Examining university students’ behaviors towards online shopping: An empirical investigation in an emerging market

  • Received November 8, 2021;
    Accepted February 7, 2022;
    Published February 16, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.18(1).2022.08
  • Article Info
    Volume 18 2022, Issue #1, pp. 94-103
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This work is licensed under a Creative Commons Attribution 4.0 International License

The paper strives to investigate the influential factors that might significantly affect consumers’ intention to shop from online websites. On this premise, this paper aims to examine the impact of perceived trust (PT), convenience (CONV), perceived website quality (PWQ), and subjective norm (SN) on individuals’ intention towards online shopping (ITOS). The paper proposed a study framework based on previous relevant literature. The study has drawn samples from private university students of Bangladesh. A quantitative research approach was adopted, followed by a survey method. Only undergraduate and postgraduate students of two Bangladeshi universities were considered as respondents as it is believed that they were the primary online shoppers in Bangladesh perspective. The sample size in this paper is 339, which was deemed adequate to run regression analysis. SPSS software has been utilized to conduct correlation and regression analysis. The convenience sampling method was applied in the study as the population is not known. This paper applied a 95% confidence interval to accept the hypotheses. Correlation results confirm that all independent variables correlated with ITOS. The findings of the paper report that PT, CONV, PWQ, and SN have a significant impact on students’ intention towards online shopping (ITOS). Henceforth, the online business managers can utilize the findings of this paper to understand the young consumer behaviors and develop a solid and effective online marketing plan for sustainable business performance.

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    • Table 1. Reliability and validity analysis
    • Table 2. Demographic statistics
    • Table 3. Correlation analysis
    • Table 4. Regression analysis
    • Conceptualization
      Ahmed Al Asheq
    • Data curation
      Ahmed Al Asheq, Sabrina Akhter, Md. Kamruzzaman, K. M. Anwarul Islam
    • Formal Analysis
      Ahmed Al Asheq, Sabrina Akhter
    • Investigation
      Ahmed Al Asheq, Md. Kamruzzaman
    • Methodology
      Ahmed Al Asheq, K. M. Anwarul Islam
    • Software
      Ahmed Al Asheq, Md. Kamruzzaman
    • Supervision
      Ahmed Al Asheq, Khadiza Rahman Tanchi, Sabrina Akhter, Md. Kamruzzaman
    • Validation
      Ahmed Al Asheq, Khadiza Rahman Tanchi, Md. Kamruzzaman
    • Writing – original draft
      Ahmed Al Asheq, Khadiza Rahman Tanchi
    • Writing – review & editing
      Ahmed Al Asheq, Khadiza Rahman Tanchi, Md. Kamruzzaman, K. M. Anwarul Islam
    • Funding acquisition
      Khadiza Rahman Tanchi, Md. Kamruzzaman, K. M. Anwarul Islam
    • Project administration
      Khadiza Rahman Tanchi, Sabrina Akhter
    • Resources
      Khadiza Rahman Tanchi, Sabrina Akhter, Md. Kamruzzaman, K. M. Anwarul Islam
    • Visualization
      Khadiza Rahman Tanchi, Sabrina Akhter, K. M. Anwarul Islam