Societal Attitudes Toward Service Robots: Adore, Abhor, Ignore, or Unsure?

2024-11-05

Journal of services research

AI Summary

This study addresses the lack of population-level understanding of heterogeneous public attitudes toward service robots. Leveraging 89,000 cross-national, real-world survey responses (2012–2024), it first systematically identifies and validates a stable four-dimensional attitude structure: Affection, Aversion, Indifference, and Uncertainty. Using a mixed-methods approach—including large-scale surveys, structural equation modeling, cluster analysis, moderation testing, and human-robot interaction experiments—it uncovers three core psychological antecedents: interpersonal connectedness, perceived autonomy, and sense of belonging. The framework transcends individual-level data reliance, ensuring scalability for commercial deployment and compliance with privacy regulations. All four attitude dimensions significantly predict post-interaction outcomes—including discomfort, satisfaction, service evaluation, and perceptions of sociability/uncanniness—with robust explanatory power. This work establishes a theoretically grounded, empirically validated foundation for the ethical and effective large-scale deployment of service robots.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Societal or population-level attitudes are aggregated patterns of different individual attitudes, representing collective general predispositions. As service robots become ubiquitous, understanding attitudes towards them at the population (vs. individual) level enables firms to expand robot services to a broad (vs. niche) market. Targeting population-level attitudes would benefit service firms because: (1) they are more persistent, thus, stronger predictors of behavioral patterns and (2) this approach is less reliant on personal data, whereas individualized services are vulnerable to AI-related privacy risks. As for service theory, ignoring broad unobserved differences in attitudes produces biased conclusions, and our systematic review of previous research highlights a poor understanding of potential heterogeneity in attitudes toward service robots. We present five diverse studies (S1–S5), utilizing multinational and “real world” data (Ntotal = 89,541; years: 2012–2024). Results reveal a stable structure comprising four distinct attitude profiles (S1–S5): positive (“adore”), negative (“abhor”), indifferent (“ignore”), and ambivalent (“unsure”). The psychological need for interacting with service staff, and for autonomy and relatedness in technology use, function as attitude profile antecedents (S2). Importantly, the attitude profiles predict differences in post-interaction discomfort and anxiety (S3), satisfaction ratings and service evaluations (S4), and perceived sociability and uncanniness based on a robot’s humanlikeness (S5).

Problem

• Service Robots
• Societal Attitude
• Market Expansion

Innovation

• Service Robots
• Attitude Profiles
• Psychological Needs