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An Interpretive Structural Modelling Approach for Modelling the Factors Affecting Consumer Online Buying Behavior


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1 The North Cap University, Near Rotary Public School Cartarpuri Alias, Huda, Sector 23A, Gurugram, Haryana 122 017, India

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E-commerce industry is one the fastest growing sectors worldwide. It is not confined to any specific category or demography. Online shopping done at the click of a button is so convenient that consumers can easily navigate through large number of brands competing in the market and make the best possible choice. The present paper aims to explore the factors that endeavor consumers to purchase online. Subsequently, an interpretive structural modelling approach is employed to detect the interrelationship among these factors. Finally, an ISM model is formed that depicts the interrelationship among the factors that impact online buying behavior of consumers. Further MICMAC analysis is performed to categorize the factors on the basis of their driving and dependence power. Factors identified include price, product details, perceived risk, perceived benefits, attitude, trust, e-loyalty, and subjective norms. An ISM model depicting four levels of hierarchy is developed. MICMAC analysis reveals that price and perceived risk are dependent factors, whereas, perceived benefit, attitude, and trust are linking factors. Last, product details, e-loyalty, and subjective norms are categorized as independent factors.

Keywords

Consumer Behavior, ISM Approach, MICMAC Analysis, Online Buying.
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  • D. K. Das, and A. Ara,"Growth of e-commerce in India,"International Journal of Core Engineering and Management, vol. 2, no.4, pp. 25 – 33, 2015.
  • S. Bhattacharya, "Propelling India towards global leadership in e-commerce," PwC India, 2018.[Online]. Available:https://www.pwc.in/research -insights/2018/propelling-india-towards-global-leadership-in-e-commerce.html
  • Deepali, "Study on growth of online shopping in India," International Journal of Computer Science and Mobile Computing, vol. 2,no. 6, pp. 65 – 68, 2013. [Online].Available :https://www.ijcsmc.com/docs/papers/June2013/V2I6201 328.pdf
  • A. Sivakumar and A. Gunasekaran, "An empirical study on the factors affecting online shopping behavior of millennial consumers," Journal of Internet Commerce, vol. 16, no.3, pp. 219 – 230, 2017.
  • J. Bucko, L. Kakalejcik, and M. Ferencova, "Online shopping: Factors that affect consumer purchasing behavior," Cogent Business & Management, vol. 5, no.1, 2018.
  • M. Zendehdel, L. H. Paim, and S. B. Osman,"Students'online purchasing behavior in Malaysia: Understanding online shopping attitude," Cogent Business & Management ,vol.2, no.1,2015 .doi : https://doi.org/10.1080/23311975.2015.1078428
  • H. Uzun and M. Poturak, "Factors affecting online shopping behavior of consumers," European Journal of Social and Human Sciences, vol. 3, no.3, pp. 163 – 170, 2014. [Online]. Available: 10.5539/ijbm.v8n13p56
  • C.-H.Park and Y.-G. Kim,”Identifying key factors affecting consumer purchase behavior in an online shopping context,” International Journal of Retail & Distribution Management, vol. 31, no. 1, pp. 16–29, 2003.
  • M. H. M. Javadi, H. R. Dolatabadi, M. Nourbakhsh, A. Poursaeedi, and A. R. Asadollahi, "An analysis of factors affecting on online shopping behavior of consumers," International Journal of Marketing Studies, vol.4,no.5,2012.doi :http://dx.doi.org/10.5539/ijms.v4n5p81
  • M. Limayem, M. Khalifa, and A. Frini, "What makes consumers buy from internet? A longitudnal study of online shopping," IEEE Transactios on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 30, no. 4, pp. 421 – 432, 2000. [Online]. Available: https://pdfs.semanticscholar.org/176d/77bfdf55a180926 153fc1da065b602033600.pdf
  • D. Narges, H. Laily and K. Ali, "Students' online shopping behavior: An empirical study," Journal of American Science, 6(1), pp. 137 – 147, 2010. [Online]. A v a i l a b l e :https://pdfs.semanticscholar.org/adfb/bcab431cc7daa0e7 62e4c98f7644413e9d01.pdf
  • G. Nagra and R. Gopal, "A study of factors affecting on online shopping behavior of consumers," International Journal of Scientific and Research Publications, vol. 3, no. 6, 2013. [Online]. Available: https://pdfs.semanticscholar.org/adfb/bcab431cc7daa0e 762e4c98f7644413e9d01.pdf
  • M. Al-Debei, M. N. Akroush, and M.I. Ashouri, "Consumer attitudes towards online shopping," Internet Research, vol. 25, no.5, pp. 707 – 733, 2015. doi: doi:https://doi.org/10.1108/IntR-05-2014-0146
  • Z. M. Jusoh and G. H. Ling, "Factors influencing consumers' attitude towards e-commerce purchases through online shopping," International Journal of Humanities and Social Science, vol. 2, no.4, pp.223–230,20 12.[Online].Available: https://www.semanticscholar.org/paper/FACTORS-INFLUENCING -CONSUMERS'- ATTITUDE-TO WARDS-Jusoh-Ling/327f0ec65bd0e0dabad23c42514d0e2ac8b05a97
  • D. F. Cox, , and S. U. Rich, “Perceived risk and consumer decision-making—The case of telephone shopping,”Journal of Marketing Research, 1(4), pp. 32–39,1964. doi : https://doi.org/10.1177%2F002224376400100405
  • S. Taylor, S., and P. Todd, “Assessing IT usage: The role of prior experience,” MIS Quaterly, vol. 19, no.4, pp.561–570, 1995. doi: 10.2307/249633
  • M. Gommans, K. S. Krishnan, and K. B. Scheffold, “ From brand to e-loyalty: A conceptual framework,” Journal of Economics and Social Research,vol. 3, no. 1, pp. 43–58, 2001.
  • R. Attri, N. Dev, and V. Sharma, "Interpretive Structural Modelling Approach: An overview," Research Journal of Management Sciences, vol. 2, no.2, pp. 3 – 8, 2013.
  • P. Jha, R. Aggarwal, and P. Kapur, "Social media marketing barriers for indian industries:Studying the Hierarchical Relationships throough ISM Methodology,"International Journal of Computer Applications, vol. 178, no. 16, 2019.
  • J. N. Warfield, “Towards Interpretation of Complex Structural Models,” IEEE Transactions on Systems Man, and Cybernetics, vol. 4, no.5, pp. 405 – 417, 1974.
  • F. Talib, Z. Rahman, and M. Qureshi, "An interpretive structural modelling approach for modelling the practices of the total quality management in service sector," International Journal Modelling in Operations Management, vol. 1, no.3, pp. 223–250, 2011.
  • P. S. Poduval,V. R. Pramod, and V.P. J. Raj, "Interpretive Structural Modeling (ISM) and its applicationin analyzing factors inhibiting implementation of Total Productive Maintenance(TPM)," International Journal of Quality & Realibility Management, vol. 32, pp. 308 – 331, 2015.
  • R. Raeesi, M. Dastranj, S. Mohammadi, and E. Rasouli, "Understanding the Interactions among the barriers to entrepreneurship using Interpretive Structural Modeling,” International Journal of Business and Management, vol. 8, no. 13, pp. 56 – 72, 2013. doi: 10.5539/ijbm.v8n13p56.

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  • An Interpretive Structural Modelling Approach for Modelling the Factors Affecting Consumer Online Buying Behavior

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Authors

Trishala Chauhan
The North Cap University, Near Rotary Public School Cartarpuri Alias, Huda, Sector 23A, Gurugram, Haryana 122 017, India
Ruchi Nayyar
The North Cap University, Near Rotary Public School Cartarpuri Alias, Huda, Sector 23A, Gurugram, Haryana 122 017, India

Abstract


E-commerce industry is one the fastest growing sectors worldwide. It is not confined to any specific category or demography. Online shopping done at the click of a button is so convenient that consumers can easily navigate through large number of brands competing in the market and make the best possible choice. The present paper aims to explore the factors that endeavor consumers to purchase online. Subsequently, an interpretive structural modelling approach is employed to detect the interrelationship among these factors. Finally, an ISM model is formed that depicts the interrelationship among the factors that impact online buying behavior of consumers. Further MICMAC analysis is performed to categorize the factors on the basis of their driving and dependence power. Factors identified include price, product details, perceived risk, perceived benefits, attitude, trust, e-loyalty, and subjective norms. An ISM model depicting four levels of hierarchy is developed. MICMAC analysis reveals that price and perceived risk are dependent factors, whereas, perceived benefit, attitude, and trust are linking factors. Last, product details, e-loyalty, and subjective norms are categorized as independent factors.

Keywords


Consumer Behavior, ISM Approach, MICMAC Analysis, Online Buying.

References





DOI: https://doi.org/10.17010/ijcs%2F2020%2Fv5%2Fi4-5%2F154784