Personality traits and entrepreneurial intention among Chartered Accountancy students

  • Received March 19, 2021;
    Accepted July 2, 2021;
    Published August 10, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.19(3).2021.12
  • Article Info
    Volume 19 2021, Issue #3, pp. 136-147
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Entrepreneurial intention plays a decisive role in the process of becoming an entrepreneur. It is not only the prerequisite for establishing a business but it also influences an individual’s behavior. This study aims to investigate the impact of three key personality traits of a prospective entrepreneur (entrepreneurial passion, creativity, and self-efficacy) in shaping their entrepreneurial intention. It is proposed that entrepreneurial passion, creativity, and self-efficacy positively influence entrepreneurial intention. Primary data were collected from 408 Chartered Accountancy (CA) students of the National Capital Region (NCR) of India with the help of a structured questionnaire. The cluster sampling method was used to select the sample from the targeted population. The data were analyzed using structural equation modeling (SEM). The results establish that the entrepreneurial intention of CA students is significantly influenced by their creativity, entrepreneurial passion, and self-efficacy, as all three traits allow an entrepreneur to identify opportunities with profit potential. A student who is passionate, creative, and confident in exploiting the new business idea will develop an intention to become an entrepreneur. The present study contributes to the literature by investigating the entrepreneurial intention of CA students, which has not been explored earlier. The significance of this study facilitates academicians and accounting organizations to focus on the personality traits of students, which helps in developing entrepreneurial intention in them.

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    • Figure 1. Structural model of the relationship between personality traits and entrepreneurial intention
    • Table 1. Descriptive and reliability analysis
    • Table 2. Regression weights
    • Table 3. Summary of a measurement model
    • Table 4. Discriminant validity
    • Table 5. Statistical fitness indices
    • Table 6. Regression weights of SEM analysis
    • Table 7. Statistical fitness indices
    • Conceptualization
      Alisha Soni, Kanupriya Misra Bakhru
    • Data curation
      Alisha Soni
    • Formal Analysis
      Alisha Soni
    • Investigation
      Alisha Soni
    • Methodology
      Alisha Soni
    • Project administration
      Alisha Soni, Kanupriya Misra Bakhru
    • Resources
      Alisha Soni, Kanupriya Misra Bakhru
    • Software
      Alisha Soni, Kanupriya Misra Bakhru
    • Validation
      Alisha Soni
    • Visualization
      Alisha Soni, Kanupriya Misra Bakhru
    • Writing – original draft
      Alisha Soni
    • Writing – review & editing
      Alisha Soni, Kanupriya Misra Bakhru