The Importance of Platform Producers’ Reputation Signals and Product Type on Product Performance in Peer-to-Peer Platforms

On two-sided peer-to-peer platforms there exists a supply side (producers) and a demand side (consumers). Platform owners provide the platforms that assist in efficiently matching producers and consumers and an infrastructure that producers can take advantage of to signal quality to consumers. This study examines the effects of producer signals on product performance in the context of Airbnb, a peer-to-peer home sharing platform. Adjusting for producers with multiple listings, the analysis uses 77,445 listings from the platform to produce regression models which tests whether signals are positively related to product performance and if the relationship between producer signals and product performance is moderated by product type. Results show that while producer signals are important to product performance, there is minimal support for the assumption that signals vary by product type. Results also show that certain product attributes may be more important than producer signals in some contexts. Based on these findings, business and theoretical implications are discussed as well as directions for future research

The Relationship between Motivation to Use Airbnb and Guests’ Repurchase Intention: Moderating Effect of Consideration Set

With the rise of the peer-to-peer sharing economy, Airbnb has become the leading platform as an online marketplace that allows homeowners to contract short-term leases or rents to tourists. It has expanded rapidly around the globe. This study comprehensively reviews previous literature with respect to the marketing of Airbnb. The study then summarized and categorized 12 motivators linked as causative agents for guests choosing Airbnb. Using the push-pull theory, along with research on the consideration set, this study examines the relationships between Airbnb motivators and guests’ repurchase intention. This study further examines how the consideration set moderates these relationships. Seventy-eight Airbnb guests were surveyed using a paper-based survey questionnaire in a pilot study. It shows that 13 factors were identified in principal component analysis. In the main study, a sample of 397 complete usable surveys were collected from an online platform. Supporting predictions, all motivators significantly predict repurchase intention. However, contrary to predictions, consideration set did not moderate the relationship between motivators and repurchase intention. The study’s theoretical and practical implications are discussed

Airbnb customer satisfaction through online reviews

With the development and better access to the Internet, mobile devices and social media, people began to post online their opinions and reviews of products and services. These comments influence new customer buying decisions and qualify companies to gain superior insight into their customers’ experience and satisfaction. Thus, it has become essential for companies to adopt methods capable of analyzing this information and extracting its value in order to better serve their customers’ unmet needs. The area of tourism and hospitality was one of the most affected by this trend. For this reason, this study will focus on the reviews of an online platform, Airbnb, so that it also studies the technological disruption in the mentioned industry. This new method of home-sharing has gained more and more followers for its advantages and differences compared to common hotels, which has triggered increasing researcher. Airbnb’s guest reviews describe each guest’s experiences (the positive and negative aspects of their stay) and will be studied through Text Mining. This consists of several methods capable of analyzing large amounts of unstructured information such as Big Data, in order to better understand overall customer satisfaction, including the factors that will influence it. Results show that distinct dimensions are valued by guests and they are different in different areas of Sintra.Com o desenvolvimento e maior acesso à Internet, dispositivos móveis e redes sociais, as pessoas começaram a publicar online as suas opiniões e avaliações de produtos e serviços. Estes comentários influenciam as decisões de compra de novos clientes e permitem às empresas obter um maior conhecimento sobre a experiência e satisfação dos seus clientes. Assim, tornou-se imprescindível para as estas, adotarem métodos capazes de analisar esta informação e extrair valor da mesma de modo a conseguirem atender de forma mais ajustada às necessidades dos seus clientes. A área da hospitalidade foi uma das mais afetadas por esta tendência. Por esse motivo, este estudo vai ser focado nas reviews de uma plataforma online, o Airbnb, juntando assim também uma disrupção tecnológica desta mesma área. Este novo método de alojamento partilhado tem ganho cada mais seguidores pelas suas vantagens e diferenças em relação a hotéis mais comuns, mas também tem sido um assunto cada vez mais estudado por investigadores. Os comentários estudados do Airbnb descrevem as experiências de cada hóspede relativamente ao alojamento onde permaneceram e são estudados através de Text Mining. Este consiste em vários métodos capazes de analisar grandes volumes de informação não estruturados como Big data para consequentemente compreender melhor a satisfação geral dos clientes, nomeadamente os fatores que a vão influenciar. Os resultados mostram que existem várias dimensões valorizadas e diferentes para as zonas estudadas em Sintra

Redliking: When Redlining Goes Online

Airbnb\u27s structure, design, and algorithm create a website architecture that allows user discrimination to prevent minority hosts from realizing the same economic benefits from short-term rental platforms as White hosts, a phenomenon this Article refers to as redliking. For hosts with an unused home, a spare room, or an extra couch, Airbnb provides an opportunity to create new income streams and increase wealth. Airbnb encourages prospective guests to view host photographs, names, and personal information when considering potential accommodations, thereby inviting bias, both implicit and overt, to permeate transactions. This bias has financial consequences. Empirical research on host earning rates found that White hosts earn significantly more than minorities, even when controlling for location, size, and amenities. Airbnb\u27s algorithm augments the effects and propensity of individual user bias, creating a system wherein allegedly race-neutral variables serve as proxies for discrimination. Contemporary redliking perpetuates historic inequality related to housing wealth. In the early twentieth century, redlining maps were used to justify withholding investments from Black communities. Today, redliking continues the practice of directing wealth to White communities, reinforces systemic real property barriers by depriving minority hosts of important revenue streams, and exacerbates the racial wealth gap. This Article examines the liability of Airbnb and similar websites for discrimination experienced by minority short-term rental hosts. The ability of the Fair Housing Act and Civil Rights Act, laws originally enacted to abolish housing discrimination and protect minority consumers, to combat redliking is complicated by the fact that sites such as Airbnb serve multiple purposes; while guests use the platform to identify and book lodging, hosts use the site to advertise available accommodations. Looking to judicial interpretation of platform liability in the context of online speech, this Article proposes two approaches - a general-function test and a fragmented function test - to determine website liability for discrimination against short-term rental hosts. Noting the limitations of the existing antidiscrimination legal framework, this Article argues that eradicating redliking requires incorporating lessons on platform design from behavioral economics as well as eliminating opportunities for website algorithms to amplify and operationalize user discrimination

Características post-covid19 de las aplicaciones de alquiler de corta duración. La flexibilidad como aspecto central de airbnb en Barcelona

The Airbnb phenomenon in Barcelona has come to occupy a central role in the local public debate. This has all been substantially altered by the global spread of COVID-19. This scenario has promoted a reformulation of the tourist rental scene, of both regular rentals and those that were also involved in the local tourism scene on an informal and/or irregu-lar basis. In this article, we use this context as a starting point in order to analyse how this adaptation of the tourist rental sector has taken place in Barcelona, in relation to a flexible interpretation of the legislation on urban leases. In order to do that, the methodology uses is basically qualitative, although with the support of a survey in a way of triangulation system of researchEl fenómeno Airbnb en Barcelona ha pasado a ocupar un papel central en el debate público local. Todo esto ha sido sustancialmente alterado por la propagación global de COVID-19.Este escenario ha promovido una reformulación del escenario del alquiler turístico, tanto de los alquileres regulares como de aquellos que también se involucraban en el escenario turístico local de manera informal y/o irregular. En este artículo, tomamos este contexto como punto de partida para analizar cómo se ha producido esta adaptación del sector del alquiler turístico en Barcelona, en relación con una interpretación flexible de la legislación sobre arrendamientos urbanos. Para ello, la metodología utilizada es básicamente cualitativa, aunque con el apoyo de una encuesta a modo de sistema de triangulación de la investigació