The DOI matches its Crossref record; 133 of the 133 references it cites were found in a registry or an index.
Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing
Open accessAbstract
The Web is a constantly evolving, complex system, with important implications for both marketers and consumers. In this paper, we contend that over the next five to ten years society will see a shift in the nature of the Web, as consumers, firms and regulators become increasingly concerned about privacy. In particular, we predict that, as a result of this privacy-focus, various information sharing and protection practices currently found on the Dark Web will be increasingly adapted in the overall Web, and in the process, firms will lose much of their ability to fuel a modern marketing machinery that relies on abundant, rich, and timely consumer data. In this type of controlled information-sharing environment, we foresee the emersion of two distinct types of consumers: (1) those generally willing to share their information with marketers (Buffs), and (2) those who generally deny access to their personal information (Ghosts). We argue that one way marketers can navigate this new environment is by effectively designing and deploying conversational agents (CAs), often referred to as “chatbots.” In particular, we propose that CAs may be used to understand and engage both types of consumers, while providing personalization, and serving both as a form of differentiation and as an important strategic asset for the firm—one capable of eliciting self-disclosure of otherwise private consumer information.
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“Thomaz et al. (2020) identify two types of consumers who vary in willingness to share information.”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Thomaz et al. (2020)
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Cites
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Items 201–204 of 204
When the Vault is Never Opened: AI‐Enabled Inference and the Future of Consumer Privacy
Brough, A. R. (2026) · Consumer Psychology Review · doi:10.1002/arcp.70014 · Cited by 2
The Fake Friend Dilemma: Relational Trust and the Political Economy of Conversational AIPreprint
Erickson, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7471958
López-López, D., Marco-Simó, J. M., Martínez, R. G., Cruz-Suárez, A., & Torres-Pruñonosa, J. (2026) · Applied Artificial Intelligence · doi:10.1080/08839514.2026.2684087
Marx, T., Hubert, M., & Mariani, M. M. (2026) · Psychology and Marketing · doi:10.1002/mar.70280
Metadata
- Authors
- Thomaz, F., Salge, C., Karahanna, E., & Hulland, J.
- Affiliations
- University of Oxford (GB)
- Wake Forest University (US)
- University of Georgia (US)
- Corresponding author
- Thomaz, Felipe
- Venue
- Journal of the Academy of Marketing Science
- Published
- 2019-11-11
- Volume
- 48
- Issue
- 1
- Pages
- 43-63
- ISSN
- 0092-0703 (ISSN-L)
- 1552-7824
- OpenAlex
- W2986577019
- Cited by
- 204
- References
- 131
- Citations by year
- 2026: 25 · 2025: 30 · 2024: 38 · 2023: 38 · 2022: 36 · 2021: 23 · 2020: 13 · 2019: 1
- FWCI
- 8.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of the Academy of Marketing Science (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- University of Oxford
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- dark web, chatbots, consumer privacy, customer data, self-disclosure, personalization, customer segmentation, marketing strategy, information sharing
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