Universität zu Köln

Gemmer, Lars ORCID: 0000-0003-2650-2861 (2023). Antecedents of ESG-Related Corporate Misconduct: Theoretical Considerations and Machine Learning Applications. PhD thesis, Universität zu Köln.

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Abstract

The core objective of this cumulative dissertation is to generate new insights in the occurrence and prediction of unethical firm behavior disclosure. The first two papers investigate predictors and antecedents of (severe) unethical firm behavior disclosure. The third paper addresses frequently occurring methodological issues when applying machine learning approaches within marketing research. Hence, the three papers of this dissertation contribute to two recent topics within the field of marketing: First, marketing research has already focused intensively on the consequences of corporate misconduct and the accompanying media coverage. Meanwhile, the prediction and the process of occurrence of such threatening events have been examined only sporadically so far. Second, companies and researchers are increasingly implementing machine learning as a methodology to solve marketing-specific tasks. In this context, the users of machine learning methods often face methodological challenges, for which this dissertation reviews possible solutions. Specifically, in study 1, machine learning algorithms are used to predict the future occurrence of severe threatening news coverage of corporate misconduct. Study 2 identifies relationships between the specific competitive situation of a company within its industry and unethical firm behavior disclosure. Study 3 addresses machine learning-based issues for marketing researchers and presents possible solutions by reviewing the computer science literature.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Gemmer, Lars gemmer@wiso.uni-koeln.de orcid.org/0000-0003-2650-2861 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-709908
Date:	September 2023
Language:	English
Faculty:	Faculty of Management, Economy and Social Sciences
Divisions:	Faculty of Management, Economics and Social Sciences > Business Administration > Marketing > Professorship for Business Administration, Marketing and Market Research
Subjects:	News media, journalism, publishing General statistics Management and auxiliary services
Uncontrolled Keywords:	Keywords Language ESG-Related Corporate Misconduct English Machine Learning English Marketing Analytics English Prediction English Unethical Firm Behavior English
Date of oral exam:	29 August 2023
Referee:	Name Academic Title Fischer, Marc Prof. Dr. Reinartz, Werner Prof. Dr.
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/70990