Graduation Semester and Year
2022
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Management
Department
Management
First Advisor
Dr. Sridhar Nerur
Second Advisor
Dr. Edmund Prater
Abstract
Industry 4.0 has received significant attention from academia and industry in the last decade. Despite its growing popularity, this area is relatively understudied. There is a lack of thorough understanding of the disparate thematic areas under the umbrella term "Industry 4.0" in the literature. My first essay aims to elucidate the intellectual structure of Industry 4.0 publications using: (a) bibliometric techniques; and (b) topic modeling. The synthesized analyses unraveled diverse themes of Industry 4.0 and deepened our understanding of academic research on Industry 4.0. Such an understanding is important to identify opportunities to advance the boundaries of scholarship on Industry 4.0. The purpose of the second essay is threefold. First, I conceptualized and developed a novel measure, ‘Industry 4.0 Capability Index’, using machine learning and text analytics to assess Industry 4.0 and the digital innovation capabilities of companies. Second, I investigated the influence of CEO demographics, such as CEO age and gender, on a firm’s Industry 4.0 capabilities. I found that the CEO age has a negative and statistically significant relationship with Industry 4.0 Capability Index. Finally, I examined the impact of advanced Industry 4.0 capabilities of firms on their financial performance. Our results suggest that Industry 4.0 and the digital capabilities of firms have a positive and statistically significant impact on their financial performance. This study makes significant contributions to the literature on CEO characteristics, Industry 4.0, and innovation. This study is among the first to derive a measure of Industry 4.0 and the digitalization capabilities of organizations using machine learning algorithms and text analytics. Our findings provide invaluable insights for academics and practitioners alike.
Keywords
Industry 4.0, Topic modeling, BERTopic, LDA, Author co-citation analysis, Innovation, Machine learning, Text analytics, CEO characteristics, Firm performance
Disciplines
Business | Business Administration, Management, and Operations
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Ganji, Vidya Laxmanrao, "CEO CHARACTERISTICS, INDUSTRY 4.0 CAPABILITIES, AND FIRM PERFORMANCE" (2022). Management Dissertations. 13.
https://mavmatrix.uta.edu/management_dissertations/13
Comments
Degree granted by The University of Texas at Arlington