Complex AI2AI Adoption Intention in Service Organizations
基于400名希腊酒店经理的调查数据，运用复杂性理论、模糊集定性比较分析和必要条件分析，识别出四种AI2AI采纳路径，并通过访谈验证，揭示服务组织中AI2AI采纳意向的复杂非线性特征。
AI2AI holds great potential as a technological innovation that may significantly change business operations. However, AI2AI adoption intentions remain under-researched in business contexts. This study addresses this gap by examining service managers' intentions to adopt AI2AI through the lens of complexity theory. Drawing on survey data from 400 Greek hotel managers, the study investigates how combinations of organisational and contextual factors shape AI2AI adoption intention. Fuzzy-set Qualitative Comparative Analysis (fsQCA) identifies sufficient configurational pathways, while Necessary Condition Analysis (NCA) determines constraining conditions. Robustness is assessed through alternative data-cleaning procedures and sensitivity analyses across calibration thresholds and consistency cut-offs. The analysis identifies four configurational pathways to AI2AI adoption (beneficial impacts; supported innovativeness; benefit–barrier nexus; competitiveness-driven adoption). Follow-up semi-structured interviews with industry stakeholders provide contextual corroboration and interpretive insight into how these adoption logics are understood and evaluated in practice. The findings highlight the complex and non-linear nature of AI2AI adoption intentions and offer theoretical and practical implications.