Relationship Quality in Customer-service Robot Interactions in Industry 5.0: An Analysis of Value Recipes

Abstract

The paper studies the interactions between customers and robots within the framework of Industry 5.0-driven services. Prior studies have explored several factors contributing to the quality of these interactions, with perceived value being a crucial aspect. This study uses value recipes, which refer to specific configurations of how different benefits and costs are weighed up/evaluated, as a theoretical framework to investigate the quality of relationships between customers and service robots. The study aims to shed light on the complex interplay between different value dimensions that shape customers' relationships with robots. To achieve this goal, the authors analyze what value configurations facilitate or impede high-quality relationships between customers and service robots. Fuzzy set qualitative comparative analysis (fsQCA) was used to analyze data from 326 consumers. The data reveal that value recipes comprising positive values (such as relational benefit, novelty, control, personalization, excellence, and convenience) and negative values (about privacy and effort) prove highly effective in augmenting relationship quality. Results also underscore those negative values either in isolation or in conjunction with positive values, do not impede relationship quality. The theoretical contribution of this study lies in presenting new insights into relationship dynamics between customers and service robots in an Industry 5.0 value-driven context. From a practical standpoint, the findings suggest guidelines for successfully infusing the retail landscape with more intelligent service robots.