Analysing online reviews to investigate customer behaviour in the sharing economy

A global-scale analysis of the sharing economy model - an AirBnB case study

The sharing economy model has changed the way in which people engage in a variety of activities, including travelling, trading, working, and lending/borrowing money. Several studies exist that aim to understand, quantify and model such phenomenon, but most such studies are geographically focused on countries in the Western World. Knowledge about the penetration and adoption of this novel market model in non-Western countries is much more limited, and almost completely lacking when it comes to emerging markets, where it was touted to bring the biggest benefits and be a game changer to uplift people economically. To close the gap, we chose Airbnb as an example of sharing economy model with worldwide market penetration, and performed a large-scale quantitative study of its penetration and adoption in seven cities in Asia, five cities in Latin America. We compared findings against seven cities in the Western World, and observed patterns to be similar across all locales, with two notable exceptions: the geographic penetration of such services, and the experience that guests travelling to such destinations shared in their reviews.

The Effects of Gender Signals and Performance in Online Product Reviews

This work quantifies the effects of signaling gender through gender specific user names, on the success of reviews written on the popular amazon.com shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed next to products. Differences in reviews, perceived-consciously or unconsciously-with respect to gender signals, can lead to crucial biases in determining what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names to select reviews where the author's likely gender can be inferred. Using reviews authored by these gender-signaling authors, we train a deep learning classifier to quantify the gendered writing style (i.e., gendered performance) of reviews written by authors who do not send clear gender signals via their user name. We contrast the effects of gender signaling and performance on the review helpfulness ratings using matching experiments. This is aimed at understanding if an advantage is to be gained by (not) signaling one's gender when posting reviews. While we find no general trend that gendered signals or performances influence overall review success, we find strong context-specific effects. For example, reviews in product categories such as Electronics or Computers are perceived as less helpful when authors signal that they are likely woman, but are received as more helpful in categories such as Beauty or Clothing. In addition to these interesting findings, we believe this general chain of tools could be deployed across various social media platforms.

A decade of systematic literature review on Airbnb: the sharing economy from a multiple stakeholder perspective

Airbnb, which launched its business in 2009, has experienced explosive growth by creating value through the sharing economy business model. The Airbnb business model helps property owners exploit underutilized assets. However, along with its rapid growth, controversies have arisen among many stakeholders, especially the traditional hotel industry, communities, and policymakers. This study reviews academic articles to pinpoint the factors involved in the relationships among Airbnb and its multiple stakeholders. The aim is to identify the benefits, drawbacks, and issues surrounding Airbnb. The analysis is based on the perspectives of six Airbnb stakeholders: guests, hosts, employees, communities, competitors, and policymakers. A variety of scholarly journals indexed in the Scopus database were reviewed, with 282 included in the final analysis. The analysis will be useful for academics, practitioners, and policymakers alike, as it summarizes the Airbnb relevant actors, identifies key factors that influence stakeholder behavior, and assesses the power and level of influence of each stakeholder. Ultimately, the study points to potential directions for future research on Airbnb.