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Societal attitudes toward service robots: Adore, abhor, ignore, or unsure?

Journal: Journal of Service Research

Date: 2024

Author: Vignesh Yoganathan, Victoria-Sophie Osburg, Andrea Fronzetti Colladon, Vincent Charles, Waldemar Toporowski

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).

Link: Google Scholar

Background and Context

Service Robot Market Growth

The global service robot market is worth $28.9 billion in 2024, triple the industrial robot market, growing to $34.7 billion by 2028.

Research Methodology

Five diverse studies using data from 89,541 participants across multiple countries spanning 2012-2024, including real-world interactions and reviews.

Research Objective

Understanding attitudes toward service robots at population level to help firms expand robot services to broader markets.

Four Distinct and Stable Attitude Profiles Toward Service Robots

ADORE Low Risk, High Benefit 23% of Population ABHOR High Risk, Low Benefit 19% of Population IGNORE Low Risk, Low Benefit 4% of Population UNSURE High Risk, High Benefit 54% of Population
  • Four distinct attitude profiles emerged consistently across all five studies, representing different risk-benefit perceptions of service robots.
  • The ambivalent "unsure" profile is the most common (54%), revealing mixed feelings are prevalent in the population.
  • Previous research only considered positive and negative attitudes, missing the critical indifferent and ambivalent profiles.

Psychological Need for Human Contact Drives Less Favorable Attitudes

KEY PSYCHOLOGICAL NEEDS • Need for Relatedness • Need for Interaction with Staff • Need for Autonomy • Need for Competence Negative (Abhor) Ambivalent (Unsure) Indifferent (Ignore) Positive (Adore)
  • Need for relatedness and interaction with service staff are the strongest predictors of attitude profile membership.
  • People's desire for human contact, not technological competence, primarily drives less favorable attitudes toward robots.
  • Surprisingly, competence needs showed minimal influence, suggesting tech ability concerns aren't the main adoption barrier.

Different Profiles Experience Varying Levels of Post-Interaction Discomfort and Anxiety

  • The positive profile shows the lowest discomfort and anxiety after interacting with a service robot.
  • The negative profile experiences the highest levels of discomfort and anxiety during robot interactions.
  • Indifferent profiles show minimal emotional response, while ambivalent profiles display moderate negative reactions.

Attitude Profiles Predict Different Levels of Customer Satisfaction with Robot Hotels

  • Satisfaction ratings from 4,696 online reviews of robot hotels reveal clear differences between attitude profiles.
  • Positive profiles gave the highest ratings, while negative profiles showed the lowest satisfaction scores.
  • Surprisingly, indifferent profiles rated experiences more favorably than ambivalent profiles despite lower initial interest.

Medium-Humanlike Robots Appeal to All Attitude Profiles

High Humanlikeness (Nadine) Medium Humanlikeness (Nimbro) Low Humanlikeness (Basic Robot) Medium Humanlikeness: Best Sociability, Lowest Uncanniness Highly Humanlike Robots: Evoke Uncanniness in All Profiles
  • Robots with medium humanlikeness (like Nimbro) received the best evaluations across all attitude profiles.
  • Highly humanlike robots (like Nadine) triggered uncanniness and negative evaluations even in positive profiles.
  • Finding the right balance of humanlikeness is crucial for broader market acceptance of service robots.

Contribution and Implications

  • For negative profiles, firms should emphasize human staff availability and enable choice between robot and human service.
  • For ambivalent profiles, balance technology with conventional value-adding features and ensure reliable robot performance.
  • For indifferent profiles, create memorable experiences with robots and avoid overemphasizing robot services in marketing.
  • For positive profiles, continuously enhance robot services and develop loyalty programs with exclusive robot interactions.
  • Population-level targeting is more effective than individual targeting, reducing reliance on personal data and privacy concerns.

Data Sources

  • Four Attitude Profiles: Based on Study 1's latent profile analysis of European Commission survey data (n=82,453).
  • Psychological Antecedents: Based on Study 2's analysis of need fulfillment measures (n=950).
  • Post-Interaction Effects: Based on Study 3's experimental interaction with virtual service robot (n=301).
  • Customer Satisfaction: Based on Study 4's psycholinguistic analysis of online reviews of robot hotels (n=4,696).
  • Humanlikeness Effects: Based on Study 5's evaluation of five robot types with varying humanlikeness (n=1,141).