Paper Number
ECIS2026-1678
Paper Type
CRP
Abstract
This paper develops configurational propositions about how employees form intentions to use AI-driven business analytics (AI-BA). Drawing on Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) theory, we use fuzzy-set Qualitative Comparative Analysis (fsQCA) on survey data from Greek employees to theorize adoption as the outcome of complementary bundles of beliefs, capabilities and contextual fit. The findings show that behavioural intention emerges when different conditions align, rather than from any single driver. We further propose that fit is itself conditional, strengthening adoption only when technological quality and user readiness are high. Finally, low intention stems from imbalanced or conflicting cognitive and contextual conditions, not simply weak beliefs. Together, these propositions recast AI-BA adoption as a set of equifinal “adoption logics” and advance configurational theorizing in technology adoption research.
Recommended Citation
Mamakou, Xenia J., "Explaining Intentions To Use AI-Driven Business Analytics: An FSQCA Integration of TAM and TTF" (2026). ECIS 2026 Proceedings. 6.
https://aisel.aisnet.org/ecis2026/bus_analytics/bus_analytics/6
Explaining Intentions To Use AI-Driven Business Analytics: An FSQCA Integration of TAM and TTF
This paper develops configurational propositions about how employees form intentions to use AI-driven business analytics (AI-BA). Drawing on Technology Acceptance Model (TAM) and Task-Technology Fit (TTF) theory, we use fuzzy-set Qualitative Comparative Analysis (fsQCA) on survey data from Greek employees to theorize adoption as the outcome of complementary bundles of beliefs, capabilities and contextual fit. The findings show that behavioural intention emerges when different conditions align, rather than from any single driver. We further propose that fit is itself conditional, strengthening adoption only when technological quality and user readiness are high. Finally, low intention stems from imbalanced or conflicting cognitive and contextual conditions, not simply weak beliefs. Together, these propositions recast AI-BA adoption as a set of equifinal “adoption logics” and advance configurational theorizing in technology adoption research.
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