Paper Type
Short
Paper Number
PACIS2026-1871
Description
Artificial Intelligence (AI) is regarded as key enabler for digital transformation in the public sector. Yet, its adoption remains limited and fragmented. Although research has highlighted the importance of leadership for AI adoption, little is known about how specific leadership behaviors influence employees’ AI acceptance in public-sector organizations. This study addresses this gap by linking transformational leadership (TFL) to the Unified Theory of Acceptance and Use of Technology (UTAUT). We develop a theory-based framework and examine how TFL dimensions relate to acceptance determinants and underlying mechanisms. Using a qualitative-to-quantitative mixed-methods design, we present preliminary results from semi-structured interviews with German police employees across four hierarchical levels (N=12). These results support most of the proposed TFL-UTAUT links, while indicating differences across hierarchical levels. The study contributes a dimension-level understanding of TFL-UTAUT links and concrete leadership behaviors that can help address AI adoption challenges in public-sector organizations.
Recommended Citation
Schober, Lisa-Maria; Riederer, Sebastian; and Welpe, Isabell M., "Leadership Behaviors Shaping AI Adoption in the Public Sector: A Mixed-Methods Study" (2026). PACIS 2026 Proceedings. 13.
https://aisel.aisnet.org/pacis2026/it_strategy/it_strategy/13
Leadership Behaviors Shaping AI Adoption in the Public Sector: A Mixed-Methods Study
Artificial Intelligence (AI) is regarded as key enabler for digital transformation in the public sector. Yet, its adoption remains limited and fragmented. Although research has highlighted the importance of leadership for AI adoption, little is known about how specific leadership behaviors influence employees’ AI acceptance in public-sector organizations. This study addresses this gap by linking transformational leadership (TFL) to the Unified Theory of Acceptance and Use of Technology (UTAUT). We develop a theory-based framework and examine how TFL dimensions relate to acceptance determinants and underlying mechanisms. Using a qualitative-to-quantitative mixed-methods design, we present preliminary results from semi-structured interviews with German police employees across four hierarchical levels (N=12). These results support most of the proposed TFL-UTAUT links, while indicating differences across hierarchical levels. The study contributes a dimension-level understanding of TFL-UTAUT links and concrete leadership behaviors that can help address AI adoption challenges in public-sector organizations.
Comments
11-Strategy