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.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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)
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
Adomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,539 · cited 5×
“Adomavicius and Tuzhilin (2005).”
BackgroundMethodInfluentialHierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A. J., & Hovy, E. H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1174 · Cited by 5,022 · cited 1×
“However, emotion and sentiment tracking are generally based on the current interaction (e.g., Yang et al. 2016, Chen et al. 2016) and can be used to personalize interaction with Buff and Ghost consumers alike.”
MethodAlone together: why we expect more from technology and less from each other
Fernández Pedemonte, D. (2011) · Choice Reviews Online · doi:10.5860/choice.48-7239 · Cited by 4,608 · cited 3×
“…and execute tasks (Sankar & Balakrishnan, 2016; Shum, He, & Li, 2018), in addition to providing more enjoyable experiences (Brandtzaeg & Følstad, 2018) and a sense of companionship (Turkle 2017; Brandtzaeg & Følstad, 2017), CAs can nudge consumers to voluntarily share personal data with companies.”
BackgroundWarren, S. D., & Brandeis, L. D. (1890) · University of Washington Press eBooks · doi:10.1515/9780295803661-014 · Cited by 4,092
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 3×
“…conscious attention to a subset of contextual cues that trigger various scripts from the past (Langer, 1992; Moon, 2000, 2003; Nass & Moon, 2000; Nass, Steuer, & Tauber, 1994)) that are applied mindlessly even when such behaviors seem irrational, inappropriate, or unnecessary (Nass & Moon, 2000).”
BackgroundBig Data : The Management Revolution
マカフィー, ア., ブリニョルフソン, エ., & 有賀, 裕. (2013) · Harvard business review · Cited by 3,545
The Uncanny Valley [From the Field]
Mori, M., MacDorman, K., & Kageki, N. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2192811 · Cited by 3,542 · cited 1×
“Excessive anthropomorphism can also trigger consumer discomfort (also known as the “ uncanny valley ” concept — see Mori, MacDorman, & Kageki, 2012; van Doorn et al., 2017) leading to decreased favorability toward the CA (Mende et al., 2019).”
BackgroundAn Extended Privacy Calculus Model for E-Commerce Transactions
Dinev, T., & Hart, P. (2006) · Information Systems Research · doi:10.1287/isre.1060.0080 · Cited by 2,640 · cited 2×
“Consumers weigh privacy concerns and related risks against the benefits of information disclosure (e.g., Dinev & Hart, 2006).”
Background(2012) · PubMed · Cited by 2,326
Nass, C., Steuer, J., & Tauber, E. R. (1994) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/191666.191703 · Cited by 2,148 · cited 1×
“…of conscious attention to a subset of contextual cues that trigger various scripts from the past (Langer, 1992; Moon, 2000, 2003; Nass & Moon, 2000; Nass, Steuer, & Tauber, 1994)) that are applied mindlessly even when such behaviors seem irrational, inappropriate, or unnecessary (Nass & Moon,…”
Research works printed but not linked: 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
- Tuzhilin, A. (2008). Personalization: The state of the art and future directions. In Business Computing. Retrieved from https://www.researchgate.net/publication/302958247_Personalization_The_state_of_the_art_and_future_directions
Cited by
Sorted by citations
Items 101–200 of 204
Pavone, G., Meyer‐Waarden, L., & Munzel, A. (2024) · Recherche et Applications en Marketing (English Edition) · doi:10.1177/20515707241283311 · Cited by 15
“The papers in this cluster mainly focus on the ethics of AI (Du and Xie, 2020), for instance on issues related to privacy, biases, social influences, and risk of manipulation (Thomaz et al., 2020).”
Loyal customers’ tipping points of spending for services: a reciprocity perspective
Teichmann, K. (2021) · European Journal of Marketing · doi:10.1108/ejm-10-2019-0781 · Cited by 14
A chatbot response generation system
Feine, J., Morana, S., & Maedche, A. (2020) · Mensch und Computer (MuC) · doi:10.1145/3404983.3405508 · Cited by 14
“to adapt to individual users [14, 54, 58], act as emotion regulators [48, 53], eectively support collaborative collocated talk-in-action”
BackgroundThe AI chatbot anthropomorphism dilemma
Nikolov, A. N., Iyer, P., Rokonuzzaman, M., Batra, G., Eskridge, B., & Sen, S. (2025) · Marketing Intelligence & Planning · doi:10.1108/mip-09-2024-0623 · Cited by 15
Eng, P. H., & Liu, R. L. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-61463-7_2 · Cited by 14
Tan, T. M., Salo, J., & Aspara, J. (2024) · Journal of business research · doi:10.1016/j.jbusres.2023.114367 · Cited by 11
Bodily feelings as information: the embodied cognition perspective on robotic services
Xiong, X., Wong, I. A., & Yang, F. X. (2025) · European Journal of Marketing · doi:10.1108/ejm-06-2023-0457 · Cited by 13
The consumption of dark narratives: A systematic review and research agenda
Luna-Cortés, G., López‐Bonilla, L. M., & López‐Bonilla, J. M. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.013 · Cited by 12
Public Policy Issues and Technoethics in Marketing Research in the Digital Age
Mandal, P. C. (2020) · International Journal of Technoethics · doi:10.4018/ijt.2021010105 · Cited by 12
Consumer (dis)engagement coping profiles using online services in managing health‐related stressors
Keeling, D. I., de Ruyter, K., & Laing, A. W. (2022) · Psychology and Marketing · doi:10.1002/mar.21717 · Cited by 10
The Digital and Physical Footprint of Dark Net Markets
Thomaz, F. (2020) · Journal of International Marketing · doi:10.1177/1069031x19898678 · Cited by 10
“…markets and deployed to solve problems related to illegal trade and trafficking, so can work developed in black markets test boundaries of existing theory, generate managerial insights, and develop knowledge that can be applied and transferred to standard marketing practice (Thomaz et al. 2020).”
BackgroundSelf-citationUnlock Happy Interactions: Voice Assistants Enable Autonomy and Timeliness
Mo, L., Zhang, L., Sun, X., & Zhou, Z. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19020053 · Cited by 11
Is being human-like beneficial? The effect of anthropomorphism on chatbot persuasion in e-commerce
Yang, M., Peng, X., Wang, Q., Zhao, Y. C., & Wang, X. (2025) · Internet Research · doi:10.1108/intr-10-2023-0866 · Cited by 11
Metaverse and B2B marketing: untapped research opportunities
Bakeshloo, K. A., Agnihotri, R., & Mohammadzadeh, M. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115592 · Cited by 11
Mer, A., & Virdi, A. S. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83753-750-120251014 · Cited by 8
Oyserman, D., & Schwarz, N. (2020) · Journal of Consumer Psychology · doi:10.1002/jcpy.1189 · Cited by 8
“Companies are aware that people are more willing to reveal extra information in anthropomorphized interchanges (Thomaz, Salge, Karahanna, & Hulland, 2020) and make efforts to anthropomorphize themselves, their brands, and speci fi c products (Aggarwal & McGill, 2007; Chan-dler & Schwarz, 2010;…”
Designing Interactive Chatbot Development Systems
Feine, J., Morana, S., & Maedche, A. (2020) · Journal of the Association for Information Systems · Cited by 8
Chaudhary, A., & Khandelwal, S. (2024) · Advanced series in management · doi:10.1108/s1877-63612024000034a011 · Cited by 8
Mehmood, K., Rehman, M. A., Abbass, A., & Woyo, E. (2025) · Journal of Consumer Behaviour · doi:10.1002/cb.70016 · Cited by 7
Mohammed, A., & Ferraris, A. (2025) · The Bottom Line Managing Library Finances · doi:10.1108/bl-11-2024-0189 · Cited by 9
Use of Online Shop Chatbots: How Trust in Seller Moderates Brand Preference and Purchase Intention
Illescas-Manzano, M. D., Martínez, S., Cardoso, P. R., & López, C. S. (2024) · Springer proceedings in business and economics · doi:10.1007/978-3-031-70488-8_10 · Cited by 8
Regulating sharing platforms in lateral exchange markets: the role of power and trust
Tang, X., Yong, L., Zhou, P., & Lowe, B. (2023) · European Journal of Marketing · doi:10.1108/ejm-06-2021-0432 · Cited by 9
Kronemann, B., Kizgin, H., & Rana, N. P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15342-6_6 · Cited by 8
Shared digital artifacts – Co-creators as beneficiaries in microlearning development
de Gafenco, M. T., Weinert, T., Janson, A., Klusmeyer, J., & Leimeister, J. M. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12074-z · Cited by 8
“Future research should focus on just-in-time support features, such as chat-bots and prompts, while overall designing a less rigid procedure of work process documentation (Bittner et al., 2021; Thomaz et al., 2020).”
AI-Based Digital Product ‘Robo-Advisory’ for Financial Investors
Khanna, P., & Jha, S. (2024) · Vision The Journal of Business Perspective · doi:10.1177/09722629241298399 · Cited by 6
Building an AI/ML Based Classification Framework for Dark Web Text Data
Murty, C. A. S., Rana, H., Verma, R., Pathak, R., & Rughani, P. H. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0604-6_9 · Cited by 6
Sartono, E. S., Wardhana, C. S. P., Princes, E., Karmawan, I. G. M., Ikhsan, R. B., & Gui, A. (2023) · International Conference on Information Technology, Computer, and Electrical Engineering · doi:10.1109/icitacee58587.2023.10277654 · Cited by 6
“Organizations choose chatbots to instantaneously engage with consumers due to their efficiency and advantages since semantic analysis technology enables them to converse autonomously to comprehend and respond to user requests [8][9][10][11][12].”
BackgroundDoes Fun Matter? Using Chatbots for Customer Services
Wut, T. M., Chan, E. A.-H., & Wong, H. S.-M. (2024) · Informatics · doi:10.3390/informatics11040094 · Cited by 8
“Communication style is the most easily controlled factor for the chatbots’ human-like development [34].”
Should I Use ChatGPT Travel Insurance Recommendations? A Dual‐Process Theory Perspective
Herjanto, H., Amin, M., & Çobanoğlu, C. (2025) · International Journal of Consumer Studies · doi:10.1111/ijcs.70044 · Cited by 4
The dark web: A hidden menace or a tool for privacy protection
Kaur, G., Mukherjee, D., Moza, B., Pahwa, V., Kaur, K., & Kaur, K. (2024) · IP International Journal of Forensic Medicine and Toxicological Sciences · doi:10.18231/j.ijfmts.2023.034 · Cited by 5
Beyond the Onion Routing: Unmasking Illicit Activities on the Dark Web
Bidv, V., & Waghmare, A. S. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24may1698 · Cited by 5
Pavone, G., Meyer‐Waarden, L., & Munzel, A. (2024) · Recherche et Applications en Marketing (French Edition) · doi:10.1177/07673701241270105 · Cited by 6
“Les articles de ce groupe se concentrent principalement sur l’éthique de l’IA (Du et Xie, 2021), par exemple sur les questions liées à la vie privée, aux biais, aux influences sociales et au risque de manipulation (Thomaz et al., 2020).”
A Bibliometric Analysis of Artificial Intelligence and Human Resource Management
Sahoo, M., & Mishra, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-5652-4_4 · Cited by 5
Hyper-personalization and Its Impact on Customer Buying Behaviour
Kumar, S., Rajan, R. A., Swaminathan, A., & Johnson, E. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-19-6004-8_50 · Cited by 5
Generative AI: capturing strategic value in businesses
do Prado, P. F., & Mantovani, D. M. N. (2025) · Revista de Gestão · doi:10.1108/rege-02-2025-0026 · Cited by 5
Lee, C.-H., Hwang, Y., & Song, H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2491024 · Cited by 4
“Recent advances in artificial intelligence and natural language processing have led to the emergence of conversational interfaces (Dale, 2016; Thomaz et al., 2020).”
Artificial intelligence and value-based strategy: a literature review and future research directions
Islami, X., Mulolli, E., & Mustafa, N. (2025) · Management Review Quarterly · doi:10.1007/s11301-025-00549-x · Cited by 6
Paul, S., Mitra, A., Shambhavi, S., Ghosh, S., & Bandyopadhyay, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.503 · Cited by 4
Does a chatbots location influence consumer attitude and intentions
Roozen, I. T. M., Raedts, M., & Waetermans, G. (2022) · International Journal of Internet Marketing and Advertising · doi:10.1504/ijima.2022.10047372 · Cited by 4
Artificial Intelligence in Human Resource Management: Personnel Marketing and Recruiting
Lüersmann, A. (2023) · Studies in systems, decision and control · doi:10.1007/978-3-031-27506-7_23 · Cited by 5
Short Text Processing for Analyzing User Portraits: A Dynamic Combination
Ding, Z., Yan, C., Liu, C., Ji, J., & Liu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61616-8_59 · Cited by 4
Measuring and Evaluating Internal Communication
O’Neil, J., Ewing, M. E., Smith, S. L., & Williams, S. D. (2021) · New perspectives in organizational communication · doi:10.1007/978-3-030-78213-9_12 · Cited by 4
Sayyed, H., & Paul, S. R. (2025) · Cogent Social Sciences · doi:10.1080/23311886.2025.2479654 · Cited by 3
“…privacy challenges in the context of national security and law enforcement, particularly in india, where the digital landscape is rapidly evolving (thomaz et al., 2020). as the country grapples with the growing threat of cyber terrorism, there is a need to balance the fundamental rights to…”
Haji, I. H. A., Temprano-García, V., & Peluso, A. M. (2025) · European Journal of Marketing · doi:10.1108/ejm-08-2023-0629 · Cited by 4
Wang, H., Wang, N., Wang, H., & Wang, J. (2026) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-06-2025-1104 · Cited by 3
Jiang, Y., & Duan, X. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2631679 · Cited by 4
“Users typically lack openness regarding how the customized solutions are produced (Thomaz et al., 2020), what data are being prioritized, and if the GenAI is under influence by hidden prejudices or mistakes, hence amplifying the sense of vulnerability.”
Impulsive consumer digital shift: virtual reality as a new mode of physical experience
Nayyar, V. (2024) · Total Quality Management & Business Excellence · doi:10.1080/14783363.2024.2436560 · Cited by 3
“In spite of this, if companies can adopt the conversational agent ideology, which has been proven remarkable and useful to companies when they are facilitating segmented populations due to their obligation to disclose personal information (Thomaz et al., 2020).”
Kyriakidou, N., Trem, K., Ogbemudia, J., & Mahtab, N. (2022) · Palgrave studies in cross-disciplinary business research, in association with EuroMed Academy of Business · doi:10.1007/978-3-031-07765-4_8 · Cited by 2
Comparing eigenvector and degree dispersion with the principal ratio of a graph
Clark, G. J. (2022) · Linear and Multilinear Algebra · doi:10.1080/03081087.2022.2158171 · Cited by 2
“These networks feature structural properties which are distinct from their legal counterparts making them more resilient to external monitoring and intervention [17, 18].”
BackgroundXinshuo, L., & Yingjian, W. (2023) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip60502.2023.10387095 · Cited by 2
“Not surprisingly, business interest in chatbots is soaring based on the above-mentioned huge business advantages (Thomaz et al., 2019).”
BackgroundHelp-seeking practices and behaviours in organizations and AI-driven chatbot: a scoping review
Pagnozzi, F., Renkema, M., & Antonelli, G. (2024) · puntOorg International Journal · doi:10.19245/25.05.of.13.06.24.1 · Cited by 4
Artificial Intelligence Automation in Advent of Human Resource Management
Kathuria, S., Duggal, A. S., & Lande, J. (2024) · Parul International Conference on Engineering and Technology (PICET) · doi:10.1109/picet60765.2024.10716197 · Cited by 2
Cryptomarkets: History, Structure and Operations
Harinam, V., & Ariel, B. (2024) · Springer eBooks · doi:10.1007/978-3-031-62821-4_2 · Cited by 1
Nayyar, V. (2024) · Information Discovery and Delivery · doi:10.1108/idd-04-2024-0054 · Cited by 1
Eichler, K. S., & Schwab, E. (2025) · Journal of Applied Business and Economics · doi:10.33423/jabe.v27i2.7607 · Cited by 1
“The current state of research indicates that the more conversational robo-advisors are tailored to individual consumers, the more likely clients are to perceive the interaction as social rather than mechanistic, fostering affective trust (Hildebrand & Bergner, 2021; Thomaz et al., 2020).”
Mele, G., Spagnolo, A., Passıante, G., & Fiorentini, A. (2025) · Springer proceedings in complexity · doi:10.1007/978-3-031-78623-5_8 · Cited by 1
Textual or visual: consumer responses to compensation after AISA service failure
Xie, C., Zhao, M., Yang, C., & Chang, E.-C. (2025) · European Journal of Marketing · doi:10.1108/ejm-07-2023-0524 · Cited by 1
Privacy and security in mobile technology: A bibliometric analysis for marketing strategies
Euch, H. K. E., Said, F. B., & Jallouli, R. (2025) · Management & Marketing · doi:10.2478/mmcks-2025-0015 · Cited by 1
“While these innovations o ff er unprecedented opportunities, their ethical deployment requires a commitment to transparency, privacy protection, and continuous adaptation (Manda et al., 2024; Sachdev, 2020; Thomaz et al., 2019).”
Duy, P. K., Hieu., C. V., Cong, T. H., Phang, L. H. V., & Nghia, N. C. (2026) · Journal of Consumer Behaviour · doi:10.1002/cb.70152 · Cited by 1
Talking AI: how conversational styles shape consumer attitudes in hedonic and utilitarian contexts
Li, S.-W., Pan, Y., Kim, K.-M., & Lee, S. (2026) · Marketing Intelligence & Planning · doi:10.1108/mip-03-2025-0206 · Cited by 1
The Ethical Interaction Trilogy Among Companies, Customers, and AI-Chatbots
Ragab, F. A., & Belal, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3361-8.ch005 · Cited by 1
The Impact of Internet Content Regulation Policies on the Promotion of Targeted Consumer Behavior
Wang, J., Grebennikova, V. M., Хазова, С. А., & Malkov, A. A. (2023) · Springer proceedings in business and economics · doi:10.1007/978-3-031-27785-6_15 · Cited by 1
Agent-Based Modelling for Criminal Network Interventions
Harinam, V., & Ariel, B. (2024) · Springer eBooks · doi:10.1007/978-3-031-62821-4_5 · Cited by 1
Does a chatbot's location influence consumer attitude and intentions
Roozen, I. T. M., Raedts, M., & Waetermans, G. (2024) · International Journal of Internet Marketing and Advertising · doi:10.1504/ijima.2024.139294 · Cited by 1
Ghosts in the Dark: How to Prepare for Times of Hyper-Privacy
Thomaz, F. (2021) · NIM Marketing Intelligence Review · doi:10.2478/nimmir-2021-0006 · Cited by 1
Self-citationDynamic Web Page Graphic Design Method for Internet Big Data Information System
Tao, D. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/6753671 · Cited by 1
“Experiments show that this method is suitable for single-query and multiquery forms with adaptive weight characteristics [7].”
MethodKenny, D., Fox, G., Sinclair, G., & Lynn, T. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.576
“We find that while current literature on the DarkNet has explored some relevant topics (i) privacy [5, 10], (ii) cybersecurity [18, 16], (iii) drug research [19] and (iv) the study of cybercriminal communities, [16, 20] (particularly DarkNet marketplaces), no research to this point has considered how the unique Procee…”
BackgroundInfluentialHow institutional pressures influence data-driven corporate communications
Economou, E. (2021) · Queensland University of Technology · doi:10.5204/thesis.eprints.209153
Humanizing Chatbots: The Effect of Fear from the COVID-19 Pandemic
Dinh, C.-M., & Park, S. S. (2022) · Springer proceedings in business and economics · doi:10.1007/978-3-031-05728-1_4
Privacy and Intimacy Concerns in Digital Marketing
Vila-Boix, L., Miotto, G., & Blanco‐González, A. (2023) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-6684-8958-1.ch009
Xue, K. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.242761 · Cited by 1
AI-Based Chatbot Agents as Drivers of Purchase Intentions: An Interdisciplinary Study
Tyagi, P., & Jain, A. (2023) · Internet of things · doi:10.1007/978-3-031-33808-3_5 · Cited by 1
Whatsapp no trabalho e esgotamento profissional: pesquisa com profissionais da indústria têxtil
de Oliveira, I. D., Césa, E. S., Nobre, A. C. D. S., Neto, R. A. D. S., & Andrade, C. G. M. D. (2023) · Revista de Gestão e Secretariado (Management and Administrative Professional Review) · doi:10.7769/gesec.v14i9.2547
ПЕРСОНАЛІЗАЦІЯ В ІНТЕРАКТИВНОМУ МАРКЕТИНГУ: РОЛЬ ТЕХНОЛОГІЙ В НАДАННІ ПЕРСОНАЛІЗОВАНОГО СЕРВІСУ
Сікорського», Н. Т. У. У. «. П. І. І. І., & Лебеденко, С. О. (2023) · Международная научно-практическая конференция «Актуальные проблемы науки и образования в условиях современных вызовов» · doi:10.36059/978-966-397-323-4-19
Chatbots in the Service Sector: A Successful Customer Experience
BENABDELOUAHED, R., & Elkhatibi, Y. (2023) · International Journal of Innovative Research in Multidisciplinary Education · doi:10.58806/ijirme.2024.v3i1n14
The impact of smart voice assistants' humor styles on perceived morality
Hua, M., & Park, H. J. (2024) · Korean Journal of Human Ecology · doi:10.5934/kjhe.2024.33.6.903
Etyang, F. O., Pavithran, P., Mwendwa, G., Yoosaf, S., & Mandela, N. (2025) · Research Square · doi:10.21203/rs.3.rs-5677244/v1
The Future of Shopping and Service Marketing
Vetrivel, S. C., Arun, V. P., & Vidhyapriya, P. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0164-8.ch001
Frank, K., Mücksch, J., Siems, F., & Schubert, K. (2025) · Forum Dienstleistungsmanagement · doi:10.1007/978-3-658-48325-8_25
Alain, O., Kokkinaki, A. I., & Chedrawi, C. (2025) · Sustainable landscape planning and natural resources management · doi:10.1007/978-3-031-82419-7_12
The mystery of Musify (an AI-based recommendation system): Safe and sound?
Fernando, A. (2025) · Journal of Information Technology Teaching Cases · doi:10.1177/20438869251364891
Hou, J.-R., Chang, Y.-C., Kuo, Y., & Lin, Y.-H. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-09945-7_1
The fake friend dilemma: relational trust and the political economy of conversational AI
Erickson, J. (2026) · Frontiers in Communication · doi:10.3389/fcomm.2026.1921101 · Cited by 2
“In these contexts, manipulation stems less from visual interfaces and more from socially responsive interaction that exploits perceived intimacy and trust, enabling deeper forms of data collection and access to private information (Thomaz et al., 2020).”
Thomaz, F., & Hulland, J. S. (2026) · Journal of Business Research · doi:10.1016/j.jbusres.2026.115989
Self-citationPrivacy-centric marketing: managerial insights for compliance and competitive differentiation
Vallejo-Blanxart, A., Peláez, A., Ibáñez, D. G., & Armengol, J. V. (2026) · Review of Managerial Science · doi:10.1007/s11846-026-00976-4
“In scenarios where extreme anonymity is critical (e.g., highly sensitive segments or journalists, activists or users under restrictive regimes) the use of anonymizing networks (Thomaz et al. 2020), popularly known as the Dark Web, such as the TOR network, has been proposed.”
Digital Human: The New Frontier of Artificial Intelligence for Customer Experience
Spilotro, C., Secundo, G., & Vitulano, F. (2026) · Springer proceedings in complexity · doi:10.1007/978-3-032-09066-9_10
Chen, J. V., Chen, Y.-S., & Le, T.-M. H. (2026) · Computers & Security · doi:10.1016/j.cose.2026.104858 · Cited by 1
Anthropomorphism and Familiarity in AI Adoption
Sboui, M., Sfar, N., & Baati, O. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2747-1.ch015
Q-Commerce as a Metachannel Catalyst
Reis, J. L., Aleixo, L., Pinto, A. S., & Rodrigues, L. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-4591-8.ch011
Balancing Innovation and Values: How Culture Shapes AI Regulation
Dutta, D. (2026) · Lecture notes in operations research · doi:10.1007/978-3-032-13116-4_5
Do Not Think Ahead of Me: High‐Personalization Chatbots Can Backfire During the Goal Formation Stage
Chen, X., Huang, M., & Xie, X. (2026) · Psychology and Marketing · doi:10.1002/mar.70189
“…chatbots) differ in terms of information processing (e.g., whether filtering information based on consumer behaviors; Kocaballi et al. 2019; Thomaz et al. 2020; Whang et al. 2022) and information provision (e.g., whether providing targeted and goal‐directed information; Kocaballi et al.…”
Data Protection Trends in the E-commerce Industry: 2025 and Beyond
Gowrishankkar, V., Patil, S., Ottman, K., & Ahamed, S. B. I. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898816780126010008
Anthropomorphic Robotics in Branding and Service Experience
Farooqi, A. H. A., Goyal, N. K., Chaturvedi, A., & Dave, S. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3184-1.ch002
Human-AI Interaction in Marketing
Bhuiyan, M. R. I., Mehadi, G., Rakib, M. R. H. K., Husain, T., & Faria, T. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9325-4.ch002
Das, P., & Das, S. (2026) · Vision The Journal of Business Perspective · doi:10.1177/09722629261471614
Premi, P., & Purani, K. (2026) · European Journal of Marketing · doi:10.1108/ejm-11-2025-0973
Contreras, C. R., & Valette‐Florence, P. (2026) · Journal of Product & Brand Management · doi:10.1108/jpbm-02-2025-5785
Lopes, M., Rocha, F., & Souza, B. (2026) · Journal of Digital Business and Management Studies · doi:10.68159/u178105711
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
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
