Reviewing the Factors Distressing Consumers Buying Behaviour During COVID-19 Pandemic with Special Reference to Kolkata using Principal Component Analysis

Authors

  • Rupa Mondal Ph.D. Research Scholar, Department of Commerce, University of Kolkata, West Bengal, India
  • Dr. Supti Kotal Associate Professor, Maharaja Srischandra College, West Bengal, India
  • Dr. Raktim Ghosh State Aided College Teacher, Department of Commerce, Maharaja Srischandra College, West Bengal, India

DOI:

https://doi.org/10.54741/mjar.3.2.1

Keywords:

principal component analysis, kmo, bartlett, consumers buying behaviour, kolkata, pandemic

Abstract

The authors make an effort to empirically reviews the factors distressing consumers buying behaviour during the COVID-19 pandemic with special reference to Kolkata by applying the principal component analysis. The study is conducted from August 29, 2022, to February 6, 2023, using a convenient sampling technique. Different statistical tests like principal component analysis (PCA), Cronbach’s alpha, along with frequency statistics are used for the analysis of the data using IBM SPSS Statistics 21. The study is both primary and secondary in nature. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is close to 1 and Bartlett's Test of Sphericity is significant in nature. Moreover, 9 factors were retrieved from the existing 38 questions. Hence, it was concluded that the factors of taste and preferences, discouraging offline purchases, after-sale service, purchasing decision, the role of reference groups, change in purchase behaviour, searching the information, the role of family members, and meeting expectations are the decisive factors distressing the consumer buying behaviour in Kolkata during the COVID-19 pandemic.

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https://www.statisticshowto.com/kaiser-meyer-olkin/.

Published

2023-04-29

How to Cite

Rupa Mondal, Dr. Supti Kotal, & Dr. Raktim Ghosh. (2023). Reviewing the Factors Distressing Consumers Buying Behaviour During COVID-19 Pandemic with Special Reference to Kolkata using Principal Component Analysis. Management Journal for Advanced Research, 3(2), 1–11. https://doi.org/10.54741/mjar.3.2.1

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Articles