WARMTH, COMPETENCE, OR BOTH? DUAL-PATHWAY CONCEPTUALISATION OF ROBOTIC RESTAURANT ENCOUNTER AND CUSTOMER REVISIT INTENTION

Service robots have become a routine presence in restaurants across Asia, yet customer responses to them remain ambivalent. Studies of service robots in hospitality typically bundle the attributes of the machine into a single quality score and regress satisfaction on it. Social cognition research suggests a different structure. People judge any agent, human or artificial, along two largely independent dimensions, competence and warmth, and these dimensions carry different emotional and behavioural consequences. This paper applies that logic to the robotic restaurant encounter and proposes a dual-pathway conceptual model. Robotic service quality and robotic efficiency are specified as competence cues, robotic anthropomorphism and robotic empathy as warmth cues; the two sets of cues form separate appraisals that jointly determine customer satisfaction and, through it, revisit intention. Eight propositions are advanced, including a non-monotonic relationship between anthropomorphism and perceived warmth, and two boundary conditions: cultural service expectations and the utilitarian or hedonic character of the dining occasion. The model offers a way of explaining why robots that perform flawlessly can still disappoint, and it treats the mixed case of a competent but cold robot as the commercially common outcome rather than a measurement anomaly. It also predicts that a service failure met with a warm response will damage satisfaction less than one met with correct but impassive handling. For operators, the framework gives a basis for allocating investment between the two pathways, separating competence attributes fixed at procurement from warmth attributes that can be staged after deployment. For researchers, it specifies a validation strategy combining experimental manipulation with survey-based estimation.

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Publication Details

Journal
Quantum Journal of Social Sciences and Humanities
Published
2026-11-07
DOI
https://doi.org/10.55197/qjssh.v7si5.1759
Primary Topic
AI in Service Interactions
Type
article
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article

WARMTH, COMPETENCE, OR BOTH? DUAL-PATHWAY CONCEPTUALISATION OF ROBOTIC RESTAURANT ENCOUNTER AND CUSTOMER REVISIT INTENTION

Azita Aboo Bakar, Anderson Ngelambong, DAHLAN ABDULLAH, I KETUT SURATA et al.
5 links
Quantum Journal of Social Sciences and Humanities
AI in Service Interactions
article

WARMTH, COMPETENCE, OR BOTH? DUAL-PATHWAY CONCEPTUALISATION OF ROBOTIC RESTAURANT ENCOUNTER AND CUSTOMER REVISIT INTENTION

Azita Aboo Bakar, Anderson Ngelambong, DAHLAN ABDULLAH, I KETUT SURATA, NUR EMELIEYANA NOR HASIM, MUHAMMAD AFIF HAZIM ROSLI
article en

Abstract

Service robots have become a routine presence in restaurants across Asia, yet customer responses to them remain ambivalent. Studies of service robots in hospitality typically bundle the attributes of the machine into a single quality score and regress satisfaction on it. Social cognition research suggests a different structure. People judge any agent, human or artificial, along two largely independent dimensions, competence and warmth, and these dimensions carry different emotional and behavioural consequences. This paper applies that logic to the robotic restaurant encounter and proposes a dual-pathway conceptual model. Robotic service quality and robotic efficiency are specified as competence cues, robotic anthropomorphism and robotic empathy as warmth cues; the two sets of cues form separate appraisals that jointly determine customer satisfaction and, through it, revisit intention. Eight propositions are advanced, including a non-monotonic relationship between anthropomorphism and perceived warmth, and two boundary conditions: cultural service expectations and the utilitarian or hedonic character of the dining occasion. The model offers a way of explaining why robots that perform flawlessly can still disappoint, and it treats the mixed case of a competent but cold robot as the commercially common outcome rather than a measurement anomaly. It also predicts that a service failure met with a warm response will damage satisfaction less than one met with correct but impassive handling. For operators, the framework gives a basis for allocating investment between the two pathways, separating competence attributes fixed at procurement from warmth attributes that can be staged after deployment. For researchers, it specifies a validation strategy combining experimental manipulation with survey-based estimation.

Quantum Journal of Social Sciences and HumanitiesVol. 7(SI5)
Universiti Teknologi MARA System (MY), Politeknik Pariwisata Bali (ID), University of Kuala Lumpur (MY), Universiti Teknologi MARA (MY)
Openalex Percentile: Top 11%
AI in Service Interactions
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