Understanding customer multi-interactions, trust, social support and voluntary performance in smart restaurants

Abstract

Purpose

The study aims to propose a triadic interaction model to assess the effect of customer–customer (C2C), employee–customer and robot–customer interactions on customer voluntary performance in the context of smart dining.

Design/methodology/approach

An explanatory sequential mixed methods design was used. First, a quantitative study surveyed Foodom patrons to assess the impact of triadic interactions on customer voluntary performance. The mediating role of trust and social support and the moderating effect of the need to belong were also explored. A post hoc study (Study 2) analyzed online comments to validate and complement the survey findings.

Findings

While all interactions promote social support, the C2C interactions significantly correlate with customer trust. Moreover, customer voluntary performance is influenced by both customer trust and social support, while the need to belong remains as a moderator. Findings from Study 2 consolidate and enrich the relationships identified in Study 1.

Research limitations/implications

This research reveals that patrons in smart dining still value interactions with employees and other diners. It enriches the stream of work on interaction quality by illuminating how different types of interactions could co-create value for customers, subsequently fostering voluntary behavior in smart dining contexts.

Originality/value

This research explores how patrons perceive interactions with robots in smart hospitality, highlighting their impact on trust and social support. It also sheds light on how interactions among robots, employees and customers influence customer voluntary performance, emphasizing the role of the need to belong in moderating relationships in this setting.

Citation

Jiao, H., Wong, I.A. and Lin, Z.(C). (2024), "Understanding customer multi-interactions, trust, social support and voluntary performance in smart restaurants", Journal of Hospitality and Tourism Technology, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JHTT-11-2023-0384