Disintermediation and Its Mitigation in Online Two-sided Platforms: Evidence from Airbnb

Disintermediation, where providers and customers transact bypassing an intermediary, has challenged the business model and dwindled profits of the multi-billion-dollar platform economy. Despite the platforms’ efforts to mitigate disintermediation, little is known regarding the extent of disintermediation or efficacy of the mitigation policies, largely due to unobservability of disintermediation. We tackle these challenges by designing a geo-analytic methodology to identify and quantify disintermediation by matching online Airbnb booking and offline granular mobile location data. We further leverage DiD with matching samples to causally examine the efficacy of four Airbnb policies; and finally propose a cost-and-benefit conceptual framework to interpret the findings and guide platform designs of mitigation policies. We find, for instance, a 5.4% of disintermediation in Austin, TX over Summer 2019; and Instant Bookable reduces disintermediation by 9%, with a stronger effect among the hosts without preference for long-term lease, with more repeated guests, and more hosting experience

The Structure and Evolution of Online Rating Biases in the Sharing Economy

A wave of sharing economy companies are profoundly changing the market landscape, disrupting traditional businesses alongside the social fabrics of exchange. A critical challenge to their growth, however, is that how to generate trust from online to offline transactions. Users in many online platforms rely on reputational systems such as ratings to infer quality and make decisions. However, ratings are biased by behavioral tendencies, such as homophily and power dependence. Our project examines the structure and evolution of rating biases by analyzing massive amount of platform data. Using big data techniques on leading sharing economy platforms, we identify the structure and evolution of biases, attempting to correct the tendencies in system design. We examine rating biases and their relationships to social distance among heterogeneous user populations. The coevolution of reputational systems and trust further implies long-term behavioral trends, which are critical to investigate for business growth

Exploring the effects of consumers’ trust : a predictive model for satisfying buyers’ expectations based on sellers’ behaviour in the marketplace

In recent years, Consumer-to-Consumer (C2C) marketplaces have become very popular among Internet users. However, compared to traditional Business-to-Consumer (B2C) stores, most modern C2C marketplaces are reported to be associated with stronger negative sentiments among consumers. These negative sentiments arise from the inability of sellers to meet certain buyers’ expectations and are linked to the low trust relationship among sellers and buyers in C2C marketplaces. The growth of these negative emotions might jeopardize buyers’ decisions to opt for C2C marketplaces in their future purchase intentions. In the present study, we extend the definition of trust as an emotion to cover the digital world and demonstrate the trust model currently used by most online stores. Based on the buyer’s behaviour in the C2C marketplace, we propose a conceptual framework to predict trust between the buyer and the seller. Given that C2C marketplaces are rich sources of data for trust mining and sentiment analysis, we perform text mining on Airbnb to predict the trust level in host descriptions of offered facilities. The data are acquired from the US city of Ashville, Alabama, and Manchester in the UK. The results of the analysis demonstrate that guest negative feedback in reviews are high when the description of the host’s property has the emotion of joy only. By contrast, guest negative sentiments in reviews are at a minimum when the host’s sentiment has mixed emotions (e.g., joy and fear)

Digital Discrimination in the Sharing Economy: Evidence, Policy, and Feature Analysis

Applications (apps) of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main facilitator of economic growth and shared prosperity in modern-day societies. However, recent research has revealed that the participation of minority groups in DSE activities is often hindered by different forms of bias and discrimination. Evidence of such behavior has been documented across almost all domains of DSE, including ridesharing, lodging, and freelancing. However, little is known about the under- lying design decisions of DSE systems which allow certain demographics of the market to gain unfair advantage over others. To bridge this knowledge gap, in this dissertation, we investigate the problem of digital discrimination from a software engineering point of view. To develop an in-depth understanding of the problem, we first synthesize existing evidence on digital discrimination from interdisciplinary literature. We then analyze online user feedback, available on social media channels, to assess end-users’ awareness of discrimination issues affecting their DSE apps. We then introduce a novel protocol for drafting and evaluating nondiscrimination policies (NDPs) in the DSE market. Our objective is to assist DSE developers with drafting high quality and less ambiguous NDPs. Finally, we propose and evaluate a modeling framework for representing discrimination concerns affecting popular DSE apps along with their relations (synergies and tradeoffs) to other system features and user goals. Our objective is to visualize such complex domain knowledge using formal notations that software developers can easily understand, communicate, and utilize as an integral part of their app design process. The impact of the proposed research will extend to the entire population of DSE workers, targeting the deep racial and regional disparities in the DSE market and helping people in resource-constrained communities to overcome key barriers to participation and adaptation in one of the fastest growing software ecosystems in the world

Prosumer-to-customer exchange in the sharing economy:Evidence from the P2P accommodation context

Numerous prosumers who share their spare resources have contributed significantly to sharing economy development in recent years. Existing research on the sharing economy has primarily focused on the service demand side of consumers, thus neglecting the service supply side of individual prosumers. Understanding of the service exchange between prosumers and customers in the peer-to-peer (P2P) sharing economy remains limited. Drawing on the motivation, opportunity, and ability (MOA) model and social exchange theory, we developed a conceptual framework to explore how prosumers' service attributes influence consumers in a P2P accommodation sharing context. Using 313 questionnaires and 112 paired objective data points from prosumers in one popular P2P accommodation platform (i.e., Xiaozhu.com), this research found that prosumers' economic motivation, service flexibility, and service knowledge level have distinct effects on consumers' transactional based and relational-based participation. We also found a moderating role of prosumers' shared property management on these effects

The characteristics of quality in peer-to-peer accommodation: a host´s perspective in the Algarve

O alojamento local mudou a maneira como os turistas reservam as suas férias e se envolvem com um destino. Atualmente, existem mais de 13 milhões de unidades de alojamento local disponíveis em mais de 200 países. Com o crescente número de alojamentos locais, os hóspedes passaram a escolher os alojamentos que mais garantias de qualidade ofereciam. Este estudo visa determinar as características de qualidade da oferta turística deste tipo de estabelecimentos, com base na perspetiva dos anfitriões e criar um quadro de referência que os gestores possam utilizar no futuro. Foi realizada uma extensa revisão da literatura, e discutidos os principais constructos que a análise do tema pressupunha: economia da partilha, alojamento local, cocriação de valor e o tema da gestão das experiências. Para esta pesquisa, foi utilizada uma abordagem qualitativa. Para alcançar este objetivo foram realizadas entrevistas junto de um grupo de proprietários de alojamento local. As entrevistas basearam-se em quatro temas: 1) A motivação para hospedar, 2) A definição de qualidade de acordo com os anfitriões, 3) A gestão da experiência do cliente e 4) Hospedagem como experiência. Os resultados obtidos permitem perceber as perspetivas os gestores de unidades de alojamento local têm sobre a qualidade dos serviços prestados, as dimensões que os turistas utilizam para avaliar essa mesma qualidade e a problemática da gestão da qualidade das experiências turísticas relacionadas com este tipo de alojamento.Peer-to-peer accommodation is a relatively new concept that can be seen as a byproduct of the sharing economy and it is estimated that there are over 13,5 million available properties. Currently, peer-to-peer accommodations lack recognisable labels that identify quality attributes. The purpose of this study is to determine those characteristics from the perspective of the hosts. To achieve this, a thorough investigation of the academic literature was first produced. The topics of sharing economy, peer-to-peer accommodation, value co-creation and experience management were determined to be all interconnected. The literature pointed out emerging concepts such as customer experience and value co-creation as important to the peer-to-peer accommodation market as they have had a visible impact on the way tourist consume hospitality services. Based on the literature findings, and the exploratory nature of the study, the most appropriate method of analysis available to use is qualitative. This allows a more flexible approach in determining how hosts perceive quality. Using the main themes discovered during the literature review, interviews with hosts were conducted and were guided by four themes, their motivation to host, their definition of quality, how they managed guest experience and their experience as a host. The findings from these interviews emphasised the complex nature of defining quality outright but did provide key practices and feelings that could help determine what is perceived as quality for guests. It also showed that current available management tools are not necessarily adequate for this different type of holiday accommodation provider. By combining the current academic literature and the findings of this research, a framework for new hosts can be established presenting all the major attributes of quality for a peer-to-peer accommodation

Judgments in the Sharing Economy: The Effect of User-Generated Trust and Reputation Information on Decision-Making Accuracy and Bias

The growing ecosystem of peer-to-peer enterprise – the Sharing Economy (SE) – has brought with it a substantial change in how we access and provide goods and services. Within the SE, individuals make decisions based mainly on user-generated trust and reputation information (TRI). Recent research indicates that the use of such information tends to produce a positivity bias in the perceived trustworthiness of fellow users. Across two experimental studies performed on an artificial SE accommodation platform, we test whether users’ judgments can be accurate when presented with diagnostic information relating to the quality of the profiles they see or if these overly positive perceptions persist. In study 1, we find that users are quite accurate overall (70%) at determining the quality of a profile, both when presented with full profiles or with profiles where they selected three TRI elements they considered useful for their decisionmaking. However, users tended to exhibit an “upward quality bias” when making errors. In study 2, we leveraged patterns of frequently vs. infrequently selected TRI elements to understand whether users have insights into which are more diagnostic and find that presenting frequently selected TRI elements improved users’ accuracy. Overall, our studies demonstrate that – positivity bias notwithstanding – users can be remarkably accurate in their online SE judgments

Judgments in the sharing economy: the effect of user-generated trust and reputation information on decision-making accuracy and bias

The growing ecosystem of peer-to-peer enterprise – the Sharing Economy (SE) – has brought with it a substantial change in how we access and provide goods and services. Within the SE, individuals make decisions based mainly on user-generated trust and reputation information (TRI). Recent research indicates that the use of such information tends to produce a positivity bias in the perceived trustworthiness of fellow users. Across two experimental studies performed on an artificial SE accommodation platform, we test whether users’ judgments can be accurate when presented with diagnostic information relating to the quality of the profiles they see or if these overly positive perceptions persist. In study 1, we find that users are quite accurate overall (70%) at determining the quality of a profile, both when presented with full profiles or with profiles where they selected three TRI elements they considered useful for their decision-making. However, users tended to exhibit an “upward quality bias” when making errors. In study 2, we leveraged patterns of frequently vs. infrequently selected TRI elements to understand whether users have insights into which are more diagnostic and find that presenting frequently selected TRI elements improved users’ accuracy. Overall, our studies demonstrate that – positivity bias notwithstanding – users can be remarkably accurate in their online SE judgments