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Information sharing and user behavior in internet-enabled peer-to-peer lending systems: an empirical study

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Journal of Information Technology

Abstract

Internet-based information systems (IS) have enabled various forms of collective intelligence, action, and resources (e.g. open source software, innovation marketplaces, crowdsourcing, and crowdfunding). Within the domain of crowdfunding, Internet-enabled Peer-to-Peer Lending Systems (IP2PLS) have emerged as a disruptive technology, with implications for the financial services sector, business capitalization strategies, and personal and community development. IS research investigating user behavior in IP2PLS has revealed the saliency of social identity and personal transparency (as expressed through information sharing) in such systems. Prior research has largely focused on a small number of IP2PLS providers, thus this study examines a very large but under-researched platform. The study tests a theoretical model based on Social Identity Theory and prior IP2PLS studies, through an analysis of 116,667 loan records, and a subsequent analysis of 1000 manually coded records, to investigate the impact of information sharing on user (lenders and borrowers) behavior. The study reveals the importance of social (vs financial) data, and further reveals relationships that frequently contradict prior findings from other IP2PLS. The study thus implies the need for a more heterogeneous view of the IP2PLS domain, and the need to more fully understand as systems that support user behavior by enabling social information exchanges.

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Acknowledgements

This paper reports on research funded by the Lewis Charitable Foundation (USA) through the Technology-Enabled Organizational Openness and Transparency (TOTO) project hosted at University College Cork, Ireland.

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  1. Department of Accounting, Finance and Information Systems, University College Cork, Cork, Ireland

    Joseph Feller, Rob Gleasure & Stephen Treacy

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  1. Joseph Feller
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Correspondence to Joseph Feller.

Appendices

Appendix A

Table A1

Table A1 List of Hypotheses Predicting How Explicitly Expected Hard Data May Impact Investment and Repayment in Lending Club
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Appendix B

Table B1

Table B1 List of Hypotheses Predicting How Implicitly Expected Soft Data May Impact Investment and Repayment in Lending Club
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Appendix C

Table C1

Table C1 Manual Coding Examples
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Feller, J., Gleasure, R. & Treacy, S. Information sharing and user behavior in internet-enabled peer-to-peer lending systems: an empirical study. J Inf Technol 32, 127–146 (2017). https://doi.org/10.1057/jit.2016.1

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