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Research Articles

The Disciplinary Identity of HCI Research: An Investigation Using Configurational Theory

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Pages 859-869 | Received 14 Sep 2021, Accepted 22 Sep 2022, Published online: 20 Oct 2022
 

Abstract

HCI is an eclectic intellectual community comprised of researchers from different academic backgrounds applying diverse methodologies to an array of research questions. What is it that unites this community? What makes HCI an academic discipline? Researchers have proposed different frames—science and problem-solving, for example—to make sense of HCI as a unified, cohesive discipline. In this paper, we investigate whether a configurational theory of HCI can contribute to the discourse. As part of our investigation, we propose a configurational theory that is potentially suitable for HCI research. Our examination has led to an understanding of HCI that emphasizes its core object of study rather than its methodologies or tendency towards problem solving. We explore how this approach complements and extends existing discussions of HCI’s disciplinary identity. We end by discussing some potential benefits that a configurational theory might have, such as its ability to serve as a frame of reference for researchers, help researchers position their work in relation to existing work, identify and evaluate research claims and evidence, and assess contribution and knowledge growth in the field.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Erik Stolterman

Erik Stolterman is Professor in Informatics at the Luddy School of Informatics, Computing, and Engineering at Indiana University, Bloomington. His work examines the areas of HCI, interaction design, design practice, and theory of design. Stolterman has published many articles books, including “The Design Way” and “Things That Keep Us Busy”.

Jordan Beck

Jordan Beck is a Senior UX Researcher at Amazon Web Services (AWS). His academic interests include design research, problem framing, and applications of machine learning to UX research and design.

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