The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Chatbot advertising effectiveness: When does the message get through?
Open accessAbstract
Abstract Chatbots on social networking sites are a recent innovation in computer-mediated marketing communication. In this study, 245 Facebook users between 18 and 35 years of age (Mage = 25.97, SD = 4.92) were asked to order tickets for the movies through Cinebot, a Facebook chatbot specifically built for the study. Afterwards, they were asked to evaluate their experiences via an online survey. The first purpose of this article was to investigate whether and how perceived helpfulness and usefulness of a chatbot consulted on the Facebook Messenger platform affected perceived intrusiveness of chatbot-initiated advertising in a later stage. In a second analysis, the relation between perceived intrusiveness and patronage intentions (i.e. purchase and recommendation intention of the product) was investigated. In addition, the role of message acceptance as a mediator and perceived message relevance as a moderator in this latter model were explored. As, to the best of our knowledge, our study is the first to investigate chatbot advertising, our research findings may hold important managerial implications.
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
Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007) · Multivariate Behavioral Research · doi:10.1080/00273170701341316 · Cited by 9,973
Introduction to mediation, moderation, and conditional process analysis :
Hayes, A. F., & names (2015) · Cited by 7,004
(1985) · Cited by 6,185
Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement
Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983) · Journal of Consumer Research · doi:10.1086/208954 · Cited by 4,610
Baker, J., Parasuraman, A., Grewal, D., & Voss, G. B. (2002) · Journal of Marketing · doi:10.1509/jmkg.66.2.120.18470 · Cited by 2,257
Consumer trust in an internet store: a cross-cultural validation
Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (2006) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.1999.tb00337.x · Cited by 1,882
Intentions to Use Mobile Services: Antecedents and Cross-Service Comparisons
Nysveen, H. (2005) · Journal of the Academy of Marketing Science · doi:10.1177/0092070305276149 · Cited by 1,291
Acquisti, A., Taylor, C., & Wagman, L. (2016) · Journal of Economic Literature · doi:10.1257/jel.54.2.442 · Cited by 1,283
Managing User Trust in B2C e-Services
Gefen, & Straub (2003) · e-Service Journal · doi:10.2979/esj.2003.2.2.7 · Cited by 1,074
Cited by
Artificial Intelligence in Supply Chain Management: A Systematic Literature Review
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.009 · Cited by 1,020
“Chatbots on social networking platforms and websites represent a new innovation in computer-based marketing communication (Van den Broeck et al., 2019).”
BackgroundTaxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764
“In customer service chatbots, users more often accepted “intrusiveness” from chatbots that were perceived to be more helpful and useful [183], suggesting that”
BackgroundUnderstanding the attitude and intention to use smartphone chatbots for shopping
Kasilingam, D. L. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101280 · Cited by 636
Ethical and social risks of harm from Language Models
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04359 · Cited by 477
Roy, R., & Naidoo, V. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.12.051 · Cited by 389
Metadata
- Authors
- Van den Broeck, E., Zarouali, B., & Poels, K.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 98
- Pages
- 150-157
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2938704773
- Cited by
- 257
- References
- 58
- FWCI
- 11.51
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Chatbot advertising effectiveness: When does the message get through? https://app.cochat.ai/papers/10.1016/j.chb.2019.04.009
- The paper
- Van den Broeck, E., Zarouali, B., & Poels, K. (2019). Chatbot advertising effectiveness: When does the message get through? Computers in Human Behavior. https://doi.org/10.1016/j.chb.2019.04.009
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.
