Paper Type
Complete
Abstract
Despite their shared focus on human-centered systems, Information Systems (IS) and Human–Computer Interaction (HCI) disciplines have evolved into largely separate research communities represented by the AIS SIGHCI and ACM SIGCHI. Prior efforts to connect these communities have been short-lived and primarily institutional in nature. This study examines whether common ground exists by systematically comparing research and publication practices. We analyzed 2,422 conference papers from both communities using LLM-based information extraction and structural topic modeling. Specifically, we compared research topics, theoretical foundations, methods, user framing, sample sizes, institutional affiliations, and funding patterns. While both communities primarily focus on micro-level phenomena and share a strong interest in Human–AI Interaction, they differ in their epistemic orientation and methodological preferences. We advocate for collaboration grounded in shared phenomena, particularly Human–AI Interaction.
Paper Number
1421
Recommended Citation
Gießmann, Nico; Jetter, Hans-Christian; Calero Valdez, André; and Heine, Moreen, "Clicking Past Each Other: Rediscovering Common Ground Between IS and HCI Disciplines" (2026). AMCIS 2026 Proceedings. 7.
https://aisel.aisnet.org/amcis2026/sig_hci/sig_hci/7
Clicking Past Each Other: Rediscovering Common Ground Between IS and HCI Disciplines
Despite their shared focus on human-centered systems, Information Systems (IS) and Human–Computer Interaction (HCI) disciplines have evolved into largely separate research communities represented by the AIS SIGHCI and ACM SIGCHI. Prior efforts to connect these communities have been short-lived and primarily institutional in nature. This study examines whether common ground exists by systematically comparing research and publication practices. We analyzed 2,422 conference papers from both communities using LLM-based information extraction and structural topic modeling. Specifically, we compared research topics, theoretical foundations, methods, user framing, sample sizes, institutional affiliations, and funding patterns. While both communities primarily focus on micro-level phenomena and share a strong interest in Human–AI Interaction, they differ in their epistemic orientation and methodological preferences. We advocate for collaboration grounded in shared phenomena, particularly Human–AI Interaction.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIG HCI