Impact of Perceived Benefits of E-Tailing on Urban and Rural Female Consumers Online Buying Intentions
Keywords:
E-Tailing, Traditional/ conventional shopping, perceived benefits, Consumer online buying Intentions
Abstract
The web has been changing the retail market in many ways. E-Tailing is the latest booming trend of buying and selling
variety of products and services via internet. It is one of the advance technological booming trends influencing the lives
of commoners. The present research aims to examine various perceived benefits that affect consumer buying intentions
via e-tailing. People are now paying more attention to latest online shopping trends rather than traditional/ conventional
shopping methods because of which they are able to buy everything from home. The study explores the impact of five
major variables such as availability, convenience, time saving, variety of products, low rates, and unique/latest trends
derived from literature which influences urban and rural female consumer online buying intentions. Data were collected
from public and private sector universities of Dera Ismail Khan using self-constructed and close-ended questionnaire
from the sample of 298 female students. Empirical analysis of the data reveals that time saving and variety of products
greatly influences online purchase intentions of female consumers residing in urban and rural areas.
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Forsythe, S., Liu, C., Shannon, D. and Gardner, L.C., (2006). Development of a scale to measure the perceived benefits
and risks of online shopping. Journal of interactive marketing, 20(2), 55-75.
Horrigan, J.A., (2008). Online shopping. Pew Internet & American Life Project Report, 36, 1-24.
Hsu, C.L., Chuan-Chuan Lin, J. & Chiang, H.S., (2013). The effects of blogger recommendations on customers’ online
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Johnson, M. D., Gustafsson, A., Andreassen, T. W., Lervik, L., & Cha, J. (2001). The Evolution and future of national
customer satisfaction index models. http://scholarship.sha.cornell.edu/articles.
Kim, D.J., Ferrin, D.L. and Rao, H.R., (2008). A trust-based consumer decision-making model in electronic commerce:
The role of trust, perceived risk, and their antecedents. Decision support systems, 44(2), 544-564
Kukar-Kinney, M. and Close, A.G., (2010). The determinants of consumers’ online shopping cart abandonment, Journal
of the Academy of Marketing Science, 38(2), 240-250.
Leung, L., (2013). Generational differences in content generation in social media: The roles of the gratifications sought
and of narcissism. Computers in Human Behavior, 29(3), 997-1006.
Lin, H.F. (2008). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic
Commerce Research and Applications, 6(4), 433-442
Li, H., Kuo, C., & Rusell, M.G., (1999). The impact of perceived channel utilities, shopping orientations, and
demographics on the consumer's online buying behavior. Journal of Computer Mediated Communication, 5(2)
Control. Journal of Experimental Social Psychology, 22, 453-474. http://dx.doi.org/10.1016/0022-
1031(86)90045-4
Ajzen, I. (1991) The Theory of Planned Behavior,” Organizational Behavior & Human Decision Processes 15(3), 179-
211
Ajzen, I., & Fishbein. M. (1980). Understanding attitudes and predicting social behavior. New Jersey, Englewood Cliffs,
Prentice-Hall. Taylor, S., & Todd, P. A. Assessing IT Usage: The Role of Prior Experiences. MIS Quarterly,
19(3), 561-570. http://dx.doi.org/10.2307/249633
Akroush, M.N., & Al-Debei, M.M., (2015). An integrated model of factors affecting consumer attitudes towards online
shopping. Business Process Management Journal, 21(6), 1353-1376.
Ariff, M. S. M., Sylvester, M., Zakuan, N., Ismail, K., & Ali, K. M. (2014). Consumer Perceived Risk, Attitude and
Online Shopping Behaviour; Empirical Evidence from Malaysia. In IOP Conference Series: Materials Science
and Engineering, 58(1)
Ayoola, & Akinade, J. (2016). Investigation of The Usage of Social Networking Websites for Personal and Educational
Development Among Secondary Schools Students in Osun State. https://researchbase.com.ng/investigation-ofthe-usage-of-social-networking-websites-for-personal-and-educational-development-among-secondary-schoolsstudents/
Chang, C., (2011). The effect of the number of product subcategories on perceived variety and shopping experience in an
online store. Journal of Interactive Marketing, 25(3), 159-168.
Chen, L.D., & Tan, J., 2004. Technology Adaptation in E-commerce: Key Determinants of Virtual Stores Acceptance.
European Management Journal, 22(1), 74-86.
Chiang, K.P., & Dholakia, R.R., (2003). Factors driving consumer intention to shop online: an empirical investigation.
Journal of Consumer psychology, 13(1-2), 177-183.
Davis, F.D. (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology” MIS
Quarterly, 13(3), 319-340.
Delafrooz, N., Paim, L.H., Haron, S.A., Sidin, S.M. & Khatibi, A., (2009). Factors affecting students' attitude toward
online shopping. African Journal of Business Management, 3(5),
Delafrooz, N., Paim, L. H., & Khatibi, A. (2011). Understanding consumer's internet purchase intention in Malaysia.
African Journal of Business Management, 5(7)
Doolin, B., Dillon, S., Thompson, F., & Corner, J.L. (2005) Perceived risk, the internet shopping experience and online
purchasing behaviour: A New Zealand perspective, Journal of Global Information Management, 13(2),
ABI/INFORM Global
Forsythe, S., Liu, C., Shannon, D. and Gardner, L.C., (2006). Development of a scale to measure the perceived benefits
and risks of online shopping. Journal of interactive marketing, 20(2), 55-75.
Horrigan, J.A., (2008). Online shopping. Pew Internet & American Life Project Report, 36, 1-24.
Hsu, C.L., Chuan-Chuan Lin, J. & Chiang, H.S., (2013). The effects of blogger recommendations on customers’ online
shopping intentions. Internet Research, 23(1), 69-88.
Johnson, M. D., Gustafsson, A., Andreassen, T. W., Lervik, L., & Cha, J. (2001). The Evolution and future of national
customer satisfaction index models. http://scholarship.sha.cornell.edu/articles.
Kim, D.J., Ferrin, D.L. and Rao, H.R., (2008). A trust-based consumer decision-making model in electronic commerce:
The role of trust, perceived risk, and their antecedents. Decision support systems, 44(2), 544-564
Kukar-Kinney, M. and Close, A.G., (2010). The determinants of consumers’ online shopping cart abandonment, Journal
of the Academy of Marketing Science, 38(2), 240-250.
Leung, L., (2013). Generational differences in content generation in social media: The roles of the gratifications sought
and of narcissism. Computers in Human Behavior, 29(3), 997-1006.
Lin, H.F. (2008). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic
Commerce Research and Applications, 6(4), 433-442
Li, H., Kuo, C., & Rusell, M.G., (1999). The impact of perceived channel utilities, shopping orientations, and
demographics on the consumer's online buying behavior. Journal of Computer Mediated Communication, 5(2)
Published
2023-03-10
How to Cite
Mehreen, K., Khan, S., & Roshan, R. (2023). Impact of Perceived Benefits of E-Tailing on Urban and Rural Female Consumers Online Buying Intentions. Journal of Social Sciences and Media Studies, 6(1), 17-25. https://doi.org/10.58921/jossams.06.01.0223
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Articles
