How do service robots generate customer value? Rethinking service robot acceptance model (sRAM) through necessary...

This study reframes the Service Robot Acceptance Model (sRAM) as a value-centred framework. It examines how functional, social, relational and individual factors shape customer value in robot-mediated restaurant encounters and how this value drives re-patronage intention, word-of-mouth intention and robot-human rapport across generational cohorts. On-site intercept surveys were conducted with 351 diners in Kuala Lumpur restaurants using service robots. Using a disjoint two-stage PLS-SEM, the four second-order antecedent blocks predicted customer value and three post-use outcomes. Multi-group analysis tested generational moderation (Baby Boomers, Generation X, Generation Y, Generation Z). Fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) identified cohort-specific configurations leading to high customer value. Relational and functional factors are the strongest overall predictors of customer value, followed by social and individual factors. Customer value, in turn, consistently predicts re-patronage intention, word-of-mouth intention and robot-human rapport. Generational results show distinct value-formation logics: Baby Boomers combine functional, relational and social drivers, Generation X relies mainly on social cues, Millennials on individual and relational factors, and Generation Z primarily on functional performance. fsQCA reveals equifinal configurations, with different combinations of functional, social, relational and individual conditions producing high value by cohort. Managers should intentionally design robot encounters by aligning functional, social, relational, and individual value drivers with the expectations of different generational cohorts, moving beyond uniform interaction styles toward cohort-sensitive value orchestration. The study extends sRAM beyond adoption by positioning customer value as the central post-use mechanism, integrating four antecedent blocks with generational and configurational perspectives to explain how service robots generate loyalty, word of mouth and rapport in hospitality settings.