2.1. Structural Topic Modeling (STM)
Advanced text analytical models or machine learning algorithms (also broadly known as big data analytics techniques) are needed to process and analyze large text corpora [12
]. Topic modeling is a statistical modeling method to extract latent topics or themes from large collections of texts, such as online reviews and social media data (e.g., microblogging posts) [13
]. Among different topic models explored over the last decade, Latent Dirichlet Allocation (LDA) has become the most popular tool for mining big text data [14
]. LDA is a probabilistic topic model assuming that each document contains a mixture of topics with different probabilistic proportions, and that latent topics can be inferred from the distribution of all the words in a text corpus. In LDA, each topic is represented as a distribution of words with different expected proportions [14
]. The only information affecting the finding of latent topics is the distribution of words in a corpus. LDA represents an unsupervised statistical machine learning approach to text analysis and thus does not require the input data to be labeled or annotated. This makes the method suitable for big data analysis.
LDA is also known as a generative model (Figure 1
), in that the document-topic proportions (θ
) and the probabilistic distribution of words over each topic (β
) are drawn from a Dirichlet prior distribution. The topic assignment (z
) is sampled from θ
per word (w
) in each document (d
). The result is that each document (or review) is represented as a mixture of k
topics in different proportions, and each topic is a mixture of words with different probabilistic contributions (β
) to the topic. LDA takes a corpus of documents as the input for this generative process.
LDA is developed in Computer Science where the focus is to understand the overall themes from a large corpus. On the other hand, social scientists and behavioral researchers often have additional information about documents or customer reviews. For example, a Yelp review provides meta-data, which include star rating, reviewer type, review date, review length, restaurant type, and location. These covariates are important in hospitality and tourism research when exploring UGC. Structural Topic Modeling (STM) [15
] is a relatively new probabilistic topic model, incorporating covariates or additional review-level information in the process of inferring topics.
Specifically, STM adds two components to the extent probabilistic topic model, LDA: topic prevalence and topic content. Topic prevalence allows covariates (X) such as the gender and age (e.g., young, old) of reviewers to influence the topic proportion (θ). For example, if reviews by young people contain topics such as atmosphere and delivery, while reviews by older people focus more on staff service and food quality, researchers can postulate that a covariate (age) affects topic prevalence. This means that the topic proportion (θ) of a document is influenced by covariate X, rather than by a Dirichlet prior.
Topic content considers that certain covariates (Y) affect the words representing each topic. For example, some words (e.g., Chow Mein) representing a topic (“food menu”) for Chinese restaurants may be different from those (e.g., Pasta Primavera) of Italian restaurants. Thus, the words representing a topic can vary by covariate Y.
2.2. Green Restaurant Attributes and Customer Perceptions
The range of environmental impacts of the restaurant industry is wide and intensive, from excessive use of water, energy, and resources to high carbon footprints made during the production and delivery of goods, and the transportation of customers and employees [17
]. Although there have been attempts to define green attributes, there is a lack of consensus upon which researchers, managers, and customers can agree [18
A green restaurant framework by Choi and Parsa [20
] suggested three perspectives in green restaurant practices: health, environmental, and social. Kwok, et al. [21
] proposed an alternative framework for green restaurants to include food-, environment-, and administration-focused green practices, based on health and environmental perspectives [20
]. The administration-focused practice in this framework measures restaurateurs’ efforts to get a green certification or to train employees. Ham and Lee [22
] outlined eight categories of green practices (i.e., water efficiency/conservation, waste reduction and recycling, sustainable furnishings, building materials or resources, use of healthy/sustainable food, energy, disposables, chemical and pollution reduction, and organizational green practices) to evaluate restaurants’ sustainability practices. Also, Chen, et al. [23
] developed the Green Restaurant Service Quality scale (GRSERV scale) by conducting an extensive review of the previous literature on green restaurants and service quality and by performing in-depth interviews with experts in the field.
Grounded in the green restaurant framework, previous studies used predetermined measurements to measure green restaurant customer perceptions [8
]. Following this self-report method, however, it is difficult to examine the noticeability of green practices as green attributes are already present in the measurements. To overcome this issue, this study chose free-recalled texts written by customers who actually visited the green restaurants.
2.3. Factors Influencing Customer Perception of Green Practices
Customers who are experiencing the same product or service may pay attention to different aspects of the product/service and respond differently depending on their personal interests [25
]. In the green restaurant context, it is plausible that customers who experienced “green practices” may have different degrees of interest or recognition for their experiences, depending on their personal values in relation to green practices. Previous studies empirically supported that customers who are conscious about green issues are more likely to perceive green practices as well to have more behavioral intention, such as revisit intention and word-of-mouth (WOM) [4
Involvement is defined as the level of psychological link between a stimulus product/purchase and an individual [25
]. Customer involvement is dependent upon intrinsic factors, such as the individual customer’s traits and values [28
], and this serves as a major motivator to comprehend certain information and drive explicit behaviors [25
]. For instance, Cameron [31
] proposed the role of involvement in information processing, arguing that involvement in a particular stimulus can increase people’s attention to trigger cognitive processing of the corresponding stimulus. Therefore, people with a high level of involvement in the particular attribute are more likely to process the particular stimulus among numerous encountered stimuli [32
]. The fact that customers have become more knowledgeable and/or conscious of health or environmental issues demonstrates that customers’ personal interest in green practices has increased [34
]. With enhanced customer interest in green practices, such practices may be perceived as more important, and thus, more customers may pay attention to green practices in restaurants.
In addition to customers’ personal interest in the products, situational factors play an important role in customer involvement [28
]. Accordingly, external/situational factors (e.g., physical environment or product information) are other determinants for the customer comprehension process and behaviors [25
]. As green practices have become increasingly considered a core activity, many restaurateurs have implemented such practices [22
]. In turn, it is more likely that customers come to recognize a restaurant’s efforts to implement green practices.
Based on the extant literature, this study suggests that customers’ involvement can be influenced by situational factors (e.g., green practice implementation) and/or customers’ personal interest in green practices. Also, customers with high green practice involvement may be inclined to focus on the related information, which ultimately influences customers’ recognition of green practices.