The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective
Open accessAbstract
Artificial intelligence (AI) is (re)shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications (will) provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being.
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
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 13×
“It emphasizes the importance of safety, security, and privacy as well as the prevention of risks and any harm— both accidentally/unintentionally (overuse) and deliberately (misuse) caused (Floridi et al., 2018; Jobin et al., 2019).”
BackgroundMethodInfluentialFloridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · cited 5×
“This multiperspectivity further accounts for the AI-forsocial-good perspective stressed by prior AI ethics literature (e.g., Cowls et al., 2021; Floridi et al., 2018, 2020; Taddeo & Floridi, 2018).”
BackgroundInfluentialThe ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567
Universal dimensions of social cognition: warmth and competence
Fiske, S. T., Cuddy, A. J. C., & Glick, P. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.11.005 · Cited by 4,567
On seeing human: A three-factor theory of anthropomorphism
Epley, N., Waytz, A., & Cacioppo, J. T. (2007) · Psychological Review · doi:10.1037/0033-295x.114.4.864 · Cited by 4,069
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232
Cited by
AI-powered marketing: What, where, and how?
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102783 · Cited by 509
Corporate digital responsibility in service firms and their ecosystems
Wirtz, J., Kunz, W. H., Hartley, N., & Tarbit, J. (2023) · Journal of Service Research · doi:10.1177/10946705221130467 · Cited by 291
Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology
Rivas, P., & Zhao, L. (2023) · AI · doi:10.3390/ai4020019 · Cited by 191
Figueroa-Armijos, M., Clark, B. B., & da Motta Veiga, S. P. (2023) · Journal of Business Ethics · doi:10.1007/s10551-022-05166-2 · Cited by 164
Artificial Intelligence Disclosure in Cause-Related Marketing: A Persuasion Knowledge Perspective
Qiu, X., Wang, Y., Zeng, Y., & Cong, R. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20030193 · Cited by 35
Metadata
- Authors
- Hermann, E.
- Venue
- Journal of Business Ethics
- Published
- 2021-05-26
- Volume
- 179
- Issue
- 1
- Pages
- 43-61
- ISSN
- 0167-4544 (ISSN-L)
- 1573-0697
- OpenAlex
- W3164960502
- Cited by
- 403
- References
- 65
- FWCI
- 54.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective. https://app.cochat.ai/papers/10.1007/s10551-021-04843-y
- The paper
- Hermann, E. (2022). Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective. Journal of Business Ethics. https://doi.org/10.1007/s10551-021-04843-y
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.