Description
The modern sharing economy paradigm is tied with using novel technologies. This study defines and investigates actual sharing economy participation. The role of demographic, economic, locational, and social capital factors, along with attitude towards sustainability and trust on participation by hosts of shared accommodations is examined to understand participation motivations. Building upon social exchange theory and sharing economy literature, the conceptual model of sharing economy participation is developed. The model is tested with data obtained from Airbnb. Socio-economic spatial data is sourced from public datasets. Location attributes of the model are analyzed using spatial statistics techniques to avoid spatial bias. We anticipate our results to predict sharing economy participation. Theoretically, findings of our research will provide a framework for IS researchers to study spatial patterns of sharing economy and participation therein. In practice, we expect our results to be generalizable for non-accommodation forms of collaborative consumption.
Recommended Citation
Koohikamali, Mehrdad; Sarkar, Avijit; and Pick, James B., "Motivations to participate in sharing economy: How location matters?" (2017). AMCIS 2017 Proceedings. 18.
https://aisel.aisnet.org/amcis2017/DataScience/Presentations/18
Motivations to participate in sharing economy: How location matters?
The modern sharing economy paradigm is tied with using novel technologies. This study defines and investigates actual sharing economy participation. The role of demographic, economic, locational, and social capital factors, along with attitude towards sustainability and trust on participation by hosts of shared accommodations is examined to understand participation motivations. Building upon social exchange theory and sharing economy literature, the conceptual model of sharing economy participation is developed. The model is tested with data obtained from Airbnb. Socio-economic spatial data is sourced from public datasets. Location attributes of the model are analyzed using spatial statistics techniques to avoid spatial bias. We anticipate our results to predict sharing economy participation. Theoretically, findings of our research will provide a framework for IS researchers to study spatial patterns of sharing economy and participation therein. In practice, we expect our results to be generalizable for non-accommodation forms of collaborative consumption.