The following is a review of the paper “Reviews and price on online platforms: Evidence from sentiment analysis of Airbnb reviews in Boston” by Lawani et al., (2019).
With the rise of platforms such as Airbnb, that now provides access to more than five million rooms in approximately 191 countries, the power dynamic in the traditional hospitality industry has shifted (Airbnb, n.d.). What are the reasons behind the relatively recent success of these peer-to-peer platforms, with a focus on hospitality in particular? Technological advances is one explanation for the sharing economy (P2P markets) as it made the process of connecting people with each other faster and more efficient, and significantly reduced overall costs. Subsequently, it facilitated the development of reputational systems, which is considered a major influence in overcoming moral hazard and adverse selection (Horton & Zeckhauser, 2016).
The process of making a decision on what accommodation you would like to stay at, is dependent on a multitude of factors and personal preferences. The amount of bedrooms, proximity to the city center, amenities, and price to name only a few things that can be taken under consideration. But ultimately, when the guests that came before you have merely negative experiences with the accommodation, it is unlikely that you will follow their footsteps. Lawani at al. (2019) studied the relationship between content of reviews and accommodation price in Boston. While previous research mainly focused on one-dimensional ratings such as number of reviews and star rating, this paper uses reviews as a proxy for quality.
The research is focused on two main components, namely first the difference between the effect of the unidimensional ratings on price and the effect of several separate quality measures (which they constructed themselves) on the price, and secondly how quality opinions from sentiment analysis of the reviews affect price. A previous study on the effect of online reviews on sales by Hu et al. (2008) states that product reviews are one of the main indicators of quality perceived by consumers. Since research by Zervas et al. (2017) has indicated that Airbnb rentals have a negative effect on hotel revenue, they are seen as substitutes. Overall, the importance of word-of-mouth on consumer decisions has been highlighted extensively throughout multiple researches. The paper looks at the characteristics of the platform Airbnb, where hosts can determine the price of their accommodation and which services are included, while the consumers have their quality preferences and their willingness-to-pay. Guests are positively impacted by competition on the supply side, as hosts might have to lower prices or increase their quality to competitors’ standards.
Methods and main findings
Retrieved from Inside Airbnb, the researchers used a dataset of 2051 individual hosts in Boston, gathered in September 2016. This accommodation data was connected to Boston’s economic data originating from the American Community Survey, which included neighborhood variables, income measures, and education level. To overcome the unidimensionality of ratings, Lawani et al. (2019) also focus on sentiment of reviews. Furthermore, they dissect quality as a construct and develop seven other variables that each represent an aspect of quality. A sentiment analysis of reviews resulted in a score for quality for the accommodations. The remaining six quality variables are accuracy, cleanliness, check-in, communication, location, and value of the accommodation.
Among the main results from the theoretical models they find that prices for short-term accommodations are strategic complements, meaning hosts’ adopt their prices according to their competitors. Secondly, from the empirical analysis they find that the sentiment score is a better quality proxy than the rating score, since the opinions in reviews can represent more indicators of quality than a singular score. Ratings are easier to understand, while reviews provide better insights (Tsekouras, 2019). However, the six quality component variables mentioned before are better predictors of price that the sentiment score. The number of bedrooms and bathrooms as well as overall capacity are associated with higher prices. Furthermore, they found that cleanliness of the accommodation followed by accuracy of the description are the quality measures that influence price the most, which has implications for hosts that are looking to improve their competitiveness.
The researchers opt for a vertical product differentiation model to depict competition. One of the strengths of the paper is the comprehensibility of the profit-maximization framework. They denote that competition between Airbnb hosts in a city usually takes place within a certain mile radius, instead of over the whole city. In addition, the sentiment analysis is conducted on the reviews that were posted on Airbnb in 2016 only. Having a topical dataset is important for the analysis of reviews since consumers usually only read the most recent reviews. More generally, this research is one of the first to look at the relation between consumer review content and its effect on the price on a sharing economy platform.
One weakness of the study that is highlighted as well is that the conceptual model is completely tailored around traditional profit maximization. It is also possible that hosts deliberately lower their accommodation price to have more options to choose from. Additionally, the researchers show little regard for the implications and relevance for Airbnb hosts, especially on how they can directly derive value from reviews. Besides, it is to be expected that sentiment score from review content is a better indicator of quality than a unidimensional rating. A rating represents the overall impression of the accommodation, but can also reflect discontent with only one aspect of the experience, while reviews allow for a more complete image. The academic relevance of that result is therefore lower compared to the other findings.
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