Sustainable human-robot services: ESG, diversity, and attractiveness in job pursuit

Purpose This study aims to examine how attractiveness factors (job, organization, individual) and environmental, social and governance (ESG) aspects influence job pursuit intentions in hospitality’s human-robot collaborative environments. It investigates relationships between these factors and job pursuit intention. Design/methodology/approach The study combines mixed methods using generalized linear modeling (GLM) with Student’s t distribution and fuzzy-set Qualitative Comparative Analysis (fsQCA). Data was collected via online survey from 492 US hospitality job seekers through Amazon’s Mechanical Turk (MTurk). The research applies complexity theory to uncover nonlinear relationships and multiple pathways to outcomes. Findings GLM results show organization attractiveness, individual interest and perceived ESG environmental and governance factors significantly predict job pursuit intention. Notably, while GLM showed job attractiveness and perceived ESG social factors as insignificant individual predictors, fsQCA revealed their critical roles within specific causal configurations. Practical implications Organizations should enhance employer branding and clearly communicate their environmental and governance ESG initiatives. Companies need targeted approaches emphasizing sustainability and governance practices, while preparing employees for human-robot collaboration (HRC) through training programs highlighting growth opportunities rather than job displacement. Originality/value This research uniquely examines employee perspectives on HRC in hospitality, addressing a gap focused predominantly on consumer reactions. It integrates attractiveness factors with ESG aspects using complexity theory and fsQCA methodology.