Paper Type

Complete

Abstract

Information Systems (IS) doctoral programs face a structural timing mismatch: dissertations unfold over multiple years, whereas digital technologies diffuse rapidly. This study employs a single-institution corpus of 94 IS doctoral dissertation abstracts (2006–2025) to examine whether doctoral research remains anchored in a socio-technical core or fragments into technology-centric silos. We applied a GenAI GPT-5.1–assisted keyword extraction procedure (20 keywords/abstract), followed by systematic manual validation and standardization. A keyword co-occurrence network with temporal overlay was constructed in VOSviewer. The resulting network is organized around a central “information systems” hub and yields eight thematic clusters, including advanced AI/deep learning, AI adoption, cybersecurity, risk and compliance behavior, data governance and ethics, organizational analytics, healthcare IS, social and collaborative systems, and user engagement/decision support. Findings suggest that, within this single-institution corpus, dissertation abstract topics evolve alongside emerging technologies while remaining structurally integrated around enduring socio-technical concerns.

Paper Number

1272

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Aug 15th, 12:00 AM

Doctoral Education in Times of Transformation: An Institutional Case Study of IS Dissertation Themes

Information Systems (IS) doctoral programs face a structural timing mismatch: dissertations unfold over multiple years, whereas digital technologies diffuse rapidly. This study employs a single-institution corpus of 94 IS doctoral dissertation abstracts (2006–2025) to examine whether doctoral research remains anchored in a socio-technical core or fragments into technology-centric silos. We applied a GenAI GPT-5.1–assisted keyword extraction procedure (20 keywords/abstract), followed by systematic manual validation and standardization. A keyword co-occurrence network with temporal overlay was constructed in VOSviewer. The resulting network is organized around a central “information systems” hub and yields eight thematic clusters, including advanced AI/deep learning, AI adoption, cybersecurity, risk and compliance behavior, data governance and ethics, organizational analytics, healthcare IS, social and collaborative systems, and user engagement/decision support. Findings suggest that, within this single-institution corpus, dissertation abstract topics evolve alongside emerging technologies while remaining structurally integrated around enduring socio-technical concerns.

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