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The effect of green restaurant attributes on customer satisfaction using a structural theme model in online customer reviews
Eunhye (Olivia) Park 1, *, Bongsug (Kevin) Chae 2, Junehee Kwon 3 and Woo-Hyuk Kim 4, *
Received: 20 February 2020 / Revised: 27 March 2020 / Accepted: 1 April 2020 / Published: 2 April 2020
Although green practices are increasingly being adopted in the restaurant industry, there is still little research examining the impact of green practices on customer satisfaction. This study used user-generated content of customers of green restaurants to identify various aspects of green restaurants, including perceived green restaurant practices. Our data is based on US certified green restaurants listed on Yelp. Structured text models were used to detect latent restaurant attributes from user-generated content. Using a longitudinal approach, changes in customer interest in green practices were assessed. Finally, general restaurant attributes and green attributes were used to predict customer satisfaction. This study will contribute to marketing strategies for the restaurant industry.
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The restaurant industry in the United States (US) includes more than one million operations with 15.3 million employees [1]. Given the restaurant industry’s close relationship with local communities, implementing sustainable (also known as green) restaurant practices has become an important marketing strategy for gaining competitive advantage and organizational legitimacy [2, 3]. The implementation of green practices has become an important tactic to satisfy the public’s needs related to health and environmental concerns, which in turn leads to positive brand image and positive customer attitudes [4, 5].
However, introducing green practices in a restaurant may not always be successful in creating positive feelings among customers. For example, green restaurant practices are not as visible to customers as other marketing efforts [6]. Therefore, it is difficult to determine whether these attributes are sufficiently communicated to customers or whether green attributes produce positive results [4, 7]. In other words, green practices must be addressed before they can influence customer attitudes.
Generally, green practices can inspire positive customer attitudes only when they recognize a restaurant’s efforts to implement them [8]. Popular green practices can provide additional benefits to customers by satisfying their emotional needs related to sustainable concerns and foster positive customer attitudes [9]. However, the effect of green practices on customer satisfaction may be small compared to the main characteristics of restaurants, especially among customers who are indifferent to green practices [8, 10]. Therefore, it should be considered along with the green practices perspective to assess the value of green practices compared to other restaurant attributes.
Therefore, the purpose of this study is to examine consumers’ attitudes toward green practices using machine learning. The human associative memory theory perspective is taken as a theoretical framework. This theoretical perspective suggests that customer descriptions of the product or service experience are then positively related to customer satisfaction. In this regard, the study has four objectives: (1) to identify green restaurant practices adopted and described in user-generated content (UGC), (2) to examine changes in customer attitudes toward green experiences, (3) to explore perceived effects of greenness; Experience in terms of customer satisfaction levels and finally (4) compare the customer satisfaction levels of adopted green practices with restaurant quality attributes.
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To capture customer recognition and the comparative impact of green restaurant practices on customer satisfaction levels, this study analyzes a large amount of UGC collected by Yelp.com. Online customer reviews are useful for studying whether customers recognize green practices when visiting restaurants or how such recognition affects customer satisfaction levels.
Advanced text analysis models or machine learning algorithms (also known as big data analysis techniques) are needed to process and analyze large text corpora [12]. Topic modeling is a statistical modeling technique for extracting hidden topics or themes from large text collections such as online reviews and social media data (for example, microblog posts) [13]. Among the various topic models studied in the last decade, Latent Dirichlet Allocation (LDA) has become the most popular tool for mining large text data [14]. LDA is a probabilistic topic model that assumes that each document contains a mixture of texts with different probability levels and extracts latent texts from the distribution of each text in the text corpus. In LDA, each topic is represented as a distribution of words of different expected sizes [14]. The only information that affects the discovery of hidden topics is the distribution of words in the corpus. LDA stands for Unsupervised Statistical Machine Learning, so the input data must be labeled or unlabeled. This makes the method suitable for large data analysis.
LDA is also called a meaning-generating model (Figure 1) in which the proportions of a document’s text (θ) and the probability distribution of words per topic (β) derive from a Dirichlet distribution. A text label (z) is selected from θ in each document (d) word (w). As a result, each document (or view) is represented as a mixture of k texts of different sizes, and each text is a mixture of words with different possible contributions (β) to the text. LDA receives many documents as input for this creative process.
LDA was developed in computer science, where the focus is on understanding common themes from the underlying corpus. On the other hand, social scientists and behavioral researchers often obtain additional information about documents or customer reviews. For example, a Yelp review includes a star rating, reviewer type, review date, review length, restaurant type, and location. These changes are important in hospitality and tourism research when UGC is examined. Structured topic models (STM) [15, 16] are a new probabilistic topic that incorporates covariates or additional review-level information into the text extraction process.
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Specifically, STM adds two components to the probabilistic topic model dimension, LDA: topic frequency and topic content. Subject frequency allows covariates such as observer gender and age (eg, young, old) to influence subject engagement (θ). For example, if younger people’s reviews include topics such as atmosphere and supply, while older people’s reviews focus more on staff service and food quality, researchers can predict that variation (age) affects the prevalence of topics. This means that the text fraction (θ) of the document is affected by the covariate X rather than the Dirichlet prior.
The content of the text assumes that several topics (Y) affect the words that represent each topic. For example, some words that indicate a theme (“food menu”) for a Chinese restaurant (eg, Chow Mein) may be different for an Italian restaurant (eg, Pasta Primavera). Therefore, the terms representing the same subject may be different from the covariate Y.
The restaurant industry’s environmental impact is extensive and intense, from excessive use of water, energy, and resources, to high carbon footprints produced in product manufacturing and distribution, and transportation of customers and workers [17]. Although there have been attempts to define green attributes, there is no consensus that researchers, managers, and customers can agree on [18, 19].
The green restaurant framework developed by Choi and Parsa [20] proposed three perspectives in green restaurant experience: health, environment, and lifestyle. Kwok et al. [21] proposed an alternative framework for
