Using AI-enabled services to enhance customer satisfaction in quick-service restaurants: mediating role of personalised engagements and operational efficiency and moderating role of technology knowledge

Purpose The study aims to investigate how AI-enabled services (AESs) influence customer satisfaction (CS) in quick service restaurants (QSRs). It also aims to examine the mediating roles of personalised engagements (PE) and operational efficiency (OE) in enhancing the AES impact on CS. Furthermore, the research explores the role of technology knowledge (TK) in moderating the AES–PE and AES–OE linkages. Design/methodology/approach A quantitative research approach using a structured questionnaire was adopted, and responses were collected from 342 QSR customers in the Tricity Region in India. Exploratory factor analysis and structural equation modelling were used to test the hypothesised relationships among AES, PE, OE, TK and CS. Findings The results reveal that AESs significantly improve PE, OE and CS in QSRs. Both PE and OE mediate the relationship between AES and CS. Furthermore, TK is found to positively moderate the AES–PE and AES–OE relationships. Research limitations/implications The study contributes significantly to technology adoption in the QSR segment by analysing the role of mediating (PE and OE) and moderating (TK) variables. Practical implications By integrating AESs, QSRs can elevate the CS by delivering PE and elevating OE. The outlets should also invest in making the masses digitally aware to maximise gains on the CS front. Social implications The study encourages QSRs to adopt customer-oriented AESs to deliver not only enhanced CS but also to bridge the digital divide prevailing in society. Originality/value The study is unique in terms of analysing predictor, mediator and moderator variables in a single integrative framework that impact CS in the QSR category.