artificial intelligence and consumer behaviour research paper

• DOI: 10.1002/cb.2233
• Corpus ID: 261150599

Artificial intelligence consumer behavior: A hybrid review and research agenda

• Varsha Jain , Ketan Wadhwani , J. Eastman
• Published in Journal of Consumer Behaviour 23 August 2023
• Computer Science, Business, Psychology

10 Citations

How generative ai is (will) change consumer behaviour: postulating the potential impact and implications for research, practice, and policy, leveraging ai to enhance marketing and customer engagement strategies in the french market, a review of machine learning algorithms in consumer behavior: the missing link in impulse buying, users, ai, or professional designers the impacts of inspiration stimuli on customers' willingness to participate in user design, mapping the terrain of open innovation in consumer research: insights and directions from bibliometrics, purchase spillovers from the metaverse to the real world: the roles of social presence, trialability, and customer experience, to share or not to share: when is influencer self‐disclosure perceived as appropriate, edge impulse potential to enhance object recognition through machine learning, artificial intelligence and consumer behavior: from predictive to generative ai, mapping research on the subjective well-being of knowledge workers: a systematic enquiry deploying bibliometrics, 105 references, consumers’ reasons and perceived value co-creation of using artificial intelligence-enabled travel service agents, using ai predicted personality to enhance advertising effectiveness, the implications of artificial intelligence on the digital marketing of financial services to vulnerable customers, artificial intelligence in e-commerce: a bibliometric study and literature review, artificial intelligence and declined guilt: retailing morality comparison between human and ai, ai increases unethical consumer behavior due to reduced anticipatory guilt, consumer trust and perceived risk for voice-controlled artificial intelligence: the case of siri, i, robot, you, consumer: measuring artificial intelligence types and their effect on consumers emotions in service, the convenience of shopping via voice ai: introducing aidm, artificial intelligence and the new forms of interaction: who has the control when interacting with a chatbot, related papers.

Showing 1 through 3 of 0 Related Papers

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

AI in Consumer Behavior

• First Online: 03 October 2021

Cite this chapter

• Dimitris C. Gkikas 8 &
• Prokopis K. Theodoridis 8  

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 22))

1955 Accesses

6 Citations

1 Altmetric

E-commerce is one of the fastest changing industries and consumers try to purchase most of the goods online. Following the internet revolution, retail and promotion, daily data generation led to a point where marketers could not further process, using the traditional statistical ways, the new demands of vast data volume. The use of big data analytics emerged, and the rise of AI came along with predictive analytics including machine learning and data mining solutions. Artificial Intelligence has both evolved and affected the way marketing is implemented. AI is proven to be a sophisticated way of analyzing and processing data as well as decision making. The privilege of its use is considered an asset to the business executives who understand its potential. Considering AI as a tool, it may provide massive data processing and prediction accuracy. Referring to marketing science, marketers have witnessed the benefits of AI by predicting consumers behavior. Since consumers behavior varies, brands struggle for customer satisfaction, they invest time and money to highlight their products or services potentials, better define their market share, and classify customers’ needs. Marketers have been in pursuit of customer satisfaction using AI tools to read web metrics and optimize reach and conversions strategies. Machine learning, natural language processing, expert systems, voice, vision, planning, and robotics are the main AI branches that companies use to stay ahead of the competition. Business objectives aim to attract new customers, predict consumer behavior, along with the capability to personalize and predict demand using “smart” systems which allow them to increase sales, mitigate the decision-making risk and increase customer satisfaction, customer loyalty, and sales predictions. This chapter tries to map the stages of AI contribution to consumer behavior describing the essential marketing milestones.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Artificial Intelligence (AI) in Marketing: How AI Supports Marketers Throughout the Consumer Journey

AI-Based Digital Marketing Strategies—A Review

The Future of Digital Marketing: How Would Artificial Intelligence Change the Directions?

V. Sankaran, Empowering consumer research with data science, in Amity International Conference on Artificial Intelligence (AICAI) (Dubai, United Arab Emirates, 2019), pp. 340–344, https://doi.org/10.1109/AICAI.2019.8701356

C. Katawetawaraks, L. Cheng, Online shopper behavior: influences of online shopping decision. Asian J. Bus. Res. 1 (2013). Available at SSRN: https://ssrn.com/abstract=2345198 . Accessed 21 Aug 2020

S. Gochhait, O. Mazumdar, S. Chahal et al., Role of artificial intelligence (AI) in understanding the behavior pattern: a study on e-commerce, in ICDSMLA 2019. Lecture Notes in Electrical Engineering , vol. 601, eds. A. Kumar, M. Paprzycki, V. Gunjan (Springer, Singapore, 2020), https://doi.org/10.1007/978-981-15-1420-3_166

Rajanarthagi, Impact of artificial intelligence on digital marketing (2019), https://gecdesigns.com/blog/impact-of-artificial-intelligence-on-digital-marketing . Accessed 15 Aug 2020

L. Alexander, The Who, What, Why, & How of Digital Marketing (2020), https://blog.hubspot.com/marketing/what-is-digital-marketing . Accessed 21 Aug 2020

A. Orogun, O. Bukola, Predicting consumer behaviour in digital market: a machine learning approach. 8 :8391–8402 (2019). https://doi.org/10.15680/IJIRSET.2019.0808006

Oracle, Can virtual experiences replace reality? (2020), https://www.oracle.com/webfolder/s/delivery_production/docs/FY16h1/doc35/CXResearchVirtualExperiences.pdf?source=%3Aow%3Alp%3Acpo%3A%3A . Accessed 25 Aug 2020

J. Kietzmann, J. Paschen, E. Treen, Artificial intelligence in advertising: how marketers can Leverage artificial intelligence along the consumer journey. J. Advert. Res. 58 (3), 263–267 (2018). https://doi.org/10.2501/JAR-2018-035.J

Article   Google Scholar  

S. Vázquez, O. Muñoz-García, I. Campanella et al., A classification of user-generated content into consumer decision journey stages. Neural Netw. 58 , 68–81 (2014), ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2014.05.026

J.J. Sheu, Y.W. Chang, K.T. Chu, Applying decision tree data mining for online group buying consumers’ behavior. Int. J. Electr. Cust. Relat. Manage. 2 (2), 140 (2008). https://doi.org/10.1504/ijecrm.2008.019929

R. Thiebaut, AI revolution: How data can identify and shape consumer behavior in ecommerce, (2019), https://doi.org/10.1108/978-1-78973-233-720191012

T. Taqwa, A. Suhendra, M. Hermita et al., Implementation of Naïve Bayes method for product purchasing decision using neural impulse actuator in neuromarketing, in 2015 International Conference on Information & Communication Technology and Systems (ICTS) (Surabaya, 2015), pp. 113–118, https://doi.org/10.1109/ICTS.2015.7379882

R.A.E. Ahmeda, M.E. Shehaba, S. Morsya et al., Performance study of classification algorithms for consumer online shopping attitudes and behavior using data mining, in 2 015 Fifth International Conference on Communication Systems and Network Technologies (Gwalior, 2015), pp. 1344–1349, https://doi.org/10.1109/CSNT.2015.50

X. Niu, C. Li, X. Yu, Predictive analytics of e-commerce search behavior for conversion. AMCIS. H. Le-wei and L. Yu-sheng, Exploration in commercial websites on the association rules base on the consumers’ behavior, in 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference , chongqing, 2011, (2017) pp. 42–44, https://doi.org/10.1109/ITAIC.2011.6030146

C. Hou, C. Chen, J. Wang, Tree-based feature transformation for purchase behavior prediction, in IEICE Transactions on Information and Systems , vol. E101.D (5), pp 1441–1444, Released, Online ISSN 1745-1361, Print ISSN 0916-8532, (2018), https://doi.org/10.1587/transinf.2017EDL8210

H. Valecha, A. Varma, I. Khare et al., Prediction of consumer behaviour using random forest algorithm, in 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (Gorakhpur, 2018), pp. 1–6, https://doi.org/10.1109/UPCON.2018.8597070

P. Chandon, J.W. Hutchinson, E.T. Bradlow et al., Does in-store marketing work? Effects of the number and position of shelf facings on brand attention and evaluation at the point of purchase. J. Mark. 73 (6), 1–17 (2009). https://doi.org/10.1509/jmkg.73.6.1

K. Gidlöf, W. Annika, D. Richard et al., Using eye tracking to trace a cognitive process: gaze behaviour during decision making in a natural environment. J. Eye Mov. Res. 6 (2013)

Google Scholar  

S. Goyal, K.P. Miyapuram, U. Lahiri, Predicting consumer's behavior using eye tracking data, in 2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI) (Hong Kong, 2015), pp. 126–129, https://doi.org/10.1109/ISCMI.2015.26

T. Kowatsch, W. Maass, In-store consumer behavior: how mobile recommendation agents influence usage intentions, product purchases, and store preferences. Comput. Hum. Behav. 26 (4), 697–704 (2010), ISSN 0747-5632, https://doi.org/10.1016/j.chb.2010.01.006

D. Liciotti, E. Frontoni, A. Mancini et al., Pervasive system for consumer behaviour analysis in retail environments, in Video Analytics. Face and Facial Expression Recognition and Audience Measurement. VAAM 2016, FFER 2016 . Lecture Notes in Computer Science, ed. K. Nasrollahi et al. vol. 10165 (Springer, Cham, 2017), https://doi.org/10.1007/978-3-319-56687-0_2

Ł. Łysik, R. Kutera, P. Machura, Behavioural and technical factors of influence on purchase behaviour of mobile consumers, in Proceedings of the 18th International Academic Mindtrek Conference on Media Business, Management, Content & Services (AcademicMindTrek ’14, 2014), https://doi.org/10.1145/2676467.2676494

L. Muzellec, E. O’Raghallaigh, Mobile technology and its impact on the consumer decision-making journey. J. Advert. Res. 58 (1), 12–15 (2017). https://doi.org/10.2501/jar-2017-058

M. Zhang, G. Chen, Q. Wei, Discovering consumers’ purchase intentions based on mobile search behaviors, in Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing , ed. T. Andreasen et al., vol. 400 (Springer, Cham., 2016) https://doi.org/10.1007/978-3-319-26154-6_2

S. Moro et al., Predicting social media performance metrics and evaluation of the impact on brand building: a datamining approach. J. Bus. Res. (2016). https://doi.org/10.1016/j.jbusres.2016.02.010

C. Kim, S.U. Yang, Like, comment, and share on Facebook: how each behavior differs from the other. Public Relations Rev. 43 (2), 441–449 (2017). https://doi.org/10.1016/j.pubrev.2017.02.006

K. Hao, What is AI? We Drew You a Flowchart to Work it Out (2018), https://www.technologyreview.com/2018/11/10/139137/is-this-ai-we-drew-you-a-flowchart-to-work-it-out . Accessed 26 Aug 2020

B.J. Copeland, Artificial Intelligence (2020), https://www.britannica.com/technology/artificial-intelligence . Accessed 15 Aug 2020

Accenture, Artificial Intelligence (2020), https://www.accenture.com/us-en/insights/artificial-intelligence-summary-index . Accessed 15 Aug 2020

Nexus Integra, Advantages and disadvantages of artificial intelligence (2019), https://nexusintegra.io/advantages-disadvantages-artificial-intelligence . Accessed 15 Aug 2020

H. Jasim, H. Hiba, H. Shnain et al., (2015) Big data and 5 V’s characteristics. 2393–2835

J. Hall, How Artificial Intelligence is Transforming Digital Marketing (2019), https://www.forbes.com/sites/forbesagencycouncil/2019/08/21/how-artificial-intelligence-is-transforming-digital-marketing/#26a4a36421e1 . Accessed 13 Aug 2020

The Keen Folks, AI Marketing: How to Leverage New Technology to Maximize Results (2020), https://thekeenfolks.com/keensights/artificial-intelligence-marketing . Accessed 15 Aug 2020

Smartboost, How to Predict Consumer Behavior Using AI Marketing (2020), https://smartboost.com/blog/how-to-predict-consumer-behavior-using-ai-marketing . Accessed 15 Aug 2020

K. Baker, The Ultimate Guide to Content Marketing in 2020 (2020), https://blog.hubspot.com/marketing/content-marketing . Accessed 15 Aug 2020

C. Myers (2019) How Will a Transform the Digital Marketing in 2019? https://www.digitaldoughnut.com/articles/2019/february/how-will-ai-transform-the-digital-marketing . Accessed 15 Aug 2020

J. Miller, 5 Ways AI is Transforming Marketing (2018), https://aibusiness.com/document.asp?doc_id=760620&site=aibusiness . Accessed 15 Aug 2020

Google Analytics, Data Import (2020), https://support.google.com/analytics/topic/6065609?hl=en&ref_topic=1727148&authuser=0 . Accessed 15 Aug 2020

M. Olmez, How to Use AI in E-Commerce (2018), https://www.accenture.com/nl-en/blogs/insights/how-to-use-ai-in-e-commerce . Accessed 15 Aug 2020

E. Van den Broeck, K. Poels, M. Walrave, A factorial survey study on the influence of advertising place and the use of personal data on user acceptance of Facebook ads. Am. Behav. Sci. 61 , 000276421771756 (2017). https://doi.org/10.1177/0002764217717560

Smart Insights, Chatbot Conversations Just Got More Human (2020), https://www.smartinsights.com/user-experience/chatbot-conversations-just-got-human . Accessed 24 Aug 2020

B. Zarouali, E. Van den Broeck, M. Walrave et al., Predicting consumer responses to a chatbot on Facebook. Cyberpsychol. Behav. Soc. Network. 21 (2018) https://doi.org/10.1089/cyber.2017.0518

OMDIA TRACTICA, Artificial Intelligence Software Revenue to Reach $59.8 Billion Worldwide by 2025 (2017), https://tractica.omdia.com/newsroom/press-releases/artificial-intelligence-software-revenue-to-reach-59-8-billion-worldwide-by-2025 . Accessed 15 Aug 2020

Juniper Research, Chatbot Conversations to Deliver $8 Billion in Cost Savings by 2022 (2020), https://www.juniperresearch.com/new-trending/analystxpress/july-2017/chatbot-conversations-to-deliver-8bn-cost-saving . Accessed 18 Aug 2020

A. Butt, Artificial Intelligence, Natural Language Processing, Machine Learning and Voice Search. A Match Made in Heavens (2019), https://quoracreative.com/article/voice-search-evolution . Accessed 15 Aug 2020

S. Mohanadasan, Visual Search Guide: Who Uses it, Benefits, and Optimization Tip (2020), https://www.semrush.com/blog/visual-search-guide-benefits-optimizatiion/#header2 . Accessed 22 Aug 2020

L. Heller, Visual Search Wins Over Text as Consumers’ Most Trusted Information Source (2019), https://www.businesswire.com/news/home/20190204005613/en/Visual-Search-Wins-Text-Consumers%E2%80%99-Trusted-Information . Accessed 15 Aug 2020

C. Boyd, Visual Search - The Ultimate Guide: Statistics, News, Trends, and Tips (2018), https://medium.com/@clarkboyd/visual-search-trends-statistics-tips-and-uses-in-everyday-life-d20084dc4b0a . Accessed 15 Aug 2020

M. Kaput, Artificial Intelligence in Email Marketing (2021). https://www.marketingaiinstitute.com/blog/ai-in-email-marketing . Accessed 27 Jan 2021

S.R. Chowdhuri, The Impact of Artificial Intelligence on Social Media (2020), https://www.manipalprolearn.com/blog/impact-artificial-intelligence-social-media . Accessed 11 Aug 2020

PPCExpo, The Ultimate Guide to Google Smart Campaigns (2020), https://ppcexpo.com/blog/smart-campaigns . Accessed 15 Aug 2020

Facebook for Business, Making Creative Impact on Facebook Platforms with Text, Timing and Format (2020), https://www.facebook.com/business/news/insights/making-creative-impact-on-facebook-platforms-with-text-timing-and-format . Accessed 15 Aug 2020

Google Ads, Smart Campaigns (2020), https://support.google.com/google-ads/answer/9367181?hl=en&ref_topic=9024774 . Accessed 15 Aug 2020

K. Novoseltseva, Artificial Intelligence in E-Commerce: Benefits, Statistics, Facts, Use Cases & Case Studies (2018), https://apiumhub.com/tech-blog-barcelona/artificial-intelligence-ecommerce . Accessed 15 Aug 2020

PAT Research, What is predictive analytics (2020), https://www.predictiveanalyticstoday.com/what-is-predictive-analytics . Accessed 25 Aug 2020

C. Crawford, Social Media Analytics: The Complete Guide (2020), https://www.socialbakers.com/blog/social-media-analytics-the-complete-guide . Accessed 21 Aug 2020

J. Gube, What is User Experience Design? Overview, Tools and Resources (2010), https://www.smashingmagazine.com/2010/10/what-is-user-experience-design-overview-tools-and-resources . Accessed 21 Aug 2020

ISO, ISO 9241-220:2019 Ergonomics of Human-System Interaction — Part 220: Processes for Enabling, Executing and Assessing Human-Centred Design Within Organizations (2020), https://www.iso.org/obp/ui/#iso:std:iso:9241:-220:ed-1:v1:en:sec:E . Accessed 16 Aug 2020

UXPA, User Experience Professionals Association (2020), http://uxpa.org . Accessed 15 Aug 2020

N. Davis, The T-Model and strategies for hiring a practitioners: Part 1 (2011), https://www.uxmatters.com/mt/archives/2011/10/the-t-model-and-strategies-for-hiring-ia-practitioners-part-1.php . Accessed 21 Aug 2020

R.F. Baumeister, E.A. Sparks, T.F. Stillman et al., Free will in consumer behavior: Self-control, ego depletion, and choice. J. Consum. Psychol. 18 , 4–13 (2008). https://doi.org/10.1016/j.jcps.2007.10.002

P. Mani, Consumer behaviour. Int. J. Innova. Res. Comput. Commun. Eng. 8 4 (2019), https://doi.org/10.35940/ijitee.L1127.10812S19

M. Brenner, Top Benefits & Uses of AI in Digital Marketing (2020), https://marketinginsidergroup.com/artificial-intelligence/5-benefits-of-ai-for-digital-marketers/ . Accessed 15 Dec 2020

D. Court, D. Elzinga, S. Mulder, O.J. Vetvik, The Consumer Decision Journey (2009), https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-consumer-decision-journey . Accessed 19 Aug 2020

A. Rennie, J. Protheroe, How people decide what to buy lies in the ‘messy middle’ of the purchase journey (2020), https://www.thinkwithgoogle.com/consumer-insights/navigating-purchase-behavior-and-decision-making . Accessed 22 Aug 2020

P. Hudson, The Impact of Artificial Intelligence on Consumer Behavior (2020), https://blog.flexmr.net/ai-consumer-behaviour . Accessed 15 Jan 2021

Robotic Marketer, Does artificial intelligence influence consumer behavior? (2020), https://www.roboticmarketer.com/does-artificial-intelligence-influence-consumer-behavior . Accessed 21 Aug 2020

K. Szatylowicz, 5 ways retail AI Will Influence Consumer Behavior (2019), https://blog.crobox.com/article/ai-consumer-behavior . Accessed 15 Aug 2020

AMA, Consumer Behavior (2020), https://www.ama.org/topics/consumer-behavior . Accessed 15 Aug 2020

G. Asamoah, George Asamoah Factors Which Influence the Buying Behaviours of Customers with Multiple Regular Customer Cards (2012), https://core.ac.uk/download/pdf/38067117.pdf . Accessed 15 Jan 2021

C. Everett, What is the ‘Autonomous Customer’ - and Why is it Important? (2015), https://www.mycustomer.com/marketing/strategy/what-is-the-autonomous-customer-and-why-is-it-important . Accessed 15 Aug 2020

Data Ethics, Danish Companies Behind Seal for Digital Responsibility (2019), https://dataethics.eu/danish-companies-behind-seal-for-digital-responsibility . Accessed 15 Aug 2020

M. Alemany Oliver, J. Vayre, Big data and the future of knowledge production in marketing research: ethics, digital traces, and abductive reasoning. J. Market Anal. 3 , 5–13 (2015). https://doi.org/10.1057/jma.2015.1

Algorithm Watch, In the Realm of Paper Tigers – Exploring the Failings of AI Ethics Guidelines (2021), https://algorithmwatch.org/en/ai-ethics-guidelines-inventory-upgrade-2020 . Accessed 21 Jan 2021

Y. Bathaee, The artificial intelligence black box and the failure of intent and causation. Harvard J. Law Technol. 31 , 889 (2018)

M. Kaput, AI for Advertising: What You Need to Know (2020), https://www.marketingaiinstitute.com/blog/ai-in-advertising . Accessed 17 Aug 2020

M. Kaput, AI for Social Media: What You Need to Know (2020), https://www.marketingaiinstitute.com/blog/ai-for-social-media . Accessed 13 Aug 2020

S. Okazaki, A.M. Díaz-Martín, M. Rozano et al., Using Twitter to engage with customers: a data mining approach. Internet Res. 25 (3), 416–434 (2015). https://doi.org/10.1108/intr-11-2013-0249

Q. André, Z. Carmon, K. Wertenbroch et al., Consumer choice and autonomy in the age of artificial intelligence and big data. Cust. Needs Solut. (2017). https://doi.org/10.1007/s40547-017-0085-8

L.B. Said, T. Bouron, A. Drogoul, Agent-based interaction analysis of consumer behavior, in Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems Part 1 - AAMAS ’02 (2002). https://doi.org/10.1145/544741.544787

Smart Insights, How Artificial Intelligence is Reshaping Email Marketing (2019) , https://www.smartinsights.com/email-marketing/how-artificial-intelligence-is-reshaping-email-marketing . Accessed 25 Aug 2020

Smart Insights, Key Features Putting E-Commerce Personalization at the Top of the Retail Agenda (2019), https://www.smartinsights.com/ecommerce/key-features-putting-e-commerce-personalization-at-the-top-of-the-retail-agenda . Accessed 23 Aug 2020

Smart Insights, Personalization is Key to Improving Lead Generation Quality for 60% of Marketers (2019), https://www.smartinsights.com/lead-generation/lead-generation-strategy/personalization-key-improving-lead-generation . Accessed 28 Aug 2020

Statista, Big Data Revenue Worldwide from 2016 to 2027, by Major Segment (2020), https://www.statista.com/statistics/301566/big-data-factory-revenue-by-type . Accessed 20 Aug 2020

Statista, Volume of Data/Information Created Worldwide from 2010 to 2024 (2020), https://www.statista.com/statistics/871513/worldwide-data-created . Accessed 29 Aug 2020

Download references

Author information

Authors and affiliations.

University of Patras, 2 Seferi G. Str., 30100, Agrinio, Greece

Dimitris C. Gkikas & Prokopis K. Theodoridis

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Dimitris C. Gkikas .

Editor information

Editors and affiliations.

Department of Informatics, University of Piraeus, Piraeus, Greece

Maria Virvou

George A. Tsihrintzis

Director of Applied Intelligent Systems Laboratory, Department of Nuclear Engineering, Purdue University, West Lafayette, IN, USA

Lefteri H. Tsoukalas

KES International, Shoreham-by-Sea, UK

Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Gkikas, D., Theodoridis, P. (2022). AI in Consumer Behavior. In: Virvou, M., Tsihrintzis, G.A., Tsoukalas, L.H., Jain, L.C. (eds) Advances in Artificial Intelligence-based Technologies. Learning and Analytics in Intelligent Systems, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-80571-5_10

Download citation

DOI : https://doi.org/10.1007/978-3-030-80571-5_10

Published : 03 October 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-80570-8

Online ISBN : 978-3-030-80571-5

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Search this journal
• Search all journals
• View access options
• View profile
• Create profile

Add email alerts

You are adding the following journal to your email alerts

Consumers and Artificial Intelligence: An Experiential Perspective

• Commentary: How AI Shapes Consumer Experiences and Expectations
• Commentary: Artificial Intelligence: The Marketer’s Dilemma
• Commentary: The Ethical Use of Powerful Words and Persuasive Machines

Cite article

Share options, information, rights and permissions, metrics and citations, get full access to this article.

View all access and purchase options for this article.

Download to reference manager

If you have citation software installed, you can download article citation data to the citation manager of your choice

Share this article

Share with email, share on social media, share access to this article.

Sharing links are not relevant where the article is open access and not available if you do not have a subscription.

For more information view the Sage Journals article sharing page.

Information

Published in.

• artificial intelligence
• customer experience
• technology marketing
• discrimination
• replacement

Rights and permissions

Affiliations.

Author Contributions

Journals metrics.

This article was published in Journal of Marketing .

Article usage *

Total views and downloads: 93527

* Article usage tracking started in December 2016

See the impact this article is making through the number of times it’s been read, and the Altmetric Score. Learn more about the Altmetric Scores

Articles citing this one

Receive email alerts when this article is cited

Web of Science: 0

Crossref: 371

• Between artificial intelligence and customer experience: a literature ... Go to citation Crossref Google Scholar
• Navigating the perils of artificial intelligence: a focused review on ... Go to citation Crossref Google Scholar
• Can we build a relationship through artificial intelligence (AI)? Unde... Go to citation Crossref Google Scholar
• The human touch vs. AI efficiency: How perceived status, effort, and l... Go to citation Crossref Google Scholar
• From explainable to interactive AI: A literature review on current tre... Go to citation Crossref Google Scholar
• Is Smarter Better? A Moral Judgment Perspective on Consumer Attitudes ... Go to citation Crossref Google Scholar
• AI-powered marketing: What, where, and how? Go to citation Crossref Google Scholar
• Can AI robots foster social inclusion? Exploring the role of immersive... Go to citation Crossref Google Scholar
• The artificial intelligence-enabled customer experience in tourism: a ... Go to citation Crossref Google Scholar
• When powerful artificial intelligence backfires Go to citation Crossref Google Scholar
• Artificial intelligence and consumer behavior: From predictive to gene... Go to citation Crossref Google Scholar
• How Artificial Intelligence Constrains the Human Experience Go to citation Crossref Google Scholar
• Giving AI a Human Touch: Highlighting Human Input Increases the Percei... Go to citation Crossref Google Scholar
• AI and Culture: Culturally dependent responses to AI systems Go to citation Crossref Google Scholar
• Paradoxes, challenges, and opportunities in the context of ethical cus... Go to citation Crossref Google Scholar
• The transformative potential of AI-enabled personalization across cult... Go to citation Crossref Google Scholar
• Smart Data Analysis and Prediction of Responsible Customer Behaviour i... Go to citation Crossref Google Scholar
• Artificial intelligence, self-efficacy and engagement in religious tou... Go to citation Crossref Google Scholar
• The dark side of artificial intelligence in marketing: meta-analytics ... Go to citation Crossref Google Scholar
• Analytical modeling of perceived authenticity in AI assistants: applic... Go to citation Crossref Google Scholar
• Strangers or friends? Examining chatbot adoption in tourism through ps... Go to citation Crossref Google Scholar
• Artificial intelligence vs. autonomous decision-making in streaming pl... Go to citation Crossref Google Scholar
• Assessing the Impact of Recommendation Novelty on Older Consumers: Old... Go to citation Crossref Google Scholar
• When the bridge is not human: Algorithmic interference in forming soci... Go to citation Crossref Google Scholar
• Cracking The Consumers’ Code: A Framework For Understanding The Artifi... Go to citation Crossref Google Scholar
• Assessing AI Receptivity Through A Persuasion Knowledge Lens Go to citation Crossref Google Scholar
• Psychology of AI: How AI impacts the way people feel, think, and behav... Go to citation Crossref Google Scholar
• Beyond Code: Consumer Response to Chatbots Go to citation Crossref Google Scholar
• Unveiling the Adverse Effects of Artificial Intelligence on Financial ... Go to citation Crossref Google Scholar
• Perceived retailer innovativeness and its impact on customer engagemen... Go to citation Crossref Google Scholar
• The Impact of Digital Economy on Employment in Countries Along the Bel... Go to citation Crossref Google Scholar
• Out of the fog: fog computing-enabled AI to support smart marketing ma... Go to citation Crossref Google Scholar
• RAISE: leveraging responsible AI for service excellence Go to citation Crossref Google Scholar
• AI is Changing the World: For Better or for Worse? Go to citation Crossref Google Scholar
• Integrating Artificial Intelligence and Customer Experience Go to citation Crossref Google Scholar
• The Rise of Human–Machine Collaboration: Managers’ Perceptions of Leve... Go to citation Crossref Google Scholar
• aiWOM: Artificial Intelligence Word-of-Mouth. Conceptualizing Consumer... Go to citation Crossref Google Scholar
• Research on consumers’ purchase intention of cultural and creative pro... Go to citation Crossref Google Scholar
• The Role of AI Self-Efficacy in Religious Contexts in Public Sector: T... Go to citation Crossref Google Scholar
• Love it or hate it? Deconstructing consumers' attitudes towards ... Go to citation Crossref Google Scholar
• Technology paternalism: Development and validation of a measurement sc... Go to citation Crossref Google Scholar
• Study on the moderating effect of social distance on the bonus distrib... Go to citation Crossref Google Scholar
• Why do people buy virtual clothes? Go to citation Crossref Google Scholar
• Are they humans or are they robots? The effect of virtual influencer d... Go to citation Crossref Google Scholar
• Synthetic reality mapping of real estate using deep learning-based obj... Go to citation Crossref Google Scholar
• How to build a competitive advantage for your brand using generative A... Go to citation Crossref Google Scholar
• Examining the Impact of AI and Digital Marketing on Consumer Purchase ... Go to citation Crossref Google Scholar
• AI-Driven Algorithms for Optimizing Social Media Advertising Go to citation Crossref Google Scholar
• Consumer responses to human-AI collaboration at organizational frontli... Go to citation Crossref Google Scholar
• Pairing up with anthropomorphized artificial agents: Leveraging employ... Go to citation Crossref Google Scholar
• Assessment Scale and Behavioral Model Construction for AI Chat Informa... Go to citation Crossref Google Scholar
• AMIBO: intelligent social conversational agent using artificial intell... Go to citation Crossref Google Scholar
• Multisensory experiences and technology in the context of wine experie... Go to citation Crossref Google Scholar
• A shared journey: Experiential perspective and empirical evidence of v... Go to citation Crossref Google Scholar
• Engaging consumers through artificially intelligent technologies: Syst... Go to citation Crossref Google Scholar
• Is Chatgpt a menace for creative writing ability? An experiment Go to citation Crossref Google Scholar
• When anthropomorphism backfires: Anticipation of negative social roles... Go to citation Crossref Google Scholar
• Personalized touchpoints and customer experience: A conceptual synthes... Go to citation Crossref Google Scholar
• How consumers respond to service failures caused by algorithmic mistak... Go to citation Crossref Google Scholar
• The Caring Machine: Feeling AI for Customer Care Go to citation Crossref Google Scholar
• Unintended effects of algorithmic transparency: The mere prospect of a... Go to citation Crossref Google Scholar
• Will Artificial Intelligence be a Performance Booster to Agritech Star... Go to citation Crossref Google Scholar
• Serving customers through chatbots: positive and negative effects on c... Go to citation Crossref Google Scholar
• ChatGPT-powered chatbot as a green evangelist: an innovative path towa... Go to citation Crossref Google Scholar
• The dark side of artificial intelligence in services Go to citation Crossref Google Scholar
• How is entrepreneurial marketing shaped by E-commerce technology: a ca... Go to citation Crossref Google Scholar
• Value perceptions of first-party content on multi-sided platforms: Fin... Go to citation Crossref Google Scholar
• The Importance and Limitations of Artificial Intelligence Ethics and D... Go to citation Crossref Google Scholar
• Eliminating customer experience pain points in complex customer journe... Go to citation Crossref Google Scholar
• How technological and natural consumption experiences impact consumer ... Go to citation Crossref Google Scholar
• Tales of Two Channels: Digital Advertising Performance Between AI Reco... Go to citation Crossref Google Scholar
• Tracking technologies in eHealth: Revisiting the personalization-priva... Go to citation Crossref Google Scholar
• Internet of Artificial Intelligence (IoAI): the emergence of an autono... Go to citation Crossref Google Scholar
• How consumer-initiated platforms shape family and consumption Go to citation Crossref Google Scholar
• Automated social presence in AI: Avoiding consumer psychological tensi... Go to citation Crossref Google Scholar
• The mind in the machine: Estimating mind perception's effect on user s... Go to citation Crossref Google Scholar
• Empathic voice assistants: Enhancing consumer responses in voice comme... Go to citation Crossref Google Scholar
• Benefits or concerns of AI: A multistakeholder responsibility Go to citation Crossref Google Scholar
• Fog Computing-Based Smart Consumer Recommender Systems Go to citation Crossref Google Scholar
• Scientometric visualization of data on artificial intelligence and mar... Go to citation Crossref Google Scholar
• The lean link: exploring industry 4.0's influence on sustainable opera... Go to citation Crossref Google Scholar
• “Can AI really help me land a job?” Student reactions to the use of ar... Go to citation Crossref Google Scholar
• Consumer reactions to technology in retail: choice uncertainty and red... Go to citation Crossref Google Scholar
• How does AI surpassing humans influence public innovativeness? A multi... Go to citation Crossref Google Scholar
• A consumer perspective of AI certification – the current certification... Go to citation Crossref Google Scholar
• Anthropomorphic chatbots' for future healthcare services: Effects of p... Go to citation Crossref Google Scholar
• Artificial intelligence in interactive marketing: a conceptual framewo... Go to citation Crossref Google Scholar
• Algorithmic personalization and brand loyalty: An experiential perspec... Go to citation Crossref Google Scholar
• Does Artificial Intelligence Promote Firms’ Innovation Efficiency: Evi... Go to citation Crossref Google Scholar
• Explorer les interactions anticipées par les consommateurs avec les ob... Go to citation Crossref Google Scholar
• “Threatened and empty selves following AI-based virtual influencers”: ... Go to citation Crossref Google Scholar
• Why can’t you be all talk to get things done? Consumer acceptance of v... Go to citation Crossref Google Scholar
• Quo Vadis Traditional Cultural Expressions Protection: Threats from Pe... Go to citation Crossref Google Scholar
• Artificial intelligence and value co-creation: a review, conceptual fr... Go to citation Crossref Google Scholar
• AI credibility and consumer-AI experiences: a conceptual framework Go to citation Crossref Google Scholar
• Can ethics be assembled? Consumer ethics in the age of artificial inte... Go to citation Crossref Google Scholar
• Marketing in the Health Care Sector: Disrupted Exchanges and New Resea... Go to citation Crossref Google Scholar
• Customer experience in AI-enabled products: Scale development and vali... Go to citation Crossref Google Scholar
• Responses to AI and Human Recommendations in a Joint-Consumption Conte... Go to citation Crossref Google Scholar
• Me and my AI: Exploring the effects of consumer self‐construal and AI‐... Go to citation Crossref Google Scholar
• Already here: Metaverse in touch and sound Go to citation Crossref Google Scholar
• The Metaverse: A new digital frontier for consumer behavior Go to citation Crossref Google Scholar
• The impact of customer privacy concerns on service robot adoption inte... Go to citation Crossref Google Scholar
• Customer experience quality with social robots: Does trust matter? Go to citation Crossref Google Scholar
• On Ads as Aesthetic Objects: A Thematic Review of Aesthetics in Advert... Go to citation Crossref Google Scholar
• Intersectionality in Marketing: A Paradigm for Understanding Understud... Go to citation Crossref Google Scholar
• An S-Frame Agenda for Behavioral Public Policy Go to citation Crossref Google Scholar
• Role of Emotional Experience in AI Voice Assistant User Experience in ... Go to citation Crossref Google Scholar
• Optimizing Wealth Management Strategies with Random Forest Prediction ... Go to citation Crossref Google Scholar
• When AI Meets Art: the Algorithm Appreciation Effect in Creative Conte... Go to citation Crossref Google Scholar
• Adoption of Metaverse in Healthcare Sector: An Empirical Analysis of i... Go to citation Crossref Google Scholar
• Computer Big Data Analysis and Cloud Computer Networking Technology in... Go to citation Crossref Google Scholar
• Artificial Intelligence and Going Beyond Cherished Assumptions Go to citation Crossref Google Scholar
• An Exploratory Study on the Dark Sides of Artificial Intelligence Adop... Go to citation Crossref Google Scholar
• Consumer Reactions to Generative AI: An Exploratory Study Using Neuros... Go to citation Crossref Google Scholar
• Co-creation with service robots and employee wellbeing: a self-determi... Go to citation Crossref Google Scholar
• When Post Hoc Explanation Knocks: Consumer Responses to Explainable A... Go to citation Crossref Google Scholar
• Implementation of deep neural networks and statistical methods to pred... Go to citation Crossref Google Scholar
• Anthropomorphic chatbots as a catalyst for marketing brand experience:... Go to citation Crossref Google Scholar
• Being Alone or Together: How Frontline Anthropomorphized Robots Affect... Go to citation Crossref Google Scholar
• Disruptive business value models in the digital era Go to citation Crossref Google Scholar
• Physical and Digital Privacy: How Developed and Developing Countries D... Go to citation Crossref Google Scholar
• Recommendation agents: an analysis of consumers’ risk perceptions towa... Go to citation Crossref Google Scholar
• Sensing privacy: Extending consumer privacy research through a consume... Go to citation Crossref Google Scholar
• Unrevealing Voice Search Behaviors: Technology Acceptance Model Meets ... Go to citation Crossref Google Scholar
• A rhizomatic reflection on market systems dynamics research Go to citation Crossref Google Scholar
• Gen Z’s Engagement with the Metaverse: Mediated Role of Symbolic Grati... Go to citation Crossref Google Scholar
• Aspects driving customers' intention to use automated purchasing proce... Go to citation Crossref Google Scholar
• Unveiling the Mind of the Machine Go to citation Crossref Google Scholar
• How about non-human tour guides? The influence of AI tour guides’ dres... Go to citation Crossref Google Scholar
• AI and XR (AIXR) Marketing in Industry 5.0 or Society 5.0 Go to citation Crossref Google Scholar
• Deploying artificial intelligence in services to AID vulnerable consum... Go to citation Crossref Google Scholar
• Consumer bias against evaluations received by artificial intelligence:... Go to citation Crossref Google Scholar
• Firm AI Adoption Intensity and Marketing Performance Go to citation Crossref Google Scholar
• When consumers need more interpretability of artificial intelligence (... Go to citation Crossref Google Scholar
• Pathways to Service System Smartness for Firms Go to citation Crossref Google Scholar
• Matching digital companions with customers: The role of perceived simi... Go to citation Crossref Google Scholar
• Do chatbots establish “humanness” in the customer purchase journey? An... Go to citation Crossref Google Scholar
• Meaning of Manual Labor Impedes Consumer Adoption of Autonomous Produc... Go to citation Crossref Google Scholar
• Paradigms for Progress: An Anomaly-First Framework for Paradigm Develo... Go to citation Crossref Google Scholar
• The positive–negative–competence (PNC) model of psychological response... Go to citation Crossref Google Scholar
• Trademarks in the Age of Automated Commerce: Consumer Choice and Auton... Go to citation Crossref Google Scholar
• A model validation of robo-advisers for stock investment Go to citation Crossref Google Scholar
• Uncovering the dark side of AI-based decision-making: A case study in ... Go to citation Crossref Google Scholar
• The cold-start problem in nascent AI strategy: Kickstarting data netwo... Go to citation Crossref Google Scholar
• How Chatbot Language Shapes Consumer Perceptions: The Role of Concrete... Go to citation Crossref Google Scholar
• The effect of AI shopping assistant’s motivated consumer innovativenes... Go to citation Crossref Google Scholar
• Impacto de la inteligencia artificial (IA) en la efectividad de las es... Go to citation Crossref Google Scholar
• Artificial intelligence-enabled personalization in interactive marketi... Go to citation Crossref Google Scholar
• Artificial Intelligence and the Value Co-Creation Process in Higher Ed... Go to citation Crossref Google Scholar
• The psychological and functional factors driving metaverse resistance Go to citation Crossref Google Scholar
• Exploring the Darkverse: A Multi-Perspective Analysis of the Negative ... Go to citation Crossref Google Scholar
• The negative effect of service robots’ affective human-likeness on con... Go to citation Crossref Google Scholar
• AI Through a CSR Lens: Consumer Issues and Public Policy Go to citation Crossref Google Scholar
• Using machine learning to investigate consumers' emotions: the spillov... Go to citation Crossref Google Scholar
• Partisan Media Sentiment Toward Artificial Intelligence Go to citation Crossref Google Scholar
• Turning Data Into Insights: Leveraging Artificial Intelligence for Bet... Go to citation Crossref Google Scholar
• Impact of AI Affordance on AI Adoption: The Mediating Role of Self-Mon... Go to citation Crossref Google Scholar
• Overcoming the blockchain technology credibility gap Go to citation Crossref Google Scholar
• The downside of artificial intelligence (AI) in green choices: How AI ... Go to citation Crossref Google Scholar
• How intelligent automation, service robots, and AI will reshape servic... Go to citation Crossref Google Scholar
• Examining the context‐specific reasons and adoption of artificial inte... Go to citation Crossref Google Scholar
• Mechanisms and Constraints Underpinning Ethically Aligned Artificial I... Go to citation Crossref Google Scholar
• Delving into brand anthropomorphisation strategies in the experiential... Go to citation Crossref Google Scholar
• Human vs. Automated Sales Agents: How and Why Customer Responses Shift... Go to citation Crossref Google Scholar
• Organizational frontlines in the digital age: The Consumer–Autonomous ... Go to citation Crossref Google Scholar
• AI on the street: Context-dependent responses to artificial intelligen... Go to citation Crossref Google Scholar
• Consumers’ multistage data control in technology-mediated environments Go to citation Crossref Google Scholar
• Sharing information with AI (versus a human) impairs brand trust: The ... Go to citation Crossref Google Scholar
• Exploring the effect of artificial intelligence intellect on consumer ... Go to citation Crossref Google Scholar
• Artificial intelligence (AI) in FinTech decisions: the role of congrui... Go to citation Crossref Google Scholar
• A contemplative overview of smart communities: a hybrid analytical app... Go to citation Crossref Google Scholar
• Value co-creation via machine learning from a configuration theory per... Go to citation Crossref Google Scholar
• Interfaces, Interactions, Time, and the Frontline Nexus: Foundational ... Go to citation Crossref Google Scholar
• Influence of Customer Perception Factors on AI-Enabled Customer Experi... Go to citation Crossref Google Scholar
• Artificial intelligence in customer retention: a bibliometric analysis... Go to citation Crossref Google Scholar
• Elucidating the impact of cognitive and behavioral responses to web ba... Go to citation Crossref Google Scholar
• Toward Better Digital Advertising: The Role of the Anthropomorphic Vir... Go to citation Crossref Google Scholar
• Effect of AI Chatbot’s Interactivity on Consumers’ Negative Word-of-Mo... Go to citation Crossref Google Scholar
• “Kind of Mine, Kind of Not”: Digital Possessions and Affordance Misali... Go to citation Crossref Google Scholar
• Using Artificial Intelligence to Promote Branded Color Cosmetics: Evid... Go to citation Crossref Google Scholar
• How does material adaptivity of smart objects shape infusion use? The ... Go to citation Crossref Google Scholar
• Creating marketing innovation abroad: The value of marketing professio... Go to citation Crossref Google Scholar
• The perceptual antecedents of brand anthropomorphism in the name-brand... Go to citation Crossref Google Scholar
• The effect of implementing chatbot customer service on stock returns: ... Go to citation Crossref Google Scholar
• Practice co-evolution: Collaboratively embedding artificial intelligen... Go to citation Crossref Google Scholar
• Information Disclosure in the Era of Voice Technology Go to citation Crossref Google Scholar
• Understanding retail exclusion and promoting an inclusive customer exp... Go to citation Crossref Google Scholar
• Exploring the Intersection of Digital Marketing and Retail Go to citation Crossref Google Scholar
• Trustworthy Artificial Intelligence and Machine Learning Go to citation Crossref Google Scholar
• Redefining the Performance Management using Emotional Artificial Intel... Go to citation Crossref Google Scholar
• Certification Labels for Trustworthy AI: Insights From an Empirical Mi... Go to citation Crossref Google Scholar
• Is Marketing a Victim of its Own Success? A Restoration Process to Unl... Go to citation Crossref Google Scholar
• Monetization of customer futures through machine learning and artifici... Go to citation Crossref Google Scholar
• A parametric analysis of AVA to optimise Netflix performance Go to citation Crossref Google Scholar
• Functions and applications of artificial intelligence in droplet micro... Go to citation Crossref Google Scholar
• Technology-enabled well-being in the era of IR4.0: marketing and publi... Go to citation Crossref Google Scholar
• Corporate Digital Responsibility in Service Firms and Their Ecosystems Go to citation Crossref Google Scholar
• Overcoming Consumer Resistance to AI in General Health Care Go to citation Crossref Google Scholar
• Information Technologies and Consumers’ Well-Being: Latest Research an... Go to citation Crossref Google Scholar
• Examining post-purchase consumer responses to product automation Go to citation Crossref Google Scholar
• A systematic review of global research on natural user interface for s... Go to citation Crossref Google Scholar
• Hi, May AI help you? An analysis of the barriers impeding the implemen... Go to citation Crossref Google Scholar
• Voice artificial intelligence service failure and customer complaint b... Go to citation Crossref Google Scholar
• Brand Privacy Policy and Brand Trust Reference in Online Business Comm... Go to citation Crossref Google Scholar
• Bodies as machines. Machines as bodies Go to citation Crossref Google Scholar
• The trinity of extended service quality, distinct perceived value, and... Go to citation Crossref Google Scholar
• When Recommender Systems Snoop into Social Media, Users Trust them Les... Go to citation Crossref Google Scholar
• Dashed expectations in service experiences. Effects of robots human-li... Go to citation Crossref Google Scholar
• Promoting smart wearable devices in the health-AI market: the role of ... Go to citation Crossref Google Scholar
• Characteristics and Optimization Strategies of A* Algorithm and Ant Co... Go to citation Crossref Google Scholar
• Anthropomorphism in Artificial Intelligence: A Review of Empirical Wor... Go to citation Crossref Google Scholar
• ‘An Infinite Deal of Nothing’: critical ruminations on ChatGPT and the... Go to citation Crossref Google Scholar
• Speaking vs. listening? Balance conversation attributes of voice assis... Go to citation Crossref Google Scholar
• The cost of freedom: Creative ideation boosts both feelings of autonom... Go to citation Crossref Google Scholar
• The role of values and ethics in influencing consumers’ intention to u... Go to citation Crossref Google Scholar
• Programmatic advertising in online retailing: consumer perceptions and... Go to citation Crossref Google Scholar
• The Impact of AI Technologies on E-Business Go to citation Crossref Google Scholar
• Robots make no effort! Service evaluation and consumer mindset Go to citation Crossref Google Scholar
• AI-Enabled marketing capabilities and the hierarchy of capabilities: C... Go to citation Crossref Google Scholar
• The perks and perils of artificial intelligence use in lateral exchang... Go to citation Crossref Google Scholar
• Towards better interaction between salespeople and consumers: the role... Go to citation Crossref Google Scholar
• Vocalizing Search: How Voice Technologies Alter Consumer Search Proces... Go to citation Crossref Google Scholar
• Artificial Intelligence, Marketing, and the History of Technology: Kra... Go to citation Crossref Google Scholar
• AI Experience Predicts Identification with Humankind Go to citation Crossref Google Scholar
• How sociotechnical imaginaries shape consumers’ experiences of and res... Go to citation Crossref Google Scholar
• Experiential AR/VR: a consumer and service framework and research agen... Go to citation Crossref Google Scholar
• Digitales Marketing-Management Go to citation Crossref Google Scholar
• Customer Experience: Trust Experience an Human Touchpoints des Versich... Go to citation Crossref Google Scholar
• Consumers and producers: whose personalisation is it? Go to citation Crossref Google Scholar
• Why do we prefer humans to artificial intelligence in telemarketing? A... Go to citation Crossref Google Scholar
• Artificial Intelligence in Marketing and Organizational Decision-Makin... Go to citation Crossref Google Scholar
• AI in Customer Service: A Service Revolution in the Making Go to citation Crossref Google Scholar
• Opportunities and Challenges of AI-Driven Customer Service Go to citation Crossref Google Scholar
• Engaging Self-service in a Customer Service Ecosystem Go to citation Crossref Google Scholar
• Use of Artificial Intelligence with Ethics and Privacy for Personalize... Go to citation Crossref Google Scholar
• Choice freedom Go to citation Crossref Google Scholar
• The Digital Drivers of Consumer Behavior: Industry 4.0, Web 3.0, and A... Go to citation Crossref Google Scholar
• Intelligent Automation, Service Robots, and AI – the Service Revolutio... Go to citation Crossref Google Scholar
• The Narrow-Taste Effect: When Consumers Display Narrow Tastes to Algor... Go to citation Crossref Google Scholar
• Influencer Marketing 2.0: The Role of Realism in Virtual Influencer En... Go to citation Crossref Google Scholar
• Existing Barriers Faced by and Future Design Recommendations for Direc... Go to citation Crossref Google Scholar
• Introduction Go to citation Crossref Google Scholar
• Robots in Retail Go to citation Crossref Google Scholar
• How and Why the Use of AI (vs. Human) Agents Influences Sales Conversi... Go to citation Crossref Google Scholar
• The Physical Presence and Relationship Distance for Efficient Consumer... Go to citation Crossref Google Scholar
• Exploring the Effect of Intelligent Recommendation Systems on Users’ E... Go to citation Crossref Google Scholar
• How Chatbots’ Anthropomorphism Affects User Satisfaction: The Mediatin... Go to citation Crossref Google Scholar
• Unveiling the Effects of LLMs: Shifting UGC Contribution in an Online ... Go to citation Crossref Google Scholar
• Unleashing the Predators: Autonomous Predation and Manipulation Throug... Go to citation Crossref Google Scholar
• Make chatbots more adaptive: Dual pathways linking human-like cues and... Go to citation Crossref Google Scholar
• Estado del arte de la inteligencia artificial en marketing y el compor... Go to citation Crossref Google Scholar
• Artificial intelligence in customer relationship management: literatur... Go to citation Crossref Google Scholar
• Potential Consumers Attitudes on AI Doctor: Based on a Survey on Colle... Go to citation Crossref Google Scholar
• Users taking the blame? How service failure, recovery, and robot desig... Go to citation Crossref Google Scholar
• Contexts of Consumption and Their Evolution in the Digital Age: Beyond... Go to citation Crossref Google Scholar
• Reassembling the elderly consumption ensemble: retaining independence ... Go to citation Crossref Google Scholar
• Conscious Empathic AI in Service Go to citation Crossref Google Scholar
• Thinking Skills Don’t Protect Service Workers from Replacement by Arti... Go to citation Crossref Google Scholar
• How consumer digital signals are reshaping the customer journey Go to citation Crossref Google Scholar
• Technology devalues luxury? Exploring consumer responses to AI-designe... Go to citation Crossref Google Scholar
• Trojan horse or useful helper? A relationship perspective on artificia... Go to citation Crossref Google Scholar
• The effect of marketing department power on investor responses to anno... Go to citation Crossref Google Scholar
• Artificial empathy in marketing interactions: Bridging the human-AI ga... Go to citation Crossref Google Scholar
• An overview, empirical application, and discussion of the future resea... Go to citation Crossref Google Scholar
• Conceptualizing Value Creation in the Platform Era Go to citation Crossref Google Scholar
• The influence of religiosity on consumers' evaluations of brands using... Go to citation Crossref Google Scholar
• Consumer reactions to AI design: Exploring consumer willingness to pay... Go to citation Crossref Google Scholar
• Consumer Experiences with Marketing Technology: Solving the Tensions B... Go to citation Crossref Google Scholar
• Asking Questions of AI Advertising: A Maieutic Approach Go to citation Crossref Google Scholar
• Exploring the role of AI algorithmic agents: The impact of algorithmic... Go to citation Crossref Google Scholar
• Artificial Intelligence, Consumers, and the Experience Economy Go to citation Crossref Google Scholar
• Does the Personality of Consumers Influence the Assessment of the Expe... Go to citation Crossref Google Scholar
• Technologies, expériences, services et au-delà ! Plaidoyer pour dévelo... Go to citation Crossref Google Scholar
• The nonlinear effect of service robot anthropomorphism on customers’ u... Go to citation Crossref Google Scholar
• The effect of required warmth on consumer acceptance of artificial int... Go to citation Crossref Google Scholar
• Does the learning ability of smart products lead to user resistance? Go to citation Crossref Google Scholar
• Explaining how algorithms work reduces consumers' concerns regarding t... Go to citation Crossref Google Scholar
• Investigating the double-edged sword effect of AI awareness on employe... Go to citation Crossref Google Scholar
• Fogyasztói magatartás a likvid modernitásban – Szakirodalmi áttekintés... Go to citation Crossref Google Scholar
• Algorithmic consumer culture Go to citation Crossref Google Scholar
• Advocacy for expanding research on technologies, experiences, services... Go to citation Crossref Google Scholar
• Ability is in the eye of the beholder: How context and individual fact... Go to citation Crossref Google Scholar
• AI anthropomorphism and its effect on users' self-congruence and self–... Go to citation Crossref Google Scholar
• Artificial intelligence in marketing: a network analysis and future ag... Go to citation Crossref Google Scholar
• Enhancing Marketing Provision through Increased Online Safety That Imb... Go to citation Crossref Google Scholar
• The role of wisdom in navigating social media paradoxes: Implications ... Go to citation Crossref Google Scholar
• A transdisciplinary review and framework of consumer interactions with... Go to citation Crossref Google Scholar
• Non-dyadic human–robot interactions and online brand communities Go to citation Crossref Google Scholar
• The Bulletproof Glass Effect: Unintended Consequences of Privacy Notic... Go to citation Crossref Google Scholar
• Thumbs Up or Down: Consumer Reactions to Decisions by Algorithms Versu... Go to citation Crossref Google Scholar
• The Service-Profit Chain: Reflections, Revisions, and Reimaginations Go to citation Crossref Google Scholar
• Leveraging Artificial Intelligence in Marketing for Social Good—An Eth... Go to citation Crossref Google Scholar
• Effects of different service failure types and recovery strategies on ... Go to citation Crossref Google Scholar
• The Effect of Artificial Intelligence on End-User Online Purchasing De... Go to citation Crossref Google Scholar
• Social appreciation of the interaction with an anthropomorphic robot Go to citation Crossref Google Scholar
• Apology or gratitude? The effect of communication recovery strategies ... Go to citation Crossref Google Scholar
• Smart Business and the Social Value of AI Go to citation Crossref Google Scholar
• Smiling AI agents: How anthropomorphism and broad smiles increase char... Go to citation Crossref Google Scholar
• A Call for Research at the Public Policy–Marketing Strategy Interface Go to citation Crossref Google Scholar
• A Review of Research on Artificial Intelligence Life Cycle Based on Bi... Go to citation Crossref Google Scholar
• The effects of gender and personality of robot assistants on customers... Go to citation Crossref Google Scholar
• Alexa, know your limits: developing a framework for the accepted and d... Go to citation Crossref Google Scholar
• Examining artificial intelligence (AI) technologies in marketing via a... Go to citation Crossref Google Scholar
• A global perspective on the marketing mix across time and space Go to citation Crossref Google Scholar
• Inclusive Growth in the Era of Automation and AI: How Can Taxation Hel... Go to citation Crossref Google Scholar
• Consumer Timework Go to citation Crossref Google Scholar
• The impact of social class and service type on preference for AI servi... Go to citation Crossref Google Scholar
• “Now You See Me, Now You Don’t”: How Digital Consumers Manage Their On... Go to citation Crossref Google Scholar
• AI-based innovation in B2B marketing: An interdisciplinary framework i... Go to citation Crossref Google Scholar
• Trust as a second-order construct: Investigating the relationship betw... Go to citation Crossref Google Scholar
• Frontiers: Supporting Content Marketing with Natural Language Generati... Go to citation Crossref Google Scholar
• Overcoming the pitfalls and perils of algorithms: A classification of ... Go to citation Crossref Google Scholar
• Consumer Health in the Digital Age Go to citation Crossref Google Scholar
• AI in marketing, consumer research and psychology: A systematic litera... Go to citation Crossref Google Scholar
• Experiencing the sense of the brand: the mining, processing and applic... Go to citation Crossref Google Scholar
• The Promethean biohacker: on consumer biohacking as a labour of love Go to citation Crossref Google Scholar
• Trust me, I'm a bot – repercussions of chatbot disclosure in different... Go to citation Crossref Google Scholar
• Service robots, agency and embarrassing service encounters Go to citation Crossref Google Scholar
• To talk or to touch: Unraveling consumer responses to two types of hot... Go to citation Crossref Google Scholar
• Retail in a Brand-New World Go to citation Crossref Google Scholar
• References Go to citation Crossref Google Scholar
• Recommendation Agents? I don’t Know Any and I don’t Trust Them! Go to citation Crossref Google Scholar
• Consumer Intention to Accept AI-Based Products and Services Go to citation Crossref Google Scholar
• Artificial intelligence (AI) applications for marketing: A literature-... Go to citation Crossref Google Scholar
• The Impact of Artificial Intelligence Voice Assistants on Consumer Exp... Go to citation Crossref Google Scholar
• Threat to Nature Connectedness: How Does It Influence Consumers’ Prefe... Go to citation Crossref Google Scholar
• Social Media Analytics Go to citation Crossref Google Scholar
• Making sense of sensory brand experience: Constructing an integrative ... Go to citation Crossref Google Scholar
• Algorithmic Fairness and Service Failures: Why Firms Should Want Algor... Go to citation Crossref Google Scholar
• Role of Privacy Management and Human-Centered Artificial Intelligence ... Go to citation Crossref Google Scholar
• The Past, Present and Future of Measuring Customer Satisfaction with A... Go to citation Crossref Google Scholar
• Power in Value Cocreation Process: The Emergence of a New Dimension Go to citation Crossref Google Scholar
• A Survey of Learning-Based Methods for Cold-Start, Social Recommendati... Go to citation Crossref Google Scholar
• A Review of Learning-Based E-commerce Go to citation Crossref Google Scholar
• Individuals' motivations to purchase virtual clothes Go to citation Crossref Google Scholar
• Hospitality services in the post COVID-19 era: Are we ready for high-t... Go to citation Crossref Google Scholar
• Guest Editorial Go to citation Crossref Google Scholar
• Artificial intelligence: The light and the darkness Go to citation Crossref Google Scholar
• AI-chatbots on the services frontline addressing the challenges and op... Go to citation Crossref Google Scholar
• Artificial intelligence in services: current trends, benefits and chal... Go to citation Crossref Google Scholar
• Creating Responsible Subjects: The Role of Mediated Affective Encounte... Go to citation Crossref Google Scholar
• Service Research Priorities: Managing and Delivering Service in Turbul... Go to citation Crossref Google Scholar
• “Alexa, order me a pizza!”: The mediating role of psychological power ... Go to citation Crossref Google Scholar
• The most human bot: Female gendering increases humanness perceptions o... Go to citation Crossref Google Scholar
• Artificial Intelligence in Marketing Go to citation Crossref Google Scholar
• The adoption, diffusion & categorical ambiguity trifecta of social rob... Go to citation Crossref Google Scholar
• Incentive Mechanisms for Acceptance and Adoption of Automated Systems Go to citation Crossref Google Scholar
• Special Issue: The Implications of Something Different: Bright Side, D... Go to citation Crossref Google Scholar
• Capturing Marketing Information to Fuel Growth Go to citation Crossref Google Scholar
• Commentary: How AI Shapes Consumer Experiences and Expectations Go to citation Crossref Google Scholar
• Commentary: Artificial Intelligence: The Marketer’s Dilemma Go to citation Crossref Google Scholar
• Commentary: Managing Human Experience as a Core Marketing Capability Go to citation Crossref Google Scholar
• Commentary: The Ethical Use of Powerful Words and Persuasive Machines Go to citation Crossref Google Scholar
• Marketing Thinking and Doing Go to citation Crossref Google Scholar
• Challenges and Opportunities for Marketing Scholars in Times of the Fo... Go to citation Crossref Google Scholar

Figures and tables

Figures & media, view options, access options.

If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:

I am signed in as:

I can access personal subscriptions, purchases, paired institutional access and free tools such as favourite journals, email alerts and saved searches.

Login failed. Please check you entered the correct user name and password.

Access personal subscriptions, purchases, paired institutional or society access and free tools such as email alerts and saved searches.

loading institutional access options

Click the button below for the full-text content

AMA members can access this journal content using society membership credentials.

Alternatively, view purchase options below:

Purchase 24 hour online access to view and download content.

Access journal content via a DeepDyve subscription or find out more about this option.

View options

You currently have no access to this content. Visit the access options page to authenticate.

Also from Sage

• CQ Library Elevating debate opens in new tab
• Sage Data Uncovering insight opens in new tab
• Sage Business Cases Shaping futures opens in new tab
• Sage Campus Unleashing potential opens in new tab
• Sage Knowledge Multimedia learning resources opens in new tab
• Sage Research Methods Supercharging research opens in new tab
• Sage Video Streaming knowledge opens in new tab
• Technology from Sage Library digital services opens in new tab

Research Article

Artificial Intelligence in  Consumer Behaviour: A Systematic Literature Review

Muhammad Farooq, Yuen Yee Yen

This is a preprint; it has not been peer reviewed by a journal.

https://doi.org/ 10.21203/rs.3.rs-3875906/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

This systematic literature analysis examines the many effects of artificial intelligence (AI) on consumer behavior. It consolidates data from a carefully chosen to set of English-language papers acquired using a Web of Science search. The selected studies provide useful insights into the complex interaction between AI, consumer attitudes, preferences, decision-making, and the larger consequences for companies. These research cover many areas of AI applications in marketing and consumer domains. The research covers various topics, such as the positive impact of AI on consumer attitudes, potential drawbacks of AI recommendations, the influence of AI-driven recommendation agents on privacy risk, personalized engagement marketing, consumer evaluations of GAN-generated fashion products, AI in ethnic clothing consumption, the adoption of AI in the leisure economy, AI in digital marketing, automation of services using AI in Industry 4.0, AI-powered applications in the service profit chain, the role of AI-powered learning apps in education, AI in B2B settings, the security of AIoT using the HoneyNet approach, the impact of digital AI technologies in India, and the role of AI in the Internet of Things (IoT). This study presents a thorough analysis of the current state of AI and consumer behavior research, providing insights that are relevant for both academic and industrial sectors.

Artificial Intelligence (AI)

Consumer Behavior

AI Applications and Systematic Literature Review

Introduction

Artificial intelligence (AI) has become a powerful force in the era of digital transformation, significantly altering several aspects of marketing and consumer behavior. This systematic literature review attempts to present a thorough and inclusive analysis of the many ways in which AI impacts consumer attitudes, preferences, and decision-making processes. The carefully chosen assortment of English-language publications, obtained from the Web of Science, showcases a diverse range of study carried out by scientists exploring the convergence of AI, marketing, and consumer dynamics.

The papers incorporated in this study encompass a wide range of topics, elucidating the complex connections between AI and consumer behavior. Olan et al. ( 2021 ) utilize the fsQCA approach to construct a metaframework that forecasts consumer behavior by considering AI, consumer attitudes, and knowledge-sharing activities. Their analysis reveals the favorable impacts on customer attitudes and engagement. Chen et al. ( 2022 ) provide empirical data about the influence of AI recommendations, demonstrating enhanced consumer preferences while also warning against the building of information cocoons.

Rohden & Zeferino ( 2023 ) explore the domain of AI-powered recommendation agents and their impact on customer perceptions of data privacy risk, highlighting the crucial significance of consumer trust. Kumar et al. ( 2019 ) examine customized engagement marketing and investigate how AI is transforming consumer interaction and its potential effects on global branding strategies. Sohn et al. ( 2021 ) analyze how consumers perceive fashion goods created by generative adversarial networks (GANs), offering valuable information for companies contemplating the use of GANs in the fashion retail industry.

This review delves into several aspects, including the favorable influence of AI on the consumption of ethnic clothes (Peng & Krutasaen, 2022 ), the integration of AI in the leisure sector (Xian, 2021 ), and its significant role in transforming digital marketing (Tchelidze, 2019 ). In addition, the study conducted by Dwivedi and Wang ( 2022 ) on the use of AI in B2B settings, as well as the research by Tan et al. ( 2022 ) on the importance of AI in increasing the security of AIoT, contribute to a more comprehensive understanding of the extensive ramifications of AI. The next sections explore each topic cluster, revealing the intricacies and insights of these research in enhancing our understanding of AI's diverse influence on consumer behavior.

Methodology

This systematic literature review utilizes a rigorous approach to thoroughly assess and synthesize the study findings on the influence of artificial intelligence (AI) on consumer behavior. The first step was a methodical exploration of the Web of Science, a highly regarded scholarly database, to locate pertinent publications written in English. The search approach included a blend of keywords, including "AI in marketing," "AI and consumer behavior," and similar phrases, to guarantee the incorporation of a wide range of research covering various topics.

The inclusion criteria consisted of studies that primarily focused on the convergence of AI and consumer behavior, encompassing a wide range of applications in marketing, decision-making, and preferences. A meticulous screening process was employed to guarantee the pertinence and excellence of the chosen articles.

After identifying the pertinent research, a thorough data extraction procedure was carried out. For each paper that was included, we methodically retrieved important details such as the authors, publication year, study methodology used, and major findings. The systematic grouping of the research enabled a structured synthesis, enabling a thorough examination of the many aspects of AI's impact on consumer behavior.

The technique employed for this systematic review conforms to accepted principles for conducting thorough and transparent literature reviews. This study intends to offer a complete and informative summary of the existing understanding of how AI affects consumer behavior. It will achieve this by conducting a systematic search, conducting rigorous screening, and organizing data extraction.

Literature Review

The literature surrounding the influence of artificial intelligence (AI) on consumer behavior is marked by a diverse array of studies that delve into the multifaceted implications of this technological integration. Olan et al. ( 2021 ) utilize the fsQCA technique to predict consumer behavior, revealing a positive influence of AI on attitudes and knowledge-sharing. Chen et al. ( 2022 ) contribute empirical evidence, cautioning against potential information cocoons stemming from AI recommendations.

Rohden and Zeferino ( 2023 ) focus on AI-driven recommendation agents and their impact on consumer perceptions of data privacy risk, emphasizing the role of consumer trust. Kumar et al. ( 2019 ) explore personalized engagement marketing, highlighting AI's role in reshaping consumer engagement and predicting its impact on branding.

The intersection of AI and fashion evaluation is addressed by Sohn et al. ( 2021 ), who compare consumer evaluations of products generated by generative adversarial networks (GAN). Peng and Krutasaen ( 2022 ) shift the focus to ethnic clothing consumption, employing AI decision-making and the Internet of Things (IoT) to identify factors influencing consumer psychology.

Xian ( 2021 ) examines the adoption of AI in the leisure economy, introducing personal innovativeness as a determinant. Tchelidze ( 2019 ) emphasizes the role of AI in digital marketing, underlining the skills required for effective utilization. The automation of services using AI in Industry 4.0 is discussed by Flavian and Casaló ( 2021 ), while Wei and Prentice ( 2022 ) explore AI-powered applications in the service profit chain

Categorizing Themes in AI and Consumer Behavior Research: An Overview

AI in Marketing and Consumer Attitudes

Olan et al., 2021 explore the impact of AI on marketing and consumer behavior, revealing a positive influence on consumer attitudes. The study incorporates the fsQCA technique, developing a metaframework predicting behavior based on AI, attitudes, and knowledge-sharing.

AI Recommendations and Decision Quality

Chen et al., 2022 contribute empirical evidence on the impact of AI recommendations on consumer preferences and decision quality. The study, based on experiments, warns against potential information cocoons and highlights the need for regulating AI behaviors.

AI-Driven Recommendation Agents and Privacy Risk

Rohden & Zeferino, 2023 delve into the impact of AI-driven recommendation agents on consumer perceptions of data privacy risk. The study, utilizing in-depth interviews and surveys, identifies factors contributing to privacy risk perception, emphasizing the role of consumer trust.

Personalized Engagement Marketing

Kumar et al., 2019 focus on personalized engagement marketing, exploring how AI curates personalized offerings. The paper predicts the impact of AI on branding and customer management practices in both developed and developing countries.

Consumer Evaluations of GAN-Generated Fashion Products

Sohn et al., 2021 examine consumer evaluations of fashion products generated using generative adversarial network (GAN). The study reveals positive effects on willingness to pay, with the disclosure of GAN technology influencing consumer evaluations.

AI in Ethnic Clothing Consumption

Peng & Krutasaen, 2022 employ AI decision-making and IoT to study factors influencing ethnic wear consumption. The research emphasizes the positive impact of cultural scope and commodity variety on ethnic clothing consumption.

Adoption of AI in the Leisure Economy

Xian, 2021 analyze the adoption of AI in the leisure economy, exploring psychological factors influencing AI acceptance. The study introduces personal innovativeness as a new factor, contributing to the understanding of AI acceptance determinants.

AI in Digital Marketing

Tchelidze, 2019 investigate the role of AI in digital marketing, emphasizing the importance of self-learning machines for understanding online consumer behavior. The research highlights skills required for digital marketers to leverage AI effectively.

Automation of Services Using AI in Industry 4.0

Flavian & Casaló, 2021 discuss the automation of services using AI in the context of Industry 4.0. The paper introduces six papers from a special issue, providing an overview, summarizing key findings, and identifying future research possibilities.

AI-Powered Applications in Service Profit Chain

Wei & Prentice, 2022 draw on service profit chain theory, considering AI-powered applications as service products. The study examines the influence of AI service quality on customer loyalty, exploring emotional intelligence as a moderator.

AI-Powered Learning Apps in Education

Ko et al., 2022 investigate compensatory behavior in students during a pandemic, exploring the role of AI-powered learning apps. The findings reveal nuanced patterns in app usage, demonstrating compensatory behavior for learning loss.

AI in B2B Settings

Dwivedi & Wang, 2022 address the gap in AI research in industrial markets, presenting 16 articles exploring various aspects of AI in B2B settings. The studies cover AI's impact on marketing, organizational behavior, product innovation, supply chain management, and customer relationship management.

Security of AIoT Using HoneyNet Approach

Tan et al., 2022 focus on the security of AIoT, proposing a HoneyNet approach for threat detection and situational awareness. The study utilizes Docker technology and deep learning models to enhance AIoT security.

Digital AI Technologies Impact in India

Bag et al., 2022 address the impact of digital AI technologies on user engagement and conversion in India. The study explores the relationship between AI technologies, user engagement, satisfying user experience, and repurchase intention.

Big Data and AI in Hospitality and Tourism

Lv et al., 2022 conduct a systematic review of big data and AI in hospitality and tourism research. The review identifies themes and trends in 270 relevant studies, covering the definition of big data, types used, AI applications, and major research themes.

AI on the Internet of Things (IoT)

Liu & Liu, 2022 examine the role of AI in the IoT, focusing on accurate node positioning and its applications in geographic and network location services. The study discusses the broad application prospects of IoT technology-oriented AI, emphasizing the need to address potential risks in public safety.

Discussion on Key Findings

The amalgamation of research data from several studies investigating the intersection of artificial intelligence (AI) and consumer behavior reveals intricate patterns, difficulties, and prospects. This conversation explores significant issues, elucidating the ramifications of artificial intelligence in the domains of marketing, decision-making, privacy, customization, and cultural settings.

The study conducted by Olan et al. ( 2021 ) demonstrates that AI has a beneficial effect on consumer sentiments, offering marketers valuable information to improve customer engagement. The metaframework established using the fsQCA approach functions as a significant instrument for forecasting and comprehending customer behavior. As artificial intelligence becomes increasingly essential to marketing efforts, these valuable insights are expected to be critical for decision-makers.

AI suggestions and Decision Quality: Chen et al. ( 2022 ) provide empirical data that raises concerns about the possible disadvantages of AI suggestions. AI-powered suggestions, although enhancing consumer preferences, might result in the construction of information cocoons, which can negatively affect the quality of decision-making. It is crucial to regulate AI activities in order to maintain a balance between customized suggestions and ensuring that customers receive a diverse range of information.

Rohden and Zeferino ( 2023 ) examine the privacy consequences associated with AI-powered recommendation bots. The results emphasize the significance of openness and confidence from consumers in reducing perceived risks. In light of increasing concerns over data privacy, it is imperative for businesses to give utmost importance to transparent communication and ethical standards in the field of artificial intelligence in order to preserve customer confidence.

Personalized Engagement Marketing: According to Kumar et al. ( 2019 ), AI plays a significant role in transforming personalized engagement marketing. The report forecasts a transformation in consumer involvement and administration methods, with ramifications for both advanced and emerging markets. With the ongoing advancement of AI, organizations have the opportunity to utilize these valuable information in order to customize their marketing campaigns to suit various customer groups.

Evaluations from consumers of fashion products generated by GANs:

Sohn et al. ( 2021 ) provide valuable insights into the field of fashion by showcasing the favorable influence of GAN-generated items on consumers' willingness to spend. The revelation of GAN technology also impacts consumer assessments. Integrating AI technology in the fashion sector, while clearly articulating their application, can improve customer attitudes and increase their readiness to spend.

Peng and Krutasaen ( 2022 ) examine the role of AI in ethnic apparel consumption and emphasize its favorable impact on cultural diversity and product selection. Comprehending these aspects is crucial for firms aiming to advertise ethnic clothing, highlighting the capacity of AI to overcome cultural differences in customer preferences.

AI Adoption in the Leisure sector: According to Xian's (2021) analysis, the use of AI in the leisure sector is influenced by personal innovativeness. Understanding the many psychological factors that impact the acceptability of AI helps in formulating effective approaches to promote its wider adoption. These effects extend beyond leisure services and also impact the acceptability of technology in other fields.

Tchelidze ( 2019 ) emphasizes the significance of autonomous robots in comprehending online customer behavior in the realm of digital marketing. Flavian and Casaló ( 2021 ) examine the utilization of artificial intelligence (AI) to automate services within the framework of Industry 4.0. These studies collectively emphasize the significant impact that AI may have on several businesses, underscoring the importance of ongoing learning and flexibility.

Wei and Prentice ( 2022 ) enhance the service profit chain theory by including AI-powered applications as service goods. An in-depth comprehension of the complex interconnections among AI service quality, customer happiness, and emotional intelligence provides organizations with a clear path to improve customer loyalty.

The authors Dwivedi and Wang ( 2022 ) aim to fill the void in AI research inside industrial marketplaces by conducting a thorough investigation of several aspects in B2B settings. Tan et al. ( 2022 ) specifically examine the security of Artificial Intelligence of Things (AIoT) and suggest a HoneyNet strategy to improve the identification of potential threats. These studies demonstrate the adaptability of AI applications in many sectors, particularly emphasizing challenges related to security.

Bag et al. ( 2022 ) examine the influence of digital AI technologies on user engagement and conversion rates in India. Lv et al. ( 2022 ) do a comprehensive analysis of the existing literature on big data and AI in the hotel and tourist industry, uncovering recurring topics and patterns. These studies make valuable contributions to the worldwide discussion on the uses of AI, by demonstrating regional differences and emphasizing the potential of AI to revolutionize various markets.

The study conducted by Liu and Liu ( 2022 ) examines the utilization of AI in the context of the Internet of Things (IoT), with a particular emphasis on achieving precise node location. The report highlights the imperative to tackle possible hazards linked to AI technology in the realm of public safety. Ensuring appropriate and successful adoption of AI in IoT will require addressing safety concerns as a top priority.

To summarize, this systematic literature review explores a wide range of research, offering a comprehensive perspective on the many effects of AI on consumer behavior. The results emphasize the profound impact that AI may have on many sectors, highlighting the need for careful management of customization, privacy, and ethical concerns. As organizations increasingly use AI technology, the knowledge obtained from these studies will act as valuable references for understanding and adapting to the ever-changing environment of customer preferences, decision-making, and market trends. The classification of topics furthermore enables a subtle comprehension of the many uses of AI, establishing the foundation for future investigation and real-world applications. As the field of AI progresses, it is crucial to continuously explore and reflect on these topics to ensure ethical and consumer-focused integration of AI in many global settings.

Declarations

Author contribution.

Both authors contributed equally.

• Bag, S., Srivastava, G., Al Bashir, M. M., Kumari, S., Giannakis, M., & Chowdhury, A. H. (2022). Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion. BENCHMARKING-AN INTERNATIONAL JOURNAL , 29 (7), 2074–2098. https://doi.org/10.1108/BIJ-07-2021-0415
• Chen, S. H., Qiu, H., Zhao, S. F., Han, Y. Y., He, W., Siponen, M., Mou, J., & Xiao, H. (2022). When more is less: The other side of artificial intelligence recommendation. JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING , 7 (2), 213–232. https://doi.org/10.1016/j.jmse.2021.08.001 WE  - Emerging Sources Citation Index (ESCI)
• Dwivedi, Y. K., & Wang, Y. C. (2022). Guest editorial: Artificial intelligence for B2B marketing: Challenges and opportunities. INDUSTRIAL MARKETING MANAGEMENT , 105 , 109–113. https://doi.org/10.1016/j.indmarman.2022.06.001
• Flavian, C., & Casaló, L. V. (2021). Artificial intelligence in services: current trends, benefits and challenges. SERVICE INDUSTRIES JOURNAL , 41 (13–14), 853–859. https://doi.org/10.1080/02642069.2021.1989177 WE  - Social Science Citation Index (SSCI)
• Ko, G. Y., Shin, D., Auh, S., Lee, Y., & Han, S. P. (2022). Learning Outside the Classroom During a Pandemic: Evidence from an Artificial Intelligence-Based Education App. MANAGEMENT SCIENCE . https://doi.org/10.1287/mnsc.2022.4531
• Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. CALIFORNIA MANAGEMENT REVIEW , 61 (4), 135–155. https://doi.org/10.1177/0008125619859317 WE  - Social Science Citation Index (SSCI)
• Liu, H. Q., & Liu, T. (2022). The Application of Artificial Intelligence Oriented to the Internet of Things Technology in the Complexity of Digital Crossborder Trade. WIRELESS COMMUNICATIONS & MOBILE COMPUTING , 2022 . https://doi.org/10.1155/2022/2339399 WE  - Science Citation Index Expanded (SCI-EXPANDED)
• Lv, H., Shi, S., & Gursoy, D. (2022). A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism. JOURNAL OF HOSPITALITY MARKETING & MANAGEMENT , 31 (2), 145–175. https://doi.org/10.1080/19368623.2021.1937434
• Olan, F., Suklan, J., Arakpogun, E. O., & Robson, A. (2021). Advancing Consumer Behavior: The Role of Artificial Intelligence Technologies and Knowledge Sharing. IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT . https://doi.org/10.1109/TEM.2021.3083536
• Peng, Y., & Krutasaen, W. (2022). Consumer Psychology of Ethnic Clothing Based on Artificial Intelligence Decision-Making and Internet of Things. WIRELESS COMMUNICATIONS & MOBILE COMPUTING , 2022 . https://doi.org/10.1155/2022/8805010 WE   - Science Citation Index Expanded (SCI-EXPANDED)
• Rohden, S. F., & Zeferino, D. G. (2023). Recommendation agents: an analysis of consumers’ risk perceptions toward artificial intelligence. ELECTRONIC COMMERCE RESEARCH , 23 (4), 2035–2050. https://doi.org/10.1007/s10660-022-09626-9
• Sohn, K., Sung, C. E., Koo, G., & Kwon, O. (2021). Artificial intelligence in the fashion industry: consumer responses to generative adversarial network (GAN) technology. INTERNATIONAL JOURNAL OF RETAIL & DISTRIBUTION MANAGEMENT , 49 (1), 61–80. https://doi.org/10.1108/IJRDM-03-2020-0091
• Tan, L., Yu, K. P., Ming, F. P., Cheng, X. F., & Srivastava, G. (2022). Secure and Resilient Artificial Intelligence of Things: A HoneyNet Approach for Threat Detection and Situational Awareness. IEEE CONSUMER ELECTRONICS MAGAZINE , 11 (3), 69–78. https://doi.org/10.1109/MCE.2021.3081874 WE  - Science Citation Index Expanded (SCI-EXPANDED)
• Tchelidze, C. L. (2019). POTENTIAL AND SKILL REQUIREMENTS OF ARTIFICIAL INTELLIGENCE IN DIGITAL MARKETING. QUALITY-ACCESS TO SUCCESS , 20 , 73-78 WE-Emerging Sources Citation Index (ESCI).
• Wei, H., & Prentice, C. (2022). Addressing service profit chain with artificial and emotional intelligence. JOURNAL OF HOSPITALITY MARKETING & MANAGEMENT , 31 (6), 730–756. https://doi.org/10.1080/19368623.2022.2058671
• Xian, X. L. (2021). Psychological Factors in Consumer Acceptance of Artificial Intelligence in Leisure Economy: A Structural Equation Model. JOURNAL OF INTERNET TECHNOLOGY , 22 (3), 697–705. https://doi.org/10.3966/160792642021052203018 WE  - Science Citation Index Expanded (SCI-EXPANDED)

Additional Declarations

No competing interests reported.

The impact of artificial intelligence on consumer behaviour and changes in business activity due to pandemic effects

• Tiago Dias + −

• Renato Lopes da Costa + −

The COVID-19 pandemic has impacted the world economy, and the restrictions have shaken business models. E-commerce has skyrocketed as the only way to purchase products and AI has received closer consideration as social distancing has become imperative. This research aims to find whether the COVID-19 has translated into an opportunity for the use of AI by companies. A survey incorporating consumers and companies was conducted to analyse the positioning of consumers regarding the use of AI, as well as the perception of companies regarding their possible use of AI. It was concluded that due to COVID-19 there was an increase in the relevance that companies give to AI, the main drivers being the companies' views on AI and the benefits from its use. Regarding consumer behaviour, consumers are more receptive to AI use, favouring a fully automated experience, with half of the sample preferring to buy online.

• Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411 DOI: https://doi.org/10.1037/0033-2909.103.3.411
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327 DOI: https://doi.org/10.1007/BF02723327
• Burgess, A. (2018). The Executive Guide to Artificial Intelligence. In The Executive Guide to Artificial Intelligence. Springer International Publishing. https://doi.org/10.1007/978-3-319-63820-1 DOI: https://doi.org/10.1007/978-3-319-63820-1
• Butzmann, L., Daweke, E., Geimer, J., Kolev, N., & Stiller, M. (2017). From mystery to mastery: Unlocking the business value of Artificial Intelligence in the insurance industry. Deloitte Digital, November, 1–45. https://www2.deloitte.com/content/dam/Deloitte/ru/Documents/financial-services/artificial-intelligence-in-insurance.pdf
• Carmo, H., & Ferreira, M. (1998). Metodologia da Investigação: Guia para Auto-aprendizagem. Universidade Aberta, Lisboa.
• Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38-68. DOI: https://doi.org/10.1177/14707853211018428
• Coombs, C. (2020). Will COVID-19 be the tipping point for the Intelligent Automation of work? A review of the debate and implications for research. International Journal of Information Management, June, 102182. https://doi.org/10.1016/j.ijinfomgt.2020.102182 DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102182
• Coombs, C., Hislop, D., Taneva, S. K., & Barnard, S. (2020). The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review. Journal of Strategic Information Systems, July 2017, 101600. https://doi.org/10.1016/j.jsis.2020.101600 DOI: https://doi.org/10.1016/j.jsis.2020.101600
• Davenport, T., & Kalakota, R. (2019). DIGITAL TECHNOLOGY The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/ DOI: https://doi.org/10.7861/futurehosp.6-2-94
• De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91-105. DOI: https://doi.org/10.1016/j.intmar.2020.04.007
• Denning, S. (2018). What Is Strategic Agility? Forbes. https://www.forbes.com/sites/stevedenning/2018/01/28/what-is-strategic-agility/amp/
• Dew, N., & Sarasvathy, S. D. (2016). Exaptation and niche construction: Behavioral insights for an evolutionary theory. Industrial and Corporate Change, 25(1), 167–179. https://doi.org/10.1093/icc/dtv051 DOI: https://doi.org/10.1093/icc/dtv051
• Dickson, B. (2020). 3 ways AI is transforming the insurance industry. https://thenextweb.com/growth-quarters/2020/02/24/3-ways-ai-is-transforming-the-insurance-industry/
• Ehiorobo, O. A. (2020). STRATEGIC AGILITY AND AI-ENABLED RESOURCE CAPABILITIES FOR BUSINESS SURVIVAL IN POST-COVID-19 GLOBAL ECONOMY. International Journal of Information, Business and Management, 12(4), 201–214. https://search.proquest.com/docview/2438206567?pq-origsite=gscholar&fromopenview=true
• El-Sheikh, A. A., Abonazel, M. R., & Gamil, N. (2017). A review of software packages for structural equation modeling:A Comparative Study. Applied Mathematics and Physics, 5(3), 85–94. https://doi.org/10.12691/amp-5-3-2
• Finlay, S. (2018). Artificial Intelligence and Machine Learning for Business. In Artificial Intelligence and Machine Learning for Business for Non-Engineers (3rd ed.). Relativistic. https://www.goodreads.com/book/show/35270840-artificial-intelligence-and-machine-learning-for-business
• Fornell, C., & Larcker, F. D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(3), 39–50. DOI: https://doi.org/10.1177/002224378101800104
• Gavetti, G., Helfat, C. E., & Marengo, L. (2017). Searching, Shaping, and the Quest for Superior Performance. Strategy Science, 2(3), 194–209. https://doi.org/10.1287/stsc.2017.0036 DOI: https://doi.org/10.1287/stsc.2017.0036
• Güngör, H. (2020). Creating Value with Artificial Intelligence: A Multi-stakeholder Perspective. Journal of Creating Value, 6(1), 72–85. https://doi.org/10.1177/2394964320921071 DOI: https://doi.org/10.1177/2394964320921071
• Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. Journal of Strategic Information Systems, 26(3), 191–209. https://doi.org/10.1016/j.jsis.2017.07.003 DOI: https://doi.org/10.1016/j.jsis.2017.07.003
• Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925 DOI: https://doi.org/10.1177/0008125619864925
• Hair, J. F., Hult, G. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling. In Sage Publication. DOI: https://doi.org/10.15358/9783800653614
• Hao, F., Xiao, Q., & Chon, K. (2020). COVID-19 and China’s Hotel Industry: Impacts, a Disaster Management Framework, and Post-Pandemic Agenda. International Journal of Hospitality Management, 90(June), 102636. https://doi.org/10.1016/j.ijhm.2020.102636 DOI: https://doi.org/10.1016/j.ijhm.2020.102636
• Haque, A., Fernando, M., & Caputi, P. (2019). The Relationship Between Responsible Leadership and Organisational Commitment and the Mediating Effect of Employee Turnover Intentions: An Empirical Study with Australian Employees. Journal of Business Ethics, 156(3), 759–774. https://doi.org/10.1007/s10551-017-3575-6 DOI: https://doi.org/10.1007/s10551-017-3575-6
• Harris, P., Dall’Olmo Riley, F., Riley, D., & Hand, C. (2017). Online and store patronage: a typology of grocery shoppers. International Journal of Retail and Distribution Management, 45(4), 419–445. https://doi.org/10.1108/IJRDM-06-2016-0103 DOI: https://doi.org/10.1108/IJRDM-06-2016-0103
• Henke, N., & Kaka, N. (2018). McKinsey: Analytics comes of age. McKinsey Analytics, January, 1–100. https://www.mckinsey.com/~/media/McKinsey/Business Functions/McKinsey Analytics/Our Insights/Analytics comes of age/Analytics-comes-of-age.ashx
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
• Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. DOI: https://doi.org/10.1007/s11747-020-00749-9
• Johne, A. (1999). Successful market innovation. European Journal of Innovation Management, 2(1), 6–11. https://doi.org/10.1108/14601069910248838 DOI: https://doi.org/10.1108/14601069910248838
• Judeh, M. (2014). What is your concept of strategic agility? ResearchGate. https://www.researchgate.net/post/What_is_your_concept_of_strategic_agility
• Kim, W., & Mauborgne, R. (2005). Blue Ocean Strategy: FROM THEORY TO PRACTICE. In E. Nofzinger, P. Maquet, & M. Thorpy (Eds.), Neuroimaging of Sleep and Sleep Disorders (Vol. 47, Issue 3). https://journals.sagepub.com/doi/pdf/10.1177/000812560504700301
• Kjellberg, H., Azimont, F., & Reid, E. (2015). Market innovation processes: Balancing stability and change. Industrial Marketing Management, 44, 4–12. https://doi.org/10.1016/j.indmarman.2014.10.002 DOI: https://doi.org/10.1016/j.indmarman.2014.10.002
• Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons and Fractals, 139. https://doi.org/10.1016/j.chaos.2020.110059 DOI: https://doi.org/10.1016/j.chaos.2020.110059
• Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2017). Brain Intelligence: Go beyond Artificial Intelligence. Mobile Networks and Applications, 23(2), 368–375. https://doi.org/10.1007/s11036-017-0932-8 DOI: https://doi.org/10.1007/s11036-017-0932-8
• Luksha, P. (2008). NICHE CONSTRUCTION: THE PROCESS OF OPPORTUNITY CREATION IN THE ENVIRONMENT. Strategic Entrepreneurship Journal, 2(4), 269–283. https://doi.org/10.1002/sej DOI: https://doi.org/10.1002/sej.57
• Maritz, A. (2020). A multi-disciplinary business approach to COVID-19: La Trobe Business School perspectives. IJOI The International Journal of Organizational Innovation, 13(1), 2020–1095. http://www.ijoi-online.org/
• McGrath, R. (2019). Seeing Around Corners: How to Spot Inflection Points before They Happen. Houghton Mifflin Harcourt. https://www.goodreads.com/book/show/43261121-seeing-around-corners
• Morton, J., Stacey, P., & Mohn, M. (2018). Building and maintaining strategic agility: An agenda and framework for executive IT leaders. California Management Review, 61(1), 94–113. https://doi.org/10.1177/0008125618790245 DOI: https://doi.org/10.1177/0008125618790245
• Naudé, W. (2020). Artificial Intelligence against COVID-19: An Early Review. IZA Discussion Papers, 13110, 1–14. https://www.iza.org/publications/dp/13110/artificial-intelligence-against-covid-19-an-early-review DOI: https://doi.org/10.2139/ssrn.3568314
• Nenonen, S, & Storbacka, K. (2018). Smash: Using Market Shaping to Design New Strategies for Innovation, Value Creation, and Growth. In Emerald Publishing (1st ed.). Emerald Publishing Limited. https://books.google.pt/books?id=2vBJDwAAQBAJ&printsec=copyright&redir_esc=y#v=onepage&q&f=false DOI: https://doi.org/10.1108/9781787437975
• Nenonen, Suvi, Kjellberg, H., Pels, J., Cheung, L., Lindeman, S., Mele, C., Sajtos, L., & Storbacka, K. (2014). A new perspective on market dynamics: Market plasticity and the stability–fluidity dialectics. Marketing Theory, 14(3), 269–289. https://doi.org/10.1177/1470593114534342 DOI: https://doi.org/10.1177/1470593114534342
• Nenonen, Suvi, Storbacka, K., & Windahl, C. (2019). Capabilities for market-shaping: triggering and facilitating increased value creation. Journal of the Academy of Marketing Science, 47, 617–639. https://doi.org/10.1007/s11747-019-00643-z DOI: https://doi.org/10.1007/s11747-019-00643-z
• Nielsen. (2020). COVID-19: The Unexpected Catalyst for Tech Adoption. Nielsen CPG, FMCG & Retail. https://www.nielsen.com/za/en/insights/article/2020/covid-19-the-unexpected-catalyst-for-tech-adoption/
• Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 1–8. https://doi.org/10.7189/jogh.08.020303 DOI: https://doi.org/10.7189/jogh.08.020303
• Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business Research, 116(May), 209–213. https://doi.org/10.1016/j.jbusres.2020.05.036 DOI: https://doi.org/10.1016/j.jbusres.2020.05.036
• Patrick, B. (2020). What is artificial intelligence? Journal of Accountancy. https://www.journalofaccountancy.com/issues/2020/feb/what-is-artificial-intelligence.html
• Patvardhan, S., & Ramachandran, J. (2020). Shaping the future: Strategy making as artificial evolution. Organization Science. https://doi.org/10.1287/orsc.2019.1321 DOI: https://doi.org/10.1287/orsc.2019.1321
• Perrault, R., Shoham, Y., Brynjolfsson, E., Clark, J., Etchemendy, J., Grosz, B., Lyons, T., Manyika, J., Mishra, S., & Niebles, J. C. (2019). Artificial Intelligence Index 2019 Annual Report. AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, 291. https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf
• Pettersen, L. (2019). Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work. Work, Employment and Society, 33(6), 1058–1067. https://doi.org/10.1177/0950017018817489 DOI: https://doi.org/10.1177/0950017018817489
• Preacher, K. J., & Hayes, S. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. DOI: https://doi.org/10.3758/BRM.40.3.879
• Priem, R. L., Butler, J. E., & Li, S. (2013). Toward reimagining strategy research: Retrospection and prospection on the 2011 amr decade award article. Academy of Management Review, 38(4), 471–489. https://doi.org/10.5465/amr.2013.0097 DOI: https://doi.org/10.5465/amr.2013.0097
• Provdanov, C. C., & Freitas, E. C. De. (2013). Metodologia do trabalho científico: métodos e técnicas da pesquisa e do trabalho acadêmico. In Novo Hamburgo: Feevale. https://doi.org/10.1017/CBO9781107415324.004 DOI: https://doi.org/10.1017/CBO9781107415324.004
• Ringle, C. M., Henseler, J., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 DOI: https://doi.org/10.1007/s11747-014-0403-8
• Scor. (2018). The Impact of Artificial Intelligence on the (re)insurance sector. Risk Analysis, 7(3), 277–280. https://doi.org/10.1111/j.1539-6924.1987.tb00460.x DOI: https://doi.org/10.1111/j.1539-6924.1987.tb00460.x
• Shabbir, J., & Anwer, T. (2015). Artificial Intelligence and its Role in Near Future. Journal of Latex Class Files, 14(8), 1–11. http://arxiv.org/abs/1804.01396
• Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059 DOI: https://doi.org/10.1016/j.jbusres.2020.05.059
• Sreeharsha, V. (2020). Computer Vision Could Help Enforce Social- Distancing in the Workplace. The Wall Street Journal PRO Artificial Intelligence. https://www.wsj.com/articles/computer-vision-could-help-enforce-social-distancing-in-the-workplace-11587720601
• Tarka, P. (2018). An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Quality and Quantity, 52(1), 313–354. https://doi.org/10.1007/s11135-017-0469-8 DOI: https://doi.org/10.1007/s11135-017-0469-8
• Tarski, A. (1977). Introducción a la Lógica y a la Metodología de las Ciencias. Investigación en Ciencias Sociales, Interamericana: México, D. F.
• Vergara, S. (2006). Projectos e relatórios de pesquisa em administração. São Paulo: Atlas.
• Vilelas, J. (2009). Investigação – o processo de construção do conhecimento. Lisboa: Sílabo. www.silabo.pt
• Wang, Y., & Wang, H. (2022). Reinventing the Wheel of Marketing: Assessing the Impact of Artificial Intelligence (AI) on Digital Marketing and Consumer Buying Behavior. In Innovative Computing: Proceedings of the 4th International Conference on Innovative Computing (IC 2021) (pp. 827-834). Springer Singapore.
• Willcocks, L. P., & Lacity, M. C. (2016). A New Approach to Automating Services. MIT Sloan Management Review, 58(1), 40–49. http://eprints.lse.ac.uk/68135/1/Willcocks_New approach_2016.pdf
• Williams, C. (2007). Research methods. Journal of Business & Economic Research, 5(3), 65–72.
• Yin, R. (1994). Case Study Research Design and Methods (Sage (ed.); 2a edição).

How to Cite

Download citation.

Research on e-commerce product evaluation based on multi-source heterogeneous data fusion

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, index terms.

General and reference

Document types

General literature

Recommendations

Multi-source data fusion study in scientometrics.

This paper provides an introduction to multi-source data fusion (MSDF) and comprehensively overviews the ingredients and challenges of MSDF in scientometrics. As compared to the MSDF methods in the sensor and other fields, and considering the features ...

Synthetic Evaluation Methods of E-commerce Product Quality based on Multi-dimensional Information Fusion

It is difficult for customers to make correct purchasing decisions due to multiple types and large quantities of product-related data as well as false advertising and promotion at e-commerce platforms during their online shopping process. Combing multi-...

A General Multi-Source Data Fusion Framework

With the development of the Internet, the increase of information sources and speed of information release and transmission have led to a sharp increase in the amount of information. To enable users finding more accurate and reliable information in the ...

Information

Published in.

Association for Computing Machinery

New York, NY, United States

Publication History

Permissions, check for updates.

• Research-article
• Refereed limited

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View Options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

View options.

View or Download as a PDF file.

View online with eReader .

HTML Format

View this article in HTML Format.

Share this Publication link

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Free Cyber Services #protect2024 Secure Our World Shields Up Report A Cyber Issue

With Open Source Artificial Intelligence, Don’t Forget the Lessons of Open Source Software

By: Jack Cable, Senior Technical Advisor, and Aeva Black, Section Chief, Open Source Security

The accelerated development of new artificial intelligence (AI) capabilities, including with large language models (LLMs), has spurred international debates around the potential impact of “open source AI” models. Does open sourcing a model benefit society because it enables developers to rapidly innovate by studying, using, sharing, and collaboratively iterating on these state-of-the-art models? Or do such capabilities pose security threats, allowing adversaries to leverage these models for greater harm? 

Fortunately, the conversation isn’t starting from scratch. Developers of all AI models, including open foundation models, can learn from existing work to secure software. As the Cybersecurity and Infrastructure Security Agency’s (CISA) leads on open source software (OSS) security, we’ve spent significant time immersed in open source communities. OSS faced similar debates during the 1990s, and we know that there are many lessons to be learned from the history of OSS.

Last month, CISA responded to the National Telecommunications and Information Administration’s (NTIA) Request for Information on Dual Use Foundation Artificial Intelligence Models With Widely Available Model Weights. At CISA, we see significant value in open foundation models to help strengthen cybersecurity, increase competition, and promote innovation. Our response highlighted that the global AI community should (1) learn from existing software security work and (2) continue to promote the responsible development and release of open foundation models while mitigating their potential harms. There is a tremendous wealth of experience from OSS community that shouldn’t be lost when considering open foundation models.

While there is not yet a consensus on the definition of what constitutes “open source AI”, the Open Source Initiative , which maintains the “Open Source Definition” and a list of approved OSS licenses, has been “driving a multi-stakeholder process to define an ‘Open Source AI’” . Therefore, in the interest of being more precise, we use the term “open foundation models” to refer to AI models with widely available weights.

Learn from Existing Software Security Work

OSS facilitates extensive innovation in every sector. A recent paper from Harvard and the University of Toronto found that the total cost to produce the world’s OSS is $4.15 billion, while the value created is magnitudes larger: $8.8 trillion. It’s safe to say that many innovations of the digital age would not have been possible without OSS.

We must all work to ensure that OSS doesn’t fall victim to the tragedy of the commons. At CISA, we continue to emphasize that every software manufacturer should be a responsible consumer of the OSS that it uses, and that means also being a sustainable contributor back to the open source ecosystem. This same principle applies to open foundation models – everyone ought to do their part to ensure a safe, secure, and sustainable community.

Additionally, we have observed the benefits of open source tools in cybersecurity. While there has been a decades-long debate on the open sourcing of dual-use cybersecurity tools (i.e., tools that can both aid cyber defenders and be used maliciously by threat actors), the general consensus among the security community is that the benefits of open sourcing security tools for defenders outweigh the harms that might be leveraged by adversaries – who, in many cases, will get their hands on tools whether or not they are open sourced. While we cannot anticipate all the potential use cases of AI, lessons from cybersecurity history indicate that we can stand to benefit from dual-use open source tools.

CISA has been hard at work in recent years to help secure the OSS ecosystem. Our Open Source Software Security Roadmap , published last year, starts with the recognition that open source software is supported by an inherently global community – and government’s role is to show up as a community member to support this community. We collaborated with the open source community to release principles for the security of package repositories and highlighted actions that five major package repositories – npm, PyPI, Crates.io, Composer, and Maven Central – are taking in line with this framework.

The AI community should heed these and other lessons from the open source community. Operators of package repositories in the AI ecosystem – such as platforms that distribute AI source code, models, weights, or training data – should work towards the items in the Principles for Package Repository Security framework and think about what unique considerations might apply. Tool developers should begin incorporating traceability and artifact composition analysis techniques. Model developers should include diverse viewpoints early and throughout the development lifecycle, ensuring that trust and safety is a core consideration during model development.

Promote the responsible development and release of open foundation models while mitigating their potential harms

In our response, we define two sets of potential harms of foundation models. Our definitions are based on whether the deployer of the model – who is the entity that runs the model – intends for those harms to be caused, or if they seek to prevent them. The first class of harms are those deliberately sought by the deployer of the model, such as using the model to conduct cyberattacks or to generate non-consensual intimate imagery (NCII). The second class of harms are those which are not desired by the deployer of the model, such as a cybersecurity vulnerability in a model deployed by a critical infrastructure entity.

The first class of harms must be addressed with a multipronged risk reduction approach. This includes additional research and investment to limit abuse of the technology (which should draw on existing trust and safety work), although we know that many protections will inevitably be circumvented by malicious deployers. Therefore, domain-specific risk mitigations are also needed. For NCII, this might involve discouraging the training of specific capabilities in models that are widely distributed (such as by filtering training data) and societal approaches to support victims in abuse. For cybersecurity vulnerabilities, the best approach to all forms of threats – including those enabled by foundation models – is to ensure that software is built in a secure by design manner resilient to the most common classes of vulnerabilities.

Most of the conversation about open foundation models has been dominated by the abuse of such models by malicious deployers. We certainly should work to study and mitigate these harms. With that said, the second class of harms is equally deserving of attention; and, promisingly — as the deployer of these models does not want harms to occur — building protections against risks into models is more readily possible. For instance, the developer of an open source model could take a secure by design approach and build the model in a responsible manner, resilient to common classes of vulnerabilities. Such a developer could also train the model in a publicly verifiable way, or on publicly available data, thereby allowing others to more fully study the model’s behavior and gain confidence that it does not contain vulnerabilities or backdoors. Much as transparency strengthens the security of OSS, allowing for public study and verification can help secure open foundation models.

We recognize that today’s open foundation models exist along a spectrum of openness and applaud efforts to train open foundation models on appropriate public data. When a model’s weights and training code are published without disclosure of training data or pre-training, even though it may be modifiable by users in some ways, users of that model have only a limited ability to understand, verify, or mitigate any vulnerabilities in the model. This lack of transparency prevents inspection and research into how these models operate. Thus, developers of open weight foundation models without open data have a responsibility to ensure the outputs of their models are safe, secure, and trustworthy.

CISA is committed to ensuring that OSS, including AI models, can continue to be deployed in a safe and secure manner to foster innovation. We encourage readers to review our full response to NTIA and to learn more about our work in OSS Security .

As always, we can be contacted at [email protected] . 

CISA does not endorse any commercial entity, product, or service. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation, or favoring by CISA.

Related Articles

Safecom releases new resource for cloud adoption, ncswic’s planning, training, and exercise committee releases “set your pace plan” flyer, safecom and ncswic publish fall 2023 joint safecom-ncswic bi-annual meeting executive summaries, ncswic planning training, and exercise committee releases the human factors resource guide.

The August 2024 issue of IEEE Spectrum is here!

For IEEE Members

Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more about ieee →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more about ieee →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., what can ai researchers learn from alien hunters, the seti institute’s approach has lessons for research on artificial general intelligence.

Edmon Begoli is the founding director of Oak Ridge National Lab's Center for AI Security Research.

Amir Sadovnik is the research lead for Oak Ridge National Lab's Center for AI Security Research.

The emergence of artificial general intelligence (AGI)—systems that can perform any task a human can—could be the most important event in human history, one that radically affects all aspects of our collective lives. Yet AGI, which could emerge soon , remains an elusive and controversial concept. We lack a clear definition of what it is, we don’t know how we will detect it, and we don’t know how to deal with it if it finally emerges.

What we do know, however, is that today’s approaches to studying AGI are not nearly rigorous enough. Within industry, where many of today’s AI breakthroughs are happening, companies like OpenAI are actively striving to create AGI, but include research on AGI’s social dimensions and safety issues only as their corporate leaders see fit . While the academic community looks at AGI more broadly, seeking the characteristics of a new intelligent life-form, academic institutions don’t have the resources for a significant effort.

Thinking about AGI calls to mind another poorly understood and speculative phenomenon with the potential for transformative impacts on humankind. We believe that the SETI Institute ’s efforts to detect advanced extraterrestrial intelligence demonstrate several valuable concepts that can be adapted for AGI research. Instead of taking a dogmatic or sensationalist stance, the SETI project takes a scientifically rigorous and pragmatic approach—putting the best possible mechanisms in place for the definition, detection, and interpretation of signs of possible alien intelligence.

The idea behind SETI goes back 60 years, to the beginning of the space age. In their 1959 Nature paper , the physicists Giuseppe Cocconi and Philip Morrison described the need to search for interstellar communication. Assuming the uncertainty of extraterrestrial civilizations’ existence and technological sophistication, they theorized about how an alien society would try to communicate and discussed how we should best “listen” for messages. Inspired by this position, we argue for a similar approach to studying AGI, in all its uncertainties.

AI researchers are still debating how probable it is that AGI will emerge and how to detect it. However, the challenges in defining AGI and the difficulties in measuring it are not a justification for ignoring it or for taking a “we’ll know when we see it” approach. On the contrary, these issues strengthen the need for an interdisciplinary approach to AGI detection, evaluation, and public education, including a science-based approach to the risks associated with AGI .

We need a SETI-like approach to AGI now

The last few years have shown a vast leap in AI capabilities. The large language models (LLMs) that power chatbots like ChatGPT , which can converse convincingly with humans, have renewed the discussion about AGI. For example, recent articles have stated that ChatGPT shows “sparks” of AGI , is capable of reasoning , and outperforms humans in many evaluations.

While these claims are intriguing and exciting, there are reasons to be skeptical. In fact, a large group of scientists argue that the current set of tools won’t bring us any closer to true AGI . But given the risks associated with AGI, if there is even a small likelihood of it occurring, we must make a serious effort to develop a standard definition of AGI, establish a SETI-like approach to detecting it, and devise ways to safely interact with it if it emerges.

Challenge 1: How to define AGI

The crucial first step is to define what exactly to look for. In SETI’s case, researchers decided to look for so-called narrow-band signals distinct from other radio signals present in the cosmic background. These signals are considered intentional and only produced by intelligent life.

In the case of AGI, matters are far more complicated. Today, there is no clear definition of “artificial general intelligence” (other terms, such as strong AI, human-level intelligence, and superintelligence are also widely used to describe similar concepts). The term is hard to define because it contains other imprecise and controversial terms. Although “intelligence” is defined in the Oxford Dictionary as “the ability to acquire and apply knowledge and skills,” there is still much debate on which skills are involved and how they can be measured. The term “general” is also ambiguous. Does an AGI need to be able to do everything a human can do? Is generality a quality we measure as a binary or continuous variable?

One of the first missions of a “SETI for AGI” construct must be to clearly define the terms “general” and “intelligence” so the research community can speak about them concretely and consistently. These definitions need to be grounded in the disciplines supporting the AGI concept, such as computer science, measurement science, neuroscience, psychology, mathematics, engineering, and philosophy. Once we have clear definitions of these terms, we’ll need to find ways to measure them.

There’s also the crucial question of whether a true AGI must include consciousness, personhood, and self-awareness. These terms also have multiple definitions, and the relationships between them and intelligence must be clarified. Although it’s generally thought that consciousness isn’t necessary for intelligence, it’s often intertwined with discussions of AGI because creating a self-aware machine would have many philosophical, societal, and legal implications. Would a new large language model that can answer an IQ test better than a human be as important to detect as a truly conscious machine?

Challenge 2: How to measure AGI

In the case of SETI, if a candidate narrow-band signal is detected , an expert group will verify that it is indeed an extraterrestrial source. They’ll use established criteria—for example, looking at the signal type and source and checking for repetition—and conduct all the assessments at multiple facilities for additional validation.

How to best measure computer intelligence has been a long-standing question in the field. In a famous 1950 paper , Alan Turing proposed the “imitation game,” now more widely known as the Turing Test, which assesses whether human interlocutors can distinguish if they are chatting with a human or a machine. Although the Turing Test has been useful for evaluations in the past, the rise of LLMs has made it clear that it’s not a complete enough test to measure intelligence. As Turing noted in his paper, the imitation game does an excellent job of testing if a computer can imitate the language-generation process, but the relationship between imitating language and thinking is still an open question. Other techniques will certainly be needed.

These appraisals must be directed at different dimensions of intelligence. Although measures of human intelligence are controversial, IQ tests can provide an initial baseline to assess one dimension. In addition, cognitive tests on topics such as creative problem-solving, rapid learning and adaptation, reasoning, goal-directed behavior, and self-awareness would be required to assess the general intelligence of a system.

These cognitive tests will be useful, but it’s important to remember that they were designed for humans and might contain certain assumptions about basic human capabilities that might not apply to computers, even those with AGI abilities. For example, depending on how it’s trained, a machine may score very high on an IQ test but remain unable to solve much simpler tasks. In addition, the AI may have other communication modalities and abilities that would not be measurable by our traditional tests.

There’s a clear need to design novel evaluations to measure AGI or its subdimensions accurately. This process would also require a diverse set of researchers from different fields who deeply understand AI, are familiar with the currently available tests, and have the competency, creativity, and foresight to design novel tests. These measurements will hopefully alert us when meaningful progress is made toward AGI.

Once we have developed a standard definition of AGI and developed methodologies to detect it, we must devise a way to address its emergence.

Challenge 3: How to deal with AGI

Once we have discovered this new form of intelligence, we must be prepared to answer questions such as: Is the newly discovered intelligence a new form of life? What kinds of rights does it have? What kinds of rights do we have regarding this intelligence? What are the potential safety concerns, and what is our approach to handling the AGI entity, containing it, and safeguarding ourselves from it?

Here, too, SETI provides inspiration. SETI has protocols for handling the evidence of a sign of extraterrestrial intelligence. SETI’s post-detection protocols emphasize validation, transparency, and cooperation with the United Nations, with the goal of maximizing the credibility of the process, minimizing sensationalism, and bringing structure to such a profound event.

As with extraterrestrial intelligence, we need protocols for safe and secure interactions with AGI. These AGI protocols would serve as the internationally recognized framework for validating emergent AGI properties, bringing transparency to the entire process, ensuring international cooperation, applying safety-related best practices, and handling any ethical, social, and philosophical concerns.

We readily acknowledge that the SETI analogy can only go so far. If AGI emerges, it will be a human-made phenomenon. We will likely gradually engineer AGI and see it slowly emerge, so detection might be a process that takes place over a period of years, if not decades. In contrast, the existence of extraterrestrial life is something that we have no control over, and contact could happen very suddenly.

The discovery of a true AGI would be the most profound development in the history of science, and its consequences would be also entirely unpredictable. To best prepare, we need a methodical, comprehensive, principled, and interdisciplinary approach to defining, detecting, and dealing with AGI. With SETI as an inspiration, we propose that the AGI research community establish a similar framework to ensure an unbiased, scientific, transparent, and collaborative approach to dealing with possibly the most important development in human history.

• World Builders Put Happy Face On Superintelligent AI ›
• The AI Apocalypse: A Scorecard ›
• AGI: ARC Prize Offers $1 Million to Inspire AI Development - IEEE Spectrum ›
• AI Self-Recognition Creates Chances for New Security Risks - IEEE Spectrum ›
• Planning for AGI and beyond | OpenAI ›

Edmon Begoli is the founding director of Oak Ridge National Lab's new Center for AI Security Research , where he co-leads ORNL's internal AI research initiative with focus on AI safety and security applications. He specializes in the research, design, and development of resilient, secure, and scalable machine learning and analytic architectures.

Amir Sadovnik is the research lead for the new Center for AI Security Research at Oak Ridge National Lab, where he leads multiple research projects related to AI risk, adversarial AI, and large language model vulnerabilities.

Two Companies Plan to Fuel Cargo Ships With Ammonia

Video friday: uc berkeley's little humanoid, can a large language model recognize itself, related stories, new competition seeks to unstall agi progress, the ai apocalypse: a scorecard, apple, microsoft shrink ai models to improve them.

Can LLMs be Fooled? Investigating Vulnerabilities in LLMs

• Sara Abdali ,
• CJ Barberan ,
• Richard Anarfi

Publication

The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP). While their capabilities are undeniably impressive, it is crucial to identify and scrutinize their vulnerabilities especially when those vulnerabilities can have costly consequences. One such LLM, trained to provide a concise summarization from medical documents could unequivocally leak personal patient data when prompted surreptitiously. This is just one of many unfortunate examples that have been unveiled and further research is necessary to comprehend the underlying reasons behind such vulnerabilities. In this study, we delve into multiple sections of vulnerabilities which are model-based, training-time, inference-time vulnerabilities, and discuss mitigation strategies including “Model Editing” which aims at modifying LLMs behavior, and “Chroma Teaming” which incorporates synergy of multiple teaming strategies to enhance LLMs’ resilience. This paper will synthesize the findings from each vulnerability section and propose new directions of research and development. By understanding the focal points of current vulnerabilities, we can better anticipate and mitigate future risks, paving the road for more robust and secure LLMs.

• Follow on X
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram
• Subscribe to our RSS feed

Share this page:

• Share on Facebook
• Share on LinkedIn
• Share on Reddit