Examining determinants of digital entrepreneurial intention: A case of graduate students

  • Received April 1, 2022;
    Accepted July 25, 2022;
    Published August 3, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.20(3).2022.13
  • Article Info
    Volume 20 2022, Issue #3, pp. 153-163
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This work is licensed under a Creative Commons Attribution 4.0 International License

This study aims to identify the determining factors of digital entrepreneurial intention among university graduate students in Bangladesh. The study considered university students as a study sample (n = 358) who were either in their final year of bachelor’s program or in the master’s program from three private universities located in Dhaka city, Bangladesh. This study was quantitative in nature, and a survey questionnaire was used based on the previous studies. There were three parts: a questionnaire, demographic information analysis, and a Likert-based measurement of study variables. A Cronbach (α) coefficient value of 0.70 or above was regarded to examine the reliability of the constructs. A factor loading value of 0.50 or above was considered to measure the research validity of all constructs’ items. Regression analysis was run to test the hypotheses. A Google form-based online survey questionnaire was used to collect the data, followed by a non-probability sampling method. After scrutiny, incomplete responses were discarded, and finally, 358 responses were deemed usable. The paper used SPSS version 26.0 to perform relevant statistical analyses. The results show that digital entrepreneurial self-efficacy, digital literacy, entrepreneurship education, innovativeness, and creativity positively and significantly impact university students becoming digital entrepreneurs. Regression result shows that students’ innovativeness and entrepreneurship education have more impact on their digital entrepreneurial intentions, implying that policymakers and universities should design their academic policy to promote innovative and entrepreneurship activities in the academic pedagogy.

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    • Figure 1. Research model
    • Figure 2. Regression coefficient
    • Table 1. Reliability and validity analysis
    • Table 2. Demographic information analysis
    • Table 3. Regression coefficient analysis
    • Formal Analysis
      Ayeasha Akhter, K. M. Anwarul Islam, Md. Mobarak Karim, Wasib Bin Latif
    • Funding acquisition
      Ayeasha Akhter, K. M. Anwarul Islam, Md. Mobarak Karim
    • Methodology
      Ayeasha Akhter, Md. Mobarak Karim, Wasib Bin Latif
    • Project administration
      Ayeasha Akhter, K. M. Anwarul Islam, Md. Mobarak Karim, Wasib Bin Latif
    • Resources
      Ayeasha Akhter, K. M. Anwarul Islam, Wasib Bin Latif
    • Software
      Ayeasha Akhter, K. M. Anwarul Islam, Md. Mobarak Karim, Wasib Bin Latif
    • Visualization
      Ayeasha Akhter
    • Writing – review & editing
      Ayeasha Akhter, Md. Mobarak Karim, Wasib Bin Latif
    • Supervision
      K. M. Anwarul Islam
    • Validation
      K. M. Anwarul Islam
    • Writing – original draft
      K. M. Anwarul Islam, Md. Mobarak Karim
    • Conceptualization
      Md. Mobarak Karim
    • Data curation
      Md. Mobarak Karim, Wasib Bin Latif
    • Investigation
      Md. Mobarak Karim, Wasib Bin Latif