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.
“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
Adomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,347 · cited 5× · see citing works
“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 4,874 · cited 1× · see citing works
“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 4,606 · cited 3× · see citing works
“…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.”
BackgroundThe social psychology of telecommunications
Short, J., Williams, E., & Christie, B. (1976) · Cited by 4,435 · see citing works
Warren, S. D., & Brandeis, L. D. (1890) · University of Washington Press eBooks · doi:10.1515/9780295803661-014 · Cited by 4,092 · see citing works
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× · see citing works
“…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 · see citing works
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× · see citing works
“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× · see citing works
“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 · see citing works
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× · see citing works
“…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,…”
Information Privacy Research: an Interdisciplinary Review1
Smith, H. J., Dinev, T., & Xu, H. (2011) · MIS Quarterly · doi:10.2307/41409970 · Cited by 2,013 · cited 1× · see citing works
“” The control-based definition of privacy is broadly accepted and has been used as the foundation of most information privacy research (Smith et al. 2011).”
Westin, A. F. (1967) · Cited by 1,908 · see citing works
Privacy and human behavior in the age of information
Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015) · Science · doi:10.1126/science.aaa1465 · Cited by 1,879 · cited 1× · see citing works
“This discrepancy between attitudes and behaviors is what scholars refer to as the “ privacy paradox ” (Acquisti et al., 2015).”
BackgroundDiscovery of Frequent Episodes in Event Sequences
Mannila, H., Toivonen, H. T. T., & Verkamo, A. I. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009748302351 · Cited by 1,468 · cited 1× · see citing works
“The sequence approach uses processual browsing activities to infer consumer preferences (Mannila, Toivonen, & Verkamo, 1997; Niu, Yan, Zhang, & Zhang, 2002).”
BackgroundExpanding understanding of service exchange and value co-creation: a social construction approach
Edvardsson, B., Tronvoll, B., & Gruber, T. (2010) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-010-0200-y · Cited by 1,425 · see citing works
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400 · cited 1× · see citing works
“Understanding the message involves semantic encoding and intent understanding, that is, understanding the purpose of the message (e.g., Tur & Deng 2011; Vinyals & Le, 2015).”
BackgroundExploring the implications of the Internet for consumer marketing
Peterson, R. A., Balasubramanian, S., & Bronnenberg, B. J. (1997) · Journal of the Academy of Marketing Science · doi:10.1177/0092070397254005 · Cited by 1,373 · cited 1× · see citing works
“…forcing firms to quickly “ race to the bottom, ” given the perception that consumers would have full information about competitive offerings (e.g., Peterson, et al. 2012), and morphing banner advertising (Urban et al., 2013), which are made possible by leveraging user profiling techniques…”
Backgroundvan Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017) · Journal of Service Research · doi:10.1177/1094670516679272 · Cited by 1,344 · cited 2× · see citing works
“…the customer service infrastructure, by replacing or augmenting tasks traditionally performed by sales employees (Larivière et al., 2017; Verhagen, van Nes, Feldberg, & van Dolen, 2014) and by providing consumers with successful service encounters (Larivière et al., 2017; van Doorn et al., 2017).”
BackgroundSelf-citationThe Environment and Social Behavior: Privacy, Personal Space, Territory, Crowding.
Gove, W. R., & Altman, I. (1978) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2065073 · Cited by 1,312 · cited 1× · see citing works
“This focus on privacy enables Dark Web users to personally control (and thus limit) access to information about themselves (Altman 1975; Westin, 1967).”
BackgroundAutomatic personalization based on Web usage mining
Mobasher, B., Cooley, R., & Srivastava, J. (2000) · Communications of the ACM · doi:10.1145/345124.345169 · Cited by 1,230 · cited 1× · see citing works
“…we discuss here, CAs must first elicit and collect data from the consumers ( data collection ), use this data to estimate their preferences and build profiles ( building profile ) that they will consequently use to tailor the interaction (Mobasher, 2007; Mobasher, Cooley, & Srivastava, 2000).”
BackgroundMende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019) · Journal of Marketing Research · doi:10.1177/0022243718822827 · Cited by 1,197 · cited 1× · see citing works
“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).”
BackgroundWestin, A. F. (1970) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,184 · see citing works
Araujo, T. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.03.051 · Cited by 1,156 · see citing works
Lamberton, C., & Stephen, A. T. (2016) · Journal of Marketing · doi:10.1509/jm.15.0415 · Cited by 1,127 · see citing works
Is That Car Smiling at Me? Schema Congruity as a Basis for Evaluating Anthropomorphized Products
Aggarwal, P., & McGill, A. L. (2007) · Journal of Consumer Research · doi:10.1086/518544 · Cited by 1,078 · cited 1× · see citing works
“While anthropomorphization can generate positive marketing results (Aggarwal & McGill, 2007), too much of it can lead to negative effects.”
BackgroundPrivacy Concerns and Consumer Willingness to Provide Personal Information
Phelps, J., Nowak, G., & Ferrell, E. (2000) · Journal of Public Policy & Marketing · doi:10.1509/jppm.19.1.27.16941 · Cited by 1,027 · cited 1× · see citing works
“…show preliminary evidence of this dynamic, whereby consumers weigh privacy concerns and related risks against the benefits of information disclosure, and sometimes end up trading privacy for monetary rewards (e.g., Caudill & Murphy 2000; Hann et al. 2008; Phelps et al. 2000; Xu et al. 2010).”
BackgroundThe role of data privacy in marketing
Martin, K. D., & Murphy, P. E. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0495-4 · Cited by 902 · cited 2× · see citing works
“Customer privacy is central to this potential problem (Martin & Murphy 2017, Stewart 2017).”
BackgroundIntimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers
Moon, Y. (2000) · Journal of Consumer Research · doi:10.1086/209566 · Cited by 842 · cited 7× · see citing works
“…an-thropomorphize 5 CAs, as individuals often feel less inhibited when interacting with anthropomorphic computers, sharing private information (Leong & Selinger, 2019; Turkle, 2017), and even developing personal relationships (Moon, 2000) (see Appendix 1, Table 7 for a review of prior studies).”
BackgroundInfluentialThe Role of Push-Pull Technology in Privacy Calculus: The Case of Location-Based Services
Xu, H., Teo, H., Tan, B. C. Y., & Agarwal, R. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222260305 · Cited by 816 · see citing works
Brandtzaeg, P. B., & Følstad, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-70284-1_30 · Cited by 788 · see citing works
“Service Encounter 2.0”: An investigation into the roles of technology, employees and customers
Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., Wünderlich, N. V., & De Keyser, A. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2017.03.008 · Cited by 779 · cited 2× · see citing works
“…becoming critical components of the customer service infrastructure, by replacing or augmenting tasks traditionally performed by sales employees (Larivière et al., 2017; Verhagen, van Nes, Feldberg, & van Dolen, 2014) and by providing consumers with successful service encounters (Larivière et…”
BackgroundFrom Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749 · cited 11× · see citing works
“7 According to Shum et al. (2018, p. 6), a CA “ may generate responses in attractive styles (e.g., having a sense of humor) that improve user engagement.”
BackgroundMethodInfluentialQiu, L., & Benbasat, I. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222250405 · Cited by 737 · cited 4× · see citing works
“In examining specific anthropomorphic features, researchers have shown that certain personality characteristics such as friendliness and expertise (Verhagen et al., 2014) as well as embodiment and communication style (Qiu & Benbasat, 2009) influence perceptions of social presence.”
BackgroundInfluentialGhose, A., Ipeirotis, P. G., & Li, B. (2012) · Marketing Science · doi:10.1287/mksc.1110.0700 · Cited by 649 · see citing works
Tam, K. Y., & Ho, S. Y. (2006) · MIS Quarterly · doi:10.2307/25148757 · Cited by 609 · cited 1× · see citing works
“To personalize the consumer interaction, one must understand the consumer (Adomavicius & Tuzhilin, 2005; Johar, Mookerjee, & Sarkar, 2014; Shum et al. 2018; Tam & Ho, 2006).”
BackgroundMcTear, M., Callejas, Z., & Griol, D. (2016) · doi:10.1007/978-3-319-32967-3 · Cited by 594 · see citing works
Creating Effective Online Customer Experiences
Bleier, A., Harmeling, C. M., & Palmatier, R. W. (2018) · Journal of Marketing · doi:10.1177/0022242918809930 · Cited by 546 · cited 1× · see citing works
“Social presence resulting from anthropomor-phization has been associated with trust, engagement, and satisfaction (Kumar & Benbasat, 2006; Picard, 1997; Qiu & Benbasat, 2009; Turkle, 2017; Bleier, Harmeling, & Palmatier 2019).”
BackgroundModeling the Clickstream: Implications for Web-Based Advertising Efforts
Chatterjee, P., Hoffman, D., & Novak, T. (2003) · Marketing Science · doi:10.1287/mksc.22.4.520.24906 · Cited by 544 · see citing works
Modeling Online Browsing and Path Analysis Using Clickstream Data
Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. (2004) · Marketing Science · doi:10.1287/mksc.1040.0073 · Cited by 540 · cited 1× · see citing works
“The resulting Digital, Social Media, and Mobile (DSMM) ecosystem has been considered as a source of intelligence (Lamberton & Stephen 2016) for observing, analyzing, and predicting behavior (Bucklin & Sismeiro 2003; Chatterjee, Hoffman, & Novak 2003; Montgomery et al. 2004).”
Anderson, C. L., & Agarwal, R. (2011) · Information Systems Research · doi:10.1287/isre.1100.0335 · Cited by 533 · cited 1× · see citing works
“…emotions impact the privacy calculus of consumers, with positive affect leading to lowered perceptions of risk (Li, Sarathy, & Xu, 2011), higher intentions to disclose information (Anderson & Agarwal, 2011), and more self-disclosure (Kehr, Kowatsch, Wentzel, & Fleisch, 2015; Yu, Hu, & Cheng, 2015).”
BackgroundThe Importance of Trust for Personalized Online Advertising
Bleier, A., & Eisenbeiss, M. (2015) · Journal of Retailing · doi:10.1016/j.jretai.2015.04.001 · Cited by 529 · cited 1× · see citing works
“This complex trade-off between personalization and privacy is known as the “ personalization paradox ” (Aguirre et al. 2016; Bleier & Eisenbeiss 2015).”
BackgroundWhen Does Retargeting Work? Information Specificity in Online Advertising
Lambrecht, A., & Tucker, C. E. (2013) · Journal of Marketing Research · doi:10.1509/jmr.11.0503 · Cited by 512 · see citing works
Human + Machine : Reimagining Work in the Age of AI
Daugherty, P. R. (2021) · Cited by 511 · see citing works
Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites
Kumar, N., & Benbasat, I. (2006) · Information Systems Research · doi:10.1287/isre.1060.0107 · Cited by 507 · cited 1× · see citing works
“Social presence resulting from anthropomor-phization has been associated with trust, engagement, and satisfaction (Kumar & Benbasat, 2006; Picard, 1997; Qiu & Benbasat, 2009; Turkle, 2017; Bleier, Harmeling, & Palmatier 2019).”
BackgroundPutting one-to-one marketing to work: Personalization, customization, and choice
Arora, N., Drèze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., Joshi, Y. V., Kumar, V., Lurie, N. H., Neslin, S. A., et al. (2008) · Marketing Letters · doi:10.1007/s11002-008-9056-z · Cited by 506 · cited 1× · see citing works
“…(Claycomb & Martin, 2001), since it increases sales ’ leads, customer acquisition and retention (Bojei et al., 2013; Sahni, Wheeler, & Chintagunta, 2018), firm profit, customer satisfaction, and enables the discovery of novel consumer needs and preferences (Arora et al., 2008; Huang & Rust, 2017).”
BackgroundMatters of mind: Mindfulness/mindlessness in perspective
Langer, E. J. (1992) · Consciousness and Cognition · doi:10.1016/1053-8100(92)90066-j · Cited by 443 · cited 1× · see citing works
“These social responses occur as a result 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,…”
BackgroundJournal of Consumer Psychology
Maheswaran, D. (2009) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2009.06.003 · Cited by 439 · see citing works
The Governance of Privacy: Policy Instruments in Global Perspective
Bennett, C. J., & Raab, C. D. (2006) · Cited by 436 · see citing works
A Model of Web Site Browsing Behavior Estimated on Clickstream Data
Bucklin, R. E., & Sismeiro, C. (2003) · Journal of Marketing Research · doi:10.1509/jmkr.40.3.249.19241 · Cited by 431 · see citing works
Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015) · Information Systems Journal · doi:10.1111/isj.12062 · Cited by 410 · cited 1× · see citing works
“…emotions impact the privacy calculus of consumers, with positive affect leading to lowered perceptions of risk (Li, Sarathy, & Xu, 2011), higher intentions to disclose information (Anderson & Agarwal, 2011), and more self-disclosure (Kehr, Kowatsch, Wentzel, & Fleisch, 2015; Yu, Hu, & Cheng, 2015).”
BackgroundVerhagen, T., van Nes, J. V., Feldberg, F., & van Dolen, W. (2014) · J. Comput. Mediat. Commun. · doi:10.1111/jcc4.12066 · Cited by 405 · cited 2× · see citing works
“In examining specific anthropomorphic features, researchers have shown that certain personality characteristics such as friendliness and expertise (Verhagen et al., 2014) as well as embodiment and communication style (Qiu & Benbasat, 2009) influence perceptions of social presence.”
BackgroundLi, H. Z., Sarathy, R., & Xu, H. (2011) · Decision Support Systems · doi:10.1016/j.dss.2011.01.017 · Cited by 402 · see citing works
Do You See What I See? The Future of Virtual Shopping
Burke, R. R. (1997) · Journal of the Academy of Marketing Science · doi:10.1177/0092070397254007 · Cited by 391 · cited 1× · see citing works
“…Web 1.0, there was a fear that by disseminating information widely (particularly price information), firms would lose their ability to effectively price discriminate across markets, segments, and purchase occasions, essentially collapsing markets to a lowest common denominator (e.g., Burke 1997).”
BackgroundInfluentialConsumer online privacy: Legal and ethical issues
Caudill, E. M., & Murphy, P. E. (2000) · Journal of Public Policy & Marketing · doi:10.1509/jppm.19.1.7.16951 · Cited by 382 · cited 1× · see citing works
“…show preliminary evidence of this dynamic, whereby consumers weigh privacy concerns and related risks against the benefits of information disclosure, and sometimes end up trading privacy for monetary rewards (e.g., Caudill & Murphy 2000; Hann et al. 2008; Phelps et al. 2000; Xu et al. 2010).”
BackgroundThe Role of the Management Sciences in Research on Personalization
Murthi, B. P. S., & Sarkar, S. (2003) · Management Science · doi:10.1287/mnsc.49.10.1344.17313 · Cited by 372 · cited 2× · see citing works
“Data collection Two different mechanisms currently inform the collection of data: explicit and implicit methods (Adomavicius, Huang, & Tuzhilin, 2008; Li & Karahanna, 2015; Murthi & Sarkar, 2003).”
MethodSoftware Piracy: Estimation of Lost Sales and the Impact on Software Diffusion
Givon, M. M., Mahajan, V., & Muller, E. (1995) · Journal of Marketing · doi:10.1177/002224299505900103 · Cited by 357 · cited 1× · see citing works
“…has predominantly focused on the effects of P2P file sharing (and other piracy behaviors) on price sensitivity (Jain 2008, Sinha & Mandel 2008), substitution effects (Danaher et al. 2010), diffusion (Givon, Mahajan & Muller 1995), and control mechanisms/policy (Sinha, Machado, & Sellman 2010).”
BackgroundUnderstanding the impact of web personalization on user information processing and decision outcomes
Tam, K. Y., & Ho, S. Y. (2006) · Journal of the Association for Information Systems · doi:10.5555/2017317.2017322 · Cited by 354 · see citing works
Neural Sentiment Classification with User and Product Attention
Chen, H., Sun, M., Tu, C., Lin, Y., & Liu, Z. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1171 · Cited by 349 · cited 1× · see citing works
“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.”
MethodAdomavičius, G., & Tuzhilin, A. (2005) · Communications of the ACM · doi:10.1145/1089107.1089109 · Cited by 345 · see citing works
Chatbots: changing user needs and motivations
Brandtzaeg, P. B., & Følstad, A. (2018) · Interactions · doi:10.1145/3236669 · Cited by 322 · see citing works
Schumann, J. H., von Wangenheim, F., & Groene, N. (2014) · Journal of Marketing · doi:10.1509/jm.11.0316 · Cited by 287 · cited 1× · see citing works
“…more accurate identity graphs that allow companies to market products and services more efficiently and effectively by, for example, targeting people with the right content at the right time (McAfee & Brynjolfsson, 2012; Schumann, von Wangenheim, & Groene, 2014; Spangler, Hartzel, & Gal-Or, 2006).”
BackgroundTechnology-driven service strategy
Huang, M.-H., & Rust, R. T. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0545-6 · Cited by 263 · cited 1× · see citing works
“…(Claycomb & Martin, 2001), since it increases sales ’ leads, customer acquisition and retention (Bojei et al., 2013; Sahni, Wheeler, & Chintagunta, 2018), firm profit, customer satisfaction, and enables the discovery of novel consumer needs and preferences (Arora et al., 2008; Huang & Rust, 2017).”
BackgroundThe personalization-privacy paradox: Implications for new media
Aguirre, E., Roggeveen, A. L., Grewal, D., & Wetzels, M. (2016) · Journal of Consumer Marketing · doi:10.1108/jcm-06-2015-1458 · Cited by 251 · cited 1× · see citing works
“This complex trade-off between personalization and privacy is known as the “ personalization paradox ” (Aguirre et al. 2016; Bleier & Eisenbeiss 2015).”
BackgroundPersonalization in Email Marketing: The Role of Noninformative Advertising Content
Sahni, N. S., Wheeler, S. C., & Chintagunta, P. (2018) · Marketing Science · doi:10.1287/mksc.2017.1066 · Cited by 244 · cited 1× · see citing works
“…mechanisms used by firms (Claycomb & Martin, 2001), since it increases sales ’ leads, customer acquisition and retention (Bojei et al., 2013; Sahni, Wheeler, & Chintagunta, 2018), firm profit, customer satisfaction, and enables the discovery of novel consumer needs and preferences (Arora et…”
BackgroundWang, W., & Benbasat, I. (2008) · Journal of Management Information Systems · doi:10.2753/mis0742-1222240410 · Cited by 241 · cited 1× · see citing works
“Empirical evidence (e.g., Wang & Benbasat 2008) suggests that such transparency, i.e., explaining the logic behind a particular thought, decision, or recommendation, engenders trust.”
BackgroundUsing data mining methods to build customer profiles
Adomavičius, G., & Tuzhilin, A. (2001) · Computer · doi:10.1109/2.901170 · Cited by 235 · cited 1× · see citing works
“Many systems utilize a collection of individual or aggregate consumer facts (e.g., demographic information, favorite product, amount spent in an online store) to represent factual profiles in relational databases (Adomavicius & Tuzhilin, 2001).”
BackgroundOnline Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions
Li, S., & Karahanna, E. (2015) · Journal of the Association for Information Systems · doi:10.17705/1jais.00389 · Cited by 223 · cited 3× · see citing works
“The type of approach leveraged by the CAwill heavily depend on the type of data used to build consumer profiles (Li & Karahanna, 2015), and consequently on whether the consumer is Buff or Ghost.”
MethodSelf-citationSentiCap: Generating Image Descriptions with Sentiments
Mathews, A., Xie, L., & He, X. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10475 · Cited by 215 · cited 1× · see citing works
“As such, CA personality information (e.g., age, gender, etc.) is often incorporated into the process of generating responses (e.g., see Li et al. 2006; Mathews et al. 2015, Shum et al. 2018).”
BackgroundTowards Designing Cooperative and Social Conversational Agents for Customer Service
Gnewuch, U., Morana, S., & Mädche, A. (2017) · Journal of the Association for Information Systems · Cited by 211 · see citing works
Danaher, B., Dhanasobhon, S., Smith, M. D., & Telang, R. (2010) · Marketing Science · doi:10.1287/mksc.1100.0600 · Cited by 209 · cited 1× · see citing works
“…has predominantly focused on the effects of P2P file sharing (and other piracy behaviors) on price sensitivity (Jain 2008, Sinha & Mandel 2008), substitution effects (Danaher et al. 2010), diffusion (Givon, Mahajan & Muller 1995), and control mechanisms/policy (Sinha, Machado, & Sellman 2010).”
BackgroundTalking to Bots: Symbiotic Agency and the Case of Tay
Neff, G. (2016) · International journal of communication · Cited by 196 · see citing works
Information Privacy and Marketing: What the U.S. Should (and Shouldn't) Learn from Europe
Smith, H. J. (2001) · California Management Review · doi:10.2307/41166073 · Cited by 195 · cited 2× · see citing works
“In the past, a distinction has been made (Smith 2001, pp. 1000-1001) be-tween countries that viewed privacy as a human right and passed “ sweeping privacy bills that address all the instances of data collection, use, and sharing (Bennett & Raab 2006; Dholakia & Zwick 2001) ” versus those that…”
BackgroundAdaptive personalization using social networks
Chung, T. S., Wedel, M., & Rust, R. T. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-015-0441-x · Cited by 184 · cited 13× · see citing works
“For example, whereas the Surface Web is currently grappling with the high volatility in the value of Bitcoin (the most familiar example of cryptocurrencies), the Dark Web trades in a variety of different cryptocurrencies, some of which are market-specific.”
BackgroundInfluentialCortes, C., Fisher, K. S., Pregibon, D., & Rogers, A. (2000) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347094 · Cited by 179 · cited 1× · see citing works
“Signatures are the data structures used to capture the evolving behavior learned from large streams of simple transactions (Cortes et al., 2000).”
BackgroundAn Audience of One: Behaviorally Targeted Ads as Implied Social Labels
Summers, C. A., Smith, R. W., & Reczek, R. W. (2016) · Journal of Consumer Research · doi:10.1093/jcr/ucw012 · Cited by 177 · see citing works
Preventing Digital Music Piracy: The Carrot or the Stick?
Sinha, R. K., & Mandel, N. (2008) · Journal of Marketing · doi:10.1509/jmkg.72.1.001 · Cited by 170 · cited 1× · see citing works
“This research has predominantly focused on the effects of P2P file sharing (and other piracy behaviors) on price sensitivity (Jain 2008, Sinha & Mandel 2008), substitution effects (Danaher et al. 2010), diffusion (Givon, Mahajan & Muller 1995), and control mechanisms/policy (Sinha, Machado, &…”
BackgroundAl‐Natour, S., Benbasat, I., & Cenfetelli, R. T. (2011) · Journal of the Association for Information Systems · doi:10.17705/1jais.00267 · Cited by 166 · see citing works
Digital Piracy: A Competitive Analysis
Jain, S. (2008) · Marketing Science · doi:10.1287/mksc.1070.0313 · Cited by 166 · cited 1× · see citing works
“This research has predominantly focused on the effects of P2P file sharing (and other piracy behaviors) on price sensitivity (Jain 2008, Sinha & Mandel 2008), substitution effects (Danaher et al. 2010), diffusion (Givon, Mahajan & Muller 1995), and control mechanisms/policy (Sinha, Machado, &…”
BackgroundCrumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting
Trusov, M., Ma, L., & Jamal, Z. (2016) · Marketing Science · doi:10.1287/mksc.2015.0956 · Cited by 165 · cited 1× · see citing works
“…“ race to the bottom, ” given the perception that consumers would have full information about competitive offerings (e.g., Peterson, et al. 2012), and morphing banner advertising (Urban et al., 2013), which are made possible by leveraging user profiling techniques (Trusov, Ma, & Jamal 2016).”
BackgroundSafer scoring? Cryptomarkets, social supply and drug market violence
Barratt, M. J., Ferris, J., & WINSTOCK, A. R. (2016) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2016.04.019 · Cited by 165 · see citing works
Don't Blame the Computer: When Self-Disclosure Moderates the Self-Serving Bias
Moon, Y. (2003) · Journal of Consumer Psychology · doi:10.1207/153276603768344843 · Cited by 163 · see citing works
Automation, Algorithms, and Politics| Talking to Bots: Symbiotic Agency and the Case of Tay
Neff, G., & Nagy, P. (2016) · International journal of communication · Cited by 161 · see citing works
Virtual agents in retail web sites: Benefits of simulated social interaction for older users
Chattaraman, V., Kwon, W., & Gilbert, J. E. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2012.06.009 · Cited by 153 · see citing works
Gao, M., Liu, K., & Wu, Z. (2009) · Information Systems Frontiers · doi:10.1007/s10796-009-9199-3 · Cited by 144 · see citing works
Media inequality in conversation
Shechtman, N., & Horowitz, L. M. (2003) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/642611.642661 · Cited by 139 · cited 1× · see citing works
“…& Garnier, 2012; Chakrabarti & Luger, 2015; Gnewuch et al., 2017; Table 6 Research agenda for personalizing consumer interactions through conversational agents in the era of hyper-privacy Jenkins et al., 2007; McTear, Callejas, & Griol, 2016; Schuetzler et al., 2014; Shechtman & Horowitz, 2003).”
BackgroundElkins, A. C., & Derrick, D. C. (2013) · Group Decision and Negotiation · doi:10.1007/s10726-012-9339-x · Cited by 136 · see citing works
Case study—Embodied virtual agents: An analysis on reasons for failure
Ben Mimoun, M. S., Poncin, I., & Garnier, M. (2012) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2012.07.006 · Cited by 136 · see citing works
A Theory-Driven Design Framework for Social Recommender Systems
Arazy, O., Kumar, N. S., & Shapira, B. (2010) · Journal of the Association for Information Systems · doi:10.17705/1jais.00237 · Cited by 135 · cited 2× · see citing works
“These approaches include (a) content-based , which typically uses the consumers ’ stored profile that includes historical ratings, viewing behavior, and purchases to match preferences; (b) social networks , which leverages the social connections of consumers to match their preferences assuming that people who are frie…”
MethodMimoun, M. S. B., Poncin, I., & Garnier, M. (2016) · Information & Management · doi:10.1016/j.im.2016.11.008 · Cited by 132 · see citing works
Commentary on "Exploring the Implications of the Internet for Consumer Marketing"
Deighton, J. (1997) · Journal of the Academy of Marketing Science · doi:10.1177/0092070397254006 · Cited by 124 · cited 1× · see citing works
“However, in the current evolution to Web 3.0, marketers face the very real possibility of losing these advantages if they no longer have the ability to identify consumers and connect them to their previous behaviors (e.g., Deighton 1997).”
BackgroundInfluentialDon't Think Twice, It's All Right: Music Piracy and Pricing in a DRM-Free Environment
Sinha, R. K., Machado, F. A., & Sellman, C. (2010) · Journal of Marketing · doi:10.1509/jmkg.74.2.40 · Cited by 108 · see citing works
Social cues in animated conversational agents
Louwerse, M. M., Graesser, A. C., Lu, S., & Mitchell, H. H. (2005) · Applied Cognitive Psychology · doi:10.1002/acp.1117 · Cited by 104 · see citing works
Urban, G. L., Liberali, G., MacDonald, E. F., Bordley, R. F., & Hauser, J. R. (2013) · Marketing Science · doi:10.1287/mksc.2013.0803 · Cited by 100 · cited 1× · see citing works
“…“ race to the bottom, ” given the perception that consumers would have full information about competitive offerings (e.g., Peterson, et al. 2012), and morphing banner advertising (Urban et al., 2013), which are made possible by leveraging user profiling techniques (Trusov, Ma, & Jamal 2016).”
BackgroundConsumer Privacy and Marketing Avoidance: A Static Model
Hann, I.-H., Hui, K.-L., Lee, S. T., & Png, I. (2008) · Management Science · doi:10.1287/mnsc.1070.0837 · Cited by 100 · cited 1× · see citing works
“…show preliminary evidence of this dynamic, whereby consumers weigh privacy concerns and related risks against the benefits of information disclosure, and sometimes end up trading privacy for monetary rewards (e.g., Caudill & Murphy 2000; Hann et al. 2008; Phelps et al. 2000; Xu et al. 2010).”
BackgroundPersonalization technologies: A process-oriented perspective
Adomavicius, D., & Tuzhilin, A. (2006) · WIRTSCHAFTSINFORMATIK · doi:10.1007/s11576-006-0098-7 · Cited by 99 · cited 5× · see citing works
“Adomavicius and Tuzhilin (2005).”
BackgroundMethodInfluentialRole of Affect in Self-Disclosure on Social Network Websites: A Test of Two Competing Models
Yu, J., Hu, P. J., & Cheng, T.-H. (2015) · Journal of Management Information Systems · doi:10.1080/07421222.2015.1063305 · Cited by 96 · see citing works
Building customer relationships: an inventory of service providers’ objectives and practices
Claycomb, C., & Martin, C. L. (2001) · Marketing Intelligence & Planning · doi:10.1108/eum0000000006109 · Cited by 95 · see citing works
Identifying and Profiling Key Sellers in Cyber Carding Community: AZSecure Text Mining System
Li, W., Chen, H., & Nunamaker, J. F. (2016) · Journal of Management Information Systems · doi:10.1080/07421222.2016.1267528 · Cited by 92 · see citing works
Toward algorithmic transparency and accountability
Garfinkel, S., Matthews, J. N., Shapiro, S. S., & Smith, J. M. (2017) · Communications of the ACM · doi:10.1145/3125780 · Cited by 92 · cited 1× · see citing works
“Fairness and transparency Alternatively, self-disclosure can be encouraged when firms provide assurances of algorithmic fairness and transparency (Garfinkel et al., 2017).”
BackgroundThe empirical link between relationship marketing tools and consumer retention in retail marketing
Bojei, J., Julian, C. C., Wel, C. A. C., & Ahmed, Z. U. (2013) · Journal of Consumer Behaviour · doi:10.1002/cb.1408 · Cited by 83 · cited 1× · see citing works
“…relationship-building mechanisms used by firms (Claycomb & Martin, 2001), since it increases sales ’ leads, customer acquisition and retention (Bojei et al., 2013; Sahni, Wheeler, & Chintagunta, 2018), firm profit, customer satisfaction, and enables the discovery of novel consumer needs and…”
BackgroundChakrabarti, C., & Luger, G. F. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.04.067 · Cited by 83 · cited 2× · see citing works
“…been many failures and many challenges still remain in designing CAs that provide high quality interactions (Ben Mimoun, Poncin, & Garnier, 2012; Chakrabarti & Luger, 2015; Gnewuch et al., 2017; Table 6 Research agenda for personalizing consumer interactions through conversational agents in the…”
BackgroundResults of an international drug testing service for cryptomarket users
Caudevilla, F., Ventura, M., Fornís, I., Barratt, M. J., Giné, C. V., Gil, C., Quintana, P., Muñoz, A. P. J., & Calzada, N. (2016) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2016.04.017 · Cited by 83 · see citing works
A valued agent: How ECAs affect website customers' satisfaction and behaviors
Mimoun, M. S. B., & Poncin, I. (2015) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2015.05.008 · Cited by 80 · see citing works
Al‐Natour, S., Benbasat, I., & Cenfetelli, R. T. (2006) · Journal of the Association for Information Systems · doi:10.17705/1jais.00110 · Cited by 75 · see citing works
Implementation of an inquisitive chatbot for database supported knowledge bases
Reshmi, S. N., & Balakrishnan, K. (2016) · Sadhana · doi:10.1007/s12046-016-0544-1 · Cited by 73 · see citing works
DICE-E: A Framework for Conducting Darknet Identification, Collection, Evaluation with Ethics
Benjamin, V. A., Valacich, J., & Chen, H.-C. (2019) · MIS Q. · doi:10.25300/misq/2019/13808 · Cited by 72 · cited 1× · see citing works
“…have explored traditional marketing questions, like the profitability of vendors and residual value to consumers (Holt, Smirnova, & Chua 2016), while utilizing traditional marketing approaches to guide research in this environment (Li, Chen, & Nunamaker 2016; Benjamin, Valacich, & Chen 2019).”
BackgroundInferring Capabilities of Intelligent Agents from Their External Traits
Knijnenburg, B. P., & Willemsen, M. C. (2016) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2963106 · Cited by 71 · cited 1× · see citing works
“For example, some attempts to provide overly humanized agents have created unrealistic consumer expectations that turned into frustration (Knijnenburg & Willemsen, 2016) and abuse (Neff & Nagy, 2016).”
BackgroundPersonalization from incomplete data
Padmanabhan, B., Zheng, Z., & Kimbrough, S. O. (2001) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502535 · Cited by 67 · cited 1× · see citing works
“…methods of data collection, session context modelling by CAs (e.g., Shum et al. 2018) allows them to gather and use interactive click stream data during the specific interaction between the CA and the consumer (i.e., implicit data, see Johar et al., 2014; Padmanabhan, Zheng, & Kimbrough, 2001).”
MethodCoordination problems in cryptomarkets: Changes in cooperation, competition and valuation
Bakken, S. A., Moeller, K. K., & Sandberg, S. (2017) · European Journal of Criminology · doi:10.1177/1477370817749177 · Cited by 67 · cited 1× · see citing works
“Other criminology scholars have examined the structure of illicit digital markets, and the associated efficiency and resiliency they exhibit (Bakken, Moeller & Sandberg 2018; Duxbury & Haynie 2019).”
BackgroundAnalysis of User Interaction with Service Oriented Chatbot Systems
Jenkins, M.-C., Churchill, R., Cox, S. J., & Smith, D. J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73110-8_9 · Cited by 66 · cited 1× · see citing works
“…& Garnier, 2012; Chakrabarti & Luger, 2015; Gnewuch et al., 2017; Table 6 Research agenda for personalizing consumer interactions through conversational agents in the era of hyper-privacy Jenkins et al., 2007; McTear, Callejas, & Griol, 2016; Schuetzler et al., 2014; Shechtman & Horowitz, 2003).”
BackgroundExploring and Estimating the Revenues and Profits of Participants in Stolen Data Markets
Holt, T. J., Smirnova, O., & Chua, Y. T. (2016) · Deviant Behavior · doi:10.1080/01639625.2015.1026766 · Cited by 65 · cited 1× · see citing works
“Similarly, other fields have explored traditional marketing questions, like the profitability of vendors and residual value to consumers (Holt, Smirnova, & Chua 2016), while utilizing traditional marketing approaches to guide research in this environment (Li, Chen, & Nunamaker 2016; Benjamin,…”
BackgroundTor, what is it good for? Political repression and the use of online anonymity-granting technologies
Jardine, E. (2016) · New Media & Society · doi:10.1177/1461444816639976 · Cited by 63 · cited 1× · see citing works
“Users can utilize “ anonymity-granting technologies ” to protect their privacy from government agencies, political opponents, trolls, data-hungry organizations, and even Internet service providers (Jardine, 2018).”
BackgroundLeong, B., & Selinger, E. M. (2019) · doi:10.1145/3287560.3287591 · Cited by 63 · cited 1× · see citing works
“…an-thropomorphize 5 CAs, as individuals often feel less inhibited when interacting with anthropomorphic computers, sharing private information (Leong & Selinger, 2019; Turkle, 2017), and even developing personal relationships (Moon, 2000) (see Appendix 1, Table 7 for a review of prior…”
BackgroundChoice Architecture, Framing, and Cascaded Privacy Choices
Adjerid, I., Acquisti, A., & Loewenstein, G. F. (2018) · Management Science · doi:10.1287/mnsc.2018.3028 · Cited by 61 · cited 1× · see citing works
“However, even those consumers who are fully aware of the costs, and are concerned about their privacy, often choose to disclose identifiable personal data (Adjerid et al., 2018).”
BackgroundCrime displacement in digital drug markets
Ladegaard, I. (2018) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2018.09.013 · Cited by 57 · cited 1× · see citing works
“For instance, within criminology, researchers have examined the effect of specific news on illicit sales volumes, finding counterintuitive increases in Dark Web transactions (Ladegaard 2019).”
BackgroundSee No Evil, Hear No Evil? Dissecting the Impact of Online Hacker Forums1
Yue, W. T., Wang, Q.-H., & Hui, K.-L. (2019) · MIS Quarterly · doi:10.25300/misq/2019/13042 · Cited by 56 · cited 1× · see citing works
“In a related IS study, Yue, Wang, & Hui (2019) conducted a user-generated content analysis of Dark Web hacker communities, and found evidence connecting increases in user chatter to a lower frequency of cyber-attacks.”
BackgroundCharacterising dark net marketplace purchasers in a sample of regular psychostimulant users
Buskirk, J. V., Roxburgh, A., Bruno, R., Naicker, S., Lenton, S. R., Sutherland, R., Whittaker, E., Sindicich, N., Matthews, A. J., Butler, K., et al. (2016) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2016.01.010 · Cited by 49 · see citing works
Melanson, S. (2019) · Information Communication & Society · doi:10.1080/1369118x.2019.1687746 · Cited by 49 · see citing works
Criminal network security: An agent‐based approach to evaluating network resilience*
Duxbury, S., & Haynie, D. L. (2019) · Criminology · doi:10.1111/1745-9125.12203 · Cited by 46 · cited 1× · see citing works
“Other criminology scholars have examined the structure of illicit digital markets, and the associated efficiency and resiliency they exhibit (Bakken, Moeller & Sandberg 2018; Duxbury & Haynie 2019).”
BackgroundPafko, P., & Mach, J. (2013) · PubMed · Cited by 45 · see citing works
Personalization and Recommender Systems
Adomavičius, G., Huang, Z., & Tuzhilin, A. (2008) · doi:10.1287/educ.1080.0044 · Cited by 40 · cited 6× · see citing works
“These techniques may include descriptive models, such as rules, sequences, and signatures (Tuzhilin, 2008), or predictive models such as logistic regressions, neural networks, decision trees, support vector machines (SVM), and Bayesian networks (Adomavicius et al., 2008; Murthi & Sarkar, 2003).”
BackgroundMethodInfluentialFacilitating Natural Conversational Agent Interactions: Lessons from a Deception Experiment
Schuetzler, R. M., Grimes, G. M., Giboney, J. S., & Buckman, J. R. (2014) · DigitalCommons@UNO · Cited by 38 · see citing works
Selling vs. Profiling: Optimizing the Offer Set in Web-Based Personalization
Johar, M. S., Mookerjee, V. S., & Sarkar, S. (2014) · Information Systems Research · doi:10.1287/isre.2014.0518 · Cited by 33 · cited 3× · see citing works
“To personalize the consumer interaction, one must understand the consumer (Adomavicius & Tuzhilin, 2005; Johar, Mookerjee, & Sarkar, 2014; Shum et al. 2018; Tam & Ho, 2006).”
BackgroundMethodSentiCap: Generating Image Descriptions with SentimentsPreprint
Mathews, A., Xie, L., & He, X. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.01431 · Cited by 32 · cited 1× · see citing works
“As such, CA personality information (e.g., age, gender, etc.) is often incorporated into the process of generating responses (e.g., see Li et al. 2006; Mathews et al. 2015, Shum et al. 2018).”
BackgroundIntent Determination and Spoken Utterance Classification
Tür, G., & Deng, L. (2011) · doi:10.1002/9781119992691.ch4 · Cited by 31 · cited 1× · see citing works
“Understanding the message involves semantic encoding and intent understanding, that is, understanding the purpose of the message (e.g., Tur & Deng 2011; Vinyals & Le, 2015).”
BackgroundStewart, D. W. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0504-7 · Cited by 31 · cited 1× · see citing works
“Customer privacy is central to this potential problem (Martin & Murphy 2017, Stewart 2017).”
BackgroundChoice Architecture, Framing, and Cascaded Privacy ChoicesPreprint
Adjerid, I., Acquisti, A., & Loewenstein, G. F. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2765111 · Cited by 30 · see citing works
Product hierarchy-based customer profiles for electronic commerce recommendation
Niu, L., Yan, X. S., Zhang, C., & Zhang, S. (2003) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2002.1174549 · Cited by 24 · cited 1× · see citing works
“The sequence approach uses processual browsing activities to infer consumer preferences (Mannila, Toivonen, & Verkamo, 1997; Niu, Yan, Zhang, & Zhang, 2002).”
BackgroundPrivacy and Consumer Agency in the Information Age: Between Prying Profilers and Preening Webcams
Dholakia, N., & Zwick, D. (2001) · Journal of Media Literacy Education · Cited by 19 · see citing works
Exploring the privacy implications of addressable advertising and viewer profiling
Spangler, W. E., Hartzel, K. S., & Gal‐Or, M. (2006) · Communications of the ACM · doi:10.1145/1125944.1125951 · Cited by 17 · see citing works
Hosanagar, K. (2019) · Cited by 17 · see citing works
Adomavičius, G., & Gupta, A. (2009) · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- 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.
- 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 direc- tions. 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
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059 · see citing works
“Thomaz et al. (2020) identify two types of consumers who vary in willingness to share information.”
BackgroundVrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2020.1871398 · Cited by 1,134 · see citing works
“Humanoid service robots and artificial intelligence bots, for example, are increasingly attracting industry attention (Araujo, 2018; Go & Sundar, 2019; Larivi (cid:1) ere et al., 2017; Thomaz et al., 2020).”
BackgroundBlut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00762-y · Cited by 1,194 · see citing works
The future of technology and marketing: a multidisciplinary perspective
Grewal, D., Hulland, J. S., Kopalle, P. K., & Karahanna, E. (2019) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00711-4 · Cited by 710 · see citing works
“In their paper, Thomaz et al. (2020) argue that a web-wide, hyper-private future may come to mirror practices currently observed in the Dark Web.”
BackgroundInfluentialSelf-citationLeveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective
Hermann, E. (2021) · Journal of Business Ethics · doi:10.1007/s10551-021-04843-y · Cited by 458 · see citing works
Mariani, M. M., Hashemi, N., & Wirtz, J. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113838 · Cited by 378 · see citing works
Gao, Y., & Liu, H. (2022) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-01-2022-0023 · Cited by 357 · see citing works
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 · see citing works
“In addition, good CDR practices do not allow the use of all available data and analytics if customer wellbeing is to be prioritized (Thomaz et al. 2020).”
BackgroundA framework for AI-powered service innovation capability: Review and agenda for future research
Akter, S., Hossain, M. A., Sajib, S., Sultana, S., Rahman, M., Vrontis, D., & McCarthy, G. (2023) · Technovation · doi:10.1016/j.technovation.2023.102768 · Cited by 302 · see citing works
Someone out there? A study on the social presence of anthropomorphized chatbots
Konya-Baumbach, E., Biller, M., & von Janda, S. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107513 · Cited by 283 · see citing works
Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot
Rajaobelina, L., Prom Tep, S., Arcand, M., & Ricard, L. (2021) · Psychology & Marketing · doi:10.1002/mar.21548 · Cited by 252 · see citing works
Classifying and measuring the service quality of AI chatbot in frontline service
Chen, Q., Gong, Y. Y., Lu, Y., & Tang, J. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.02.088 · Cited by 261 · see citing works
Trust me, I'm a bot – repercussions of chatbot disclosure in different service frontline settings
Mozafari, N., Weiger, W. H., & Hammerschmidt, M. (2021) · Journal of service management · doi:10.1108/josm-10-2020-0380 · Cited by 246 · see citing works
Hildebrand, C., & Bergner, A. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00753-z · Cited by 255 · see citing works
Alabed, A., Javornik, A., & Gregory-Smith, D. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121786 · Cited by 233 · see citing works
Digital transformation: A multidisciplinary perspective and future research agenda
Paul, J., Ueno, A., Dennis, C., Alamanos, E., Curtis, L. J., Foroudi, P., Kacprzak, A., Kunz, W. H., Liu, J., Marvi, R., et al. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13015 · Cited by 322 · see citing works
“…value, a practice also referred to as surveillance capitalism (Zuboff, 2015, 2019), has serious privacy and ethical implications which can even be considered inconsistent with digital human rights conventions (Thomaz et al., 2020; United Nations Human Rights Office of the High Commissioner, 2021).”
BackgroundGenerative AI in innovation and marketing processes: A roadmap of research opportunities
Cillo, P., & Rubera, G. (2024) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-024-01044-7 · Cited by 219 · see citing works
“While the marketing literature has primarily focused on consumer concerns regarding data privacy (Martin & Murphy, 2017; Martin et al., 2017; Thomaz et al., 2020 ), we highlight a major concern for firms: sharing proprietary knowledge poses the risk of diminishing the scarcity of a firm’s…”
Jan, I. U., Ji, S.-G., & Kim, C. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103440 · Cited by 193 · see citing works
Bardhan, I. R., Chen, H., & Karahanna, E. (2020) · MIS Quarterly · doi:10.25300/misq/2020/14644 · Cited by 171 · see citing works
“…in terms of text versus voice, embodiment, anthropomorphization (e.g., gender, age, race, etc.), whether to use homophilous anthropomorphism or not, personality, emotional intelligence, personalization mechanisms, and conversational structures and flow (for a discussion see Thomaz et al. 2020).”
Self-citationConsumer interaction with cutting-edge technologies: Implications for future research
Ameen, N., Hosany, S., & Tarhini, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106761 · Cited by 146 · see citing works
“One of the main issues surrounding the use and development of robotic technologies is whether robots should look like machines or take anthropomorphised forms (Thomaz et al. 2020).”
BackgroundPrivacy concerns in e‐commerce: A multilevel meta‐analysis
Maseeh, H. I., Jebarajakirthy, C., Pentecost, R. D., Arli, D. I., Weaven, S. K., & Ashaduzzaman, M. (2021) · Psychology and Marketing · doi:10.1002/mar.21493 · Cited by 162 · see citing works
The future of buyer–seller interactions: a conceptual framework and research agenda
Ahearne, M., Atefi, Y., Lam, S. K., & Pourmasoudi, M. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-021-00803-0 · Cited by 161 · see citing works
How consumer digital signals are reshaping the customer journey
Schweidel, D. A., Bart, Y., Inman, J. J., Stephen, A. T., Libai, B., Andrews, M., Rosario, A. B., Chae, I., Chen, Z., Kupor, D. M., et al. (2022) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-022-00839-w · Cited by 162 · see citing works
“With a combination of behavioral change and technology, they can become “ ghosts ” to marketers (Thomaz et al., 2020).”
BackgroundSelf-citationThe influence of chatbot humour on consumer evaluations of services
Shin, H., Bunosso, I., & Levine, L. R. (2022) · International Journal of Consumer Studies · doi:10.1111/ijcs.12849 · Cited by 143 · see citing works
AI-based chatbots in conversational commerce and their effects on product and price perceptions
Sidlauskiene, J., Joye, Y., & Auruškevičienė, V. (2023) · Electronic Markets · doi:10.1007/s12525-023-00633-8 · Cited by 163 · see citing works
“They differ in their embodiment/ disembodiment as well as engagement levels (Thomaz et al., 2020) and can perform simple tasks such as booking flights, to more complex tasks such as giving health, financial or shopping advice (Araujo 2020).”
BackgroundLiving and working with service robots: a TCCM analysis and considerations for future research
De Keyser, A., & Kunz, W. H. (2022) · Journal of Service Management · doi:10.1108/josm-12-2021-0488 · Cited by 132 · see citing works
Chatbots in frontline services and customer experience: An anthropomorphism perspective
Nguyen, M., Casper Ferm, L., Quach, S., Pontes, N., & Thaichon, P. (2023) · Psychology & Marketing · doi:10.1002/mar.21882 · Cited by 119 · see citing works
“Chatbots are an important strategic brand asset as they provide the chance to provide efficient customer service around ‐ the ‐ clock (Rajaobelina et al., 2021; Thomaz et al., 2020).”
BackgroundAiello, G., Donvito, R., Acuti, D., Grazzini, L., Mazzoli, V., Vannucci, V., & Viglia, G. (2020) · Journal of Retailing · doi:10.1016/j.jretai.2020.07.001 · Cited by 118 · see citing works
Are they humans or are they robots? The effect of virtual influencer disclosure on brand trust
Muniz, F., Stewart, K., & Magalhães, L. (2023) · Journal of Consumer Behaviour · doi:10.1002/cb.2271 · Cited by 118 · see citing works
The future of digital technologies in marketing: A conceptual framework and an overview
Plangger, K. A., Grewal, D., de Ruyter, K., & Tucker, C. E. (2022) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-022-00906-2 · Cited by 123 · see citing works
“Whereas Web 2.0 technologies forced firms and their brand managers to give up some power to customers and other stakeholders (Thomaz et al., 2020 ), Web 3.0 technologies such as blockchains appear likely to usher in an era of what we can call decentralized marketing .”
BackgroundInfluentialNoble, S. M., & Mende, M. (2023) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-023-00948-0 · Cited by 105 · see citing works
“…robotics, whether they are physically embodied (e.g., Pepper) or their social presence is exclusively virtual (e.g., Alexa) (Blut et al., 2021; Thomaz et al., 2020), or a combination of both (e.g., shape-shifting robots that oscillate between a virtual and one or multiple embodied existences)…”
BackgroundInfluentialWould an AI chatbot persuade you: an empirical answer from the elaboration likelihood model
Chen, Q., Yin, C., & Gong, Y. Y. (2023) · Information Technology and People · doi:10.1108/itp-10-2021-0764 · Cited by 89 · see citing works
Macro and Exogenous Factors in Computational Advertising: Key Issues and New Research Directions
Helberger, N., Huh, J., Milne, G. R., Strycharz, J., & Sundaram, H. (2020) · Journal of Advertising · doi:10.1080/00913367.2020.1811179 · Cited by 88 · see citing works
“As argued in Liu-Thompkins et al. (2020) in this issue, users now also play the roles of creators, metavoicers, and content propagators.”
InfluentialDrivers of privacy concerns when interacting with a chatbot in a customer service encounter
Bouhia, M., Rajaobelina, L., Tep, S. P., Arcand, M., & Ricard, L. (2022) · International Journal of Bank Marketing · doi:10.1108/ijbm-09-2021-0442 · Cited by 94 · see citing works
Chatbots in Marketing: A Literature Review Using Morphological and Co-Occurrence Analyses
Ramesh, A., & Chawla, V. (2022) · Journal of Interactive Marketing · doi:10.1177/10949968221095549 · Cited by 91 · see citing works
“Examples of the former approach are Thomaz et al. ’ s (2019) investigation of the balance between consumer privacy and chatbot personalization in future digital environments; Huang and Rust ’ s (2018) exploration of the match between skills and tasks as a basis for replacing human agents with…”
BackgroundArtificial intelligence and its implications for data privacy
Martin, K. D., & Zimmermann, J. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101829 · Cited by 85 · see citing works
A theory of multiformat communication: mechanisms, dynamics, and strategies
Moffett, J. W., Folse, J. A. G., & Palmatier, R. W. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00750-2 · Cited by 84 · see citing works
Kronemann, B., Kizgin, H., Rana, N. P., & Dwivedi, Y. K. (2023) · Spanish Journal of Marketing - ESIC · doi:10.1108/sjme-10-2022-0213 · Cited by 83 · see citing works
Employee-(ro)bot collaboration in service: an interdependence perspective
Le, K. B. Q., Sajtos, L., & Fernandez, K. V. (2022) · Journal of service management · doi:10.1108/josm-06-2021-0232 · Cited by 81 · see citing works
Xu, Y., Zhang, J., & Deng, G. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.902782 · Cited by 92 · see citing works
“Nonetheless, commercial interest in chatbot technology remains high, due to their aforementioned benefits (Thomaz et al., 2020).”
BackgroundInfluentialDekkal, M., Arcand, M., Tep, S. P., Rajaobelina, L., & Ricard, L. (2023) · Journal of Financial Services Marketing · doi:10.1057/s41264-023-00230-y · Cited by 75 · see citing works
“Privacy is an important variable in e-commerce (Thomaz et al. 2020; Hsu and Lin 2018; Baruh et al. 2017; Arcand et al. 2007).”
Serving customers through chatbots: positive and negative effects on customer experience
Ranieri, A., Bernardo, I. D., & Mele, C. (2024) · Journal of Service Theory and Practice · doi:10.1108/jstp-01-2023-0015 · Cited by 92 · see citing works
Counteracting dark sides of robo-advisors: justice, privacy and intrusion considerations
Aw, E. C., Leong, L.-Y., Hew, J.-J., Rana, N. P., Tan, T. M., & Jee, T. W. (2023) · International Journal of Bank Marketing · doi:10.1108/ijbm-10-2022-0439 · Cited by 74 · see citing works
Personalized touchpoints and customer experience: A conceptual synthesis
Weidig, J., Weippert, M., & Kuehnl, C. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114641 · Cited by 102 · see citing works
Xu, Y., Zhang, J., Chi, R., & Deng, G. (2022) · Nankai Business Review International · doi:10.1108/nbri-06-2021-0041 · Cited by 65 · see citing works
Bălan, C. (2023) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer18020051 · Cited by 68 · see citing works
“80% of the total number of reviewed articles), with the other five articles being conceptual [4,7,8,34,56].”
BackgroundChoi, S., & Zhou, J. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114127 · Cited by 58 · see citing works
Rawat, R., Rajawat, A. S., Mahor, V. K., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0749-3_57 · Cited by 53 · see citing works
Darkweb research: Past, present, and future trends and mapping to sustainable development goals
Raghu Raman, R., Nair, V. K., Nedungadi, P., Ray, I., & Achuthan, K. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e22269 · Cited by 51 · see citing works
“Researchers [61] hypothesize that over the next 5 – 10 years, society will become more concerned with privacy on the web, and information-sharing practices currently found on the Darkweb will be adapted to the overall web.”
BackgroundSwani, K., Milne, G. R., & Slepchuk, A. N. (2021) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2021.03.001 · Cited by 54 · see citing works
Querci, I., Barbarossa, C., Romani, S., & Ricotta, F. (2022) · Psychology & Marketing · doi:10.1002/mar.21705 · Cited by 42 · see citing works
Schindler, D., Maiberger, T., Koschate-Fischer, N., & Hoyer, W. D. (2024) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-023-00987-7 · Cited by 48 · see citing works
“…“natural language computer programs designed to approximate human speech (written or oral) and interact with people via a digital interface” (Thomaz et al., 2020, p. 49) and are increasingly being deployed by many companies to simplify or aid consumers’ purchase decisions (e.g., Castelo et…”
Live support by chatbots with artificial intelligence: A future research agenda
Camilleri, M. A., & Troise, C. (2022) · Service Business · doi:10.1007/s11628-022-00513-9 · Cited by 50 · see citing works
“Such automated technologies may not always be in a position to engage in non-linear conversations, especially when they have to go back and forth on a topic with online users (Thomaz et al. 2020; Van Pinxteren et al. 2020).”
BackgroundService robots’ anthropomorphism: dimensions, factors and internal relationships
Zhang, S., Lin, X., Li, X., & Ren, A. (2022) · Electronic Markets · doi:10.1007/s12525-022-00527-1 · Cited by 48 · see citing works
“The current era of service robot application calls for a more detailed and practical understanding of anthropomorphism as it plays out in human–computer interaction in service contexts (Doorn et al., 2017; Thomaz et al., 2020).”
BackgroundDouble-sided messages improve the acceptance of chatbots
Lu, M., Li, T., Shi, X., & Huang, X. (2023) · Annals of Tourism Research · doi:10.1016/j.annals.2023.103644 · Cited by 40 · see citing works
Recasting Service Quality for AI-Based Service
Noor, N., Hill, S. R., & Troshani, I. (2021) · Australasian Marketing Journal (AMJ) · doi:10.1177/18393349211005056 · Cited by 49 · see citing works
“With AISA, a spectrum of personalization capabilities can now be better realized from technology-based systems as AISA can learn and adapt to user behavior based on available data (Thomaz et al., 2020).”
BackgroundWhat Drives Consumers’ Decisions to Use Intelligent Agent Technologies? A Systematic Review
Sidlauskiene, J. (2021) · Journal of Internet Commerce · doi:10.1080/15332861.2021.1961192 · Cited by 43 · see citing works
“In the marketing context such goal-directed activities can be decision-making (Maheswarappa et al. 2017; Guan et al. 2005); interaction (Thomaz et al. 2020), negotiation (Huang and Lin 2007); information search and acquisition (Sen, Saha, and Hernandez 2007); socialization (Alvertis et al. 2018);…”
BackgroundGumusel, E. (2024) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24898 · Cited by 50 · see citing works
Saffarizadeh, K., University of Texas at Arlington, Keil, M., Georgia State University, Boodraj, M., Boise State University, Alashoor, T., & University of Navarra (2024) · Journal of the Association for Information Systems · doi:10.17705/1jais.00839 · Cited by 41 · see citing works
Humanlike service robots: A systematic literature review and research agenda
Zhang, W., Slade, E. L., & Pantano, E. (2024) · Psychology and Marketing · doi:10.1002/mar.22099 · Cited by 47 · see citing works
How Chatbots Augment Human Intelligence in Customer Services: A Mixed-Methods Study
Lin, X., Wang, X., Shao, B., & Taylor, J. (2024) · Journal of Management Information Systems · doi:10.1080/07421222.2024.2415773 · Cited by 53 · see citing works
“As a result, they might help employees acquire knowledge about their customers’ profiles and buying patterns and could target consumers with the correct products or services at the right time [18, 75, 80].”
Gelbrich, K., Roschk, H., Miederer, S., & Kerath, A. (2026) · Journal of Marketing · doi:10.1177/00222429251344139 · Cited by 38 · see citing works
“(cid:129) Virtual assistant gives advice in online shop (Thomaz et al. 2020). based but can also be text-based (i.e., holding a screen) (Castelo et al. 2023).”
Utilitarian motivations to engage with travel websites: an interactive technology adoption model
Camilleri, M. A., & Kozak, M. (2022) · Journal of Services Marketing · doi:10.1108/jsm-12-2021-0477 · Cited by 39 · see citing works
Impact of service agents on customer satisfaction and loyalty: mediating role of Chatbots
Naqvi, M. H. A., Hongyu, Z., Naqvi, M. H., & Li, K. (2023) · Journal of Modelling in Management · doi:10.1108/jm2-01-2023-0004 · Cited by 36 · see citing works
Huang, L. L., Chen, R. P., & Chan, K. W. (2024) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-024-01017-w · Cited by 38 · see citing works
Service robot anthropomorphism on consumer usage intention: curvilinear and linear effect
Yang, M., Li, J., Feng, L., Chen, S.-C., & Tseng, M.-L. (2024) · Industrial Management & Data Systems · doi:10.1108/imds-08-2023-0538 · Cited by 33 · see citing works
Fehrenbach, D., & Herrando, C. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.08.029 · Cited by 41 · see citing works
Artificial Intelligence (AI)-Powered Chatbots for Marketing and Online Shopping
Waghambare, M. A., Prabhu, S., Ashok, P., & А, Н. Р. (2023) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-6684-9576-6.ch002 · Cited by 32 · see citing works
Pan, S., Qin, Z., & Zhang, Y. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19040157 · Cited by 36 · see citing works
“Unlike with human streamers, there is little risk of virtual influencers causing a scandal that can compromise the brand that they are trying to promote [1,3–5].”
Zhang, L., & Ren, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05014-5_25 · Cited by 33 · see citing works
Artificial Intelligence in Costumer Acquisition
Elhissoufi, M., & Alla, L. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-3172-9.ch001 · Cited by 28 · see citing works
Operationalizing Artificial Intelligence-Enabled Customer Analytics Capability in Retailing
Hossain, M. A., Akter, S., Yanamandram, V. K., & Gunasekaran, A. (2022) · Journal of Global Information Management · doi:10.4018/jgim.298992 · Cited by 28 · see citing works
Why can’t we help but love mobile banking chatbots? Perspective of stimulus-organism-response
Upadhyay, N., & Kamble, A. A. (2023) · Journal of Financial Services Marketing · doi:10.1057/s41264-023-00237-5 · Cited by 28 · see citing works
“Chatbots enhance customer experience through increased accessibility, convenience and personalization (Thomaz et al. 2020).”
The Effect of Bots on Human Interaction in Online Communities
Safadi, H., Lalor, J. P., & Berente, N. (2024) · MIS Quarterly · doi:10.25300/misq/2023/17901 · Cited by 26 · see citing works
“There are a variety of bots, including conversational agents (chatbots) that help to market products (Thomaz et al., 2020), decision-making bots that manage organizational activity (Kellogg et al., 2020), and online bots that disseminate information (Salge et al., 2022).”
So, I. G., Haron, H. B., Gui, A., Princes, E., & Sari, S. A. (2021) · Sustainability · doi:10.3390/su132313023 · Cited by 26 · see citing works
Hey, Alexa! What attributes of Skills affect firm value?
Bahmani, N., Bhatnagar, A., & Gauri, D. K. (2022) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-022-00851-0 · Cited by 26 · see citing works
“Privacy concerns Consumers, firms, and regulators have become increasingly concerned about privacy and the ways in which online data are handled (Thomaz et al., 2019).”
BackgroundImpact of cultural tightness on consumers' preference for anthropomorphic AI services
Sui, J., Shen, H., & Zhou, X. (2024) · Psychology and Marketing · doi:10.1002/mar.22086 · Cited by 25 · see citing works
Eichler, K. S., & Schwab, E. (2024) · Frontiers in Behavioral Economics · doi:10.3389/frbhe.2024.1489159 · Cited by 32 · see citing works
The Impact of Chatbots on Customer Satisfaction: A Systematic Literature Review
Vergaray, A. D., Robles, W. F. P., & Jiménez, J. D. C. R. (2023) · TEM Journal · doi:10.18421/tem123-21 · Cited by 24 · see citing works
“Thus, when customer satisfaction is related to the use of the “Chatbot”, it has positive points of view, being present 2 hours a day, every day, as well as the comfort that people feel when using it [7], [8], and reducing costs for brands [9].”
BackgroundIsrafilzade, K. (2021) · JOURNAL OF LIFE ECONOMICS · doi:10.15637/jlecon.8.4.12 · Cited by 23 · see citing works
Carmichael, L., Poirier, S., Coursaris, C. K., Léger, P., & Sénécal, S. (2022) · Applied Sciences · doi:10.3390/app122412660 · Cited by 26 · see citing works
Mehmood, K., Verleye, K., Keyser, A. D., & Larivière, B. (2022) · Journal of service management · doi:10.1108/josm-10-2021-0405 · Cited by 22 · see citing works
The role of marketing analytics in the ethical consumption of online consumers
Nayyar, V. (2022) · Total Quality Management & Business Excellence · doi:10.1080/14783363.2022.2139676 · Cited by 23 · see citing works
Chatbot recommender systems in tourism: A systematic review and a benefit-cost analysis
Camilleri, M. A., & Troise, C. (2023) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1145/3589883.3589906 · Cited by 32 · see citing works
“These individuals may be concerned about the safety and security of their personal information, as they are aware that these technologies can store and use the data they provide [45].”
BackgroundResponsible Artificial Intelligence Attention and Firm Innovation: An Attention‐Based View
Xiong, M., Xu, H., Ji, J., Zuo, R., Wang, Y., & Olya, H. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.70015 · Cited by 29 · see citing works
Tazi, F., Shrestha, S., Cruz, J. D. L., & Das, S. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2020018 · Cited by 21 · see citing works
“2015 [ 4 ] 2016 [ 214 ] 2017 [ 212 ] 2018 [ 10 , 187 ] 2019 [ 213 ] 2020 [ 72 ] Figure 14.”
BackgroundAn Exploration of Personalization in Digital Communication. Insights in Fashion
Nobile, T. H., & Kalbaska, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50341-3_35 · Cited by 21 · see citing works
Xia, Y.-Q., Liu, Z.-J., Wang, S.-Q., Huang, C.-X., & Zhao, W.-Q. (2024) · Journal of the Knowledge Economy · doi:10.1007/s13132-024-02153-y · Cited by 21 · see citing works
“This will ultimately strengthen the awareness of both brand privacy security and privacy protection (Thomaz et al., 2020).”
RetractedElevating Business Experiences
Waghambare, M. A., Prabhu, S., Ashok, P., & Natraj, N. A. (2024) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-1503-3.ch001 · Cited by 20 · see citing works
Vena-Oya, J., García, J. A. C., & Burys, J. (2024) · Tourism and Hospitality · doi:10.3390/tourhosp5020018 · Cited by 19 · see citing works
“Of these, security is one of the most important dimensions, since the consumer may be reluctant to provide personal information when using a digital service [32].”
BackgroundTsekouras, D., Gutt, D., & Heimbach, I. (2024) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-024-01027-8 · Cited by 18 · see citing works
“Firms widely use chatbots across various functions and contexts, including marketing (Thomaz et al., 2020), sales (Gartner, 2018; Luo et al., 2019 ), finance (Luo et al., 2019), health care (Health Europa, 2019), and education (Tourangeau et al., 2003).”
Choi, S. (2025) · Journal of Consumer Behaviour · doi:10.1002/cb.2505 · Cited by 18 · see citing works
Zhang, C., Cui, C., & Yao, Q. (2021) · Psychology Research and Behavior Management · doi:10.2147/prbm.s336223 · Cited by 18 · see citing works
Gupta, C. P., Kumar, V. V. R., & Khurana, A. (2024) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom61295.2024.10498474 · Cited by 18 · see citing works
“Conversational agents are one common way that artificial intelligence is used in marketing [8].”
BackgroundShort-term and long-term effects of touchpoints on customer perceptions
Cambra‐Fierro, J., Polo‐Redondo, Y., & Trifu, A. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102520 · Cited by 18 · see citing works
Digital Transformation and Resilience
Abidi, O., Richet, J., & Vitari, C. (2025) · Journal of Global Information Management · doi:10.4018/jgim.367873 · Cited by 16 · see citing works
Exploring the frontier of anthropomorphism in AI agents: Trends and way forward
Chaturvedi, R., Verma, S., Srivastava, V., & Khot, S. S. (2025) · Business and Society Review · doi:10.1111/basr.70002 · Cited by 19 · see citing works
The State of Artificial Intelligence in Marketing With Directions for Future Research
Chen, J., Rosas, J. H. A., Frankwick, G. L., & Arévalo, F. R. J. (2022) · International Journal of Business Intelligence Research · doi:10.4018/ijbir.297062 · Cited by 16 · see citing works
Human Resources Management After Industry 4.0
Anayat, S. (2023) · Advances in human resources management and organizational development book series · doi:10.4018/978-1-6684-7494-5.ch005 · Cited by 15 · see citing works
Cicco, R. D., Elmashhara, M. G., Silva, S. C. L. D. C. E., & Hammerschmidt, M. (2025) · European Journal of Marketing · doi:10.1108/ejm-01-2022-0066 · Cited by 14 · see citing works
- 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
- 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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