What are we talking about?
Over the past decade, online peer-to-peer platforms such as Airbnb or Uber have become some of the most prominent upbringings of the sharing economy. But what is the sharing economy? Defining the concept is quite difficult, but one may define it as “the use of technology to facilitate the exchanged access of goods or services between two or more parties” (Miller, 2018). It is fast-rising and highly popular, with 44.8 million adults in the US using the sharing economy in 2016, and 86.5 million US users expected in 2021 (Miller, 2018).
So, how can those of the 44.8 million adults in the US using the sharing economy who provide services and products make the most out of this economy? The authors of the presented research intend to answer this question.
Abrate and Viglia (2019) argue that the success of products offered on online peer-to-peer platforms is influenced by the personal reputation of the seller, which is often indicated by the sellers’ credentials. This personal reputation increases the quality of the relationship of those involved in the peer-to-peer platform and reduces uncertainty in the transaction. However, the authors argue that to date there limited research available regarding revenue optimization and personal reputation, instead of product reputation. Thus, the authors intend to close this gap in research by disclosing the effects of both personal and product reputation on revenue optimization in the sharing economy.
According to the authors, there are five main concepts involved in the revenue optimization of products and services in the sharing economy. These five concepts are:
- Shared assets, which refers the product’s physical and service characteristics
- Product reputation, which refers to online reviews shaping consumers’ perception of a product
- Personal reputation, which refers to the expertise of the seller
- Potential revenues, which refers to the revenues which could objectively be achieved from a product or service
- Achieved revenues, which refers to the actual revenues achieved from a product or service
Based on their literature review, the authors propose the following theoretical framework:
The authors test whether product and personal reputation reduce the gap between potential and achieved revenues and whether personal reputation has a stronger effect on reducing this gap.
How is it measured?
In order to test these hypotheses, the authors make use of data from Airbnb. On Airbnb, hosts can list their accommodations and rent these to guests. Here, the shared assets are the listings and their characteristics, which constrain the maximum revenues a host can achieve. The potential revenues thus depend on factors such as location and number of bedrooms. While the host tries to maximize his revenues, achieved revenues often do not match the potential revenues.
The authors take a stochastic approach to test the hypotheses and build two regression models. Without going into more detail regarding the regression model in this blog post, the authors aim at explaining the difference in potential revenues and achieved revenues with these models. They argue that the difference can be explained by reputational variables in that good reputation allows the host to outperform other hosts and thereby close the gap between potential and achieved revenues. If the host’s reputation is bad, on the other hand, the gap between potential revenues and achieved revenues increases. For both personal and product reputation, this is reflected in the regression models built by the authors.
The researchers then gathered data from the five most popular European destinations, which are Barcelona, Istanbul, London, Paris and Rome. From these cities, the researchers identified all Airbnb listings within a 2-kilometer distance from what general tourism websites determined as the main attractions of these cities. Further, the researchers set a cap at 200 listings per city.
For these listings, the researchers retrieved information on prices, availability, characteristics of the listing and reputational attributes. Personal reputation has been measured in days since registration, profile completeness and the “superhost” qualification, which is awarded by Airbnb to hosts when certain reputation requirements are satisfied. Furthermore, product reputation has been measured by professional photos of the listing and online reviews, in terms of volume and average review score.
After validating the listings, the authors were left with a sample size of 981 listings. The authors then applied their regression models.
What are the results?
The researchers found support for all three hypotheses, meaning that both product and personal reputation reduce the gap between potential and achieved revenues, but that personal reputation has a stronger effect in reducing the gap.
What can we learn from this?
The results of this research have important implications for both scholars, and managers and practitioners. For scholars, this research bridges the gap in existing literature regarding revenue optimization in the sharing economy, which has previously mostly focused on product reputation. This research also offers insights into the importance of personal reputation, stand-alone and compared to product reputation.
For managers and practitioners, this research offers statistical proof as to how to optimize revenues in the sharing economy, especially in the case of Airbnb. Hosts on Airbnb can use these insights to take measures to increase both their product and personal reputation in order to increase their revenues. Through better personal branding and building trust, hosts can increase their personal reputation and thereby reduce uncertainty in the transaction which leads to higher achieved revenues.
Finally, what are the strengths and weaknesses of this paper?
One of the strengths of this paper is that it offers a statistical approach to revenue optimization in the sharing economy. Through regression modeling based on real data from Airbnb, the authors prove the importance of both product and personal reputation.
A weakness of this paper, however, is that reputation, which is a key variable in this research, is difficult to measure. The researchers themselves acknowledge that reputation is very subjective and may not be adequately captured in this research. In future research, this issue should be tackled, possibly by validating the indicators used for assessing reputation in this study by having guests confirm or reject the host’s product and personal reputation .
Abrate, G. & Viglia, G. (2019). Personal or Product Reputation? Optimizing Revenues in the Sharing Economy. Journal of Travel Research, 58(1), 136-148.
Miller, D. (2018). What Is The Sharing Economy (and How Is It Changing Industries)? Retrieved on March 9, 2019 from https://www.thebalancesmb.com/the-sharing-economy-and-how-it-changes-industries-4172234