To dine or not to dine: or in this case – where? Before the advent of digital technology, people traditionally relied on recommendations from friends and family to help select the restaurants and hotels worthy of their patronage. Now, however, the plugged-in generation of the internet can turn to online review websites, with access to recommendations from a much wider group of people with different experiences and opinions of featured restaurants, hotels, and destinations. Dining recommendations from a potential client’s social circle still impact that person’s subsequent restaurant choices. However, where a hungry punter picks to eat is also increasingly influenced by online review platforms.
Technology has enabled an abundance of easily accessible information for the all-important decision of where to book a table, but it’s still not clear which factors influence the specific choice of review websites used for dining decisions. Prior research studies have examined factors such as the motivation of people writing reviews and the kinds of platforms where people prefer to submit reviews. There has been a distinct lack of scientific study into reader attributes and perspectives for restaurant review websites, and the extent to which people adopt specific websites (such as Yelp or Zomato) when deciding which restaurants to visit and establishments to avoid.
PREDICTING INTENTIONS TO VISIT RESTAURANTS
Research from UCF Rosen College is set to change this dearth of insight into restaurant review websites. Dr. Juhee Kang and her collaborator designed a predictive model that has a practical application for the hospitality industry. The model was used to better understand the factors and relationships influencing a customer’s attitude towards a restaurant review website, and the impact of the selected website on subsequent dining choices. The team used the well-established technology acceptance model (TAM) as the theoretical foundation for their research. Extending the TAM model, they incorporated a number of additional factors that could offer greater explanatory power regarding factors that influence a user’s process of accepting a restaurant review website. Specifically, the research collaborators were interested in finding out the extent to which social circle (subjective norm) and website experience would influence a user’s acceptance of a specific technology, and whether this in turn would impact the user’s intention to visit a restaurant. An additional objective for the research was to understand whether there might be differences in the proposed relationships within the model, for users of different restaurant review websites. Following a review of prior research of the factors impacting a user’s acceptance of a review website, the research team incorporated four predictors and three consumer behavior constructs into a structural model.
In addition to identifying the seven constructs in the model, the research team also set out to test expected relationships between these factors based on the following hypotheses:
- Trusted opinions of a customer’s social circle – valued friends, colleagues, or family members – the ‘subjective norm’ in the model, would influence whether a customer perceived a specific website to be useful.
- The opinion of the customer’s social circle was further expected to influence a customer’s attitude towards a restaurant review website.
- A customer was expected to have a more positive attitude towards a restaurant review website if it was perceived to be useful.
- A review website with perceived ease of use was proposed to be viewed as more useful.
- The customer’s attitude was also hypothesized to be positively influenced by the overall experience of using a restaurant review website.
- A customer’s positive attitude towards a restaurant review website positively influences a customer’s intention to use that specific restaurant review website.
- The user’s intention to use a restaurant review website positively influences the user’s intention to visit the restaurant listed on the review site.
NOVEL METHODS
Using Qualtrics, an online questionnaire software, Dr. Kang and her collaborator developed a questionnaire to assess the constructs of their model. The questionnaire was hosted on Amazon’s Mechanical Turk (M-Turk), an online platform that can be used to collect data from research participants. M-Turk data has been shown to produce high-quality results that are as reliable as those gathered from data collection using traditional methods.
The target group of participants were people from the United States who use restaurant review websites to make dining decisions. Out of a total of 419 questionnaires completed on M-Turk, 364 met the criteria for inclusion in the final data analysis. Out of this group, most of the respondents (56.5%) were female, with the predominant age group of respondents being 25-34 years old (39.3% of the group), and Caucasian (78.3%). Close to half of the group (45.6%) had a university degree. The majority of research participants (69.8%) who completed the questionnaires indicated that they used Yelp for their restaurant search.
PREDICTING CUSTOMER CHOICE
Once the researchers had the information collected – what did they do? First of all, the researchers were interested in testing their model for Yelp versus other restaurant review websites, and so the respondents were split into two groups accordingly.
The data was analyzed using statistical analysis and structural equation modelling software packages to test the model’s reliability. Using confirmatory factor analysis (CFA), the researchers tested whether the data collected fitted with the model they had developed. Were the relationships described in the model borne out by the data itself? A complex data analysis of the relationships between the different factors was undertaken, and the results revealed many subtle influences that are useful to understand when marketing a restaurant through a restaurant review website. Nearly all the hypotheses for the factors and proposed relationships within the model were found to be statistically significant, meaning that these relationships did not occur by chance. The only exceptions were the hypotheses for ease of use affecting a customer’s attitude towards and intention to use a restaurant review website, which was not confirmed. Ease of use did, however, have a significant indirect effect on attitude for Yelp users but not for users of other review websites. Additionally, perceived usefulness mediated ease of use in that the more user-friendly the website was, the more useful a user found it, which in turn influenced attitude. Perceived usefulness was the most important predictor of a user’s attitude to a restaurant review website.
WHICH PLATFORM?
Restaurants can clearly benefit when customers have a positive attitude towards a particular review website on which the restaurant is represented. The relevance of these findings for the hospitality industry is that it’s important for restaurants to carefully choose the correct platform to engage with customers. A review website which is perceived to be useful is likely to strongly influence the customer’s attitude towards that website. Additionally, the relevant opinions of a customer’s social circle also influence attitude towards a review website; the social circle impact is further enhanced if the website is popular and well known, as is the case with Yelp. The study did show subtle differences between acceptance of Yelp and other restaurant review websites. For example, Yelp users seem to be more forgiving of some of the issues experienced when using the website because their social circle has a high opinion of Yelp. Given the popularity of Yelp, other restaurant review websites would need to design a user-friendly website experience, to overcome the other influences propelling customers towards Yelp.
In the final analysis, all these predictors influencing whether a customer intends to use a website (i.e., attitude, usefulness, subjective norm, and online review website experience) ultimately impact whether a customer will visit a restaurant. Well-established websites with strong images and brands, that are well-regarded in a customer’s mind, play an important part in enticing customers towards a dining experience at a restaurant. Dr. Kang and her collaborators have demonstrated that in an increasingly digital world, restaurants and diners alike benefit from well-designed restaurant review websites.
