The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs
Abstract
We empirically test the effects of explanation facilities on consumers' initial trusting beliefs concerning online recommendation agents (RAs). RAs provide online shopping advice based on user-specified needs and preferences. The characteristics of RAs that may hamper consumers' trust building in the RAs are identified, and the provision of explanation facilities is proposed as a knowledge-based approach to enhance consumers' trusting beliefs by dealing with these obstacles. This study examines the effects of three types of explanations about an RA and its use—how, why, and trade-off explanations—on consumers' trusting beliefs in an RA's competence, benevolence, and integrity. An RA was built as the experimental platform and a laboratory experiment was conducted. The results confirm the important role of explanation facilities in enhancing consumers' initial trusting beliefs and indicate that consumers' use of different types of explanations enhances different trusting beliefs: the use of how explanations increases their competence and benevolence beliefs, the use of why explanations increases their benevolence beliefs, and the use of trade-off explanations increases their integrity beliefs.
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
Agency Theory: An Assessment and Review
Eisenhardt, K. M. (1989) · The Academy of Management Review · doi:10.5465/amr.1989.4279003 · Cited by 13,338
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
An Examination of the Nature of Trust in Buyer-Seller Relationships
Doney, P. M., & Cannon, J. P. (1997) · Journal of Marketing · doi:10.2307/1251829 · Cited by 6,822
Developing and validating trust measures for e-commerce: an integrative typology
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002) · Information Systems Research · doi:10.1287/isre.13.3.334.81 · Cited by 5,550
When choice is demotivating: can one desire too much of a good thing?
Iyengar, S. S., & Lepper, M. R. (2000) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.79.6.995 · Cited by 2,878
Consumer trust in an Internet store
Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000) · Information Technology and Management · doi:10.1023/a:1019104520776 · Cited by 2,577
Building Effective Online Marketplaces with Institution-Based Trust
Pavlou, P. A., & Gefen, D. (2004) · Information Systems Research · doi:10.1287/isre.1040.0015 · Cited by 2,516
What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology
McKnight, D. H., & Chervany, N. L. (2001) · International Journal of Electronic Commerce · doi:10.1080/10864415.2001.11044235 · Cited by 2,282
A trust model for consumer Internet shopping
Lee, M. K. O., & Turban, E. (2001) · International Journal of Electronic Commerce · doi:10.1080/10864415.2001.11044227 · Cited by 2,012
Cited by
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Qiu, L., & Benbasat, I. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222250405 · Cited by 737
Self-citationHow Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
Kizilcec, R. F. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858402 · Cited by 615
Examining Trust in Information Technology Artifacts: The Effects of System Quality and Culture
Vance, A., Elie-Dit-Cosaque, C., & Straub, D. W. (2008) · Journal of Management Information Systems · doi:10.2753/mis0742-1222240403 · Cited by 599
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598
What drives Malaysian m-commerce adoption? An empirical analysis
Tsu Wei, T., Marthandan, G., Yee‐Loong Chong, A., Ooi, K., & Arumugam, S. (2009) · Industrial Management & Data Systems · doi:10.1108/02635570910939399 · Cited by 550
The effects of transparency on trust in and acceptance of a content-based art recommender
Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., & Wielinga, B. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9051-3 · Cited by 448
AI-Based Digital Assistants: Opportunities, Threats, and Research Perspectives
Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., Hinz, O., Morana, S., & Söllner, M. (2019) · Business & Information Systems Engineering · doi:10.1007/s12599-019-00600-8 · Cited by 432
Explanations as Mechanisms for Supporting Algorithmic Transparency
Rader, E., Cotter, K., & Cho, J. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173677 · Cited by 388
Wang, W., & Benbasat, I. (2008) · Journal of Management Information Systems · doi:10.2753/mis0742-1222240410 · Cited by 241
Self-citationPerceived information transparency in B2C e-commerce: an empirical investigation.
Zhou, L., Wang, W., Xu, J. (., Liu, T., & Gu, J. (2018) · Information & Management · doi:10.1016/j.im.2018.04.005 · Cited by 233
Self-citationWashington State University, Hess, T., Fuller, M., University of Massachusetts, A., Campbell, D., & Millsaps College (2009) · Journal of the Association for Information Systems · doi:10.17705/1jais.00216 · Cited by 233
Why different trust relationships matter for information systems users
Söllner, M., Hoffmann, A., & Leimeister, J. M. (2016) · European Journal of Information Systems · doi:10.1057/ejis.2015.17 · Cited by 223
Lowry, P. B., Zhang, D., Zhou, L., & Fu, X. (2010) · Information Systems Journal · doi:10.1111/j.1365-2575.2009.00334.x · Cited by 206
How do visual explanations foster end users' appropriate trust in machine learning?
Yang, F., Huang, Z., Scholtz, J., & Arendt, D. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377480 · Cited by 200
User attitudes towards algorithmic opacity and transparency in online reviewing platforms
Eslami, M., Vaccaro, K., Lee, M. K., Elazari Bar On, A., Gilbert, E., & Karahalios, K. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300724 · Cited by 184
Algorithmic Management: Bright and Dark Sides, Practical Implications, and Research Opportunities
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-022-00764-w · Cited by 180
Communicating Algorithmic Process in Online Behavioral Advertising
Eslami, M., Krishna Kumaran, S. R., Sandvig, C., & Karahalios, K. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174006 · Cited by 171
Why do users trust algorithms? A review and conceptualization of initial trust and trust over time
Cabiddu, F., Moi, L., Patriotta, G., & Allen, D. G. (2022) · European Management Journal · doi:10.1016/j.emj.2022.06.001 · Cited by 138
The effect of web communities on consumers' initial trust in B2C e‐commerce websites
Brengman, M., & Karimov, F. P. (2012) · Management Research Review · doi:10.1108/01409171211256569 · Cited by 128
I can do better than your AI: expertise and explanations
Schaffer, J., O'Donovan, J., Michaelis, J., Raglin, A., & Höllerer, T. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302308 · Cited by 119
Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior
Hostler, R. E., Yoon, V. Y., Guo, Z., Guimaraes, T., & Forgionne, G. (2011) · Information & Management · doi:10.1016/j.im.2011.08.002 · Cited by 117
Are explanations always important?: a study of deployed, low-cost intelligent interactive systems
Bunt, A., Lount, M., & Lauzon, C. (2012) · Proceedings of the 2012 ACM international conference on Intelligent User Interfaces · doi:10.1145/2166966.2166996 · Cited by 106
Acceptance of recommendations to buy in online retailing.
Baier, D., & Stüber, E. (2010) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2010.03.005 · Cited by 106
The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice
Lehmann, C. A., Haubitz, C. B., Fügener, A., & Thonemann, U. W. (2022) · Production and Operations Management · doi:10.1111/poms.13770 · Cited by 100
On the Role of Fairness and Social Distance in Designing Effective Social Referral Systems1
Hong, Y., Pavlou, P. A., Shi, N., & Wang, K. (2017) · MIS Quarterly · doi:10.25300/misq/2017/41.3.06 · Cited by 91
Explainable Artificial Intelligence: a Systematic Review
Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269
Wang, P., & Ding, H. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103732 · Cited by 68
Human–artificial intelligence collaboration in prediction: a field experiment in the retail industry
Revilla, E., Saenz, M. J., Seifert, M., & Ma, Y. (2023) · Journal of Management Information Systems · doi:10.1080/07421222.2023.2267317 · Cited by 63
Improving the radiologist-CAD interaction: designing for appropriate trust
Jorritsma, W., Cnossen, F., & van Ooijen, P. M. A. (2015) · Clinical Radiology · doi:10.1016/j.crad.2014.09.017 · Cited by 61
Wang, Y.-Y., Luse, A., Townsend, A. M., & Mennecke, B. E. (2015) · Information Systems and e-Business Management · doi:10.1007/s10257-014-0269-9 · Cited by 54
Antecedents and effects of trust in forecasting advice
Goodwin, P., Sinan Gönül, M., & Önkal, D. (2013) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2012.08.001 · Cited by 46
Repairing Integrity-Based Trust Violations in Ascription Disputes for Potential E-Commerce Customers
Deng, H., Wang, W., & Lim, K. H. (2022) · MIS Quarterly · doi:10.25300/misq/2022/15679 · Cited by 13
Self-citation
Metadata
- Authors
- Wang, W., & Benbasat, I.
- Venue
- Journal of Management Information Systems
- Publisher
- Informa UK Limited
- Published
- 2007-05
- Volume
- 23
- Issue
- 4
- Pages
- 217-246
- ISSN
- 0742-1222 (ISSN-L)
- 1557-928X
- OpenAlex
- W2001468398
- Cited by
- 498
- References
- 17
- FWCI
- 41.42
- 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.
