Comfort with Social Robots in the Pre-interaction Phase: A Field Experiment with Customers of a Retail Bank.

Abstract

This study aims to investigate the influence of two interaction designs on user comfort and intention to use during pre-interaction phase. As part of a field experiment in a retail bank, a proactive and a passive interaction design of a social robot were compared. A standardized questionnaire was used to determine how the interaction design affects the comfort, trust and usage intention of customers and what role trust plays as a mediating factor. The data analysis shows that the proactive design was rated better in terms of psychological comfort and emotional value, but not in terms of trust and intention to use. Comfort with robots positively influenced the intention to use the social robot, with trust serving as a key mediator; in the proactive variant, the effect was only indirect via trust, while in the passive variant, both direct and indirect effects were observed. According to dual processing theory, proactive designs rely on automatic, emotion-driven processes that directly influence comfort, while passive designs encourage reflective decision-making, supporting trust and increasing usage intention despite lower comfort. A balanced integration of both approaches can enhance customer comfort and trust, facilitating the successful adoption of social robots in retail.

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Titel Comfort with Social Robots in the Pre-interaction Phase: A Field Experiment with Customers of a Retail Bank.
Medien Artificial Intelligence in HCI. HCII 2025.
Verlag Springer
Herausgeber Artificial Intelligence in HCI. HCII 2025. Lecture Notes in Computer Science
Band 15822
Verfasser Carina Wiedenhöft, Anna Pilz, Prof. Dr. Alexander Piazza, Carolin Kaiser
Veröffentlichungsdatum 30.05.2025
Projekttitel 318PIA1201 - CONREC 2
Zitation Wiedenhöft, Carina; Pilz, Anna; Piazza, Alexander; Kaiser, Carolin (2025): Comfort with Social Robots in the Pre-interaction Phase: A Field Experiment with Customers of a Retail Bank.. Artificial Intelligence in HCI. HCII 2025. 15822. DOI: 10.1007/978-3-031-93429-2_17