The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
What makes consumers buy from Internet? A longitudinal study of online shopping
Abstract
The objective of this study is to investigate the factors affecting online shopping. A model explaining the impact of different factors on online shopping intentions and behavior is developed based on the theory of planned behavior. The model is then tested empirically in a longitudinal study with two surveys. Data collected from 705 consumers indicate that subjective norms, attitude, and beliefs concerning the consequences of online shopping have significant effects on consumers' intentions to buy online. Behavioral control and intentions significantly influenced online shopping behavior. The results also provide strong support for the positive effects of personal innovativeness on attitude and intentions to shop online. The implications of the findings for theory and practice are discussed.
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Citations
Cites
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Understanding information technology usage: a test of competing models
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Innovativeness: The Concept and Its Measurement
Midgley, D. F., & Dowling, G. R. (1978) · Journal of Consumer Research · doi:10.1086/208701 · Cited by 1,550 · cited 1×
“This construct has been of particular interest in innovation diffusion research in general [43], [44] and the domain of marketing in particular [ 45 ]‐[47].”
Background
Cited by
Vijayasarathy, L. R. (2004) · Information & Management · doi:10.1016/j.im.2003.08.011 · Cited by 1,218
Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics
Devaraj, S., Fan, M., & Kohli, R. (2002) · Information Systems Research · doi:10.1287/isre.13.3.316.77 · Cited by 1,205
Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services
Yeo, V. C. S., Goh, S.-K., & Rezaei, S. (2017) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2016.12.013 · Cited by 956
Technology acceptance model (TAM) and social media usage: an empirical study on Facebook
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014) · Journal of Enterprise Information Management · doi:10.1108/jeim-04-2012-0011 · Cited by 937
(2007) · Cited by 737
The theory of planned behavior and Internet purchasing
George, J. F. (2004) · Internet Research · doi:10.1108/10662240410542634 · Cited by 694
Thakur, R., & Srivastava, M. (2014) · Internet Research · doi:10.1108/intr-12-2012-0244 · Cited by 606
The role of system trust in business-to-consumer transactions
PENNINGTON, R., WILCOX, H. D., & GROVER, V. (2003) · Journal of Management Information Systems · doi:10.1080/07421222.2003.11045777 · Cited by 407
The role of consumer innovativeness and perceived risk in online banking usage
Aldás‐Manzano, J., Lassala‐Navarré, C., Ruiz‐Mafé, C., & Sanz‐Blas, S. (2009) · International Journal of Bank Marketing · doi:10.1108/02652320910928245 · Cited by 364
Factors influencing satisfaction and loyalty in online shopping: an integrated model
Lin, G. T. R., & Sun, C. (2009) · Online Information Review · doi:10.1108/14684520910969907 · Cited by 241
Going with the Flow: Predicting Online Purchase Intentions
Korzaan, M. L. (2003) · Journal of Computer Information Systems · doi:10.1080/08874417.2003.11647530 · Cited by 214
Buying apparel over the Internet
Goldsmith, R. E., & Goldsmith, E. B. (2002) · Journal of Product & Brand Management · doi:10.1108/10610420210423464 · Cited by 200
Advertising benefits from ethical Artificial Intelligence algorithmic purchase decision pathways
Rodgers, W., & Nguyen, T. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05048-7 · Cited by 137
A model of user adoption of interface agents for email notification
Serenko, A. (2008) · Interacting with Computers · doi:10.1016/j.intcom.2008.04.004 · Cited by 92
Singh, S., & Srivastava, S. (2018) · Cogent Arts & Humanities · doi:10.1080/23311983.2018.1495043 · Cited by 61
Generating Product Descriptions from User Reviews
Novgorodov, S., Guy, I., Elad, G., & Radinsky, K. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313532 · Cited by 41
Internet Lottery Commerce: An Integrated View of Online Sport Lottery Adoption
Chiu, Y.-T. H., Lee, W.-I., Liu, C.-C., & Liu, L.-Y. (2012) · Journal of Internet Commerce · doi:10.1080/15332861.2012.650990 · Cited by 37
(2005) · Journal of the Association for Information Systems · Cited by 16
Learning to Generate Personalized Product Descriptions
Elad, G., Guy, I., Novgorodov, S., Kimelfeld, B., & Radinsky, K. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3357984 · Cited by 13
Ukenna, S. I., Idoko, E. C., & Matthew, E. E. (2023) · International Journal of Electronic Business · doi:10.1504/ijeb.2023.132197 · Cited by 2
Metadata
- Authors
- Limayem, M., Khalifa, M., & Frini, A.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- Published
- 2000-07
- Volume
- 30
- Issue
- 4
- Pages
- 421-432
- ISSN
- 1083-4427 (ISSN-L)
- OpenAlex
- W2124100017
- Cited by
- 745
- References
- 4
- FWCI
- 30.75
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.