Iranian journal of management sciences

Iranian journal of management sciences

A systematic review of the role of personalized marketing in medical tourism

Document Type : Research Paper

Authors
mina khazaei aliabad 1
seyed saeed Hashemi 2
Mehdi basouli 2
1 Ph.D. Student, Department of Tourism, University of Science and Culture, Tehran, Iran
2 Associate Professor, Department of Tourism, University of Science and Culture, Tehran, Iran
100/jiams.2026.9161.7976
Abstract
Introduction: Medical tourism has emerged as one of the fastest-growing sectors in the global tourism industry, driven by factors such as rising healthcare costs in developed countries, advancements in medical technologies, and the proliferation of high-quality treatment centers worldwide. According to Grand View Research, the market is projected to exceed $208 billion by 2027, underscoring the need for innovative marketing strategies amid intensifying competition. Personalized marketing, leveraging data and digital technologies like AI, big data, and recommender systems, plays a pivotal role in attracting international patients and enhancing their satisfaction by tailoring services to individual needs. Despite substantial research growth, the literature remains fragmented, geographically limited, and lacks systematic analysis of personalization's evolution from traditional to data-driven models. This study conducts a qualitative systematic review (2005–2025) to identify trends, concepts, and gaps, proposing a conceptual framework integrating smart technologies, patient behavior, strategies, and ethics. Key research questions address temporal/geographical trends, theoretical models, thematic areas, technology's role, gaps, and a global personalization framework in medical tourism. Methods: This study adopted a qualitative systematic review design guided by the PRISMA framework to ensure methodological transparency and replicability. The review process was further supported by the Nested Knowledge platform to facilitate structured synthesis and data management. A comprehensive search was conducted in the Scopus database covering the period from 2005 to 2025. The search strategy incorporated combinations of keywords such as “personalized marketing,” “medical tourism,” “artificial intelligence,” and “recommender systems.” The initial search yielded 2660 records. After applying predefined inclusion and exclusion criteria, including limiting the selection to peer-reviewed journal articles published in English and ensuring direct relevance to personalization within the context of medical tourism, 273 empirical studies were retained for final analysis. Non-empirical publications, conference abstracts, and studies lacking substantive relevance were excluded. Data extraction and classification were structured using the PICO framework: Population (digital users and international patients), Intervention (personalized strategies and intelligent technologies), Comparison (traditional versus technology-enabled marketing approaches), and Outcome (patient satisfaction, loyalty, and related performance indicators). The analytical process involved thematic coding, chronological trend mapping, and systematic gap identification. This multi-step procedure enhanced analytical rigor and ensured the transparency and reproducibility of the review process. Finding: The temporal analysis indicates that more than 80% of the identified studies were published after 2020, reflecting the rapid maturation and widespread adoption of artificial intelligence technologies. Geographically, the body of research is predominantly concentrated in developed countries,particularly the United States and European nations,while contributions from emerging economies remain limited. Thematic clustering of the literature reveals three dominant streams: (1) digital consumer behavior, including the use of predictive algorithms; (2) technological applications, particularly AI-driven personalization and recommender systems; and (3) outcome-oriented studies focusing on enhanced patient satisfaction and loyalty. Prominent conceptual and analytical models include predictive marketing based on machine learning and AI-enabled content personalization. Despite these advancements, several critical gaps persist. Empirical validation of proposed models remains insufficient, and limited attention has been paid to ethical considerations, data privacy concerns, and institutional-level applications within the medical tourism sector. Although technologies such as artificial intelligence and big data analytics facilitate behavioral prediction and targeted advertising, underexplored areas include the protection of international patient data and cross-cultural ethical implications. Conclusions: This review highlights the uneven growth of personalized marketing research in medical tourism, emphasizing technology's potential while identifying gaps in empirical evidence, geographical diversity, and ethical frameworks. The proposed conceptual model links smart technologies, patient preferences, marketing strategies, and ethics to outcomes like loyalty and experience. Future research should prioritize field studies, emerging markets, and policy development for data privacy. Ultimately, balanced adoption of personalization can sustain medical tourism's competitiveness.

Highlights

Kyanko, L., Stube, J., & Timm, D. (2021). Balancing the benefits and risks of medical tourism: a scoping review. Journal of Travel Medicine, 28(2), taaa194. https://doi.org/10.1093/jtm/taaa194

Lim, W. M., Rasul, T., Kumar, S., & Ala, M. (2022). Past, present, and future of customer engagement. Journal of Business Research, 140,439–458. https://doi.org/10.1016/j.jbusres.2021.11.014

Mattoo A, Rathindran R. (2006). How health insurance inhibits trade in health care. Health Affairs, 25(2):358–68. http://dx.doi.org/10.1377/hlthaff.25.2.358

Mischin, J., Deirmentzoglou, G. A., Daskou, S., & Vlassi, E. (2025, May). Artificial intelligence-powered tools for personalized product recommendations in e-commerce: The role of user satisfaction on online purchase decisions. In International Conference Economies of the Balkan and Eastern European Countries (pp. 235–247). Cham, Switzerland: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-90054-9_15

Milstein, A., & Smith, M. (2006). America's new refugees—seeking affordable surgery offshore. New England Journal of Medicine, 355(16), 1637-1640. https://doi.org/10.1056/nejmp068190

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of marketing, 58(3), 20-38. https://doi.org/10.1177/002224299405800302

Okorie, G. N., Egieya, Z. E., Ikwue, U., Udeh, C. A., Adaga, E. M., DaraOjimba, O. D., & Oriekhoe, O. I. (2024). Leveraging big data for personalized marketing campaigns: a review. International Journal of Management & Entrepreneurship Research, 6(1), 216-242. https://doi.org/10.51594/ijmer.v6i1.778 

Paffhausen, A. L., Peguero, C., & Roche-Villarreal, L. (2010). Medical tourism: a survey. United Nations Economic Commission for Latin America and the Caribbean.

Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. (2014). Machine learning for targeted display advertising: Transfer learning in action. Machine learning, 95(1), 103-127. https://doi.org/10.1007/s10994-013-5375-2

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Pessot, E., Spoladore, D., Zangiacomi, A., & Sacco, M. (2021). Natural resources in health tourism: a systematic literature review. Sustainability, 13(5), 2661. https://doi.org/10.3390/su13052661

Plan and Budget Organization of the Islamic Republic of Iran. (2024). Laws of the Economic, Social and Cultural Development Plans of the Islamic Republic of Iran (First to Seventh Development Plans). Tehran, Iran: Plan and Budget Organization of the Islamic Republic of Iran. (In Persian).

Polk, J., Tassin, C., & McNellis, J. (2020). Magic Quadrant for Personalization Engines. Gartner Report Reprint, 13.

Ridderstaat, J., Singh, D., & DeMicco, F. (2019). The impact of major tourist markets on health tourism spending in the United States. Journal of Destination Marketing & Management, 11, 270-280.  https://doi.org/10.1016/j.jdmm.2018.05.003 

Rosário, A. T., Oliveira, F., & Ferreira, J. J. P. (2025). The integration of AI and IoT in marketing: A systematic literature review of research themes and future directions. Electronics, 14(9), 1854. https://doi.org/10.20944/preprints202503.2310.v1

Rust, R. T., & Huang, M. H. (2021). The feeling economy. In The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy (pp. 41-61). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-52977-2_4

Semwal, R., Tripathi, N., Rana, A., Dafouti, B. S., Bairwa, M. K., & Mathur, V. (2024, May). AI-powered personalization and emotional intelligence integration for enhanced service marketing in transformative tourism experiences. In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 1851-1856). IEEE. https://doi.org/10.1109/IC3SE62002.2024.10592909

Shojaei, P., Haqbin, A., & Saber, M. (2024). Identifying and analysing Iran medical tourism development barriers using fuzzy cognitive mapping. Journal of Health Management, 26(4), 611-623. https://doi.org/10.1177/09720634231216065

Smith, M., & Puczkó, L. (2014). Health, tourism and hospitality: Spas, wellness and medical travel. Routledge. https://dx.doi.org/10.4324/9780203083772

Smith, B., & Linden, G. (2017). Two decades of recommender systems at Amazon. com. Ieee internet computing, 21(3), 12-18.   https://doi.org/10.1109/MIC.2017.72  

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Suess, C., Baloglu, S., & Busser, J. A. (2018). Perceived impacts of medical tourism development on community wellbeing. Tourism Management, 69, 232-245.   https://doi.org/10.1016/j.tourman.2018.06.006

Suprenant, C. F., & Solomon, M. R. (1987). Predictability and personalization in the service encounter. Journal of Marketing, 51(2),86–96.   https://doi.org/10.1177/002224298705100207

Tedja, B., Al Musadieq, M., Kusumawati, A., & Yulianto, E. (2024). Systematic literature review using PRISMA: exploring the influence of service quality and perceived value on satisfaction and intention to continue relationship. Future Business Journal, 10, 39. https://doi.org/10.1186/s43093-024-00326-4

Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of marketing research, 51(5), 546-562. https://doi.org/10.1509/jmr.10.0355

Türk, E. Ş., & Arı, E. (2024). Health tourism as a competitive strategy. Health Policy, 128(2), 204-211. https://doi.org/10.1016/j.healthpol.2023.12.006

Vargo, S. L., & Lusch, R. F. (2014). Evolving to a new dominant logic for marketing. In The service-dominant logic of marketing (pp. 3-28). Routledge.

Xu, Y., Shieh, C. H., van Esch, P., & Ling, I. L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian marketing journal, 28(4), 189-199. https://doi.org/10.1016/j.ausmj.2020.03.005

Yang, N. (2025). Construction of personalized marketing model for e-commerce services based on big data. In Proceedings of the 2nd Guangdong–Hong Kong–Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence (pp. 1623–1628). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3745238.3745493

Zhao, B., & Hu, Y. (2025). Construction of e-commerce personalized marketing model under the framework of big data technology. In Proceedings of the 2025 International Conference on Digital Economy and Information Systems (pp. 23–28). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3745133.3745139

Zhu, H. (2025). Research on Precision Marketing Strategy and Customer Relationship Management Driven by Artificial Intelligence. https://dx.doi.org/10.25236/icamfss.2024.042 

 

Keywords
Personalized marketing
medical tourism
artificial intelligence
. machine learning
systematic review
Kyanko, L., Stube, J., & Timm, D. (2021). Balancing the benefits and risks of medical tourism: a scoping review. Journal of Travel Medicine, 28(2), taaa194. https://doi.org/10.1093/jtm/taaa194
Lim, W. M., Rasul, T., Kumar, S., & Ala, M. (2022). Past, present, and future of customer engagement. Journal of Business Research, 140,439–458. https://doi.org/10.1016/j.jbusres.2021.11.014
Mattoo A, Rathindran R. (2006). How health insurance inhibits trade in health care. Health Affairs, 25(2):358–68. http://dx.doi.org/10.1377/hlthaff.25.2.358
Mischin, J., Deirmentzoglou, G. A., Daskou, S., & Vlassi, E. (2025, May). Artificial intelligence-powered tools for personalized product recommendations in e-commerce: The role of user satisfaction on online purchase decisions. In International Conference Economies of the Balkan and Eastern European Countries (pp. 235–247). Cham, Switzerland: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-90054-9_15
Milstein, A., & Smith, M. (2006). America's new refugees—seeking affordable surgery offshore. New England Journal of Medicine, 355(16), 1637-1640. https://doi.org/10.1056/nejmp068190
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of marketing, 58(3), 20-38. https://doi.org/10.1177/002224299405800302
Okorie, G. N., Egieya, Z. E., Ikwue, U., Udeh, C. A., Adaga, E. M., DaraOjimba, O. D., & Oriekhoe, O. I. (2024). Leveraging big data for personalized marketing campaigns: a review. International Journal of Management & Entrepreneurship Research, 6(1), 216-242. https://doi.org/10.51594/ijmer.v6i1.778 
Paffhausen, A. L., Peguero, C., & Roche-Villarreal, L. (2010). Medical tourism: a survey. United Nations Economic Commission for Latin America and the Caribbean.
Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. (2014). Machine learning for targeted display advertising: Transfer learning in action. Machine learning, 95(1), 103-127. https://doi.org/10.1007/s10994-013-5375-2
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pessot, E., Spoladore, D., Zangiacomi, A., & Sacco, M. (2021). Natural resources in health tourism: a systematic literature review. Sustainability, 13(5), 2661. https://doi.org/10.3390/su13052661
Plan and Budget Organization of the Islamic Republic of Iran. (2024). Laws of the Economic, Social and Cultural Development Plans of the Islamic Republic of Iran (First to Seventh Development Plans). Tehran, Iran: Plan and Budget Organization of the Islamic Republic of Iran. (In Persian).
Polk, J., Tassin, C., & McNellis, J. (2020). Magic Quadrant for Personalization Engines. Gartner Report Reprint, 13.
Ridderstaat, J., Singh, D., & DeMicco, F. (2019). The impact of major tourist markets on health tourism spending in the United States. Journal of Destination Marketing & Management, 11, 270-280.  https://doi.org/10.1016/j.jdmm.2018.05.003 
Rosário, A. T., Oliveira, F., & Ferreira, J. J. P. (2025). The integration of AI and IoT in marketing: A systematic literature review of research themes and future directions. Electronics, 14(9), 1854. https://doi.org/10.20944/preprints202503.2310.v1
Rust, R. T., & Huang, M. H. (2021). The feeling economy. In The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy (pp. 41-61). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-52977-2_4
Semwal, R., Tripathi, N., Rana, A., Dafouti, B. S., Bairwa, M. K., & Mathur, V. (2024, May). AI-powered personalization and emotional intelligence integration for enhanced service marketing in transformative tourism experiences. In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 1851-1856). IEEE. https://doi.org/10.1109/IC3SE62002.2024.10592909
Shojaei, P., Haqbin, A., & Saber, M. (2024). Identifying and analysing Iran medical tourism development barriers using fuzzy cognitive mapping. Journal of Health Management, 26(4), 611-623. https://doi.org/10.1177/09720634231216065
Smith, M., & Puczkó, L. (2014). Health, tourism and hospitality: Spas, wellness and medical travel. Routledge. https://dx.doi.org/10.4324/9780203083772
Smith, B., & Linden, G. (2017). Two decades of recommender systems at Amazon. com. Ieee internet computing, 21(3), 12-18.   https://doi.org/10.1109/MIC.2017.72  
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Suess, C., Baloglu, S., & Busser, J. A. (2018). Perceived impacts of medical tourism development on community wellbeing. Tourism Management, 69, 232-245.   https://doi.org/10.1016/j.tourman.2018.06.006
Suprenant, C. F., & Solomon, M. R. (1987). Predictability and personalization in the service encounter. Journal of Marketing, 51(2),86–96.   https://doi.org/10.1177/002224298705100207
Tedja, B., Al Musadieq, M., Kusumawati, A., & Yulianto, E. (2024). Systematic literature review using PRISMA: exploring the influence of service quality and perceived value on satisfaction and intention to continue relationship. Future Business Journal, 10, 39. https://doi.org/10.1186/s43093-024-00326-4
Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of marketing research, 51(5), 546-562. https://doi.org/10.1509/jmr.10.0355
Türk, E. Ş., & Arı, E. (2024). Health tourism as a competitive strategy. Health Policy, 128(2), 204-211. https://doi.org/10.1016/j.healthpol.2023.12.006
Vargo, S. L., & Lusch, R. F. (2014). Evolving to a new dominant logic for marketing. In The service-dominant logic of marketing (pp. 3-28). Routledge.
Xu, Y., Shieh, C. H., van Esch, P., & Ling, I. L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian marketing journal, 28(4), 189-199. https://doi.org/10.1016/j.ausmj.2020.03.005
Yang, N. (2025). Construction of personalized marketing model for e-commerce services based on big data. In Proceedings of the 2nd Guangdong–Hong Kong–Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence (pp. 1623–1628). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3745238.3745493
Zhao, B., & Hu, Y. (2025). Construction of e-commerce personalized marketing model under the framework of big data technology. In Proceedings of the 2025 International Conference on Digital Economy and Information Systems (pp. 23–28). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3745133.3745139
Zhu, H. (2025). Research on Precision Marketing Strategy and Customer Relationship Management Driven by Artificial Intelligence. https://dx.doi.org/10.25236/icamfss.2024.042