout face the most substantial losses of income and employment during and after the buyout. Buyouts have no measurable impact on health outcomes and health expenditures. However, buyouts influence employees' career paths, and the health of those employees who become unemployed deteriorates, whereas those who find new jobs improves.My colleagues thought that the last two sentences somewhat contradict each other. About half of the negative effect of buyouts on employees' incomes is buffered through the state's social security system. We conclude that buyout-related restructuring has a stronger negative impact on the human capital of employees with health problems.

Author(s): 

Prof. Torsten Biemann, Chris Kaibel

Cooperation Partners:

Chair of Business Adminstration, Human Resource Management and Leadership

Description:

In experiments, researchers and practitioners commonly allocate subjects randomly to the different treatment conditions equally before the experiment starts. While this approach is intuitive, it means that new information gathered during the experiment is not utilized until after the experiment has ended. Based on methodological approaches from other scientific disciplines such as computer science and medicine, we suggest a machine learning algorithm for subject allocation in randomized experiments.

Specifically, we discuss a Bayesian multi-armed bandit algorithm for randomized controlled trials and use Monte Carlo simulations to compare its efficiency with randomized controlled trials that have a fixed and balanced subject allocation. Our findings indicate that a randomized allocation based on Bayesian multi-armed bandits is more efficient and ethical in most settings. We develop recommendations for researchers and discuss the limitations of our approach.

Results were accepted for publication by the journal “Organisational Research Methods”:

https://journals.sagepub.com/doi/full/10.1177/1094428119854153

A related tool for the deployment of Multi-armed bandits can be found here:

https://randomization-with-bandits.shinyapps.io/bayesian-bandit/

Author(s):  

Prof. Torsten Biemann, Dr. Chris Kaibel, Dr. Irmela Koch-Bayran, Max Mühlenbock

Description:

Relying on algorithm-based decisions seems to be a promising step towards more data-driven decision-making and an alternative to subjective human decisions in the recruiting and employee selection context. However, the question that remains to be answered is whether algorithm-based hiring decisions help organizations attracting and recruiting a diverse workforce with unique talents.

Building on established characteristics of procedural justice, we conducted two experimental studies to examine the effects of algorithm vs. human decision-makers on applicants’ perceptions of the selection process and organizational attractiveness. We found that applicants do not automatically perceive algorithms as purely negative or positive for the selection process. Instead, the perceptions seem to differ between outcome variables and dependent on applicants’ experiences and believes. While algorithms might help to attract applicants that have made discrimination experiences, algorithms seem to discourage applicants with unique characteristics. Implications and future research directions are discussed.

Outcomes were published in “Best Paper Proceedings” of the Academy of Management 2019.