The DOI matches its Crossref record.
Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing
Abstract
This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing—an approach to create, communicate, and deliver personalized offerings to customers. It proposes that consumers are ready for a new journey in which AI is a tool for endless options and information that are narrowed and curated in a personalized way. It also provides predictions for managers regarding the AI-driven environment on branding and customer management practices in both developed and developing countries.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cited by
Sorted by citations
Items 701–774 of 774
Tiwari, N., & Jain, V. (2026) · Springer eBooks · doi:10.1007/978-3-031-75316-9_59-1
Marcillo, F., Gavilanez, K., Fajardo, P., & Chamorro, C. (2026) · Bastcorp International Journal · doi:10.62943/bij.v5n1.2026.505
Enhancing AI Chatbot Responsiveness Through Design of a Multimodal Emotion Detection
Sah, Y., Padole, A., Dongre, M., Shinde, G., Bobhate, G., & Fatangare, S. (2026) · International Conference on Visual Analytics and Data Visualization (ICVADV) · doi:10.1109/icvadv67766.2026.11470356
Coşkun, T., Çetin, A., & Başal, M. (2026) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2026.164068
Bouchareb, N., Balbal, H., Sahnoune, F., & Khelfi, R. (2026) · Cognition Technology & Work · doi:10.1007/s10111-026-00876-3
Human-AI Collaboration for Organizational Innovation in Industry 5.0
Achrén, L., Tariq, A., Dabić, M., & Torkkeli, M. (2026) · Future of business and finance · doi:10.1007/978-3-032-06604-6_2
Kosat, A. (2026) · Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.35379/cusosbil.1661256
Harnessing Marketing 6.0 Technologies to Reshape the Healthcare Sector
Amer, S. M., Alarfaj, W., Hasnin, E. A. H., Alhumud, A. A., & Almazroa, M. N. (2026) · International Journal of Service Science Management Engineering and Technology · doi:10.4018/ijssmet.408692
Impact of Generative Artificial Intelligence on the Work of Digital Marketing Professionals
Martins, M., Alturas, B., & Lapa, T. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-16284-7_2
Determinants of Greek Banking Customers’ Intention to Use AI-Based Green Fintech Solutions
Gatzioufa, P., Saprikis, V., Avlogiaris, G., Antoniadis, I., & Panitsidis, K. (2026) · FinTech · doi:10.3390/fintech5020043
Creating Ad Sequences with Optimal Discourse Structure by LLM
Galitsky, B. (2026) · Springer eBooks · doi:10.1007/978-3-032-16524-4_8
Alcalá, A. M., López, B. J., Santoyo, G. A. R., & García, M. G. B. (2025) · Ingenieria Solidaria · doi:10.16925/2357-6014.2025.02.03
Artificial intelligence marketing for green products in E-commerce supply chain
Cui, X., Yu, Z., Sun, J., et al. (2026) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-10-2025-2185
Kraul-Flores, K., & López, K. A. C. S. (2026) · Vinculatégica EFAN · doi:10.29105/vtga12.2-1297
Svegl, L., Mikalef, P., & Škerlavaj, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6848764
AI-Powered Content for Influencers as a Tool for Profit Growth in the US Market
Verdieva, P. (2026) · Universal Library of business and economics. · doi:10.70315/uloap.ulbec.2026.0301015
Möller, J., & Herm, S. (2026) · Springer eBooks · doi:10.1007/978-3-658-51635-2_5
Do Not Think Ahead of Me: High‐Personalization Chatbots Can Backfire During the Goal Formation Stage
Chen, X., Huang, M., & Xie, X. (2026) · Psychology and Marketing · doi:10.1002/mar.70189
“For example, Sephora, Carrefour, Shopify, Taobao, and Mercari use highly personalized chatbots to actively capture current consumer behaviors (e.g., browsing and searching) and adapt services to real‐time interaction context (Forbes 2023; Kumar et al. 2019; The New York Times 2023).”
AI in Arab E-Commerce: A Thematic Analysis of SME Transformation and an Extended TOE FrameworkPreprint
Khzam, H. B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6955440
Customer Relationship Management (CRM)
Liu–Thompkins, Y. (2026) · Oxford University Press eBooks · doi:10.1093/9780197859063.003.0019
Yang, Y., Fan, L.-W., Wang, T., & Yan, R. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107325 · Cited by 1
Moharrak, M., & Mogaji, E. (2026) · International Journal of Bank Marketing · doi:10.1108/ijbm-10-2025-0816 · Cited by 1
Achoukhi, N., Akesbi, M., & Raif, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5067-7.ch008
A SYSTEMATIC NARRATIVE REVIEW (SNR) OF ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN TOURISM
Gökçe, A., Baydeni̇z, E., & Fakir, F. Z. (2026) · International Journal of Contemporary Tourism Research · doi:10.30625/ijctr.1920048
Ha, N. T. T. (2026) · International Journal of Research and Review · doi:10.52403/ijrr.20260644
Braun, A., Graf, P., & Lejpras, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-20638-1_13
Predictive Modelling of Brand Attachment in Hyper-Personalised Marketing Using Machine Learning
Gayathri, M., Therasa, C., & Surya Balaji, D. (2026) · International Conference on AI, Data Science, Cyber Security and Smart Manufacturing for Sustainable Development (ICADCS) · doi:10.1109/icadcs70036.2026.11583576
From Transactions to Transformation
Limbad, R., Agarwal, S., Tatia, A., et al. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0116-5.ch005
Elsantil, Y. G. (2026) · Corporate and Business Strategy Review · doi:10.22495/cbsrv7i3art11 · Cited by 1
Forgiving the Algorithm? Consumer Responses to AI- vs. Human-Created Misrepresentative Ads
Bhoumik, K. (2026) · Journal of Advertising Research · doi:10.1080/00218499.2026.2677339
Meng, T., Li, X., Cao, J., & Zou, X. (2026) · Marketing Intelligence & Planning · doi:10.1108/mip-01-2025-0048
Artificial Intelligence and the Creator Economy: Conceptualization and Research Propositions
Rangaswamy, P., & Sivakumar, K. (2026) · Customer Needs and Solutions · doi:10.1007/s40547-026-00167-y · Cited by 1
The personalization paradox in AI-driven social media advertising: a tripartite framework
Haq, I. U., Mazhar, B., Tareen, H. K., Maqsood, F., & Welhenage, U. A. P. (2026) · International Journal of Advertising · doi:10.1080/02650487.2026.2704450 · Cited by 1
Wang, X., Alves, R. R., Hu, Q., & Chen, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-63139-1
Zaman, H. M. F., Xu, Y., Nguyen, T. T. N., Pasha, M. A., & Lan, A. N. T. (2026) · Marketing and Management of Innovations · doi:10.21272/mmi.2026.2-02
Murty, A. K., & Mohan, A. (2026) · Cureus Journal of Business and Economics. · doi:10.7759/s44404-026-00192-z
“Recent research highlights that personalized marketing is increasingly driven by AI and predictive analytics, enabling firms to deliver hyper-personalized experiences tailored to individual consumer behavior (Kumar et al., 2019) (Davenport et al., 2020) (Huang and Rust, 2021).”
Artificial Intelligence as an Enabler of Integrated Brand Communication
Islam, W., Rayun, S. M. N., Chowdhury, S. A., Mia, M. R., & Raji, I. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9325-4.ch008
Wang, J., Fu, Z., Ren, L., & Hou, P. (2026) · Journal of Quality Assurance in Hospitality & Tourism · doi:10.1080/1528008x.2026.2711988
Personalized Up- and Cross-Selling with AI
Altendorf, M., & Gollmer, I. (2026) · Springer eBooks · doi:10.1007/978-3-658-51109-8_20
Neuro-AI in Retail: Leveraging Cognitive Computing to Decode Consumer Attention and Emotion
Mouammine, Y. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-11411-2_31
AI for Pricing Decisions: A Two-Level Taxonomy of Applications
Nardi, L., & Mauro, A. D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23684-5_47
Boukerch, Y., CHERNANE, K., & FETTIH, N. (2026) · Modern Management Review · doi:10.7862/rz.2026.mmr.07
“…emphasizing the complementary roles of predictive analytics, AI-driven automation, and personalized engagement in enhancing customer interactions and experiential outcomes (Kumar et al., 2019; Egorenkov, 2022; Huseynov, Improving customer experience with phone carriers… 19 2023; Tung, Lan, 2024).”
A Bayesian Network Framework for AI-Driven Consumer–Organization Relationship Management
Ennouri, W. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0497-5.ch009
The Use of AI in Luxury Fashion
Rizzo, C., & Troise, C. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83742-288-320261002
Siyu, D., Al-haimi, B., & Juyan, L. (2026) · Springer proceedings in business and economics · doi:10.1007/978-3-032-29346-6_23
Turan, H. (2026) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2026.105050
The Intersection of Relationship Marketing and Artificial Intelligence
Santos, J. D., & Pires, P. B. (2026) · Contributions to management science · doi:10.1007/978-3-032-22360-9_6
Goetz, M., & Faßmann, M. (2026) · Springer eBooks · doi:10.1007/978-3-658-51525-6_89
Ergin, E. A., Yalçın, G. C., Kara, K., et al. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10642-2
ڕەهەندە ئابوورییەکانی بەکارهێنانی ژیریی دەستکرد لە گۆڕینی شێوازی بەرهەمهێنانی ڕێکلامی بازرگانییدا
Karim, A. J. H., & Kakashekh, H. M. (2026) · Cihan University-Erbil Journal of Humanities and Social Sciences · doi:10.24086/cuejhss.v10n2y2026.pp67-76
Nnabuife, I., Obembe, D., Giwa, S. R., et al. (2026) · International journal of organizational analysis · doi:10.1108/ijoa-12-2025-6271
Bhati, S., & Singh, N. (2026) · Asia-Pacific Journal of Management Research and Innovation · doi:10.1177/2319510x261476112
“Kumar et al. (2019) argue that AI-driven personalisation, operating at the individual level in real time, offers substantially greater value than traditional segment-level targeting on conversion and lifetime value metrics.”
Ethical Considerations and Transparency in Intelligent Agent Operations
Beshiri, A., Bytyqi, D., & Misini, S. (2026) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc71566.2026.11650040
“These systems can automate repetitive tasks and complex cognitive workflows, reduce response times, increase service availability, and optimize large-scale systems [29], [30].”
Rethinking Marketing Management Education in the Age of Artificial Intelligence
Jaiswal, A., Naik, M. K. P., & Nadarajah, D. (2026) · Springer eBooks · doi:10.1007/978-3-031-75316-9_149-1
Jha, S., Basu, S., & Patnaik, S. (2026) · International Marketing Review · doi:10.1108/imr-03-2025-0154
Dark Side of AI in online shopping: does algorithmic targeting increase unsustainable consumption
Cao, L. T., Tran, T. T. B., Bui, K. T., Luu, T. T., & Nguyen, L. T. (2026) · Sustainable Futures · doi:10.1016/j.sftr.2026.102123 · Cited by 1
Obi-Okoye, S. O., & Nofal, R. (2026) · Sustainability · doi:10.3390/su18178925
Thai, T. C. (2026) · Marketing Education Review · doi:10.1080/10528008.2026.2729464
Wilson, G., Bennett, C., & Turner, J. (2024) · Journal of Digital Business and Management Studies · doi:10.68159/n471038146
ІНТЕЛЕКТУАЛЬНА ТРАНСФОРМАЦІЯ CRM-СИСТЕМ ЯК СТРАТЕГІЧНИЙ ІМПЕРАТИВ МАРКЕТИНГОВОГО МЕНЕДЖМЕНТУ
Vovchanska, O., Іванова, Л., & Buk, L. (2026) · Підприємництво та інновації · doi:10.32782/2415-3583/41.2
Al-Harbi, R., Al-Qahtani, O., & Nasser, S. (2025) · Journal of Digital Business and Management Studies · doi:10.68159/o255962329
Beyond Traditional Sales Training: An AI Driven Experiential Learning Framework
Singh, P. P., Saran, A., Khan, I., & Kalliny, M. (2026) · Marketing Education Review · doi:10.1080/10528008.2026.2729466
Wahab, A., Aqif, T., & Azmat, M. (2026) · Journal of Contemporary Marketing Science · doi:10.1108/jcmars-05-2025-0022
Managerial Motivations for AI Adoption in Hotels
Roget, F. M., Mourão, L. P., & Rodríguez, X. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9700-9.ch015
Oboko, W. (2026) · British Journal of Management and Marketing Studies · doi:10.52589/bjmms-lzkqlioa
The Future of AI-Powered Marketing: Exploring the Role of Chat GPT in Modern Marketing
Devi, M. P. S. A. D. P. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-38411
Consumer vulnerability in artificial intelligence-driven marketing: A paradox theory lens
Burgess, A. J., & Joubert, A. M. (2026) · Journal of Business Research · doi:10.1016/j.jbusres.2026.116559
Nadar, D. R., & Bhuvaneswari, G. (2026) · Technology and Innovation Conference (TIC) · doi:10.1109/tic68483.2026.11702827
Tintabura, C., & Poltanee, I. (2026) · Journal of Business Innovation and Sustainability (JBIS) · doi:10.71185/jbis.2026.283077
“OCS strongly drives CEB ( = 0.706, p 0.001), and CEB is the primary determinant of HBST ( = 0.776, p 0.001), consistent with Customer Engagement theory (Kumar, et al., 2019, p.135).”
InfluentialDual-Channel Brand Salience in AI-Curated Markets
Jomon Jose, M., & Mathews, J. R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9589-0.ch009
Abbas, M., Arnida, A., Younus, M., & Meiyani, E. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1962501
Simplicity over sophistication? Rule-based chatbots lead to higher return rates than LLMs in retail
Oliveira, A. P. V., Silva, S. C. L. D. C. E., & de Sousa, N. (2026) · The International Review of Retail Distribution and Consumer Research · doi:10.1080/09593969.2026.2740584 · Cited by 1
Subagja, A. S., & Shanti, M. (2026) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss71466.2026.11715034
“Hyper-personalization is a process that occurs when the creator decides, usually based on previously collected customer data and algorithm through AI-driven deep-learning that decides which product is suitable for the customer[2].”
Metadata
- Authors
- Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J.
- Affiliations
- Georgia State University (US)
- University of Virginia (US)
- Northwestern University (US)
- Venue
- California Management Review
- Publisher
- SAGE Publications
- Published
- 2019-07-04
- Volume
- 61
- Issue
- 4
- Pages
- 135-155
- ISSN
- 0008-1256 (ISSN-L)
- 2162-8564
- OpenAlex
- W2953773016
- Cited by
- 797
- References
- 0
- Citations by year
- 2026: 136 · 2025: 154 · 2024: 208 · 2023: 111 · 2022: 76 · 2021: 73 · 2020: 33 · 2019: 4
- FWCI
- 21.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- California Management Review (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Keywords
- artificial intelligence, personalized marketing, customer engagement, personalization, customer management, branding, consumer behavior, information curation, AI-driven marketing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review