Methodological analysis establishes ecological validity criteria in service research, highlighting practical design principles for managing generative AI mediation.
Ecological validity refers to how well research reflects the conditions, interactions, and behaviors of the real-world setting its findings address. This study examines it as a core criterion for evaluating real-world evidence in service research. Generative AI operates in the research process, where it produces explanations, scenarios, stimuli, and synthetic respondents at near-zero cost. Distinguishing ecological validity from internal and external validity, extending it beyond experiments, and clarifying its service-specific relevance, the study reconceptualizes it around context, interactivity, temporality, and Generative AI mediation. It then specifies four functions through which multi-study designs accumulate real-world evidence and derives one design principle from each element, with a checklist that researchers, reviewers, and editors apply. A comparison with prior calls for relevance, ecological value, and impact in marketing, consumer research, public policy, and tourism locates what is specific to the service case.
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Sung et al. (2026) studied this question.
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