Jaewoo Jung’s research while affiliated with Colorado State University and other places

Now in its third edition, this Handbook is essential for students and researchers in Strategic Management and Organizational Theory and Behaviour. The Strategy as Practice approach moves away from the disembodied and asocial study of firm assets, technologies and practices, towards the study of strategizing as an activity. Strategy is understood as something people do rather than something a firm has. This perspective explores how strategizing contributes to an organizations' daily operations at all levels. Through detailed empirical studies of the everyday activities and practice of people engaged in strategizing, the Handbook investigates who strategists are, what strategists do, how they do it, and what the consequences of their actions are. Featuring new authors and additional or fundamentally updated and revised chapters, this edition provides a state-of-the-art overview of recent reflections and works in this rapidly growing stream of strategic management, whilst also presenting a research agenda for the next decade.

How can scholars collaborate with AI in qualitative research without compromising its essence? This essay brings together different expert perspectives around this question. We take point of departure in the unique features of qualitative research such as "being there", discovery and novel theorizing, and present different reflections on how AI could enhance these features and when we should be particularly cautious. We conclude that AI is not a shortcut ; instead, it holds promise when researchers deeply invest in, innovate, and adapt their methods. Given the plurality and flexibility inherent in qualitative research, we argue that integrating AI should be approached like any other methodological choice-aligned with the research's underlying ontological and epistemological assumptions.

Text analysis, particularly custom dictionaries and topic modeling, has helped advance management and organization theory. Custom dictionaries involve creating word lists to quantify patterns and infer constructs, while topic modeling extracts themes from textual documents to help understand a theoretical domain. Building on these two approaches, we propose another text analysis approach called word-text-topic extraction (WTT), which enhances the efficiency and relevance of text analysis for the sake of theoretical advancement. Specifically, we first identify relevant words for a researcher's theoretical area of interest using word-embedding algorithms. That step is followed by extracting text segments from the textual corpus using a collocation process. Finally, topic modeling is applied to capture themes relevant to the specific theoretical area of interest. To illustrate the WTT approach, we explored one research area needing further theory development—innovation. Using 841 CEOs’ letters to shareholders, we found that our WTT approach provides nuanced features of innovation that differ across industry contexts. We guide researchers on decisions and considerations related to the WTT approach in order to facilitate its use in future studies.