ANTECEDENTS OF ESG-RELATED CORPORATE MISCONDUCT - THEORETICAL CONSIDERATIONS AND MACHINE LEARNING APPLICATIONS - Inauguraldissertation 2023

Aug 2, 2026

This inaugural dissertation examines antecedents of ESG-related corporate misconduct by combining theoretical considerations with machine learning approaches. It structures the study through three dissertation papers that predict severe negative public scrutiny, analyze how competitive conditions influence disclosure of unethical firm behavior, and review recurring problems in machine learning applications in marketing. The work develops trigger-event definitions, constructs data and firm samples, selects predictive variables, and evaluates benchmark and advanced modeling methods using assessment criteria and cost-sensitive classification.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages199

Reading Time8 h 21 min

AccessFree to download

Synopsis

Overview

Introduction

Summary of Dissertation Projects

Paper 1: When Will We Get in Trouble (Again)? Predicting the Occurrence of Severe Negative Publicity for Firms

Paper 2: When is Competition Really Healthy? Analyzing the Impact of the Firm’s Competitive Situation on the Disclosure of Unethical Firm Behavior

Paper 3: Machine Learning in Marketing – A Review of Recurring Problems and How to Solve Them

References

ANTECEDENTS OF ESG-RELATED CORPORATE MISCONDUCT: THEORETICAL CONSIDERATIONS AND MACHINE LEARNING APPLICATIONS Inauguraldissertation zur Erlangung des Doktorgrades der Wirtschafts-und Sozialwissenschaftlichen Fakultät der Universität zu Köln 2023 vorgelegt von Lars Gemmer aus Duisburg Referent: Prof. Dr. Marc Fischer Korreferent: Prof. Dr. Werner Reinartz Tag der Promotion: 29.08.2023 DEDICATION To my father Dirk and my mother Barbara – Your trust and support are the antecedents of this work. CONTENTS LIST OF FIGURES....................................................................................................................... V LIST OF TABLES .......................................................................................................................VI LIST OF ABBREVIATION....................................................................................................... VIII SYNOPSIS .................................................................................................................................... 1 1 Overview ......................................................................................................................... 1 2 Introduction .................................................................................................................... 2 3 Summary of Dissertation Projects................................................................................ 5 3.1 Paper 1: When Will We Get in Trouble (Again)? Predicting the Occurrence of Severe Negative Publicity for Firms.......................................................................... 5 3.2 Paper 2: When is Competition Really Healthy? Analyzing the Impact of the Firm’s Competitive Situation on the Disclosure of Unethical Firm Behavior .......... 7 3.3 Paper 3: Machine Learning in Marketing – A Review of Recurring Problems and How to Solve Them ................................................................................................... 9 References Synopsis ........................................................................................................... 12 PAPER 1: WHEN WILL WE GET IN TROUBLE (AGAIN)? PREDICTING THE OCCURRENCE OF SEVERE NEGATIVE PUBLICITY FOR FIRMS............................................................................. 15 Abstract ............................................................................................................................... 15 1 Introduction .................................................................................................................. 16 2 Data................................................................................................................................ 19 2.1 Definition of Trigger Events .................................................................................... 19 2.2 Population of Negative News Events ...................................................................... 20 2.3 Population of Firms ................................................................................................. 22 2.4 Identification of Trigger Events............................................................................... 23 2.5 Evolution of Negative News and Trigger Events .................................................... 24 3 Conceptual Development of the Prediction Model ................................................... 26 3.1 Insights from Related Research Fields .............................................................. 27 3.1.1 The prediction of earthquakes and crimes ......................................................... 27 3.1.2 The prediction of risks in marketing and other business disciplines ................. 29 3.1.3 Summary............................................................................................................ 29 3.2 Selecting Variables for the Prediction of Trigger Events ....

FAQs

What is the dissertation’s central focus?

The dissertation focuses on antecedents of ESG-related corporate misconduct and how they can be studied using theoretical framing and machine learning.

How does Paper 1 predict corporate trouble?

Paper 1 builds a prediction model to forecast the occurrence of severe negative publicity for firms using defined trigger events, selected variables, and benchmark plus machine learning methods.

What topics are addressed across the three dissertation papers?

The papers cover (1) prediction of negative publicity, (2) the effect of competitive situation on disclosure of unethical behavior, and (3) a review of recurring problems in machine learning applications in marketing.

Aug 2, 2026

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