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Follow the Crowd: How Social Information and Social Identity Influence Investing Decisions

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  • Stephen Kuselias

Abstract

Increasingly, jurisdictions around the world have been enacting standards that formally allow organizations to offer equity using the crowdfunding model. Using the Internet, the crowdfunding model raises capital by soliciting from a large number of people (i.e., the crowd). A key feature of crowdfunding that has been embraced by regulators is the use of social information to inform investors as a safeguard for this type of investing. However, research has not yet explored how investors utilize this information. In this study, I investigate whether social information can shift investors away from financial maximization goals. Social identity theory predicts that people can be provoked to act as representatives of their group, setting aside individual interests. I predict, and find, that positive social information focused on an equity crowdfunding organization can activate the social identity of investors who share an identity with that organization, causing them to invest more in a relatively weak organization. In a moderated mediation analysis, I find that positive social information provokes investors who share an identity with an organization to feel a stronger connection to that organization, leading them to increase investment. In addition, I explore whether investors’ perceptions of negative social information are moderated by their social identity. I find that investors who are deeply committed to an identity shared with an organization experiencing negative social information will defensively invest in that organization. The findings of this study will be of interest to investors, regulators, and crowdfunding organizations.

Suggested Citation

  • Stephen Kuselias, 2020. "Follow the Crowd: How Social Information and Social Identity Influence Investing Decisions," Abacus, Accounting Foundation, University of Sydney, vol. 56(3), pages 407-435, September.
  • Handle: RePEc:bla:abacus:v:56:y:2020:i:3:p:407-435
    DOI: 10.1111/abac.12188
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    References listed on IDEAS

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    2. Clarence Goh, 2024. "Analysts’ Earnings per Share Forecasts: The Effects of Forecast Uncertainty and Forecast Precision on Investor Judgements," Abacus, Accounting Foundation, University of Sydney, vol. 60(1), pages 172-204, March.

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