Attitude Change Via Discomfort with Contradiction: Examining Customer Participation in Dining and Yelping
Ph.D. in Hospitality Management (HRTM)
Adviser: Robin B. DiPietro
Anticipated Defense Date: March 1st, 2020
Background and significance
Customers have positive/negative attitude based on their service experience. A formed behavior based on the attitude pertains to WOM/eWOM (word-of-mouth/electronic word-of-mouth). WOM defined as “informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services, and their sellers” (Westbrook 1987, p. 261). eWOM refers to “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Hennig-Thurau, Walsh, & Walsh, 2003, p. 39). In restaurant settings, WOM and eWOM are normally reflected in customers’ opinions based on their dining experience as well as their reviews on Yelp, which is a crowd-sourced restaurant review forum (Luca ; Zervas, 2016).
On one hand, while having a formed attitude originally is affect-based, customers over time develop cognitions that will support the formed attitude of a service (Ryffel, Wirz, Kühne, ; Wirth, 2014). For example, if a person had a great affection from the first visit to a restaurant, this person will stick with such positive WOM, even if the later experience is not quite as satisfactory. If customers reviewed some positive eWOM related to a restaurant on Yelp, they will unconsciously form a positive attitude based on other users’ reviews, and expect a promising service or food when they dine in that restaurant.
On the other hand, while customers cannot change the originally formed past behavior, they often change their current attitude as it is an easier route to stay consistent with their past behavior (Festinger, 1962). For example, if a person rated a restaurant 4 star (out of 5 stars) on Yelp, this person will be writing his/her reviews relatively positive (Fogel & Zachariah, 2017). The theory of cognitive consistency (Redondo & Puelles, 2017) provided reasons for why people are in need of a quick remedy to posit the inconsistency between their attitude and behavior (Tsao, Hsieh, Shih, & Lin, 2015). The inconsistency between attitude and behavior is defined as cognitive dissonance (Festinger, 1962).
Further, the cognitive consistency theory pointed out dissonance between individuals’ perceived actions and attitudes will create an emotional discomfort that could from a unhealthy state of psychological wellbeing (Albarracin ; Shavitt, 2018; Elliot ; Devine, 1994; Morvan ; O’Connor, 2017; Pugh, Groth, & Hennig-Thurau, 2011). Therefore, in order to avoid such dissonance, individuals often use three psychological paradigms illustrated below to change attitude via discomfort with contradiction.
(1) Adding Consonant
The induced compliance paradigm (Festiger & Carlsmith, 1959) indicate customers change their attitude as they know it is easier than changing their past behavior. For example, once customers originally had a positive experience (IV1)/rated a restaurant service positively on Yelp (IV2: rating scale on 1 to 5 magnitude), their WOM (DV1)/text-based eWOM(DV2) will follow the given experience/rating. If the former experience/rating was positive, their current reviews will tend to be positive; however, if the former experience/rating was negative, customers’ follow-up WOM/eWOM will also be negative (Steward, Narus, ; Roehm, 2018). In these cases, we assume that the former experience will influence the current WOM, and a customers’ initial rating on Yelp will match with text-based eWOM as customers have a tendency to be consistent in their behaviors and attitude in pursuance of avoiding cognitive dissonance.
(2) Subtracting Dissonant Cognitions
Free-choice paradigm (Brehm, 1956) point out that when individuals make a choice, the positive aspects of the rejected alternatives and the negative aspects of the chosen alternatives spurs dissonance between attitudes and behaviors. This is because, when individual customers choose a product/service over another, they will attach more positive attributes to the chosen product/service either by ignoring relative information from the experience or solely justifying their experience based on selective memory and interpretation (Vinson, Dale, & Jones, 2016). For example, when customers decide to dine out, they will compare several restaurants and choose one over the other based on their preferences. After choosing the particular restaurant (IV3: Did you choose a restaurant against another one? Yes vs. No), customers will start emphasizing positive WOM (DV3) on that chosen restaurant and maximizing negative features from those unchosen restaurants. This also is the case when customers writing their service reviews on Yelp. For example, customers who are rating and writing their service reviews on Yelp by comparing one restaurant versus the other (IV4: Yes vs. No), they will provide more positive eWOM (DV4) on the chosen restaurant (Steward, Narus, & Roehm, 2018). Both situations suggest how customers avoid the psychological discomfort and change their attitude in order to remain the consistency with their behavior (Agarwal, Yadav, & Malik, 2018).
(3) Minimizing The Importance of the Dissonant Actions
In addition, effort justification paradigm (Aronson & Mills, 1959) propose that the more unpleasant the attempt to attain a desired goal, the more people will acknowledge the goal once it is achieved. For example, when customers are lining up a queue for a well-known restaurant (IV5: Yes vs. No), even though the line will result in slow service delivery, they would still provide positive WOM (DV5) once they obtained the food they waited for. In this case, the customers who are posting their eWOM (DV6) on Yelp will be still positive about the service in general, since they already know the waiting time will be longer in well-known restaurants (IV6: Yes vs. No). Thus, once they got what they want, their eWOM on Yelp will be positive as a way to justify their desirability of the obtained food.
Noted that a customer’s WOM will affect another customer’s assumption about the service/product in a restaurant as well as a customer’s eWOM on Yelp could affect other customers’ perceptions related to the particular restaurant, both customers’ WOM and eWOM should be concisely analyzed as it might have a negative effect on a restaurant’s business if not objectively evaluated (Pentina, Bailey, & Zhang, 2018).
Therefore, the study is designed to examine how customers’ service attitude change via discomfort when they evaluating a restaurant service that is not consistent with their attitude or expectation. Specifically, the research aims to explore how customers change their attitude when their WOM is not match with their original expectations after dine in a restaurant; and how customers weigh their online review ratings on Yelp and justify their eWOM accordingly. By studying this, the current research will provide insights on explaining the cognitive processing of customers’ WOM and eWOM comprehensively, which can not only benefit restaurant service providers by analyzing customer WOM but also give suggestions to customers on referring to online restaurant reviews from other customers wisely.
Project description, design and approach
This research first will utilize the techniques in the fields of machine learning and natural language processing to effectively extract dimensions of customer eWOM context. Based on the large sample of online reviews collected, the primary contribution of this research is the extraction of potential dimensions that affecting customers’ dissonance between attitude and behavior. The principle of extracting dimensions from online reviews will be conducted by the principal component analysis (PCA) that shrinks the number of random variables and keeps the highly characterized dimensions (Anderson, 2008). As follows, the Latent dirichlet allocation (LDA) will be conducted. LDA is a model for discovering the abstract “theme/topics” that occur in a collection of texts, which enables the discovery of underlying theme/topics from a massive amount of unstructured text data. This method will quickly discover the mixture of topics (i.e., positive or negative words that influencing customers ratings) from online Yelp reviews. By doing this, a series of specific topics in eWOM will be captured, including identifying an optimal number of dimensions (whether the eWOM is negative or positive), and assessing how identified topic dimensions match with the rantings (Blei, Andrew, ; Michael, 2003). The target population is consumers, particularly in the restaurant sector. Our empirical setting is the consumer review service panel Yelp, which is one of the most influential online review communities for collecting restaurant reviews. The data collection process will run until we have in excess of 250,000 Yelp reviews, which will possibly last for 3 months. Once the data is collected, we will identify and collect all required information in each eWOM and rating.
After extracting the main topics related to eWOM from the text-based analysis, the study will conduct scenario-based experiments for describing three different types of dissonance situations happening in real restaurant dining settings (adding consonant, subtracting dissonant cognitions, and minimizing the importance of the dissonant ones). By conducting experiments, we will be able to control variables and only looking at the relationship between independent variables and dependent variables. By conducting a survey-based experience, the researcher should be able to see how customers WOM reflected in three different experimental conditions, which will strengthen the argument in explaining different dissonance effects between customers’ attitude and behavior in actual restaurant settings. For each scenario, 100 participants are required, therefore, in total 300 valid responses are plan to be collected via the online consumer survey platform — Amazon Murk. The data collecting period is expected to last for 3 weeks.
Focus group interview
Estimating 10 customers who are having the experience of Yelping the restaurant they dined in and 10 customers who have dined in a well-known restaurants after a long queue will be recruited for the focus group interview. The interview will basically ask three different attitude change paradigms with open-ended questions. The assessment question will include the topics selected from the text-based data, and survey question from the experimental design. The study protocol will be ensured by USC Institutional Review Board. Data collectors who will participate in recruiting customers will cover customers with a variety of demographic factors. No data will be collected that could identify personal information. The validation of the text-based data and survey questions will be reconfirmed by conducting the focus group interviews with customers, and will provide more constructive findings to the study.
The anticipated outcome is to confirm the customers’ attitude change in justifying their behavior in online review circumstances. Based on the incongruity between psychological discomfort and aversive outcomes, the research will prove how customers’ resolve the dissonance by adding a positive consonant, by subtracting dissonant cognitions, or minimizing the importance of the dissonant ones. Therefore, these results will benefit service providers to see how customers self-justify their attitude change and help them to have better solutions to reply to the comments or improve the service that can minimize customer dissonance. Furthermore, other customers who rely on the existing reviews for selecting a restaurant to dine in can also know how to objectively read other customers’ reviews. The pilot study of text-data and experiment based results will be developed as a conference paper for ICHRIE (International Council on Hotel, Restaurant and Institutional Education will be held on July, 2019: The most recognized conference in hospitality and tourism industry) will be a target conference. Once the presenter gathers valuable feedback from the conference presentation, the manuscript will be developed for submitting to IJHM (International Journal of Hospitality Management will be submitted on May, 2019, which is the top ranked journal in hospitality and tourism filed).
Significance of this funding to graduate experience
The funding contributes to the career of researcher’s growth and experience. The funded research utilizes researcher’s expertise in consumer behavior and restaurant industry, which lay the foundation for future research endeavors. In addition, with the amount of the funding, it allows the researcher to commit more effort into the research with needed financial support. The lacking statistical ability will be compensated with the funding for the researcher to collaborate with someone who has expertise in big data mining, thus will support the researcher in learning more knowledge that cross scientific disciplines. The funding opportunity will help the researcher disseminate the work to professional conferences, and represent the university of South Carolina well. The final manuscript submitted to the top journal will benefit further readers in related areas and contribute to the existing literature.
Table 1. Time table for the study
IRB for Experimental design
IRB for Focus group
Focus group study
Agarwal, M., Yadav, R., ; Malik, S. (2018). Cognitive Dissonance and Mitigation Strategies: The Restaurant Industry. Int. J. Adv. Multidiscip. Res, 5(6), 38-44.
Albarracin, D., ; Shavitt, S. (2018). Attitudes and attitude change. Annual review of psychology, 69.
Anderson, C. (2008). The long Tail: Why the future of business is selling less of more.New York: Hyperion Books.
Aronson, E., ; Mills, J. (1959). The effect of severity of initiation on liking for a group. The Journal of Abnormal and Social Psychology, 59(2), 177.
Blei, D. M., Andrew, Y. N. G., ; Michael, I. J. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
Brehm, J. W. (1956). Postdecision changes in the desirability of alternatives. The Journal of Abnormal and Social Psychology, 52(3), 384.
Elliot, A. J., ; Devine, P. G. (1994). On the motivational nature of cognitive dissonance: Dissonance as psychological discomfort. Journal of personality and social psychology, 67(3), 382.
Festinger, L. (1962). A theory of cognitive dissonance (Vol. 2). Stanford university press.
Festinger, L., ; Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. The journal of abnormal and social psychology, 58(2), 203.
Fogel, J., ; Zachariah, S. (2017). Intentions to use the yelp review website and purchase behavior after reading reviews. Journal of theoretical and applied electronic commerce research, 12(1), 53-67.
Hennig-Thurau, T., Walsh, G., ; Walsh, G. (2003). Electronic word-of-mouth: Motives for and consequences of reading customer articulations on the Internet. International journal of electronic commerce, 8(2), 51-74.
Luca, M., ; Zervas, G. (2016). Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Science, 62(12), 3412-3427.
Morvan, C., ; O’Connor, A. (2017). A Theory of Cognitive Dissonance. CRC Press.
Pentina, I., Bailey, A. A., & Zhang, L. (2018). Exploring effects of source similarity, message valence, and receiver regulatory focus on yelp review persuasiveness and purchase intentions. Journal of Marketing Communications, 24(2), 125-145.
Pugh, S. D., Groth, M., & Hennig-Thurau, T. (2011). Willing and able to fake emotions: A closer examination of the link between emotional dissonance and employee well-being. Journal of Applied Psychology, 96(2), 377.
Redondo, I., & Puelles, M. (2017). The connection between environmental attitude–behavior gap and other individual inconsistencies: a call for strengthening self-control. International Research in Geographical and Environmental Education, 26(2), 107-120.
Ryffel, F. A., Wirz, D. S., Kühne, R., & Wirth, W. (2014). How emotional media reports influence attitude formation and change: The interplay of attitude base, attitude certainty, and persuasion. Media Psychology, 17(4), 397-419.
Steward, M. D., Narus, J. A., & Roehm, M. L. (2018). An exploratory study of business-to-business online customer reviews: external online professional communities and internal vendor scorecards. Journal of the Academy of Marketing Science, 46(2), 173-189.
Tsao, W. C., Hsieh, M. T., Shih, L. W., & Lin, T. M. (2015). Compliance with eWOM: The influence of hotel reviews on booking intention from the perspective of consumer conformity. International Journal of Hospitality Management, 46, 99-111.
Vinson, D.W., Dale, R., & Jones, M. (2016). Decision contamination in the wild: Sequential Dependencies in Yelp review ratings. In D. Grodner, D. Mirman, A. Papafragou, J. Trueswell, J. Nock, S. Arunachalam, S. Christie, & C. Norris (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Westbrook, Robert A. (1987), “Product/Consumption-Based Affective Responses and Postpurchase Process,” Journal of Marketing Research, 24 (3), 258–70.