The Effect of Facial Resemblance on Cooperative Behavior in the Sharing Economy

The Effect of Facial Resemblance on Cooperative Behavior in the Sharing Economy

Yajing Wang, Yunjie Calvin Xu, Xiang Ni
Copyright: © 2022 |Pages: 22
DOI: 10.4018/JGIM.315307
Article PDF Download
Open access articles are freely available for download

Abstract

In online sharing economy platforms, users often need to cooperate with strangers with minimal information. When personal photos are available on these platforms, how do users form social perceptions and consequently cooperative intention? According to the stimulus-organism-response (S-O-R) model and the kin selection theory, the authors propose that facial resemblance between two partners affects their perceptions of the cognitive trustworthiness, affective trustworthiness, attractiveness, and sociability of the partner, and that these social perceptions affect their cooperative intention. In the experiment, subjects evaluate the face of five potential partners of various levels of facial resemblance. The results support that the effect of facial resemblance on cooperative intention is mediated by social perceptions. Moreover, the transactional values and social values of the task partially moderate the effect of facial resemblance on social perceptions for female subjects. Sharing economy platforms may leverage the facial resemblance to facilitate cooperation among strangers.
Article Preview
Top

Introduction

The term sharing economy refers to the peer-to-peer sharing of access to underutilized goods and services (Huang & Lee, 2022; Zhao et al., 2020). The global e-commerce market has experienced the rapid growth of the sharing economy. For instance, according to Statista, the global revenue of ride-sharing services reached $156,176 million in 2019, and it is expected to grow 10.2% annually to reach $230,085 million by 2023 (Dadwal et al., 2020).

Matching transaction partners is a key function of the sharing economy platforms (Einav et al., 2016). A sharing economy platform needs to match a user to a partner with whom they are comfortable interacting, even when they are strangers to each other. For transactions with personal interactions, such as sharing a ride (Wang et al., 2018), a good match requires not only the objective requirements of the trip but also the type of person that the co-rider is. In the sharing economy context, a good matching function should facilitate cooperative behaviors. Cooperative behavior refers to the intention and behavior to achieve the common goal of both parties, such as participation in an online transaction with a potential partner (Axelrod & Hamilton, 1981).

In the sharing economy, social motivation is a crucial antecedent of cooperation with strangers (Bucher et al., 2016; Shiau & Chau, 2015). In traditional e-commerce, the motivation of users is often to meet transactional needs (i.e., obtaining high product and service value). However, in the context of the sharing economy, users also pursue social value, establishing social relationships with like-minded people for mutual appreciation, a sense of belonging, and companionship (Botsman & Rogers, 2010; Tussyadiah, 2016; Zhang et al., 2019). Thus, users on the sharing economy platforms may intentionally seek the social cues of others to form their social perceptions of their potential partners. Social perception refers to the perceived sociopsychological traits of others. The social perceptions of others ultimately affect one’s cooperative intention in the sharing economy.

Personal photos have been found to facilitate cooperative behaviors between strangers (Brand et al., 2012; Ert et al., 2016; Kim et al., 2020) and the formation of social connections (Hong et al., 2020; Oeldorf-Hirsch & Sundar, 2016). Personal photos might serve as important resources for users to form social perceptions of a stranger. When a user derives favorable social perceptions from a potential partner based on their personal photos, they might have a stronger cooperative intention. The mere presence of a personal photo can either enhance other users’ trust (Bente et al., 2012; Ert et al., 2016) or reduce it (Dai et al., 2018), suggesting that the characteristics of the personal photo matter.

One important characteristic of the personal photo is the facial resemblance between the two partners. Facial resemblance is defined as the similarity of one’s face to the other’s based on the look of facial features (Maloney & Dal Martello, 2006). Prior studies have found that an individual considers a user profile with photos that resemble their appearance as more trustworthy and attractive (DeBruine, 2002, 2004). However, users of the sharing economy platforms may form social perceptions other than trustworthiness and attractiveness. Furthermore, just as the development of trust can be culture-dependent (Zhao et al., 2020), the formation of social perceptions based on facial resemblance can be sensitive to culture and ethnicity. Provided that most prior studies were conducted in Western cultures (DeBruine, 2002; Krupp et al., 2008), the effects of facial resemblance need to be tested in other cultural settings.

Complete Article List

Search this Journal:
Reset
Volume 32: 1 Issue (2024)
Volume 31: 9 Issues (2023)
Volume 30: 12 Issues (2022)
Volume 29: 6 Issues (2021)
Volume 28: 4 Issues (2020)
Volume 27: 4 Issues (2019)
Volume 26: 4 Issues (2018)
Volume 25: 4 Issues (2017)
Volume 24: 4 Issues (2016)
Volume 23: 4 Issues (2015)
Volume 22: 4 Issues (2014)
Volume 21: 4 Issues (2013)
Volume 20: 4 Issues (2012)
Volume 19: 4 Issues (2011)
Volume 18: 4 Issues (2010)
Volume 17: 4 Issues (2009)
Volume 16: 4 Issues (2008)
Volume 15: 4 Issues (2007)
Volume 14: 4 Issues (2006)
Volume 13: 4 Issues (2005)
Volume 12: 4 Issues (2004)
Volume 11: 4 Issues (2003)
Volume 10: 4 Issues (2002)
Volume 9: 4 Issues (2001)
Volume 8: 4 Issues (2000)
Volume 7: 4 Issues (1999)
Volume 6: 4 Issues (1998)
Volume 5: 4 Issues (1997)
Volume 4: 4 Issues (1996)
Volume 3: 4 Issues (1995)
Volume 2: 4 Issues (1994)
Volume 1: 4 Issues (1993)
View Complete Journal Contents Listing