This lecture introduces Computational Grounded Theory (CGT), a method that integrates computational text analysis to address the limitations of traditional text analytics. While conventional text analytics excel in prediction, they often lack the explanatory power essential for formal decision-making and program evaluation. CGT bridges this gap by combining automated processes with human-in-the-loop involvement, enabling the inductive development of substantive theories directly from textual data. Grounded Theory facilitates the emergence of themes from data, offering a deeper theoretical understanding of phenomena—a stark contrast to the dominant hypothetical-deductive reasoning prevalent in IS research. The session will also highlight the application of Structural Topic Modelling (STM) as a topic modelling method, illustrated through examples in healthcare and IT management. Activities will include case studies demonstrating CGT applications and an engaging interactive Q&A session.

· Introduction to Computational Grounded Theory (CGT)

· Challenges in traditional text analytics for theory development

· The human-in-the-loop approach: Enhancing interpretation and sensemaking

· Methodological steps: Discovery, measurement, and inferencing

· Case studies: Applications of CGT in addressing complex research questions

· Opportunities for PhD research: Advancing theoretical contributions through CGT

Dr. Nina Rizun, Assistant Professor, Gdansk University of Technology, Gdansk/Poland.

Sesión online a través de Collaborate en la sala del curso de Aula Virtual del programa de doctorado, con compartición de una copia de la presentación en PDF.

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