Thing Ethnography and Generative AI: Ethical and Methodological Challenges with Illustrative Notes from Türkiye

Ceyda Taç, Anabela Mesquita (), João Batista () and B. Burak Soyer ()
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Ceyda Taç: CEOS.PP/ISCAP/Polytechnic of Porto
Anabela Mesquita: CEOS.PP/ISCAP/Polytechnic of Porto
João Batista: Aveiro Institute of Accounting and Administration/Digimedia University of Aveiro
B. Burak Soyer: Ankara Yıldırım Beyazıt University

Chapter Chapter 1 in Human Resource Development for Sustainability and Social Responsibility, 2026, pp 3-13 from Springer

Abstract: Abstract Ethnography, a foundational approach in qualitative research, has evolved from cultural-centered analysis to object-centered methodologies such as “thing ethnography,” which explore the relationships between cultural meanings and social structures. The advent of Generative Artificial Intelligence (GAI) tools—particularly ChatGPT and similar GAI tools—is reshaping these methods by introducing new forms of data generation, analysis, and interaction. Based on a structured literature review, this study investigates how GAI can enhance traditional ethnographic practices, especially in terms of interview efficiency and methodological diversity, while also raising important ethical concerns related to bias, accuracy, objectivity, and emotional distance. Focusing on the Turkish academic context, the study evaluates the cultural alignment and practical integration of GAI tools in local research practices, considering both their potential and limitations. It argues that GAI functions not only as a methodological instrument but also as an active participant in the research process—challenging conventional boundaries of ethnography and prompting a redefinition of the researcher’s role.

Keywords: Thing ethnography; Generative artificial intelligence; Large language models; Qualitative research; Türkiye; Research ethics (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-09683-8_1

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DOI: 10.1007/978-3-032-09683-8_1

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