Understanding retail quality of sporting goods stores: a text mining approach

Abstract

PurposeSporting goods retailing is a significant sector within the sport industry with the total revenue of this sector reaching $52.2 billion in 2018. Beset with formidable competition, sporting goods stores are compelled to augment their merchandise with service and improve retail quality. The purpose of this study is to investigate retail quality of sporting goods stores (RQSGS).Design/methodology/approachBased on 27,793 online reviews of 1481 stores in the United States, this study used Leximancer 4.0, a text mining software, to identify critical retail quality dimensions associated with sporting goods stores, and further explored the most salient dimensions among different levels of ratings.FindingsCustomer service and store aspects are the two higher-order dimensions of RQSGS; holistic experience, manager and staff are three themes under customer service, and product, B&M store and online–offline integration are three themes under store aspects. Furthermore, extreme reviews focus more on customer service, whereas lukewarm reviews focus more on store aspects.Practical implicationsKnowledgeable staff, managers and online–offline integration are instrumental in creating superior retail quality. Sporting goods stores should enhance hedonic and social values for consumers in order to ward off online competitions.Originality/valueThis study explored retail quality dimensions that are pertinent to sporting goods retailing utilizing text mining methods. This study to certain extent cross-validated the existing retailing literature that is developed on alternative methods.

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“…She found that reviews containing topics of food toughness, chef performance and food saltiness were associated with lower ratings on taste; reviews containing topics of urge and waiter were associated with lower ratings on service. Similarly, Mao (2021) reported that star ratings of sporting goods retailers on Yelp were associated with service attributes mentioned in the reviews. For instance, the occurrence of manager is predictive of a lower rating; the occurrence of staff is predictive of a higher rating and the occurrence of product is predictive of a mediocre rating.…”

“…In the context of sporting good retail, there is evidence to show that customers derive satisfaction primarily from customization. Mao (2021) finds staff, a customization element, is more predictive of satisfaction than product, a standardization element. Based on this reasoning, a trade-off between retail quality and productivity would likely occur in sporting goods retailing.…”

The transformative impact of big data applications in sport marketing: current and future directions

“…Similarly, Mao (2020) employed Yelp reviews to understand the retail quality of sporting goods stores. Mao (2020) collected 27,793 online reviews of 1,481 stores across seven states: Arizona, Illinois, North Carolina, Nevada, Ohio, Pennsylvania and Wisconsin. This investigation used BD to cross-validate on retail literature and indicated that knowledgeable staff beyond a salesperson can provide other value-added services.…”

“…Such insights inform our understanding of loss aversion through applying volume and variety data. Similarly, Mao (2020) employed Yelp reviews to understand the retail quality of sporting goods stores. Mao (2020) collected 27,793 online reviews of 1,481 stores across seven states: Arizona, Illinois, North Carolina, Nevada, Ohio, Pennsylvania and Wisconsin.…”

“…Subsequently, they computed regression analysis to predict game attendance as a function of sentiment analysis and other control variables. Relatedly, Mao (2020) utilized a patented probabilistic topic modeling algorithm from Leximancer 4.0 to identify critical quality dimensions associated with sporting goods stores.…”