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Designing AI-Based Systems for Qualitative Data Collection and Analysis

Rietz, Tim

Abstract (englisch):

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS.

Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. ... mehr

Volltext §
DOI: 10.5445/IR/1000135146
Veröffentlicht am 13.07.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Hochschulschrift
Publikationsdatum 13.07.2021
Sprache Englisch
Identifikator KITopen-ID: 1000135146
Verlag Karlsruher Institut für Technologie (KIT)
Umfang x, 166 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Wirtschaftsinformatik und Marketing (IISM)
Prüfungsdatum 01.07.2021
Schlagwörter Artificial Intelligence, Qualitative Data Collection, Qualitative Data Analysis, Chatbot, QDAS, Requirements Engineering, Feedback, Human-Computer Interaction
Referent/Betreuer Mädche, A.
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