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

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 (N total = 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).

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Publication Details

Journal
Journal of Service Research
Published
2024-11-05
DOI
https://doi.org/10.1177/10946705241295841
Citations
6
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.08
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article

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

Vignesh Yoganathan, Victoria‐Sophie Osburg, Andrea Fronzetti Colladon, Waldemar Toporowski et al.
6 citations
Journal of Service Research
AI in Service Interactions
2.08
article

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

Vignesh Yoganathan, Victoria‐Sophie Osburg, Andrea Fronzetti Colladon, Waldemar Toporowski, Vincent Charles
article en
6 citations

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 (N total = 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).

Journal of Service ResearchVol. 28(1)
Queen's University Belfast (GB), Aston University (GB), University of Perugia (IT), Montpellier Business School (FR), University of Göttingen (DE)
No poverty
Openalex Percentile: Top 11%
AI in Service Interactions
2.08
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