Evaluation of Methods for User Needs Extraction in Digital–Physical Product Ecosystems Using ChatGPT Text Categorization

Digital Business and Intelligent Systems: 16th International Baltic Conference (Baltic DB&IS 2024): Proceedings. Communications in Computer and Information Science. Vol.2157 2024
Alberts Pumpurs, Jānis Grabis

The identification and categorization of user needs in digital–physical product ecosystems are key starting point for developing user-centered products and improving user experiences in complex, interconnected environments. Utilizing ChatGPT for text categorization offers a new automated approach to simplify user need elicitation of a user generated content that can be applied to a traditional user needs elicitation method. The Kano model, user personas, the jobs to be done framework, and user journey mapping methods were used in this study to identify user needs in digital–physical product ecosystems. ChatGPT was used in this study to automate the process of identifying and analyzing consumer experiences using the selected methods. The findings of this study offer insights into product ecosystem user needs research and practical guidance in the use of ChatGPT to identify and categorize user needs.

Keywords
ChatGPT | Digital–physical product ecosystems | User need extraction
DOI
10.1007/978-3-031-63543-4_10
Hyperlink
https://link.springer.com/chapter/10.1007/978-3-031-63543-4_10

Pumpurs, A., Grabis, J. Evaluation of Methods for User Needs Extraction in Digital–Physical Product Ecosystems Using ChatGPT Text Categorization. In: Digital Business and Intelligent Systems: 16th International Baltic Conference (Baltic DB&IS 2024): Proceedings. Communications in Computer and Information Science. Vol.2157, Lithuania, Vilnius, 30 Jun-3 Jul., 2024. Cham: Springer, 2024, pp.141-157. ISBN 9783031635427. ISSN 1865-0929. Available from: doi:10.1007/978-3-031-63543-4_10

Publication language
English (en)