Entrepreneurial crowdfunding backer motivations: a latent Dirichlet allocation approach

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Purpose – This study furthers one’s understanding of the motivations of the crowdfunding crowd by empirically examining critical factors that influence the crowd’s decision to support a crowdfunding project.

Design/methodology/approach – Backer’s comments from a sample of the top 100 most funded technology product projects on KickStarter were collected. A latent Dirichlet allocation (LDA) analysis strategy was adopted to investigate critical motivational factors. Three experts mapped those factors to the known theoretical constructs of social exchange theory (SET).

Findings – Although backers are motivated by value, they are also motivated by far less tangible social factors including trust and a feeling of psychological ownership. Findings suggest that the crowd is far more than a passive group of investors or customers and should be viewed as participatory stakeholders. This study serves as guidance for project owners hoping to motivate the crowd and for future investigators examining backer motivations in other types of crowdsourcing projects.

Research limitations/implications – Online chatter in the form of user-generated comments is an excellent data source for researchers to mine for value and meaning.

Practical implications – Given strong feelings of psychological ownership, project owners should actively engage the crowd and solicit the crowd for advice and help in order to motivate them.

Originality/value – The study presents the first empirical exploration of backer motivations using LDA guided by theory and the knowledge of experts. A framework of latent motivational factors is proposed. Keywords Social exchange theory, Motivation (psychology), Psychological ownership, Crowdfunding, Latent Dirichlet allocation

Paper type Research paper

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.