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:
CreatorsEmailORCIDORCID Put Code
Gemmer, Larsgemmer@wiso.uni-koeln.deorcid.org/0000-0003-2650-2861UNSPECIFIED
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:
KeywordsLanguage
ESG-Related Corporate MisconductEnglish
Machine LearningEnglish
Marketing AnalyticsEnglish
PredictionEnglish
Unethical Firm BehaviorEnglish
Date of oral exam: 29 August 2023
Referee:
NameAcademic Title
Fischer, MarcProf. Dr.
Reinartz, WernerProf. Dr.
Refereed: Yes
URI: http://kups.ub.uni-koeln.de/id/eprint/70990

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