Disclosing AI’s Involvement in Advertising to Consumers: A Task-Dependent Perspective

References

• Ahn, Jungyong, Jungwon Kim, and Yongjun Sung. 2021. “AI-Powered Recommendations: The Roles of Perceived Similarity and Psychological Distance on Persuasion.” International Journal of Advertising 40 (8): 1366–1384. https://doi.org/10.1080/02650487.2021.1982529
   Web of Science ®Google Scholar
• Alibaba Cloud. 2020. “An Introduction to AI, Its Development, Application, and Future.” July 26. https://medium.datadriveninvestor.com/an-introduction-to-ai-its-development-application-and-future-fbf7143dae9a
   Google Scholar
• Arango, Luis, Stephen Pragasam Singaraju, and Outi Niininen. 2023. “Consumer Responses to AI-Generated Charitable Giving Ads.” Journal of Advertising 52 (4): 486–503. https://doi.org/10.1080/00913367.2023.2183285
   Web of Science ®Google Scholar
• Araujo, Theo, Jonathan R. Copulsky, Jameson L. Hayes, Su Jung Kim, and Jaideep Srivastava. 2020. “From Purchasing Exposure to Fostering Engagement: Brand–Consumer Experiences in the Emerging Computational Advertising Landscape.” Journal of Advertising 49 (4): 428–445. https://doi.org/10.1080/00913367.2020.1795756
   Web of Science ®Google Scholar
• Bakpayev, Marat, Tae Hyun Baek, Patrick van Esch, and Sukki Yoon. 2020. “Programmatic Creative: AI Can Think but It Cannot Feel.” Australasian Marketing Journal 30 (1): 90–95. https://doi.org/10.1016/j.ausmj.2020.04.002
   Web of Science ®Google Scholar
• Beltramini, Richard F. 1982. “Advertising Perceived Believability Scale.” In Proceedings of the Southwestern Marketing Association, 1–3. Wichita, KS: Wichita State University.
   Google Scholar
• Berger, Jonah, and Katherine L. Milkman. 2012. “What Makes Online Content Viral?” Journal of Marketing Research 49 (2): 192–205. https://doi.org/10.1509/jmr.10.0353
   Web of Science ®Google Scholar
• Bower, Alexander H., and Mark Steyvers. 2021. “Perceptions of AI Engaging in Human Expression.” Scientific Reports 11 (1): 21181. https://doi.org/10.1038/s41598-021-00426-z
   PubMed Web of Science ®Google Scholar
• Campbell, Colin, Kirk Plangger, Sean Sands, and Jan Kietzmann. 2022. “Preparing for an Era of Deepfakes and AI-Generated Ads: A Framework for Understanding Responses to Manipulated Advertising.” Journal of Advertising 51 (1): 22–38. https://doi.org/10.1080/00913367.2021.1909515
   Web of Science ®Google Scholar
• Castelo, Noah, Maarten W. Bos, and Donald R. Lehmann. 2019. “Task-Dependent Algorithm Aversion.” Journal of Marketing Research 56 (5): 809–825. https://doi.org/10.1177/0022243719851788
   Web of Science ®Google Scholar
• Chang, Yu-Ting, Hueiju Yu, and Hsi-Peng Lu. 2015. “Persuasive Messages, Popularity Cohesion, and Message Diffusion in Social Media Marketing.” Journal of Business Research 68 (4): 777–782. https://doi.org/10.1016/j.jbusres.2014.11.027
   Web of Science ®Google Scholar
• Cheguri, Preethi. 2023. “Deepfake Technology: Concerns Raised in the Advertising Industries.” Analytics Insight, January 25. https://www.analyticsinsight.net/deepfake-technology-concerns-raised-in-the-advertising-industries/#:∼:text=Deepfakes%20can%20be%20used%20to,spreading%20and%20consumers%20being%20misled
   Google Scholar
• Chen, Gang, Peihong Xie, Jing Dong, and Tianfu Wang. 2019. “Understanding Programmatic Creative: The Role of AI.” Journal of Advertising 48 (4): 347–355. https://doi.org/10.1080/00913367.2019.1654421
   Web of Science ®Google Scholar
• Chen, Huan, Slyvia Chan-Olmsted, Julia Kim, and Irene Mayor Sanabria. 2022. “Consumers’ Perception on Artificial Intelligence Applications in Marketing Communication.” Qualitative Market Research: An International Journal 25 (1): 125–142. https://doi.org/10.1108/QMR-03-2021-0040
   Web of Science ®Google Scholar
• Cheng, Xusen, Ying Bao, Alex Zarifis, Wankun Gong, and Jian Mou. 2022. “Exploring Consumers’ Response to Text-Based Chatbots in E-Commerce: The Moderating Role of Task Complexity and Chatbot Disclosure.” Internet Research 32 (2): 496–517. https://doi.org/10.1108/INTR-08-2020-0460
   Web of Science ®Google Scholar
• Cui, Yuanyuan, Patrick van Esch, and Shailendra Pratap Jain. 2022. “Just Walk out: The Effect of AI-Enabled Checkouts.” European Journal of Marketing 56 (6): 1650–83.
   Web of Science ®Google Scholar
• Damian, Rodica Ioana, and Dean Keith Simonton. 2015. “Four Psychological Perspectives on Creativity.” In Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource, edited by Robert A. Scott and Stephen Michael Kosslyn, 1–15. Wiley.
   Google Scholar
• De Keyzer, Freya, Nathalie Dens, and Patrick De Pelsmacker. 2022. “How and When Personalized Advertising Leads to Brand Attitude, Click, and WOM Intention.” Journal of Advertising 51 (1): 39–56. https://doi.org/10.1080/00913367.2021.1888339
   Web of Science ®Google Scholar
• Deng, Shasha, Chee-Wee Tan, Weijun Wang, and Yu Pan. 2019. “Smart Generation System of Personalized Advertising Copy and Its Application to Advertising Practice and Research.” Journal of Advertising 48 (4): 356–365. https://doi.org/10.1080/00913367.2019.1652121
   Web of Science ®Google Scholar
• Dolnicar, Sara, and Yolanda Jordaan. 2007. “A Market-Oriented Approach to Responsibly ManagingInformation Privacy Concerns in Direct Marketing.” Journal of Advertising 36 (2): 123–149. https://doi.org/10.2753/JOA0091-3367360209
   Web of Science ®Google Scholar
• Evans, Nathaniel J., Joe Phua, Jay Lim, and Hyoyeun Jun. 2017. “Disclosing Instagram Influencer Advertising: The Effects of Disclosure Language on Advertising Recognition, Attitudes, and Behavioral Intent.” Journal of Interactive Advertising 17 (2): 138–149. https://doi.org/10.1080/15252019.2017.1366885
   Google Scholar
• Fay, Brad, and Rick Larkin. 2017. “Why Online Word-of-Mouth Measures Cannot Predict Brand Outcomes Offline: Volume, Sentiment, Sharing, and Influence Metrics Yield Scant Online–Offline WOM Correlations.” Journal of Advertising Research 57 (2): 132–143. https://doi.org/10.2501/JAR-2017-021
   Web of Science ®Google Scholar
• Franke, Claudia, Andrea Groeppel-Klein, and Katrin Müller. 2023. “Consumers’ Responses to Virtual Influencers as Advertising Endorsers: Novel and Effective or Uncanny and Deceiving?” Journal of Advertising 52 (4): 523–539. https://doi.org/10.1080/00913367.2022.2154721
   Web of Science ®Google Scholar
• Fredrickson, Barbara L., Michele M. Tugade, Christian E. Waugh, and Gregory R. Larkin. 2003. “What Good Are Positive Emotions in Crisis? A Prospective Study of Resilience and Emotions following the Terrorist Attacks on the United States on September 11th, 2001.” Journal of Personality and Social Psychology 84 (2): 365–376. https://doi.org/10.1037/0022-3514.84.2.365
   PubMed Web of Science ®Google Scholar
• Fulgoni, Gian M., and Andrew Lipsman. 2015. “Digital Word of Mouth and Its Offline Amplification: A Holistic Approach to Leveraging and Amplifying All Forms of WOM.” Journal of Advertising Research 55 (1): 18–21. https://doi.org/10.2501/JAR-55-1-018-021
   Web of Science ®Google Scholar
• Gong, Taeshik, and Jin Nam Choi. 2016. “Effects of Task Complexity on Creative Customer Behavior.” European Journal of Marketing 50 (5/6): 1003–1023. https://doi.org/10.1108/EJM-04-2015-0205
   Web of Science ®Google Scholar
• Gosling, Samuel D., Peter J. Rentfrow, and William B. Swann, Jr. 2003. “A Very Brief Measure of the Big-Five Personality Domains.” Journal of Research in Personality 37 (6): 504–528. https://doi.org/10.1016/S0092-6566(03)00046-1
   Web of Science ®Google Scholar
• Gran, Anne-Britt, Peter Booth, and Taina Bucher. 2021. “To Be or Not to Be Algorithm Aware: A Question of a New Digital Divide?” Information, Communication & Society 24 (12): 1779–96. https://doi.org/10.1080/1369118X.2020.1736124
   Web of Science ®Google Scholar
• Granulo, Armin, Christoph Fuchs, and Stefano Puntoni. 2021. “Preference for Human (Vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts.” Journal of Consumer Psychology 31 (1): 72–80. https://doi.org/10.1002/jcpy.1181
   Web of Science ®Google Scholar
• Griner, David. 2018. “An AI Tried to Write the Perfect Lexus Ad. Here’s a Scene-by-Scene Look at What It Was Thinking.” AdWeek, May 10. https://www.adweek.com/programmatic/an-ai-tried-to-write-the-perfect-lexus-ad-heres-a-scene-by-scene-look-at-what-it-was-thinking/
   Google Scholar
• Hayes, Andrew F. 2018. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. 2nd ed. New York: Guilford Press.
   Google Scholar
• Hayes, Jameson L., Guy Golan, Brian Britt, and Janelle Applequist. 2020. “How Advertising Relevance and Consumer–Brand Relationship Strength Limit Disclosure Effects of Native Ads on Twitter.” International Journal of Advertising 39 (1): 131–165. https://doi.org/10.1080/02650487.2019.1596446
   Web of Science ®Google Scholar
• Huang, Liqiang, Jie Zhang, Hefu Liu, and Liang Liang. 2014. “The Effect of Online and Offline Word-of-Mouth on New Product Diffusion.” Journal of Strategic Marketing 22 (2): 177–189. https://doi.org/10.1080/0965254X.2013.876070
   Google Scholar
• Ishida, Koji, Lisa Slevitch, and Katia Siamionava. 2016. “The Effects of Traditional and Electronic Word-of-Mouth on Destination Image: A Case of Vacation Tourists Visiting Branson, Missouri.” Administrative Sciences 6 (4): 12–29. https://doi.org/10.3390/admsci6040012
   Web of Science ®Google Scholar
• Jarrahi, Mohammad Hossein. 2018. “Artificial Intelligence and the Future of Work: Human–AI Symbiosis in Organizational Decision Making.” Business Horizons 61 (4): 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
   Web of Science ®Google Scholar
• Jeong, Eun Ha, and Soo Cheong Shawn Jang. 2011. “Restaurant Experiences Triggering Positive Electronic Word-of-Mouth (eWOM) Motivations.” International Journal of Hospitality Management 30 (2): 356–366. https://doi.org/10.1016/j.ijhm.2010.08.005
   Web of Science ®Google Scholar
• Kang, Enoch Yi-No, Duan-Rung Chen, and Yen-Yuan Chen. 2023. “Associations between Literacy and Attitudes toward Artificial Intelligence–Assisted Medical Consultations: The Mediating Role of Perceived Distrust and Efficiency of Artificial Intelligence.” Computers in Human Behavior 139:107529. https://doi.org/10.1016/j.chb.2022.107529
   Google Scholar
• Khillar, Sagar. 2020. “Difference between Strong and Weak AI. Difference between Similar Terms and Objects.” Differencebetween.net, July 14. http://www.differencebetween.net/technology/difference-between-strong-and-weak-ai/
   Google Scholar
• Kim, Junghan, and Arun Lakshmanan. 2015. “How Kinetic Property Shapes Novelty Perceptions.” Journal of Marketing 79 (6): 94–111. https://doi.org/10.1509/jm.14.0284
   Web of Science ®Google Scholar
• King, Robert Allen, Pradeep Racherla, and Victoria D. Bush. 2014. “What We Know and Don’t Know about Online Word-of-Mouth: A Review and Synthesis of the Literature.” Journal of Interactive Marketing 28 (3): 167–183. https://doi.org/10.1016/j.intmar.2014.02.001
   Web of Science ®Google Scholar
• Lee, Kai-Fu, and Chen Qiufan. 2022. “What AI Cannot Do.” Bigthink.com, January 19. https://bigthink.com/the-future/what-ai-cannot-do/
   Google Scholar
• Li, Feng, and Timon C. Du. 2017. “The Effectiveness of Word of Mouth in Offline and Online Social Networks.” Expert Systems with Applications 88: 338–351. https://doi.org/10.1016/j.eswa.2017.07.004
   Web of Science ®Google Scholar
• Li, Hairong, Terry Daugherty, and Frank Biocca. 2002. “Impact of 3-D Advertising on Product Knowledge, Brand Attitude, and Purchase Intention: The Mediating Role of Presence.” Journal of Advertising 31 (3): 43–57. https://doi.org/10.1080/00913367.2002.10673675
   Web of Science ®Google Scholar
• Liao, Mengqi, and S. Shyam Sundar. 2022. “When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering.” Journal of Advertising 51 (2): 256–267. https://doi.org/10.1080/00913367.2021.1887013
   Web of Science ®Google Scholar
• Liu, Bingjie, and Lewen Wei. 2019. “Machine Authorship in Situ: Effect of News Organization and News Genre on News Credibility.” Digital Journalism 7 (5): 635–657. https://doi.org/10.1080/21670811.2018.1510740
   Web of Science ®Google Scholar
• Liu-Thompkins, Yuping, Ewa Maslowska, Yuqing Ren, and Hyejin Kim. 2020. “Creating, Metavoicing, and Propagating: A Road Map for Understanding User Roles in Computational Advertising.” Journal of Advertising 49 (4): 394–410. https://doi.org/10.1080/00913367.2020.1795758
   Web of Science ®Google Scholar
• Longoni, Chiara, and Luca Cian. 2022. “Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect.” Journal of Marketing 86 (1): 91–108. https://doi.org/10.1177/0022242920957347
   Web of Science ®Google Scholar
• Lou, Chen, Siu Ting Josie Kiew, Tao Chen, Tze Yen Michelle Lee, Jia En Celine Ong, and ZhaoXi Phua. 2023. “Authentically Fake? How Consumers Respond to the Influence of Virtual Influencers.” Journal of Advertising 52 (4): 540–557. https://doi.org/10.1080/00913367.2022.2149641
   Web of Science ®Google Scholar
• Luo, Xueming, Siliang Tong, Zheng Fang, and Zhe Qu. 2019. “Machines versus Humans: The Impact of AI Chatbot Disclosure on Customer Purchases.” Marketing Science 38 (6): 937–947.
   Web of Science ®Google Scholar
• Lv, Linxiang, and Minxue Huang. 2022. “Can Personalized Recommendations in Charity Advertising Boost Donation? The Role of Perceived Autonomy.” Journal of Advertising 53 (1): 36–53. https://doi.org/10.1080/00913367.2022.2109082
   Web of Science ®Google Scholar
• Lyell, David, Farah Magrabi, and Enrico Coiera. 2018. “The Effect of Cognitive Load and Task Complexity on Automation Bias in Electronic Prescribing.” Human Factors 60 (7): 1008–1021. https://doi.org/10.1177/0018720818781224
   PubMed Web of Science ®Google Scholar
• MacKenzie, Scott B., and Richard J. Lutz. 1989. “An Empirical Examination of the Structural Antecedents of Attitude toward the Ad in an Advertising Pretesting Context.” Journal of Marketing 53 (2): 48–65. https://doi.org/10.1177/002224298905300204
   Web of Science ®Google Scholar
• Malone, Sam. 2020. “The Importance of Word-of-Mouth Marketing through Social Media.” Forbes, December 7. https://www.forbes.com/sites/forbesbusinesscouncil/2020/12/07/the-importance-of-word-of-mouth-marketing-through-social-media/?sh=78e819b230a3
   Google Scholar
• Maynard, Douglas C., and Milton D. Hakel. 1997. “Effects of Objective and Subjective Task Complexity on Performance.” Human Performance 10 (4): 303–330. https://doi.org/10.1207/s15327043hup1004_1
   Web of Science ®Google Scholar
• McCall, Cade, Christine M. Tipper, Jim Blascovich, and Scott T. Grafton. 2012. “Attitudes Trigger Motor Behavior through Conditioned Associations: Neural and Behavioral Evidence.” Social Cognitive and Affective Neuroscience 7 (7): 841–849. https://doi.org/10.1093/scan/nsr057
   PubMed Web of Science ®Google Scholar
• Mogaji, Emmanuel, Sunday Olaleye, and Dandison Ukpabi. 2020. “Using AI to Personalise Emotionally Appealing Advertisement.” In Digital and Social Media Marketing, edited by Nripendra P. Rana, Emma L. Slade, Hatice Kizgin, Ganesh P. Sahu, Nitish Singh, Bidit Dey, Anabel Gutierrez, and Yogesh K. Dwivedi, 137–150. Cham: Springer.
   Google Scholar
• Moisescu, Ovidiu‐Ioan, Ioana Dan, and Oana‐Adriana Gică. 2022. “An Examination of Personality Traits as Predictors of Electronic Word‐of‐Mouth Diffusion in Social Networking Sites.” Journal of Consumer Behaviour 21 (3): 450–467. https://doi.org/10.1002/cb.1970
   Web of Science ®Google Scholar
• Qin, Xuebing, and Zhibin Jiang. 2019. “The Impact of AI on the Advertising Process: The Chinese Experience.” Journal of Advertising 48 (4): 338–346. https://doi.org/10.1080/00913367.2019.1652122
   Web of Science ®Google Scholar
• Rodgers, Shelly. 2021. “Themed Issue Introduction: Promises and Perils of Artificial Intelligence and Advertising.” Journal of Advertising 50 (1): 1–10. https://doi.org/10.1080/00913367.2020.1868233
   Web of Science ®Google Scholar
• Sabrepc. 2023. “10 Artificial Intelligence Tasks You Didn’t Know AI Could Do.” Sabrepc, January 23. https://www.sabrepc.com/blog/Deep-Learning-and-AI/10-Tasks-You-Didnt-Know-AI-Could-Do
   Google Scholar
• Statista. 2023. “Artificial Intelligence (AI)-Enabled Advertising Spending Worldwide in 2022 and 2032 (in Billion U.S. Dollars).” Statista Research Department, January 10. https://www.statista.com/statistics/1301227/ai-enabled-adspend-world/
   Google Scholar
• Sundar, S. Shyam. 2020. “Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII).” Journal of Computer-Mediated Communication 25 (1): 74–88. https://doi.org/10.1093/jcmc/zmz026
   Web of Science ®Google Scholar
• Sundar, S. Shyam, and Jinyoung Kim. 2019. “Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information.” In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow Scotland, UK, 1–9.
   Google Scholar
• Sundar, S. Shyam, T. Franklin Waddell, and Eun Hwa Jung. 2016. “The Hollywood Robot Syndrome Media Effects on Older Adults’ Attitudes toward Robots and Adoption Intentions.” In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch, New Zealand, 343–350. IEEE.
   Google Scholar
• Taylor, Charles. 2019. “Can Artificial Intelligence Eliminate Consumer Privacy Concerns For Digital Advertisers?” Forbes, July 29. https://www.forbes.com/sites/charlesrtaylor/2019/07/29/can-artificial-intelligence-eliminate-consumer-privacy-concerns-for-digital-advertisers/?sh=79bc17c2b264
   Google Scholar
• Thomas, Veronica L., and Kendra Fowler. 2021. “Close Encounters of the AI Kind: Use of AI Influencers as Brand Endorsers.” Journal of Advertising 50 (1): 11–25. https://doi.org/10.1080/00913367.2020.1810595
   Web of Science ®Google Scholar
• Thórisson, Kristinn R., Jordi Bieger, Thröstur Thorarensen, Jóna S. Sigurðardóttir, and Bas R. Steunebrink. 2016. “Why Artificial Intelligence Needs a Task Theory.” In International Conference on Artificial General Intelligence, 118–128. Springer, Cham.
   Google Scholar
• Vakratsas, Demetrios, and Xin Wang. 2021. “Artificial Intelligence in Advertising Creativity.” Journal of Advertising 50 (1): 39–51. https://doi.org/10.1080/00913367.2020.1843090
   Web of Science ®Google Scholar
• van Noort, Guda, Itai Himelboim, Jolie Martin, and Tom Collinger. 2020. “Introducing a Model of Automated Brand-Generated Content in an Era of Computational Advertising.” Journal of Advertising 49 (4): 411–427. https://doi.org/10.1080/00913367.2020.1795954
   Web of Science ®Google Scholar
• Verma, Sanjeev, and Neha Yadav. 2021. “Past, Present, and Future of Electronic Word of Mouth (EWOM).” Journal of Interactive Marketing 53 (1): 111–128. https://doi.org/10.1016/j.intmar.2020.07.001
   Google Scholar
• Voss, Kevin E., Eric R. Spangenberg, and Bianca Grohmann. 2003. “Measuring the Hedonic and Utilitarian Dimensions of Consumer Attitude.” Journal of Marketing Research 40 (3): 310–320. https://doi.org/10.1509/jmkr.40.3.310.19238
   Web of Science ®Google Scholar
• Waddell, T. Franklin. 2018. “A Robot Wrote This? How Perceived Machine Authorship Affects News Credibility.” Digital Journalism 6 (2): 236–255. https://doi.org/10.1080/21670811.2017.1384319
   Web of Science ®Google Scholar
• Wan, Anan, and Mengtian Jiang. 2023. “Can Virtual Influencers Replace Human Influencers in Live-Streaming E-Commerce? An Exploratory Study from Practitioners’ and Consumers’ Perspectives.” Journal of Current Issues & Research in Advertising 44 (3): 332–372. https://doi.org/10.1080/10641734.2023.2224416
   Web of Science ®Google Scholar
• Watts, Jameson, and Anastasia Adriano. 2021. “Uncovering the Sources of Machine-Learning Mistakes in Advertising: Contextual Bias in the Evaluation of Semantic Relatedness.” Journal of Advertising 50 (1): 26–38. https://doi.org/10.1080/00913367.2020.1821411
   Web of Science ®Google Scholar
• Westbrook, Robert A. 1987. “Product/Consumption-Based Affective Responses and Postpurchase Processes.” Journal of Marketing Research 24 (3): 258–270. https://doi.org/10.1177/002224378702400302
   Web of Science ®Google Scholar
• Wu, Linwan, and Naa Amponsah Dodoo. 2020. “Being Accepted or Ostracized: How Social Experience Influences Consumer Responses to Advertisements with Different Regulatory Focus.” Journal of Advertising 49 (3): 234–249. https://doi.org/10.1080/00913367.2020.1743217
   Web of Science ®Google Scholar
• Wu, Linwan, Naa Amponsah Dodoo, Taylor Jing Wen, and Li Ke. 2022. “Understanding Twitter Conversations about Artificial Intelligence in Advertising based on Natural Language Processing.” International Journal of Advertising 41 (4): 685–702. https://doi.org/10.1080/02650487.2021.1920218
   Web of Science ®Google Scholar
• Wu, Linwan, and Taylor Jing Wen. 2021. “Understanding AI Advertising from the Consumer Perspective: What Factors Determine Consumer Appreciation of AI-Created Advertisements?” Journal of Advertising Research 61 (2): 133–146. https://doi.org/10.2501/JAR-2021-004
   Web of Science ®Google Scholar
• Wu, Wann-Yih, and Badri Munir Sukoco. 2010. “Why Should I Share? Examining Consumers’ Motives and Trust on Knowledge Sharing.” Journal of Computer Information Systems 50 (4): 11–19.
   Web of Science ®Google Scholar
• Xu, Jingjun David. 2016. “Retaining Customers by Utilizing Technology-Facilitated Chat: Mitigating Website Anxiety and Task Complexity.” Information & Management 53 (5): 554–569. https://doi.org/10.1016/j.im.2015.12.007
   Web of Science ®Google Scholar
• Xu, Yingzi, Chih-Hui Shieh, Patrick van Esch, and I. Ling Ling. 2020. “AI Customer Service: Task Complexity, Problem-Solving Ability, and Usage Intention.” Australasian Marketing Journal 28 (4): 189–99. https://doi.org/10.1016/j.ausmj.2020.03.005
   Web of Science ®Google Scholar
• Yeomans, Michael, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. 2019. “Making Sense of Recommendations.” Journal of Behavioral Decision Making 32 (4): 403–414. https://doi.org/10.1002/bdm.2118
   Web of Science ®Google Scholar
• Zawiślak, Paulina, and Michael Sweeney. 2023. “The Demise of Third-Party Cookies in AdTech: Why Are They Being Phased Out?” Clearcode, April 27. https://clearcode.cc/blog/third-party-cookies-demise/#the-european-union%E2%80%99s-general-data-protection-regulation-(gdpr)
   Google Scholar
• Zhang, Weilu, and Shelly Rodgers. 2023. “Linking Ethnicity Targeting with Artificial Intelligence and Data Collection: Perceptions and Behavioral Responses of Black Consumers.” Journal of Current Issues & Research in Advertising 44 (3): 373–391. https://doi.org/10.1080/10641734.2023.2212022
   Web of Science ®Google Scholar