Knowing how satisfied/dissatisfied is far from enough: a comprehensive customer satisfaction analysis framework based on hybrid text mining techniques

Abstract

Purpose This study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but also identify factors leading to dissatisfaction and further quantify improvement opportunity levels. Design/methodology/approach Adopting deep learning, Cross-Bidirectional Encoder Representations Transformers (BERT) model is developed to measure customer satisfaction. Furthermore, opinion mining technique is used to extract consumers’ opinions and obtain dissatisfaction factors. Furthermore, the opportunity algorithm is introduced to quantify attributes’ improvement opportunity levels. A total of 19,133 online reviews of 31 restaurants in Universal Beijing Resort are crawled to validate the framework. Findings Results demonstrate the superiority of Cross-BERT model compared to existing models such as sentiment lexicon-based model and Naïve Bayes. More importantly, after effectively unveiling customer dissatisfaction factors (e.g. long queuing time and taste salty), “Dish taste,” “Waiters’ attitude” and “Decoration” are identified as the three secondary attributes with the greatest improvement opportunities. Practical implications The proposed framework helps managers, especially in the restaurant industry, accurately understand customer satisfaction and reasons behind dissatisfaction, thereby generating efficient countermeasures. Especially, the improvement opportunity levels also benefit practitioners in efficiently allocating limited business resources. Originality/value This work contributes to hospitality and tourism literature by developing a comprehensive customer satisfaction analysis framework in the big data era. Moreover, to the best of the authors’ knowledge, this work is among the first to introduce opportunity algorithm to quantify service improvement benefits. The proposed Cross-BERT model also advances the methodological literature on measuring customer satisfaction.

Enhancing Organizational Effectiveness Through Employee Work Attitude: Dissection of Nigeria’s Hospitality Industry

“…However, research has shown that organization can attain their effectiveness through effective service delivery, and customer satisfaction (Yang et al,2024;Ayinaddis et al, 2023;Musa, 2023;Edeh et al, 2021a). It was this affirmation that service delivery and customer satisfaction were adapted as the indicators of organizational effectiveness in the hospitality industry.…”

“…Thus, for the hospitality industry to be effective, service delivery to customers must be prompt and tangible. Service delivery is an indicator of organizational effectiveness and performance (Yang et al,2024). When customers demand services in the hospitality industry, it is the responsibility of the service providers to make their requests promptly if not they will not be satisfied, and this will affect the profits of the organization (Edeh et al, 2021b).…”

“…Customer satisfaction is the perception of a customer concerning the product or service provided by the organization in an effective manner (Hngoi et al, 2023). A customer is said to be satisfied when they feel that their purchasing experience about a product is satisfactory (Yang et al, 2024). For Dhoopar et al (2023), customer satisfaction is the customer's feeling of pleasure derived by the customer that the product or service produces the perceived performance in line with his/her expectation.…”

“…Both positive and negative behaviors at workplaces have differences in the effectiveness and efficiency of an enterprise (Edeh et al, 2021a). In another view, Yang et al (2024) contended that managers who prefer using job enlargement to influence work attitudes lack managerial acumen. Ayinaddis et al (2023) assert that broadening jobs has no relationship with either customer satisfaction or repurchase intention instead it demotivates employees.…”

Recognizing emotions in restaurant online reviews: a hybrid model integrating deep learning and a sentiment lexicon

“…Chen et al (2015) later solidified that positive and negative emotions are indeed distinct variables that coexist in restaurant settings. In addition, recent big data studies in a restaurant context (Liu et al , 2022; Mehraliyev et al , 2020; Yang et al , 2023) have also confirmed that positive and negative emotions have distinct roles, and further studies on what constitutes specific emotions and how they affect satisfaction and behavior are necessary.…”