A Netnographic Study of Entrepreneurial Traits: Evaluating classic typologies using the crowdsourcing algorithm of an online community

  • Lima M
  • Namaci L
  • Fabiani T
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Abstract

This paper evaluates how the advices of experienced entrepreneurs to young start-up creators in an online community reflect entrepreneurship traits commonly found in conceptual typologies. The overall goal is to contrast and evaluate existing models based on evidence from an online community. This should facilitate future studies to improve current typologies by ranking entrepreneurial traits according to perceived relevance. In order to achieve these objectives, we have conducted a “netnographic study” (i.e., the qualitative analysis of web-based content) of 96 answers to the question “What is the best advice for a young, first-time startup CEO?” on Quora.com. Relying on Quora’s ranking algorithm (based on crowdsourcing of votes and community prestige), we focused on the top 50% of answers (which we shall call the “above Quora 50” category) considered the most relevant by its 2000+ followers and 120,000+ viewers. We used Nvivo as a Qualitative Data Analysis Software to code all the entries into the literature categories. These codes were then later retrieved using matrix queries to compare the incidence of traits and the perceived relevance of answers. We found that among the 50% highest ranking answers on Quora, the following traits are perceived as the most important for young entrepreneurs to develop: management style, attitude in interpersonal relations, vision, self-concept, leadership style, marketing, market and customer knowledge, innovation, technical knowledge and skills, attitude to growth, ability to adapt, purpose and relations system. These results could lead to improving existing typologies and creating new models capable of better identifying people with the highest potential to succeed in new venture creation.

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APA

Lima, M. C., Namaci, L., & Fabiani, T. (2014). A Netnographic Study of Entrepreneurial Traits: Evaluating classic typologies using the crowdsourcing algorithm of an online community. Independent Journal of Management & Production, 5(3). https://doi.org/10.14807/ijmp.v5i3.171

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