Review of Professional Management- A Journal of New Delhi Institute of Management

Open Access Open Access  Restricted Access Subscription Access

Shopping Convenience:A Case of Online Retailing


Affiliations
1 New Delhi Institute of Management, Tughlakabad, New Delhi, India
 

This study examined the relative effects of dimensions of shopping convenience on customer satisfaction in Indian online retail context. First order Structural Equation Modeling (SEM) was used in order to test the relationships among study constructs with the help of 227 sample elements. The study results confirmed the access convenience (β = 0.441, p = 0.002) as most important shopping convenience dimension to ascertain customer satisfaction followed by search (β = 0.424, p <0.000), transaction (β = 0.379, p = 0.007 and possession (β = 0.279, p = 0.023). Whereas evaluation convenience (β = 0.217, p = 0.034) proved to be least important shopping convenience dimension in order to ascertain customer satisfaction. The findings of the study would help managers in better understanding of shopping convenience as perceived by customers and subsequently designing customized marketing mix for better return on efforts. In addition, it will also help marketing researchers in developing the better understanding of shopping convenience concept in online retail context.

Keywords

Electronic-Commerce, Customer Satisfaction, Confirmatory Factor Analysis, Structural Equation Modeling.
User
Notifications
Font Size

  • Anderson. R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology and Marketing. 20(2), 123-138.

  • Bagozzi, R.P. & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.

  • Beauchamp, M.B. & Ponder, N. (2010). Perceptions of retail convenience for in-store and online shoppers. The Marketing Management Journal, 20(1), 49-65.

  • Berry, L.L., Seiders, K. & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1-17.

  • Bhatnagar, Amit, Sanjog Misra & H. Raghav Rao (2000). On risk, convenience, and internet shopping behavior. Communications of the ACM, 43(11), 98-110.

  • Brown, Lew, G. (1989). The strategic and tactical implications of convenience in consumer product marketing. Journal of Consumer Marketing, 6(3), 13 – 19.

  • Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21, 230--258.

  • Colwell, S.R., Aung, M., Kanetkar, V. & Holden, A.L. (2008). Toward a measure of service convenience: multiple-item scale development and empirical test. Journal of Services Marketing, 22(2), 160-169.

  • Copeland, M.T. (1923). Relation of consumers’ buying habits to marketing methods. Harvard Business Review, 1(2), 282-289.

  • Fornell, C. & Larcker, D.F. (1981). Structural equation model with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 18(3), 382-389.

  • Gehrt, K.C. & Yale, L.J. (1993). The dimensionality of the convenience phenomenon: a qualitative reexamination. Journal of Business and Psychology, 18(2), 163-180.

  • Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. & Tatham, R.L. (2010). Multivariate data analysis, 6th Edition. Upper Saddle River, New Jersey: Pearson Prentice Hall.

  • Hoe, S. L. (2008). Issues and procedures in adopting SEM technique. Journal of Applied Quantitative Methods, 3(1), 76-83.

  • Hoelter, D. R. (1983). The analysis of covariance structures: Goodness-of-fit indices. Sociological Methods and Research, 11, 325-344.

  • Hofacker, C.F. (2001) Internet Marketing (3rd Edition), John Wiley and Sons, Inc., New York.

  • Hui, Michael K. & David K. Tse. (1996). What to tell consumers in waits of different lengths: an integrative model of service evaluation. Journal of Marketing, 60 (2), 81-90.

  • Hui, Michael K., Mrugank V. Thakor, & Ravi Gill. (1998). The effect of delay type and service stage on consumers’ reactions to waiting. Journal of Consumer Research, 24 (1), 469-479.

  • Kaltcheva, Velitchka D.; & Weitz, Barton A. (2006). When should a retailer create an exciting store environment. Journal of Marketing, 70, 107-118.

  • Kim, B.C. & Park, Y.W. (2012). Security versus convenience? an experimental study of user misperceptions of wireless internet service quality. Decision Support Systems, Vol. 53(1), 1-11.

  • King, S. F., & Liou, J-S. (2004). A framework for internet channel evaluation. International Journal of Information Management, 24(6), 473-488.

  • Kumar, Piyush, Manohar U. Kalwani, & Maqbool Dada. (1997). The impact of waiting time guarantees on consumer waiting experiences. Marketing Science, 16 (4), 295-314.

  • Ling (Alice) Jiang, Zhilin Yang, & Minjoon Jun. (2013). Measuring consumer perceptions of online shopping convenience. Journal of Service Management, 24(2), 191 – 214.

  • Moeller, S., Fassnacht, M. & Ettinger, A. (2009). Retaining customers with shopping convenience. Journal of Relationship Marketing, 8(4), 313-329.

  • Reimers, V. & Clulow, V. (2009). Retail centres: it’s time to make them convenient. International Journal of Retail & Distribution Management, 37(7), 541-562.

  • Rohm, Andrew J., & Vanitha Swaminathan. (2004). A typology of online shoppers based on shopping motivations. Journal of Business Research, 57(7), 748-757.

  • Seiders, K., Berry, L.L. & Gresham, L. (2000). Attention retailers: how convenient is your convenience strategy? Sloan Management Review, 49(3), 79-90.

  • Seiders, K., Voss, G.B., Godfrey, A.L. & Grewal, D. (2007). SERVCON: development and validation of a multidimensional service convenience scale. Journal of the Academy Marketing Science, 35, 144-156.

  • Wolfinbarger, M. & Gilly, M. (2003). eTailQ: dimensionalizing, measuring and predicting etail quality. Journal of Retailing, 79(3), 183-198.

  • https://www.ibef.org/industry/retail-india.aspx [Accessed April 18, 2018].

  • https://www.ibef.org/industry/indian-retail-industry-analysis-presentation [Accessed April 18, 2018].


Abstract Views: 353

PDF Views: 163




  • Shopping Convenience:A Case of Online Retailing

Abstract Views: 353  |  PDF Views: 163

Authors

Vikas Gautam
New Delhi Institute of Management, Tughlakabad, New Delhi, India

Abstract


This study examined the relative effects of dimensions of shopping convenience on customer satisfaction in Indian online retail context. First order Structural Equation Modeling (SEM) was used in order to test the relationships among study constructs with the help of 227 sample elements. The study results confirmed the access convenience (β = 0.441, p = 0.002) as most important shopping convenience dimension to ascertain customer satisfaction followed by search (β = 0.424, p <0.000), transaction (β = 0.379, p = 0.007 and possession (β = 0.279, p = 0.023). Whereas evaluation convenience (β = 0.217, p = 0.034) proved to be least important shopping convenience dimension in order to ascertain customer satisfaction. The findings of the study would help managers in better understanding of shopping convenience as perceived by customers and subsequently designing customized marketing mix for better return on efforts. In addition, it will also help marketing researchers in developing the better understanding of shopping convenience concept in online retail context.

Keywords


Electronic-Commerce, Customer Satisfaction, Confirmatory Factor Analysis, Structural Equation Modeling.

References





DOI: https://doi.org/10.20968/rpm%2F2018%2Fv16%2Fi1%2F175071