Mobile Ethnography : Towards a More In-Situ Research Method to Analyze Customer Experience
Book Details
Format
Paperback / Softback
ISBN-10
3658515864
ISBN-13
9783658515867
Publisher
Springer Fachmedien Wiesbaden
Imprint
Springer Gabler
Country of Manufacture
GB
Country of Publication
GB
Publication Date
May 2nd, 2026
Print length
237 Pages
Product Classification:
Market researchCustomer services
Ksh 16,200.00
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To address these gaps, the author proposes an exemplary research design to strengthen validity and reliability.Using Design Science Research Methodology, this dissertation develops mobile ethnography as a design artifact comprising the method, participant-facing mobile apps, and web-based analysis software for researchers.
This book introduces mobile ethnography as an in-situ qualitative research method that enables participants to document lived experiences on smartphones as they unfold. By capturing in-situ data such as text, photos, videos, audio, ratings, timestamps, and GPS, the method provides rich contextual insight into experiences as they are lived, not just remembered. Visualized as journey maps, customer and employee experience data can be analyzed to identify patterns, pain points, and opportunities for improvement. Grounded in service design, customer experience research, and ethnography, the book presents a systematic literature review of ethnographic research in leading information systems journals from 2000 to 2025. The review finds increasing methodological transparency, but also persistent gaps in research design, triangulation, and reflexivity. To address these gaps, the author proposes an exemplary research design to strengthen validity and reliability. Using Design Science Research Methodology, this dissertation develops mobile ethnography as a design artifact comprising the method, participant-facing mobile apps, and web-based analysis software for researchers. Based on seven iterative design cycles, the book shows how participants can become active contributors to customer experience research while maintaining data integrity and motivation. It concludes with implications for AI-supported analysis. In other words: rigorous research, but with dirt under its fingernails.
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