Cited by 204AI in Service InteractionsPrivacy, Security, and Data ProtectionDigital Marketing and Social Media

Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing

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Thomaz, Felipe, Salge, Carolina, Karahanna, Elena, Hulland, John·2020·Journal of the Academy of Marketing Science

Abstract

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.

    “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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Research works printed but not linked: 3

  1. Cortes, C., Fisher, K., Pregibon, D., Rogers, A., & Smith, F. (2000). Hancock: A language for extracting signatures from data streams. In Proc. of the 2000 ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 9-17.
  2. Mobasher, B. (2007). The Adaptive Web (P. Brusilovsky, A. Kobsa, & W. Nejdl, Eds.). Retrieved from http://dl.acm.org/citation.cfm?id= 1768197.1768201
  3. Tuzhilin, A. (2008). Personalization: The state of the art and future direc- tions. In Business Computing. Retrieved from https://www. researchgate.net/publication/302958247_Personalization_The_ state_of_the_art_and_future_directions

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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
Locations
  • Journal of the Academy of Marketing Science (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
University of Oxford
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Social Sciences › Sociology and Political Science › 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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