Understanding the motivation and satisfaction of yoga consumers is of critical importance for both leisure service providers and leisure researchers to enhance the sustainability of personal lives in terms of physical wellness and mental happiness. For this purpose, this study investigated 25,120 pairs of online ratings and reviews from 100 yoga centres in Shanghai, China using latent Dirichlet allocation (LDA)-based text mining, and successfully established the relationship between rating and review. Findings suggest that Chinese yogis are motivated by improving physical condition, improving psychological condition, gracing appearance, establishing social connection, and creating social isolation. In addition to teaching mainstream yoga, yoga centres also provide additional courses. From a consumer perspective, yogis are relatively satisfied with teachers, courses, and the environment, but complain about the supporting staff, membership price, and reservation service. Managerially, yoga centres are encouraged to continue attending to the motivations of yogis, specialising their guidance, and fostering strengths and circumventing weaknesses in their service. This study also contributes by verifying, elaborating on, and tentatively extending the framework of the Physical Activity and Leisure Motivation Scale (PALMS).
Establishing the relation between online ratings and reviews provides a potentially inexpensive and effective way for restaurants to capture quality improvement hints from customers. To this end, this study proposes an integrated approach that leverages text mining and empirical modeling to quantitatively correlate ratings with reviews. From Dianping.com (a Chinese crowd-sourced online review community), 49,080 pairs of restaurant rating and review were examined, with high-frequency words, major topics, and subtopics identified. Multilinear regression was employed to screen out the most impactful factors that influence taste, environment, and service ratings. Managerially, the idea of triggering the synergistic benefit from customer ratings and reviews is referential for market practitioners both within and beyond the catering industry.
Observing and interpreting restaurant customers’ evolution of dining patterns and satisfaction during COVID-19 is of critical importance in terms of developing sustainable business insights. This study describes and analyzes customers’ dining behavior before and after the pandemic outbreak by means of statistically aggregating and empirically correlating 651,703 restaurant-user-generated contents posted by diners during 2019–2020. Twenty review topics, mostly food, were identified by latent Dirichlet allocation, whereas analysis of variation and rating-review regression were performed to explore whether and why customers became less satisfied. Results suggest that customers have been paying fewer visits to restaurants since the outbreak, assigning lower ratings, and showing limited evidence of spending more. Interestingly, queuing, the most annoying factor for restaurant customers during normal periods, turns out to receive much less complaint during COVID-19. This study contributes by discovering business knowledge in the context of COVID-19 based on big data that features accessibility, relevance, volume, and information richness, which is transferable to future studies and can benefit additional population and business. Meanwhile, this study also provides practical suggestions to managers regarding the framework of self-evaluation, business mode, and operational optimization.
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