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Problem description: Measuring quality in the service industry remains a challenge. Existing methodologies are often costly and unscalable. Furthermore, understanding how elements of service quality contribute to the performance of service providers continues to be a concern in the service industry. In this paper, we address these challenges in the restaurant sector, a vital component of the service industry. Academic/practical relevance: Our work provides a scalable methodology for measuring the quality of service providers using the vast amount of text in social media. The quality metrics proposed are associated with economic outcomes for restaurants and can help predict future restaurant performance. Methodology: We use text present in online reviews on Yelp.com to identify and extract service dimensions using nonnegative matrix factorization for a large set of restaurants located in a major city in the United States. We subsequently validate these service dimensions as proxies for service quality using external data sources and a series of laboratory experiments. Finally, we use econometrics to test the relationship between these dimensions and restaurant survival as additional validation. Results: We find that our proposed service quality dimensions are scalable, match industry standards, and are correctly identified by subjects in a controlled setting. Furthermore, we show that specific service dimensions are significantly correlated with the survival of merchants, even after controlling for competition and other factors. Managerial implications: This work has implications for the strategic use of text analytics in the context of service operations, where an increasingly large text corpus is available. We discuss the benefits of this work for service providers and platforms, such as Yelp and OpenTable.
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3 Stars on Yelp, 4 Stars on Google Maps: A Cross-Platform Examination of Restaurant Ratings
Even though a restaurant may receive different ratings across review platforms, people often see only one rating during a local search (e.g. 'best burgers near me'). In this paper, we examine the differences in ratings between two commonly used review platforms-Google Maps and Yelp. We found that restaurant ratings on Google Maps are, on average, 0.7 stars higher than those on Yelp, with the increase being driven in large part by higher ratings for chain restaurants on Google Maps. We also found extensive diversity in top-ranked restaurants by geographic region across platforms. For example, for a given metropolitan area, there exists little overlap in its top ten lists of restaurants on Google Maps and Yelp. Our results problematize the use of a single review platform in local search and have implications for end users of ratings and local search technologies. We outline concrete design recommendations to improve communication of restaurant evaluation and discuss the potential causes for the divergence we observed.
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- PAR ID:
- 10208785
- Date Published:
- Journal Name:
- Computer supported cooperative work CSCW
- ISSN:
- 1573-7551
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3432953
