The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
The Effects of Presentation Formats and Task Complexity on Online Consumers’ Product Understanding1
Abstract
This study assesses and compares four product presentation formats currently used online: static pictures, videos without narration, videos with narration, and virtual product experience (VPE), where consumers are able to virtually feel, touch, and try products. The effects of the four presentation formats on consumers’ product understanding as well as the moderating role of the complexity of product understanding tasks were examined in a laboratory experiment. Two constructs used to measure product understanding performance are actual product knowledge and perceived website diagnosticity (i.e., the extent to which consumers believe a website is helpful for them to understand products). The experimental results show that (1) both videos and VPE lead to higher perceived website diagnosticity than static pictures; (2) under a moderate task complexity condition, VPE and videos lead to the same level of actual product knowledge, but all are more effective than static pictures; (3) under a high task complexity condition, all four presentation formats are equally effective in terms of actual product knowledge. Moreover, the results also indicate that it is perceived website diagnosticity, not actual product knowledge, that affects the perceived usefulness of websites, which further influences consumers’ intentions to revisit the websites.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003) · Information Systems Research · doi:10.1287/isre.14.2.189.16018 · Cited by 6,479
Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value
Babin, B. J., Darden, W. R., & Griffin, M. (1994) · Journal of Consumer Research · doi:10.1086/209376 · Cited by 6,020
Task-Technology Fit and Individual Performance
Goodhue, D. L., & Thompson, R. L. (1995) · MIS Quarterly · doi:10.2307/249689 · Cited by 6,006
On data-limited and resource-limited processes
Norman, D. A., & Bobrow, D. G. (1975) · Cognitive Psychology · doi:10.1016/0010-0285(75)90004-3 · Cited by 2,403
Task complexity: Definition of the construct
Wood, R. E. (1986) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(86)90044-0 · Cited by 1,393 · cited 1×
“According to Wood (1986), task complexity is a function of the number of distinct acts that must be completed and the number of distinct information cues about the attributes of the task-related stimulus object an individual has to process when performing a task.”
BackgroundExploring the implications of the Internet for consumer marketing
Peterson, R. A., Balasubramanian, S., & Bronnenberg, B. J. (1997) · Journal of the Academy of Marketing Science · doi:10.1177/0092070397254005 · Cited by 1,373
Mayer, R. E., & Moreno, R. (1998) · Journal of Educational Psychology · doi:10.1037/0022-0663.90.2.312 · Cited by 1,315 · cited 2×
“Based on these findings, Mayer and Moreno (1998, 2002) have contended that video-with-narration is more effective in provoking user memory and learning than video-without-narration.”
BackgroundMethodWhen is an illustration worth ten thousand words?
Mayer, R. E., & Gallini, J. K. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.4.715 · Cited by 1,051 · cited 2×
“…shown that appropriate use of multiple sensory cues in multimedia can effectively represent nonverbal information, enhance learning performance and experience, and help form mental representations of objects (Carney and Levin 2002; Mayer et al. 2003; Mayer and Gallini 1990; Moreno and Mayer 2002).”
BackgroundPictorial Illustrations Still Improve Students' Learning from Text
Carney, R. N., & Levin, J. R. (2002) · Educational Psychology Review · doi:10.1023/a:1013176309260 · Cited by 873
Cognitive Fit: An Empirical Study of Information Acquisition
Vessey, I., & Galletta, D. (1991) · Information Systems Research · doi:10.1287/isre.2.1.63 · Cited by 705 · cited 1×
“Vessey and Galletta (1991) suggest that different types of presentation formats significantly influence the quality of cognitive learning.”
BackgroundEvaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection
Todd, P., & Benbasat, I. (1999) · Information Systems Research · doi:10.1287/isre.10.4.356 · Cited by 312
Self-citation
Cited by
Consumer Empowerment Through Internet-Based Co-creation
Füller, J., Mühlbacher, H., Matzler, K., & Jawecki, G. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222260303 · Cited by 710
On Product Uncertainty in Online Markets: Theory and Evidence1
Dimoka, A., Hong, Y., & Pavlou, P. A. (2012) · MIS Quarterly · doi:10.2307/41703461 · Cited by 682
Integrating Technology Addiction and Use: an Empirical Investigation of Online Auction Users1
Turel, O., Serenko, A., & Giles, P. (2011) · MIS Quarterly · doi:10.2307/41409972 · Cited by 547
A Research Agenda for Trust in Online Environments
Gefen, D., Benbasat, I., & Pavlou, P. (2008) · Journal of Management Information Systems · doi:10.2753/mis0742-1222240411 · Cited by 482
Self-citationProduct fit uncertainty in online markets: nature, effects, and antecedents
Hong, Y. (., & Pavlou, P. A. (2014) · Information Systems Research · doi:10.1287/isre.2014.0520 · Cited by 453
Metadata
- Authors
- Jiang, Z. (., & Benbasat, I.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2007-09-01
- Volume
- 31
- Issue
- 3
- Pages
- 475-500
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W3125756404
- Cited by
- 585
- References
- 14
- FWCI
- 53.17
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
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.
