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Navigating generative AI in service research: the effectiveness–legitimacy matrix
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DOI：10.1080/02642069.2026.2702956.png)
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Abstract
En 中文
Generative AI is rapidly reshaping academic work, including in service research. While existing guidance predominantly focuses on task-level applications and individual best practices, less attention has been paid to the institutional and normative conditions under which service scholars’ generative AI use becomes appropriate or problematic. Drawing on a socio-technical perspective, this paper develops the Effectiveness–Legitimacy Matrix (ELM), a typology that analyses generative AI use in service research along two conceptually distinct but interrelated dimensions: effectiveness (the fit between technological capabilities and research task characteristics) and legitimacy (the alignment with institutional rules and professional norms). The ELM identifies four configurations (aligned, misguided, norm-breaking, and dead-end use), each associated with distinct methodological and governance implications. The paper offers a diagnostic framework to guide responsible integration of generative AI in service research and outlines a research agenda to understand its long-term epistemic consequences for the services research community.
Keywords:
Generative AI
affordances
norms
effectiveness
legitimacy
生成式人工智能；服务研究；可供性；规范；有效性；合法性
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