The DOI matches its Crossref record; 63 of the 67 references it cites were found in a registry or an index.
Acceptance of recommendations to buy in online retailing.
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“personalized online recommendations for products of interest improve women’s online shopping experience (Baier and Stüber, 2010).”
From Motivational and Affective Factors Underlying Consumer Dropout and Transactional Success in eCommerce: An Overview (2020) · cites this paper as Baier and Stüber, 2010
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
(1975) · PhilPapers (PhilPapers Foundation) · Cited by 36,737
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
\nThe use of partial least squares path modeling in international marketing
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) · Radboud Repository (Radboud University) · doi:10.1108/s1474-7979(2009)0000020014 · Cited by 12,535
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
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.03.005 · Cited by 6,312
(2003) · International Journal of Electronic Commerce · doi:10.1080/10864415.2003.11044275 · Cited by 5,715
Mathieson, K. (1991) · Information Systems Research · doi:10.1287/isre.2.3.173 · Cited by 4,216
Hybrid Recommender Systems: Survey and Experiments
Burke, R. (2002) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1021240730564 · Cited by 4,119
Extending the TAM for a World-Wide-Web context
Moon, J.-W., & Kim, Y.-G. (2001) · Information & Management · doi:10.1016/s0378-7206(00)00061-6 · Cited by 3,802
Assessing IT Usage: The Role of Prior Experience
Taylor, S., & Todd, P. (1995) · MIS Quarterly · doi:10.2307/249633 · Cited by 2,891
Pavlou, P. A., & Fygenson, M. (2006) · MIS Quarterly · doi:10.2307/25148720 · Cited by 2,685
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
The Technology Acceptance Model: Past, Present, and Future
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003) · Communications of the Association for Information Systems · doi:10.17705/1cais.01250 · Cited by 2,572
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
Gefen, D., & Straub, D. W. (1997) · MIS Quarterly · doi:10.2307/249720 · Cited by 2,292
The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift.
Bagozzi, R. P. (2007) · Journal of the Association for Information Systems · doi:10.17705/1jais.00122 · Cited by 2,238
Alba, J. W., Lynch, J. G., Weitz, B. A., Janiszewski, C. A., Lutz, R., Sawyer, A. G., & Wood, S. L. (1997) · Journal of Marketing · doi:10.1177/002224299706100303 · Cited by 2,211
Extending the technology acceptance model with task–technology fit constructs
Dishaw, M. T., & Strong, D. M. (1999) · Information & Management · doi:10.1016/s0378-7206(98)00101-3 · Cited by 1,772
Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids
Häubl, G., & Trifts, V. (2000) · Marketing Science · doi:10.1287/mksc.19.1.4.15178 · Cited by 1,731
The influence of online product recommendations on consumers' online choices
Senecal, S., & Nantel, J. (2004) · Journal of Retailing · doi:10.1016/j.jretai.2004.04.001 · Cited by 1,585
Re-Examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis
Segars, A. H., & Grover, V. (1993) · MIS Quarterly · doi:10.2307/249590 · Cited by 1,525
Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation1
Venkatesh, V. (1999) · MIS Quarterly · doi:10.2307/249753 · Cited by 1,428
A Cross-Cultural Study on Escalation of Commitment Behavior in Software Projects1
Keil, M., Tan, B. C. Y., Wei, K., Saarinen, T., Tuunainen, V. K., & Wassenaar, A. (2000) · MIS Quarterly · doi:10.2307/3250940 · Cited by 1,402
Consumer e-shopping acceptance: Antecedents in a technology acceptance model
Ha, S., & Stoel, L. (2009) · Journal of Business Research · doi:10.1016/j.jbusres.2008.06.016 · Cited by 1,353
Soft modelling: The Basic Design and Some Extensions
Wold, H. (1982) · Medical Entomology and Zoology · Cited by 1,314
Understanding online purchase intentions: contributions from technology and trust perspectives
van der Heijden, H., Verhagen, T., & Creemers, M. (2003) · European Journal of Information Systems · doi:10.1057/palgrave.ejis.3000445 · Cited by 1,230
Commercial Friendships: Service Provider-Client Relationships in Context
Price, L. L., & Arnould, E. J. (1999) · Journal of Marketing · doi:10.2307/1251973 · Cited by 1,201
Testing the Determinants of Microcomputer Usage via a Structural Equation Model
Igbaria, M., Guimãrães, T., & Davis, G. B. (1995) · Journal of Management Information Systems · doi:10.1080/07421222.1995.11518061 · Cited by 1,184
Porter, C. E., & Donthu, N. (2006) · Journal of Business Research · doi:10.1016/j.jbusres.2006.06.003 · Cited by 1,066
Overview of the Technology Acceptance Model: Origins, Developments and Future Directions
Chuttur, M. Y. (2009) · Cited by 1,041
Garbarino, E., & Strahilevitz, M. (2004) · Journal of Business Research · doi:10.1016/s0148-2963(02)00363-6 · Cited by 954
The Influence of Avatars on Online Consumer Shopping Behavior
Holzwarth, M., Janiszewski, C. A., & Neumann, M. M. (2006) · Journal of Marketing · doi:10.1509/jmkg.70.4.19 · Cited by 851
Internet Recommendation Systems
Ansari, A., Essegaier, S., & Kohli, R. (2000) · Journal of Marketing Research · doi:10.1509/jmkr.37.3.363.18779 · Cited by 711
An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms
Herlocker, J., Konstan, J. A., & Riedl, J. (2002) · Information Retrieval · doi:10.1023/a:1020443909834 · Cited by 662
Web retailing adoption: exploring the nature of internet users Web retailing behaviour
O’Cass, A., & Fenech, T. O. (2003) · Journal of Retailing and Consumer Services · doi:10.1016/s0969-6989(02)00004-8 · Cited by 660
Ansari, A., & Mela, C. F. (2003) · Journal of Marketing Research · doi:10.1509/jmkr.40.2.131.19224 · Cited by 639
Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses
Fitzsimons, G. J., & Lehmann, D. R. (2004) · Marketing Science · doi:10.1287/mksc.1030.0033 · Cited by 604
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs
Wang, W., & Benbasat, I. (2007) · Journal of Management Information Systems · doi:10.2753/mis0742-1222230410 · Cited by 541
Web Acceptance Model (WAM): Moderating effects of user experience
García, J. A. C., Muñoz‐Leiva, F., & Martínez, T. L. (2007) · Information & Management · doi:10.1016/j.im.2007.02.003 · Cited by 413
Lee, H., Fiore, A. M., & Kim, J. (2006) · International Journal of Retail & Distribution Management · doi:10.1108/09590550610675949 · Cited by 315
Applying the technology acceptance model to the online retailing of financial services
Mckechnie, S. A., Winklhofer, H. M., & Ennew, C. (2006) · International Journal of Retail & Distribution Management · doi:10.1108/09590550610660297 · Cited by 271
Causal flows with latent variables
Wold, H. (1974) · European Economic Review · doi:10.1016/0014-2921(74)90008-7 · Cited by 269
A comparison of three models to explain shop‐bot use on the web
Gentry, L., & Calantone, R. J. (2002) · Psychology and Marketing · doi:10.1002/mar.10045 · Cited by 260
Recommendation Systems with Purchase Data
Bodapati, A. V. (2008) · Journal of Marketing Research · doi:10.1509/jmkr.45.1.77 · Cited by 235
Yu, L., Liu, L., & Li, X. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.08.013 · Cited by 216
Krämer, T. (2007) · Journal of Marketing Research · doi:10.1509/jmkr.44.2.224 · Cited by 160
Mild, A., & Reutterer, T. (2003) · Journal of Retailing and Consumer Services · doi:10.1016/s0969-6989(03)00003-1 · Cited by 139
Reengineering IS Research and Its Intellectual Infrastructure for the Electronic Economy
Hars, A., Sawy, O. A. E., & Gosain, S. (2000) · Journal of Organizational Computing and Electronic Commerce · doi:10.1207/s15327744joce100101 · Cited by 139
Adoption of travel e‐shopping in the UK
Kamarulzaman, Y. (2007) · International Journal of Retail & Distribution Management · doi:10.1108/09590550710773255 · Cited by 133
Consumer acceptance of online auctions: An extension and revision of the TAM
Stern, B. B., Stafford, M. R., Stafford, T. F., & Bienstock, C. C. (2008) · Psychology and Marketing · doi:10.1002/mar.20228 · Cited by 110
Developments in information and communication technologies for retail marketing channels
Barlow, A., Siddiqui, N. Q., & Mannion, M. (2004) · International Journal of Retail & Distribution Management · doi:10.1108/09590550410524948 · Cited by 88
Should Recommendation Agents Think Like People?
Aksoy, L., Bloom, P. N., Lurie, N. H., & Cooil, B. (2006) · Journal of Service Research · doi:10.1177/1094670506286326 · Cited by 84
Learning by Collaborative and Individual‐Based Recommendation Agents
Ariely, D., Lynch, J. G., & Aparicio, M. (2004) · Journal of Consumer Psychology · doi:10.1207/s15327663jcp1401&2_10 · Cited by 83
Comegys, C., Hannula, M., & Väisänen, J. (2006) · Journal of Targeting, Measurement and Analysis for Marketing · doi:10.1057/palgrave.jt.5740193 · Cited by 82
Utilizing Popularity Characteristics for Product Recommendation
Ahn, H. J. (2006) · International Journal of Electronic Commerce · doi:10.2753/jec1086-4415110203 · Cited by 74
Web-based Sales: Defining the Cognitive Buyer
Zellweger, P. (2000) · Vieweg+Teubner eBooks · doi:10.1007/978-3-322-86793-3_9 · Cited by 51
Web-Based sales: Defining The Cognitive Buyer
Zellweger, P. (1997) · Electronic Markets · doi:10.1080/10196789700000030 · Cited by 38
The effectiveness of verbal prompts on sales
Ebster, C., Wagner, U., & Valis, S. (2005) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2005.08.003 · Cited by 31
A new recommender system to combine content-based and collaborative filtering systems
Kim, B.-D., & Kim, S.-O. (2001) · Journal of Database Marketing & Customer Strategy Management · doi:10.1057/palgrave.jdm.3240040 · Cited by 26
Königstorfer, J., & Gröppel‐Klein, A. (2007) · Cited by 16
The art of the ask: Maximizing verbal compliance in telefundraising
Simmel, L. L., & Berger, P. D. (2000) · Journal of Interactive Marketing · doi:10.1002/1520-6653(200022)14:3 · Cited by 11
Research works printed but not linked: 4
- Chau, 2000, Impact of information presentation modes on online shopping: an empirical evaluation of a broadband interactive shopping service, Journal of Organizational Computing and Electronic Commerce, 10, 1
- Gaul, 2002, eMarketing mittels recommendersystemen, Marketing ZFP, 24, 47
- Hansen, 2007, Empfehlungssysteme zur Verkaufsberatung im Internet. State-of-the-art und Konsumentenakzeptanz, Wirtschaftsinformatik, 49, 50
- Venkatesh, V., 1999. Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation, MIS Quarterly, 23 (2), 239-260. · DOI
Cited by
Sorted by citations
\nThe use of partial least squares path modeling in international marketing
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) · Radboud Repository (Radboud University) · doi:10.1108/s1474-7979(2009)0000020014 · Cited by 12,535
Effects of Restaurant Satisfaction and Knowledge Sharing Motivation on eWOM Intentions
Yang, F. X. (2013) · Journal of Hospitality & Tourism Research · doi:10.1177/1096348013515918 · Cited by 340
Pizzi, G., Scarpi, D., & Pantano, E. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.006 · Cited by 265
Faqih, K. M. S. (2016) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2016.01.016 · Cited by 262
Social network for the choice of tourist destination: attitude and behavioural intention
Pietro, L. D., Virgilio, F. D., & Pantano, E. (2012) · Journal of Hospitality and Tourism Technology · doi:10.1108/17579881211206543 · Cited by 225
Metadata
- Authors
- Baier, D., & Stüber, E.
- Affiliations
- Brandenburg University of Technology Cottbus-Senftenberg (DE)
- Corresponding author
- Baier, Daniel
- ORCID
- Baier, Daniel
- Venue
- Journal of Retailing and Consumer Services
- Publisher
- Elsevier BV
- Published
- 2010-05
- Volume
- 17
- Issue
- 3
- Pages
- 173-180
- ISSN
- 0969-6989 (ISSN-L)
- OpenAlex
- W2084468777
- Cited by
- 106
- References
- 67
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 10 · 2023: 2 · 2022: 12 · 2021: 7 · 2020: 11 · 2019: 7 · 2018: 7 · 2017: 3 · 2016: 6 · 2015: 10 · 2014: 7 · 2013: 2 · 2012: 9
- FWCI
- 11.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Retailing and Consumer Services (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- online retail, personalized recommendation, technology acceptance, consumer acceptance, recommendation quality, online shopping experience, customer satisfaction, personalization
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