Potential for well-being in peer-to-peer sharing economy: a systematic review and modeling

Humans pursue well-being in their lives. Well-being encompasses positive emotions, life satisfaction, healthy functioning, meaning of life, and self-growth. In ancient times, sharing was an intimate behavior that consolidated communities and led to an ideal life, which refers to well-being in the present. Sharing economy sheds light on a novel business model in which people can develop their well-being while sharing resources/goods and services among strangers. However, prior research shows that users participate in the sharing economy because of self-interest rather than social interaction or sustainability. Sustainable well-being is important not only for us but also for our communities and our planet. This study investigates how the components of well-being can be built into a sharing economy and foster users to pursue it in the long-term. It reviews and synthesizes prior studies to (1) elucidate the components of well-being in the peer-to-peer (P2P) sharing economy, (2) model the sharing ecosystem with components of well-being from a global perspective, and (3) discuss the design solutions for a P2P sharing platform to facilitate well-being. Furthermore, it provides examples of practices to illustrate the proposed model. We believe this study not only motivates platformers to consider users’ well-being but also promotes sustainable functioning of the sharing ecosystem

How Airbnb Conveys Social and Economic Value through User Representation

New platforms for renting and sharing among private individuals are emerging in today’s e-commerce landscape. Airbnb can be regarded as a representative of many such platforms. Such accommodation rental commonly implies shared usage where both host and guest occupy a space at the same time, involving social interactions that can provide additional value. Drawing on social reward theory, this paper proposes a research model that links the guest’s intention to book to the host’s user representa-tion via the pathways of social and economic value. We propose a design to evaluate our research model by means of a scenario-based online experiment, including the common elements of user repre-sentation (1) text reviews, (2) profile information (e.g., occupation, hobbies and interests), (3) star rating, and (4) the listing price. With this, we expect to contribute to a better understanding of the driving factors behind guests’ booking decisions in accommodation sharing

When Do People Trust Their Social Groups?

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals' general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups. By surveying 6,383 Facebook Groups users about their trust attitudes and examining aggregated behavioral and demographic data for these individuals, we show that (1) an individual's propensity to trust is associated with how they trust their groups, (2) smaller, closed, older, more exclusive, or more homogeneous groups are trusted more, and (3) a group's overall friendship-network structure and an individual's position within that structure can also predict trust. Last, we demonstrate how group trust predicts outcomes at both individual and group level such as the formation of new friendship ties.Comment: CHI 201

Understanding the Platform Economy: Signals, Trust, and Social Interaction

Two-sided markets are gaining increasing importance. Examples include accommodation and car sharing, resale, shared mobility, crowd work, and many more. As these businesses rely on transactions among users, central aspects to virtually all platforms are the creation and maintenance of trust. While research has considered effects of trust-building on diverse platforms in isolation, the overall platform landscape has received much less attention. However, cross-platform comparison is important since platforms vary in their degree of social interaction, which, as we demonstrate in this paper, determines the adequacy and use of different trust mechanisms. Based on actual market data, we examine the mechanisms platforms employ and how frequent users rely on them. We contrast this view against survey data on users’ perceptions of the context-specific importance of these trust-building tools. Our findings provide robust evidence for our reasoning on the relation between platforms’ degree of social interaction and the associated expressive trust cues

The effects of facial attractiveness and trustworthiness in online peer-to-peer markets

Online peer-to-peer markets, such as Airbnb, often include profile photos of sellers to reduce anonymity. Ert, Fleischer, and Magen (2016) found that more trustworthy-looking, but not more attractive-looking, Airbnb hosts from Stockholm charge higher prices for similar apartments. This suggests that people are willing to pay more for a night in an apartment if the host looks trustworthy. Here, we present a pre-registered replication testing how photo-based impressions of hosts’ attractiveness and trustworthiness influence rental prices. We extend previous investigations by (a) controlling for additional features related to price (e.g., the apartment’s location value), (b) testing for an influence of other host features, such as race and facial expression, and (c) analyzing a substantially larger sample of apartments. An analysis of 1,020 listings in New York City showed that more attractive-looking, but not more trustworthy-looking, hosts charge higher prices for their apartments. Compared to White hosts, Black (but not Asian) hosts charge lower prices for their apartments. Hosts who smile more intensely in their profile photo charge higher prices. Our results support the general conclusion that people rely on profile photos in online markets, though we find that attractiveness is more important than trustworthiness. Keywords: first impressions, peer-to-peer markets, trustworthiness, attractivenes

Investigando a Interação Anfitrião-Convidadono Airbnb

This study aims to examine the reviews of guests who had Airbnb experience in Mexico City, Athens, and Tokyo in terms of host-guest interaction. In this way, we intend to interpret guest's experience, satisfaction, recommend and revisit intention phenomenon in terms of depth and breadth of host's sincerity. We adopt an interpretive approach and a mixed method. That is, the study combines topic extracting technic by employing online secondary big data and explicating emerging themes and patterns. The paper highlights firstly sincerity of communication in sentences with host name emphasized. On the other hand, the host's responsive and communicative nature improves friendliness over time, which affects experience, satisfaction, recommend and revisit. Finally, Airbnb hosts are intermediator of local culture. In our research, a lexicon specific to our research topic (guest-host communication) was created by using topic extraction, Tf*Idf and named entity extraction techniques. The created lexicon can be used in other studies in this field. Furthermore, we conclude that using hosts’ name in reviews by guest an important attribute of interaction. Findings from this study provides important managerial implications for hospitality industry and Airbnb hosts who attempt to interact with guest accurately. Thence the study suggests a communication should enclose diverse and accurate information, host should pay attention to be friendly and communicative, respond as quickly as possible

Exploring host-guest interaction on Airbnb

This study aims to examine the reviews of guests who had Airbnb experience in Mexico City, Athens, and Tokyo in terms of host-guest interaction. In this way, we intend to interpret guest's experience, satisfaction, recommend and revisit intention phenomenon in terms of depth and breadth of host's sincerity.We adopt an interpretive approach and a mixed method. That is, the study combines topic extracting technic by employing online secondary big data and explicating emerging themes and patterns. The paper highlights firstly sincerity of communication in sentences with host name emphasized. On the other hand, the host's responsive and communicative nature improves friendliness over time, which affects experience, satisfaction, recommend and revisit. Finally, Airbnb hosts are intermediator of local culture.In our research, a lexicon specific to our research topic (guest-host communication) was created by using topic extraction, Tf*Idf and named entity extraction techniques. The created lexicon can be used in other studies in this field. Furthermore, we conclude that using hosts’ name in reviews by guest an important attribute of interaction.Findings from this study provides important managerial implications for hospitality industry and Airbnb hosts who attempt to interact with guest accurately. Thence the study suggests a communication should enclose diverse and accurate information, host should pay attention to be friendly and communicative, respond as quickly as possibl