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<p>Generative Artificial Intelligence (AI) promises efficiency gains in public administration, yet its evaluation requires more than traditional task-technology fit analyses. This paper develops the Task-Technology-Public-Value-Fit (TTPV-Fit) as an integrated analytical framework linking instrumental fit (task-technology) with normative fit (public value contribution). Based on ten semi-structured interviews at a&#xa0;municipal public corporation, three key findings emerge: Cadence Mismatch—the perceived development gap between internal and external AI tools—constitutes an independent evaluation dimension beyond classical fit concepts; systematic tensions arise between instrumental and normative fit; and perceived public value remains at the individual level while organizational impacts remain speculative. The study demonstrates that expectation management and inter-municipal cooperation are at least as important as the technical capabilities of generative AI for successful municipal AI adoption.</p>

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Task-Technology-Public-Value-Fit generativer Künstlicher Intelligenz in der Kommunalverwaltung

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Meryem Schöck,
  • Nicolai Krüger,
  • Frank Teuteberg

摘要

Generative Artificial Intelligence (AI) promises efficiency gains in public administration, yet its evaluation requires more than traditional task-technology fit analyses. This paper develops the Task-Technology-Public-Value-Fit (TTPV-Fit) as an integrated analytical framework linking instrumental fit (task-technology) with normative fit (public value contribution). Based on ten semi-structured interviews at a municipal public corporation, three key findings emerge: Cadence Mismatch—the perceived development gap between internal and external AI tools—constitutes an independent evaluation dimension beyond classical fit concepts; systematic tensions arise between instrumental and normative fit; and perceived public value remains at the individual level while organizational impacts remain speculative. The study demonstrates that expectation management and inter-municipal cooperation are at least as important as the technical capabilities of generative AI for successful municipal AI adoption.