Exploring Features and Products in E-Commerce on Consumers Behavior Using Cognitive Affective
Open accessAbstract
Technology advancement has given rise to a new online purchasing paradigm known as e-commerce. Online shopping in e-commerce platform has altered consumer behavior and purchasing decisions. However, a few studies have conducted by considering cognitive-affective process in making purchase decision on e-commerce. Therefore, the current study examines consumer behavior from perspective of stimulus-organism-response theory and the internal perception process (cognitive and affective) to understand consumer’s purchase decisions. We use Structural Equation Model-Partial Least Square technique to analyze the data of 308 consumers in Indonesia. Our findings report that two dimensions of the stimulus environment (credibility and product usefulness) significantly contributed to perceived trust; convenience has an insignificant impact on perceived trust and perceived value. In comparison, three dimensions of the stimulus environment (convenience, informativeness, and product price) contribute to consumers’ perceived trust. Furthermore, the stimulus environment indicators (credibility, informativeness, product price, and product usefulness) effect on perceived value, while convenience has an insignificant on consumers’ perceived value. The internal perception process or cognitive and affective process, perceived trust has an insignificant impact on perceived pleasure, while perceived value has a significant impact on perceived pleasure. Lastly, perceived pleasure significantly influences consumer purchase decisions in online shopping and e-commerce. The findings of this study can expand the research on e-commerce how consumer’s consider their cognitive and affective factor on product purchase decision. The study suggests that the optimize the features and products as way to improve the consumer purchase decision, and it affects toward cognitive and affective components such as perceive trust, value, and pleasure. This study has contributed both theoretically and practically to stakeholders, policy makers, and practitioner in e-commerce.
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Lazaris, C., Vrechopoulos, A., Sarantopoulos, P., & Doukidis, G. I. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102731 · Cited by 58 · see citing works
Modeling attitude constructs in movie product placements
Balasubramanian, S. K., Patwardhan, H., Pillai, D., & Coker, K. K. (2014) · Journal of Product & Brand Management · doi:10.1108/jpbm-04-2014-0552 · Cited by 57 · see citing works
Using service convenience to reduce perceived cost
Thúy, P. N. (2011) · Marketing Intelligence & Planning · doi:10.1108/02634501111153683 · Cited by 56 · see citing works
Hanzaee, K. H., & Taghipourian, M. J. (2012) · Cited by 53 · see citing works
Cross-cultural examination of apparel online purchase intention: S-O-R paradigm
Nam, C., Cho, K., & Kim, Y. D. (2021) · Journal of Global Fashion Marketing · doi:10.1080/20932685.2020.1845766 · Cited by 50 · cited 1× · see citing works
“Some studies used the SOR framework and consumer behavior, such as [11], [12], [13], and [14] in online purchasing through e-commerce.”
The influence of online cross-recommendation on consumers’ instant cross-buying intention
Zhu, D. H., Wang, Y. W., & Chang, Y. P. (2018) · Internet Research · doi:10.1108/intr-05-2017-0211 · Cited by 48 · see citing works
Xu, X., Wang, L., & Zhao, K. (2020) · Sustainability · doi:10.3390/su12072790 · Cited by 48 · see citing works
Lee, C.-H., Chen, C., Huang, S., Chang, Y.-T., & Demirci, S. (2021) · International Journal of Electronic Commerce Studies · doi:10.7903/ijecs.1971 · Cited by 46 · see citing works
Nofrizal, N., Juju, U., Sucherly, S., Arizal, N., Waldelmi, I., & Aznuriyandi, A. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103146 · Cited by 46 · see citing works
Groß, M. (2018) · Technology in Society · doi:10.1016/j.techsoc.2018.07.005 · Cited by 44 · see citing works
Liu, H. (2021) · American Journal of Industrial and Business Management · doi:10.4236/ajibm.2021.111006 · Cited by 38 · see citing works
Kalia, P., Kaur, N., & Singh, T. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2599-8.ch036 · Cited by 27 · see citing works
What factors determine reviewer credibility?
Sharma, H., & Aggarwal, A. G. (2019) · Kybernetes · doi:10.1108/k-08-2019-0537 · Cited by 18 · see citing works
Suryanegara, M. (2020) · IEEE Access · doi:10.1109/access.2020.3022365 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Le, N. T. C., Nguyen, T. T. P., & Voak, A. (2026) · Journal of Global Scholars of Marketing Science · doi:10.1080/21639159.2025.2577721 · Cited by 6 · see citing works
Nuh, A., Rizan, M., & Usman, O. (2025) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2026209 · Cited by 2 · see citing works
Yang, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-45903-5 · Cited by 2 · see citing works
Patgiri, R., & Ramakrishna, G. (2026) · Cureus Journal of Business and Economics. · doi:10.7759/s44404-026-00067-3 · Cited by 2 · see citing works
Analysis of Three-Stage Visit Behavior of Tourists Using ChatGPT: Agenda for Future Study
Pahrudin, P., Liu, L.-W., Sihombing, A. K., & Jamalulel, I. (2025) · IEEE International Conference on Social Sciences and Intelligence Management (SSIM) · doi:10.3390/engproc2025098015
Self-citationAnalysis of Student Consumer Behavior in Using E-commerce Platforms: An S-O-R Framework Approach
Wicaksono, M. R., Ryden, D., Tanuwidjaja, G. S., & Alexandra, J. (2025) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech67074.2025.11265157
Sparse Machine Learning for Predicting Dry Matter Content in Food Hyperspectral Data
Desandros, A., Henry, M. M., Imani, A., Rasyak, M. R., Isnan, M., & Pardamean, B. B. (2025) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris67789.2025.11295995
Self-citationTopic Analysis of #IndonesiaGelap Discourse Communities using BERTopic
Mola, S. A. S., Henry, M. M., Ole, T. D. I. D., Blegur, Z. R. P., Fransz, J. E. R., & Pardamean, B. B. (2025) · IEEE Information Technology International Seminar (ITIS) · doi:10.1109/itis67966.2025.11308962
Self-citationRajarajeswari, K., Reddy, R. A., Badhoutiya, A., Singh, R., Choudhary, Y., & Nair, V. (2025) · IEEE International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies (SISIMPACT) · doi:10.1109/sisimpact67725.2025.11439171
Percepciones de consumidores sobre la conveniencia de las compras en línea: Un estudio correlacional
Moreno, Á. E. P., Incio, F. A. R., & Querales, N. J. A. (2026) · CICAG: Revista del Centro de Investigación de Ciencias Administrativas y Gerenciales · doi:10.36390/y6bsmr57
- Authors
- Pahrudin, P., Caraka, R. E., Liu, L.-W., Ugiana Gio, P., Anita Yunikawati, N., & Pardamean, B.
- Affiliations
- Chaoyang University of Technology (TW)
- National Research and Innovation Agency (ID)
- Department of Mathematics, Universitas Sumatera Utara, Medan, Indonesia
- Department of Development Economics, Faculty of Economics and Business, Universitas Negeri Malang, Malang, Indonesia
- Binus University (ID)
- Venue
- IEEE Access
- Published
- 2025
- Volume
- 13
- Pages
- 63774-63791
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4408715980
- Cited by
- 11
- References
- 131
- Citations by year
- 2026: 4 · 2025: 7
- FWCI
- 19.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Vibrant Academic Community, Telkom University, through the Scholarly Publications
- Universitas Sumatera Utar
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Retail Behavior Studies
- Keywords
- e-commerce, consumer behavior, online purchase decision, stimulus-organism-response model, perceived trust, perceived value, perceived enjoyment, website credibility, product usefulness, informativeness, product price, convenience
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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