Why do people engage in collaborative consumption?


Collaborative consumption is a large scale trend which involves millions of users and constitutes a profitable business model for many companies to invest in (Botsman and Rogers, 2010). It is often associated with the sharing economy and takes place in organized systems or networks, in which participants conduct sharing activities in the form of renting, lending, trading, bartering, and swapping of goods, services, transportation solutions, space, or money (based on Owyang et al., 2014; Belk, 2014; Bardhi and Eckhardt, 2012; Botsman and Rogers, 2010; Chen, 2009).

Despite the rising importance of collaborative consumption, there is not much knowledge on why users engage in collaborative activities nor why many people are still reluctant to participate in this emerging trend. To address this gap, Möhlmann, in his paper “Collaborative Consumption: Determinants of Satisfaction and the Likelihood of Using a Sharing Economy Option Again” (2015), adopts a holistic approach to study the determinants of the usage of collaborative consumption services, providing empirical evidence from both business-to-consumer (B2C) and consumer-to-consumer (C2C) settings. As a matter of fact, collaborative consumption might refer to both B2C services, such as commercial car sharing, or C2C sharing in the form of redistribution markets or collaborative lifestyles (Bardhi and Eckhardt, 2012; Botsman and Rogers, 2010; Mont, 2004), such as accommodation sharing marketplaces. While nowadays users of sharing services can mainly be found among young age groups, the future generation will be growing up with this trend (Möhlmann, 2015).

Möhlmann (2015) analyzes ten factors that are expected to have an effect on the variable satisfaction with a sharing option, which itself has an effect on the likelihood of choosing a sharing option again. These ten determinants are: community belonging, cost savings, environmental impact, familiarity, internet capability, service quality, smartphone capability, trend affinity, trust, and utility (see Figure 1). The hypotheses of the paper suppose that each determinant has a positive effect on the two dependent variables, with satisfaction with a sharing option also having a positive impact on the likelihood to use a sharing option again. The empirical analysis was conducted on two different collaborative consumption services, specifically the B2C car sharing service car2go (study 1) and the C2C accommodation sharing service Airbnb (study 2). Two independent quantitative online studies were rolled out in July 2014, distributing questionnaires via a mailing list to students of the University of Hamburg (Germany) by a research laboratory.

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The findings (see Table 1) show that respondents seem to predominantly be driven by rational reasons, serving their self-benefit, when using collaborative consumption services. Users pay attention to the fact that collaborative consumption helps them to save money and that respective service is characterized by a high utility, in a way that it well substitutes a non-sharing option. In addition, familiarity with a service was found to be an important determinant, probably because it lowers transaction costs of getting to know the specifics of the sharing process (Henning-Thurau et al., 2007). Furthermore, both studies reveal the important role of trust as an essential determinant of the satisfaction with a sharing option. This is an interesting result because trust has not been analyzed in relation to other determinants in the context of collaborative consumption in quantitative studies so far (Möhlmann, 2015). Some differences are also present in the two studies, specifically, in study 1 (B2C car sharing context car2go), two additional determinants with significant effects were identified: community belonging and service quality. While in study 2 (C2C accommodation sharing context Airbnb), a relationship between the satisfaction with a sharing option and the variable likelihood of choosing a sharing option again was estimated. This relationship was not revealed in study 1.

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The main strength of this paper is that it is both academically and managerially relevant. Academically speaking, the results of this study contribute to close a research gap and hold valuable implications for researchers. Findings indicate that indeed there are many similarities among the determinants of the use of different collaborative consumption services. However, a detailed analysis might also reveal context or industry specifics, as shown in this paper. While for managers of B2C and C2C collaborative consumption services, the results of this paper offer important and relevant insights for the acquisition but also retention of customers. Managers of B2C and C2C services should adapt their market activities to respond to the fact that rational and self-centred determinants were found to be essential, including utility, cost savings, and familiarity. Furthermore, managers need to make sure that trust building measures are implemented and communicated to respective stakeholders.

This paper is also subject to a number of limitations. Firstly, even though it is true that collaborative consumption services are mainly used by a young age group, the fact that approximately 88% of the respondents were under the age of 30 does not provide true generalizability of the results. Especially considering that collaborative consumption is a growing trend that will soon involve people of any age group, a more heterogeneous sample should have been utilized. Secondly, it is likely that interrelations among determinants exist, which is something that has not been studied here. For example, it seems straightforward that determinants such as cost saving and utility, or familiarity and trend affinity might be correlated. Future research should construct a more comprehensive research model also considering such interdependencies. Thirdly, one of the most significant determinants in the analysis was utility, however, such variable showed low values of Cronbach alpha, respectively 0.57 in study 1 and 0.60 in study 2. Considering that the generally accepted cut-off is that alpha should be 0.70 or higher for a set of items to be considered a scale (Garson, 2012), the internal consistency of such variable is very poor. This undermines the reliability of the significant relationship between utility and the two dependent variables. Future studies should therefore create surveys which construct the utility variable in a different way.  Lastly, in this paper, only the likelihood of using a sharing option again was investigated, but not actual behaviour. A more comprehensive and reliable analysis should consider the real behaviour of users. Longitudinal studies or experimental designs can be used in future research in order to address this issue.

To conclude, it can be said that there are without doubt several determinants which can affect satisfaction with collaborative consumption services and the likelihood of choosing such services again. Future studies might consider various additional determinants such as, for example, burden of ownership (ownership is usually associated with responsibility and effort), process risk (sharing can involve procedural risks), or product variety (sharing offers a wide range of different products and services). The list goes on as the relevant causal factors can be numerous. So what other determinants do you believe to be crucial in explaining user engagement in collaborative consumption?

 

References 

Bardhi, F., & Eckhardt, G. M. (2012). Access-based consumption: The case of car sharing. Journal of consumer research, 39(4), 881-898.

Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595-1600.

Botsman, R., & Rogers, R. (2011). What’s mine is yours: how collaborative consumption is changing the way we live.

Chen, Y. (2008). Possession and access: Consumer desires and value perceptions regarding contemporary art collection and exhibit visits. Journal of Consumer Research, 35(6), 925-940.

Garson, G. D. (2012). Testing statistical assumptions. Asheboro, NC: Statistical Associates Publishing.

Hennig-Thurau, T., Henning, V., & Sattler, H. (2007). Consumer file sharing of motion pictures. Journal of Marketing, 71(4), 1-18.

Möhlmann, M. (2015). Collaborative consumption: determinants of satisfaction and the likelihood of using a sharing economy option again. Journal of Consumer Behaviour, 14(3), 193-207.

Mont, O. (2004). Institutionalisation of sustainable consumption patterns based on shared use. Ecological economics, 50(1-2), 135-153.

Owyang, J., Samuel, A., & Grenville, A. (2014). Sharing is the new buying: How to win in the collaborative economy. Vision Critical/Crowd Companies.

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