Predictors of online buying behavior
Open accessAbstract
To help identify the factors influencing online shopping, the Wharton Forum on electronic Commerce sponsored an ongoing research project begun in 1997 called the Wharton Virtual Test Market (WVTM). It seeks to understand Web consumer demographics, attitudes about online shopping and predictors of online buying behavior. Consumers worldwide can shop online 24 hours a day, 7 days a week and 365 days a year. Some market sectors, including insurance, financial services, computer hardware and software, travel, books, music, flowers, and automobiles are experiencing rapid growth in online sales. Amazon.com has become the third largest bookseller in the U.S., despite being in business only since 1995. The prototypical Web consumer leads a wired lifestyle and is time starved. It appears that Web consumers shop online to save time. The most important information for predicting shopping habits-online and offline are measures of past behavior, not demographics. This result suggests several implications for the design of online shopping environments like make it more convenient to buy standard or repeat-purchase items; customization to make a purchase decision and easy checkout process.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previous B2C e-commerce research has highlighted the importance of trust between businesses and consumers.
“Previous business-to-consumer (B2C) e-commerce research has highlighted the importance of trust between businesses and consumers [2][3][4].”
From The value of seller trustworthiness in C2C online markets (2002) · cites this paper as [2]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Applied Linear Regression Models
(1997) · Technometrics · doi:10.1080/00401706.1997.10485142 · Cited by 4,638 · see citing works
Lohse, G. L., & Spiller, P. (1998) · Communications of the ACM · doi:10.1145/278476.278491 · Cited by 603 · see citing works
Self-citationEmerging trends in the WWW user population
Pitkow, J. E., & Kehoe, C. M. (1996) · Communications of the ACM · doi:10.1145/228503.228525 · Cited by 225 · see citing works
Social Class and Life Cycle as Predictors of Shopping Behavior
Rich, S. U., & Jain, S. C. (1968) · Journal of Marketing Research · doi:10.1177/002224376800500105 · Cited by 74 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Dell Computer Corp. Fiscal 1999 in Review. Annual Report, Round Rock, Tex; see www.dell.com/corporate/access/annualreports/ report99/Dell_1999_Fiscal_in_Review.pdf.
- Meeker, M. and Pearson, S. The Internet Retailing Report. Morgan Stan- ley, Dean Witter, Discover & Co., New York (May 28, 1997); see www.ms.com/insight/misc/inetretail.html).
- Neter, J. (Ed.), Kutner, M., and Nachtsheim, C. Applied Linear Regres- sion Models, 3rd Ed. Irwin, Chicago, 1996.
Cited by
Sorted by citations
Trust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635 · see citing works
Predicting e-services adoption: A perceived risk facets perspective
Featherman, M. S., & Pavlou, P. A. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00111-3 · Cited by 3,351 · see citing works
Some antecedents and effects of trust in virtual communities
Ridings, C. M., Gefen, D., & Arinze, B. (2002) · The Journal of Strategic Information Systems · doi:10.1016/s0963-8687(02)00021-5 · Cited by 1,423 · see citing works
Vijayasarathy, L. R. (2004) · Information & Management · doi:10.1016/j.im.2003.08.011 · Cited by 1,218 · see citing works
Park, C., & Jun, J. (2003) · International Marketing Review · doi:10.1108/02651330310498771 · Cited by 414 · see citing works
Yang, Q., Pang, C., Liu, L., Yen, D. C., & Michael Tarn, J. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.03.058 · Cited by 410 · see citing works
Defaults, Framing and Privacy: Why Opting In-Opting Out1
Johnson, E. J., Bellman, S., & Lohse, G. L. (2002) · Marketing Letters · doi:10.1023/a:1015044207315 · Cited by 388 · see citing works
Self-citationAn Analysis of Factors Affecting on Online Shopping Behavior of Consumers
Moshrefjavadi, M. H., Rezaie Dolatabadi, H., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. (2012) · International Journal of Marketing Studies · doi:10.5539/ijms.v4n5p81 · Cited by 383 · see citing works
“…with brick-and-mortar shoppers, online consumers tend to be older (Bellman et al. , 1999; Donthu and Garcia, 1999; Weiss, 2001), better educated (Bellman et al. , 1999; Li et al. , 1999; Swinwyard and Smith, 2003), have higher income (Bellman et al. , 1999; Li et al. , 1999; Donthu and Garcia,…”
BackgroundAdoption of in-store mobile payment: Are perceived risk and convenience the only drivers?
de Kerviler, G., Demoulin, N. T. M., & Zidda, P. (2016) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2016.04.011 · Cited by 374 · see citing works
International and Cross-Cultural Influences on Online Shopping Behavior
Stafford, T. F., Turan, A., & Raisinghani, M. S. (2004) · Journal of Global Information Technology Management · doi:10.1080/1097198x.2004.10856373 · Cited by 139 · see citing works
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 · see citing works
The value of seller trustworthiness in C2C online markets
Strader, T. J., & Ramaswami, S. N. (2002) · Communications of the ACM · doi:10.1145/585597.585600 · Cited by 102 · see citing works
- Authors
- Bellman, S., Lohse, G. L., & Johnson, E. J.
- Affiliations
- Murdoch University (AU)
- University of Pennsylvania (US)
- Columbia University (US)
- Venue
- Communications of the ACM
- Published
- 1999-12
- Volume
- 42
- Issue
- 12
- Pages
- 32-38
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2069722994
- Cited by
- 854
- References
- 4
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 8 · 2023: 21 · 2022: 23 · 2021: 24 · 2020: 21 · 2019: 25 · 2018: 34 · 2017: 35 · 2016: 33 · 2015: 53 · 2014: 44 · 2013: 45 · 2012: 31
- FWCI
- 110.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Murdoch University Research Portal (submittedVersion)
- UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- online shopping, online shopping behavior, e-commerce, consumer behavior, consumer demographics, online shopping convenience, checkout process, repeat purchase, online retail
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 ›
Are you an author, or do you see a problem with this page? Request a review
