Abstract

A meta-review of the history of qualitative and critical theory in Information Systems (IS) and Human Computer Interaction (HCI) illustrates how source disciplines and publication outlets can filter reputation and affect methodological diversity. An academic cultures framework allows us to compare disciplines and explain different timelines for methods and theories. IS has many publication outlets, while HCI reputations depend on publication in the ACM Conference on Computer-Human Interaction (CHI). The fields deal with similar topics but are shaped by different institutional contexts. Qualitative research and critical theory arose in Information Systems during the 1980s and 1990s, while a search of HCI literature shows no critical theory until the 2000s. An open field of journals may create affordances for new approaches, whereas filtering reputation through a core conference may constrain them. The rise of platforms may blur domain distinctions through a common focus on AI, privacy and information integrity.

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Reputation Control and Theoretical Innovation in Two Academic Cultures

A meta-review of the history of qualitative and critical theory in Information Systems (IS) and Human Computer Interaction (HCI) illustrates how source disciplines and publication outlets can filter reputation and affect methodological diversity. An academic cultures framework allows us to compare disciplines and explain different timelines for methods and theories. IS has many publication outlets, while HCI reputations depend on publication in the ACM Conference on Computer-Human Interaction (CHI). The fields deal with similar topics but are shaped by different institutional contexts. Qualitative research and critical theory arose in Information Systems during the 1980s and 1990s, while a search of HCI literature shows no critical theory until the 2000s. An open field of journals may create affordances for new approaches, whereas filtering reputation through a core conference may constrain them. The rise of platforms may blur domain distinctions through a common focus on AI, privacy and information integrity.