CoChat
Sign Up
Cited by 53Digital Marketing and Social MediaAI in Service InteractionsMarketing and Advertising Strategies

The Role of Artificial Intelligence in Personalizing Social Media Marketing Strategies for Enhanced Customer Experience

Open access
Beyari, Hasan, Hashem, Tareq·2025·Behavioral Sciences
DOI 10.3390/bs15050700PMC PMC12109579
Open this paper in CoChat — free accountOpen PDF

Abstract

This paper explores the role of artificial intelligence (AI) in personalizing social media marketing strategies and its impact on customer experience, with a focus on consumers within the MENA region. Using data collected from an online questionnaire completed by 893 individuals, the study confirms that AI significantly enhances social media marketing by offering personalized content, optimizing influencer selection, and enabling real-time consumer interaction. These capabilities not only increase customer awareness but also improve user experience and purchase intentions. Key AI tools such as influencer marketing, content optimization, and customization are effective in capturing consumer attention, although further research is necessary to deepen understanding. By examining AI’s ability to analyze vast datasets and support targeted marketing efforts, the study contributes to both academic and practical discourse, offering insights that businesses can use to refine their AI-driven social media strategies. Ultimately, the research aims to guide marketers through the complexities of AI deployment, ensuring its benefits are fully realized for consumers.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/bs15050700

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In social marketing, AI can enable more effective behavior-change interventions tailored to each public's cultural particularities.

    “Aplicado al terreno del marketing social, este potencial se traduce en la posibilidad de diseñar intervenciones de cambio de comportamiento más eficaces y ajustadas a las particularidades culturales de cada público (Evans et al., 2024;Beyari & Hashem, 2025).”

    From El Reto del Marketing Social ante la Inteligencia Artificial: un análisis comparado en distintos contextos geográficos y sistemas políticos (2026) · cites this paper as Beyari & Hashem, 2025

    The Literature Review (§2.3; page unavailable) says AI enables personalized marketing messages for diverse consumer groups, supporting the tailoring aspect. It does not address social-marketing behavior-change interventions. The Conclusions (§8; page unavailable) identify differences across cultures as a topic for future research, rather than establishing that AI can tailor interventions to each public’s cultural particularities. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 34

  1. Aaker D. A. The value of brand equity Journal of Business Strategy 1992 13 4 27 32 10.1108/eb039503 · DOI
  2. Aghdaie S. F. A. Kazemi A. Mahdavinia S. H. The influence of psychological factors on customers information search behaviour International Journal of Productivity and Quality Management 2021 33 3 420 434 10.1504/IJPQM.2021.116953 · DOI
  3. Ahmad M. F. The impact of big data processing framework for artificial intelligence within corporate marketing communication International Journal of Engineering & Technology 2018 7 4 384 388
  4. Arachchi H. D. M. Samarasinghe G. D. Impact of embedded AI mobile smart speech recognition on consumer attitudes towards AI and purchase intention across Generations X and Y European Journal of Management Studies 2024 29 1 3 29 10.1108/EJMS-03-2023-0019 · DOI
  5. Babashahi L. Barbosa C. E. Lima Y. Lyra A. Salazar H. Argôlo M. Souza J. M. D. AI in the workplace: A systematic review of skill transformation in the industry Administrative Sciences 2024 14 6 127 10.3390/admsci14060127 · DOI
  6. Black W. Babin B. J. Multivariate data analysis: Its approach, evolution, and impact The great facilitator: Reflections on the contributions of Joseph F. Hair, Jr. to marketing and business research Springer 2019 121 130
  7. Brill T. M. Munoz L. Miller R. J. Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial intelligence applications The role of smart technologies in decision making Routledge 2022 35 70
  8. Chavez L. Ruiz C. Curras R. Hernandez B. The role of travel motivations and social media use in consumer interactive behaviour: A uses and gratifications perspective Sustainability 2020 12 21 8789 10.3390/su12218789 · DOI
  9. Deryl M. D. Verma S. Srivastava V. How does AI drive branding? Towards an integrated theoretical framework for AI-driven branding International Journal of Information Management Data Insights 2023 3 2 100205 10.1016/j.jjimei.2023.100205 · DOI
  10. Dhavaleshwar S. B. Innovative marketing in the digital age: The power of AI, personalization, and social media Educational Administration: Theory and Practice 2024 30 1 3677 3684 10.53555/kuey.v30i1.7415 · DOI
  11. Ebrahimi P. Hamza K. A. Gorgenyi-Hegyes E. Zarea H. Fekete-Farkas M. Consumer knowledge sharing behavior and consumer purchase behavior: Evidence from E-commerce and online retail in Hungary Sustainability 2021 13 18 10375 10.3390/su131810375 · DOI
  12. Fayyaz Z. Ebrahimian M. Nawara D. Ibrahim A. Kashef R. Recommendation systems: Algorithms, challenges, metrics, and business opportunities Applied Sciences 2020 10 21 7748 10.3390/app10217748 · DOI
  13. Gerea C. Gonzalez-Lopez F. Herskovic V. Omnichannel customer experience and management: An integrative review and research agenda Sustainability 2021 13 5 2824 10.3390/su13052824 · DOI
  14. Homburg C. Jozić D. Kuehnl C. Customer experience management: Toward implementing an evolving marketing concept Journal of the Academy of Marketing Science 2017 45 3 377 401 10.1007/s11747-015-0460-7 · DOI
  15. Jebb A. T. Ng V. Tay L. A review of key Likert scale development advances: 1995–2019 Frontiers in Psychology 2021 12 1 637547 10.3389/fpsyg.2021.637547 34017283 PMC8129175 · DOI
  16. Katz E. Foulkes D. On the use of the mass media as “escape”: Clarification of a concept Public Opinion Quarterly 1962 26 3 377 388 10.1086/267111 · DOI · Paper page
  17. Kim Y. J. Kim H. S. The impact of hotel customer experience on customer satisfaction through online reviews Sustainability 2022 14 2 848 10.3390/su14020848 · DOI
  18. Kumar A. Bapat G. Kumar A. Hota S. L. Abishek G. D. Vaz S. Unlocking brand excellence: Harnessing AI tools for enhanced customer engagement and innovation Engineering Proceedings 2024 59 1 204
  19. Liu R. Gupta S. Patel P. The application of the principles of responsible AI on social media marketing for digital health Information Systems Frontiers 2023 25 6 2275 2299 10.1007/s10796-021-10191-z 34539226 PMC8435400 · DOI
  20. Mari A. Artificial Intelligence-based Voice Assistants as Social Media Platform: Anticipated Effect on Brands during Shopping Journal of Creative Industries and Cultural Studies: JOCIS 2020 6 1 56 71 10.56140/JOCIS-v6-3 · DOI
  21. Matosas-López L. Romero-Ania A. How to improve customer engagement in social networks: A study of Spanish brands in the automotive industry Journal of Theoretical and Applied Electronic Commerce Research 2021 16 7 3269 3281 10.3390/jtaer16070177 · DOI
  22. Morrison D. G. Purchase intentions and purchase behavior Journal of Marketing 1979 43 2 65 74 10.1177/002224297904300207 · DOI
  23. Na M. Rong L. Ali M. H. Alam S. S. Masukujjaman M. Ali K. A. M. The mediating role of brand trust and brand love between brand experience and loyalty: A study on smartphones in China Behavioral Sciences 2023 13 6 502 10.3390/bs13060502 37366754 PMC10294934 · DOI
  24. Palmer A. Customer experience management: A critical review of an emerging idea Journal of Services Marketing 2010 24 3 196 208 10.1108/08876041011040604 · DOI
  25. Saurwein F. Spencer-Smith C. Automated trouble: The role of algorithmic selection in harms on social media platforms Media and Communication 2021 9 4 222 233 10.17645/mac.v9i4.4062 · DOI · Paper page
  26. Sekaran U. Research methods for business: A skill building approach John Wiley & Sons 2016
  27. Sürücü L. Maslakci A. Validity and reliability in quantitative research Business & Management Studies: An International Journal 2020 8 3 2694 2726
  28. Tulcanaza-Prieto A. B. Cortez-Ordoñez A. Lee C. W. Influence of customer perception factors on AI-enabled customer experience in the ecuadorian banking environment Sustainability 2023 15 16 12441 10.3390/su151612441 · DOI
  29. Wang R. A study on the impact of perceived value of art based on artificial intelligence on consumers’ purchase intention Journal of The Korea Society of Computer and Information 2021 26 1 275 281
  30. Wibowo A. Chen S. C. Wiangin U. Ma Y. Ruangkanjanases A. Customer behavior as an outcome of social media marketing: The role of social media marketing activity and customer experience Sustainability 2020 13 1 189 10.3390/su13010189 · DOI
  31. Yang Q. Lee Y. C. What drives the digital customer experience and customer loyalty in mobile short-form video shopping? Evidence from douyin (TikTok) Sustainability 2022 14 17 10890 10.3390/su141710890 · DOI
  32. Yin J. Qiu X. AI technology and online purchase intention: Structural equation model based on perceived value Sustainability 2021 13 10 5671 10.3390/su13105671 · DOI
  33. Yoo S. R. Lee S. W. Jeon H. M. The role of customer experience, food healthiness, and value for revisit intention in GROCERANT Sustainability 2020 12 6 2359 10.3390/su12062359 · DOI
  34. Ziakis C. Vlachopoulou M. Artificial intelligence in digital marketing: Insights from a comprehensive review Information 2023 14 12 664 10.3390/info14120664 · DOI

Cited by

Sorted by citations

Authors
Beyari, H., & Hashem, T.
Affiliations
  • Umm al-Qura University (SA)
  • Applied Science Private University (JO)
Corresponding author
Beyari, Hasan
ORCID
Venue
Behavioral Sciences
Publisher
MDPI AG
Published
2025-05-19
Volume
15
Issue
5
Pages
700
ISSN
2076-328X (ISSN-L)
DOI
10.3390/bs15050700
PMC
PMC12109579
OpenAlex
W4410517772
Cited by
53
References
3
Citations by year
2026: 39 · 2025: 14
FWCI
91.68
Type
journal article
Language
en
Open access
Licence
Locations
  • Behavioral Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
Keywords
artificial intelligence, social media marketing, personalization, customer experience, influencer marketing, content optimization, purchase intention, customer awareness

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