Universität zu Köln

Opitz, Saskia ORCID: 0000-0003-3190-7775 (2025). Individualizing Management Practices. PhD thesis, Universität zu Köln.

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Abstract

Firm performance relies heavily on employee motivation, yet individuals differ significantly in what motivates them. A substantial body of literature in economics, management, and management accounting explores the impact of management practices on firm performance and employee outcomes, revealing heterogeneity in employee responses based on characteristics such as gender, social preferences, and personality. This dissertation examines two approaches to individualizing management practices: (1) a centralized approach, using AI for tailored assignments, and (2) a decentralized approach, leveraging managerial discretion. Empirical evidence is drawn from proprietary firm data, surveys, field experiments, and online experiments. Chapter 1 explores the centralized approach, applying machine learning to assign incentives based on worker characteristics. Experiments with gig workers reveal that targeted assignments improve performance compared to assigning a single best incentive scheme. The findings underline the importance of reliably measuring worker characteristics for ensuring algorithmic assignment quality. Chapter 2 studies a decentralized approach in a spot bonus system where managers decide bonus timing. Analyses show that managers award a large portion of bonuses at year-end, reflecting a need for better information on performance. However, timely bonuses are associated with higher employee recognition, suggesting that managers face a trade-off. Chapter 3 examines non-monetary practices, testing how highlighting flexibility in job ads affects applications. A field experiment with 176 job ads shows that emphasizing remote work or flexible work hours increases the number of applications, with the improvement partly varying by job characteristics. Overall, the dissertation demonstrates the advantages and challenges of centralized and decentralized approaches, offering insights for academics and practitioners.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Opitz, Saskia saskia-opitz@outlook.com orcid.org/0000-0003-3190-7775 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-752266
Date:	2025
Language:	English
Faculty:	Faculty of Management, Economy and Social Sciences
Divisions:	Faculty of Management, Economics and Social Sciences > Business Administration > Corporate Development > Professorship for Business Administration and Human Resources Management
Subjects:	Economics Management and auxiliary services
Uncontrolled Keywords:	Keywords Language Incentives, Management Practices, Job Ads, Algorithm, Field Experiments, Individualization English
Date of oral exam:	17 February 2025
Referee:	Name Academic Title Sliwka, Dirk Prof. Dr. Heinz, Matthias Prof. Dr.
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/75226