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dc.contributor.advisorNerur, Dr. Sridhar
dc.contributor.advisorPrater, Dr. Edmund
dc.creatorGanji, Vidya Laxmanrao
dc.date.accessioned2023-06-30T15:33:11Z
dc.date.available2023-06-30T15:33:11Z
dc.date.created2022-08
dc.date.issued2022-08-11
dc.date.submittedAugust 2022
dc.identifier.urihttp://hdl.handle.net/10106/31444
dc.description.abstract**Please note that the full text is embargoed until 8/6/2024** 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectIndustry 4.0
dc.subjectTopic modeling
dc.subjectBERTopic
dc.subjectLDA
dc.subjectAuthor co-citation analysis
dc.subjectInnovation
dc.subjectMachine learning
dc.subjectText analytics
dc.subjectCEO characteristics
dc.subjectFirm performance
dc.titleCEO CHARACTERISTICS, INDUSTRY 4.0 CAPABILITIES, AND FIRM PERFORMANCE
dc.typeThesis
dc.date.updated2023-06-30T15:33:12Z
thesis.degree.departmentInformation Systems and Operations Management
thesis.degree.grantorThe University of Texas at Arlington
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy in Management Science
dc.type.materialtext
dc.creator.orcid0000-0002-8637-3996
local.embargo.terms2024-08-01
local.embargo.lift2024-08-01


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