Lilly Linne Kainde
Magister Management, Fakultas Ekonomi dan Bisnis Universitas Klabat, Manado, Indonesia

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

The Effects Of Employee Behavior On Customer Loyalty Through Customer Satisfaction: Sentiment Analysis Of Google Maps Restaurant Review In Manado City Samuel Rantung; Lilly Linne Kainde
Economics and Digital Business Review Vol. 7 No. 1 (2026)
Publisher : STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/ecotal.v7i2.4056

Abstract

This study examines the effects of employee behavior on customer satisfaction and customer loyalty by analyzing 367 Google Maps restaurant reviews from Manado City collected between 2022 and 2025. Using a lexicon-based sentiment analysis model tailored for Bahasa Indonesia and Manado dialects, the research quantifies sentiment for three key variables: employee behavior, customer satisfaction, and customer loyalty. Descriptive results indicate that customers express satisfaction more frequently than loyalty intentions, while comments related to employee behavior tend to be mixed. Correlation and regression analyses reveal that employee behavior significantly increases customer satisfaction (H1 supported), but customer satisfaction does not significantly predict loyalty, nor does employee behavior directly affect loyalty. Mediation testing using the Baron and Kenny (1986) approach confirms that customer satisfaction does not mediate the relationship between employee behavior and loyalty. These findings partially support the Service-Profit Chain Theory by confirming the link between service behavior and satisfaction but not the subsequent satisfaction–loyalty pathway. The study highlights the value of online reviews as real-time, natural reflections of customer experiences while also emphasizing that loyalty intentions are less likely to be expressed in textual feedback. Implications, limitations, and recommendations for integrating sentiment analysis with behavioral data in future research are discussed.