Behavioral Drivers of First-Time Blood Donor Retention in Yogyakarta, Indonesia

  • Mohammad Adam Jerusalem Bachelor Program of Industrial Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
  • Kartika Ratna Pertiwi Faculty of Medicine, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
  • Agung Wijaya Subiantoro Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia, Indonesia
  • Ummi Fakhriyah Jayatri Bachelor Program of Industrial Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
  • Dyantika Putry Mahmud Bachelor Program of Industrial Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
DOI: https://doi.org/10.56338/mppki.v9i5.9414
Keywords: Blood Supply Chain, Donor Retention, Clustering Analysis, Blood Donation Behaviour

Abstract

ntroduction: Voluntary blood donor retention is still challenging in blood stock issue, specifically in decentralization system like in Indonesia. Most of previous research has focused on logistic or survey-based study, hence it cannot explain the dynamics of actual donor behavior from time to time. Further, there is a lack of understanding of donor retention as a behavioral process that develops longitudinally. This research aims to identify the first-time donor retention pattern and also demographic characteristics and relevant service context as basis for developing more effective health promotion strategies.

Methods: This research implemented a retrospective longitudinal cohort design based on routine blood donor registration data of 26,170 first-time donors from five Blood Transfusion Units in the Province of Special Region of Yogyakarta during the period 2021–2024. Donor visit trajectories were analyzed using a sequence analysis approach with the optimal matching method and then grouped using the Partition Around Medoids algorithm. Cluster validity was determined using the silhouette and Dunn indeces and further analyzed descriptively and statistically to examine differences in characteristics among groups.

Results: The study found three main donor patterns, i.e. one-time donors, regular donors, and donors who have temporarily stopped donating. City of Yogyakarta has highest retention rate, while Gunung Kidul is dominated by donors-once. Male donors tend to dominant among regular donors, on the contrary female donors are more represented in temporarily stopped donating. The vital finding points out the first 6–12 months engagement after initial donation is strongly associated with donor behavioral intentions.

Conclusion: Blood donor retention is dynamic process that is influenced by demographic characteristics and service context. A limitation of this study is the lack of consideration of psychosocial as a variable. However, the use of actual longitudinal data is a major strength of this study. The managerial implication of this study is that segmentation-based strategies, strengthening interventions in the early phase, and gender-sensitive and community-based approaches are needed to increase donor retention in a sustainable manner.

References

World Health Organization. Towards 100% voluntary blood donation: a global framework for action. WHO. 2010

Raykar NP, Makin J, Khajanchi M, et al. Assessing the global burden of hemorrhage: The global blood supply, deficits, and potential solutions. Sage Open Med. 2021;9. https://doi.org/10.1177/20503121211054995

Fallahi A, Pourghazi A, Mokhtari H. A multi-product humanitarian supply chain network design problem: A fuzzy multi-objective and robust optimization approach. Int J Eng. 2024;37(5):941–958. https://doi.org/10.5829/ije.2024.37.05b.12

Mansur A, Vanany I, Arvitrida NI. Horizontal collaboration in a decentralised system: Indonesian blood supply chain. Supply Chain Forum Int J. 2023;24(4):334–350. https://doi.org/10.1080/16258312.2022.2161287

Loua A, Kasilo OMJ, Nikiema JB, Sougou AS, Kniazkov S, Annan EA. Impact of the COVID-19 pandemic on blood supply and demand in the WHO African region. Vox Sang. 2021;116(7):774–784. https://doi.org/10.1111/vox.13071

Gammon RR, Rosenbaum L, Cooke R, Friedman M, Rockwood L, Nichols T, et al. Maintaining adequate donations and a sustainable blood supply: Lessons learned. Transfusion. 2021;61(1):294–302. https://doi.org/10.1111/trf.16145

Stanworth SJ, New HV, Apelseth TO, Brunskill S, Cardigan R, Doree C, et al. Effects of the COVID-19 pandemic on supply and use of blood for transfusion. Lancet Haematol. 2020;7(10):e756–e764. https://doi.org/10.1016/S2352-3026(20)30186-1

Delabranche X, Kientz D, Tacquard C, Bertrand F, Roche A, Tran Ba Loc P, et al. Impact of COVID-19 and lockdown regarding blood transfusion. Transfusion. 2021;61(8):2327–2335. https://doi.org/10.1111/trf.16422

Pandey HC, Coshic P, CS C, Arcot PJ, Kumar K. Blood supply management in times of SARS-CoV-2 pandemic: Challenges, strategies adopted, and lessons learned from a hospital-based blood centre. Vox Sang. 2021;116(5):497–503. https://doi.org/10.1111/vox.13019

Vanany I, Maryani A, Amaliah B, Rinaldy F, Muhammad F. Blood traceability system for Indonesian blood supply chain. Procedia Manuf. 2015;4:535–542. https://doi.org/10.1016/j.promfg.2015.11.073

Mansur A, Vanany I, Arvitrida NI. Blood supply chain challenges: Evidence from Indonesia [Internet]. Thailand: IEOM Society; 2019 [cited 2024 Jun 23]. Available from: https://index.ieomsociety.org/index.cfm/article/view/ID/2045

Osorio AF, Brailsford SC, Smith HK. A structured review of quantitative models in the blood supply chain: A taxonomic framework for decision-making. Int J Prod Res. 2015;53(24):7191–7212. https://doi.org/10.1080/00207543.2015.1005766

Jabbarzadeh A, Fahimnia B, Seuring S. Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transp Res E Logist Transp Rev. 2014;70:225–244. https://doi.org/10.1016/j.tre.2014.06.003

Haghjoo N, Tavakkoli-Moghaddam R, Shahmoradi-Moghadam H, Rahimi Y. Reliable blood supply chain network design with facility disruption: A real-world application. Eng Appl Artif Intell. 2020;90:103493. https://doi.org/10.1016/j.engappai.2020.103493

Altunoglu B, Batur Sir GD. Multi-objective location-distribution optimization in blood supply chain: An application in Turkiye. BMC Public Health. 2024;24(1):3181. https://doi.org/10.1186/s12889-024-20647-x

Gunpinar S, Centeno G. Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Comput Oper Res. 2015;54:129–141. https://doi.org/10.1016/j.cor.2014.08.017

Dillon M, Oliveira F, Abbasi B. A two-stage stochastic programming model for inventory management in the blood supply chain. Int J Prod Econ. 2017;187:27–41. https://doi.org/10.1016/j.ijpe.2017.02.006

Shokouhifar M, Sabbaghi MM, Pilevari N. Inventory management in blood supply chain considering fuzzy supply/demand uncertainties and lateral transshipment. Transfusion and Apheresis Science. 2021;60(3):103103. https://doi.org/10.1016/j.transci.2021.103103

Romero-Domínguez L, Martín-Santana JD, Beerli-Palacio A. Segmenting Active Blood Donors According to Their Barriers to Develop Retention Programs. Transfus Med Rev. 2019;33(3):176–82. https://doi.org/10.1016/j.tmrv.2019.06.004

Germain M, Glynn SA, Schreiber GB, Gélinas S, King M, Jones M, Bethel J, Tu Y. Determinants of return behavior: a comparison of current and lapsed donors. Transfusion. 2007 Oct;47(10):1862-70. https://doi.org/10.1111/j.1537-2995.2007.01409.x

Godin G, Conner M, Sheeran P, Bélanger-Gravel A, Germain M. Determinants of repeated blood donation among new and experienced blood donors. Transfusion. 2007;47(9):1607–1615. https://doi.org/10.1111/j.1537-2995.2007.01331.x

Mauka WI, Mahande MJ, Msuya SE, Philemon RN. Factors associated with repeat blood donation at the Northern Zone Blood Transfusion Centre in Tanzania. J Blood Transfus. 2015;2015:717653. https://doi.org/10.1155/2015/717653

Schlumpf KS, Glynn SA, Schreiber GB, Wright DJ, Steele WR, Tu Y, et al. Factors influencing donor return. Transfusion. 2008;48(2):264–272. https://doi.org/10.1111/j.1537-2995.2007.01519.x

Nguyen DD, DeVita DA, Hirschler NV, Murphy EL. Blood donor satisfaction and intention of future donation. Transfusion. 2008;48(4):742–748. https://doi.org/10.1111/j.1537-2995.2007.01600.x

Ling LM, Hui TS, Tan AKG, Ling GS. Determinants of blood donation status in Malaysia: Profiling non-donors, occasional donors and regular donors. Kajian Malaysia. 2018;36(1):1–27..

Soedarmono YSM. Donor issues in Indonesia: A developing country in Southeast Asia. Biologicals. 2010;38(1):43–46. https://doi.org/10.1016/j.biologicals.2010.02.001

Alkahtani AS, Jilani M. Predicting return donor and analyzing blood donation time series using data mining techniques. Int J Adv Comput Sci Appl. 2019;10(8):1–8. https://doi.org/10.14569/IJACSA.2019.0100816

Kauten C, Gupta A, Qin X, Richey G. Predicting blood donors using machine learning techniques. Inf Syst Front. 2022;24(6):1–16. https://doi.org/10.1007/s10796-021-10149-1

Chan JSK, Wan WY, Yu PLH. A Poisson geometric process approach for predicting drop-out and committed first-time blood donors. J Appl Stat. 2014;41(7):1486–1503. https://doi.org/10.1080/02664763.2014.881781

Jagirdar H, Nwobi NH, Swanevelder R, Cockeran R, Bruhn R, Kaidarova Z, et al. Blood donor return behavior in South Africa and the United States before and during the COVID-19 pandemic. Transfusion. 2024;64(8):1492–1502. https://doi.org/10.1111/trf.17934

Gusfield D. Algorithms on strings, trees and sequences. Cambridge: Cambridge University Press; 1997. https://doi.org/10.1017/CBO9780511574931

Liao TW. Clustering of time series data: A survey. Pattern Recognit. 2005;38(11):1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025

Ghassempour S, Girosi F, Maeder A. Clustering multivariate time series using hidden Markov models. Int J Environ Res Public Health. 2014;11(3):2741–2763. https://doi.org/10.3390/ijerph110302741

Aassve A, Billari FC, Piccarreta R. Strings of adulthood: A sequence analysis of young British women’s work-family trajectories. Eur J Popul. 2007;23(3–4):369–388. https://doi.org/10.1007/s10680-007-9134-6

Gabadinho A, Ritschard G, Müller NS, Studer M. Analyzing and visualizing state sequences in R with TraMineR. J Stat Softw. 2011;40(4):1–37. https://doi.org/10.18637/jss.v040.i04

Sun R, Giles CL, editors. Sequence learning: Paradigms, algorithms, and applications. Berlin: Springer; 2001. https://doi.org/10.1007/3-540-44565-X

Wong GBK, Joseph D. Embracing a cluster perspective: Optimal matching analysis tutorial and tool demonstration. In: Proceedings of the Computers and People Research Conference. New York: ACM; 2024. p. 1–8. https://doi.org/10.1145/3632634.3655871

Dunn JC. Well-separated clusters and optimal fuzzy partitions. J Cybern. 1974;4(1):95–104. https://doi.org/10.1080/01969727408546059

Rousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7

Liu Y, Li Z, Xiong H, Gao X, Wu J. Understanding of internal clustering validation measures. In: Proceedings of the IEEE International Conference on Data Mining. IEEE; 2010. p. 911–916. https://doi.org/10.1109/ICDM.2010.35

Hubert L, Arabie P. Comparing partitions. J Classif. 1985;2(1):193–218. https://doi.org/10.1007/BF01908075

Brusco MJ, Singh R, Cradit JD, Steinley D. Cluster analysis in empirical OM research: Survey and recommendations. Int J Oper Prod Manag. 2017;37(3):300–320. https://doi.org/10.1108/IJOPM-08-2015-0493

Boenigk S, Leipnitz S. Acquiring potential blood donors in large cities: A preference-based donor segmentation study. J Nonprofit Public Sect Mark. 2016;28(4):364–393. https://doi.org/10.1080/10495142.2016.1238330

Martín-Santana JD, Beerli-Palacio A, Romero-Domínguez L. Recruitment strategies: Non-donor segmentation based on intrinsic and extrinsic stimuli. Vox Sang. 2020;115(1):47–59. https://doi.org/10.1111/vox.12858

Jansen P, Sümnig A, Esefeld M, Greffin K, Kaderali L, Greinacher A. Well-being and return rate of first-time whole blood donors. Vox Sang. 2019;114(2):154–161. https://doi.org/10.1111/vox.12752

Schreiber GB, Sharma UK, Wright DJ, Glynn SA, Ownby HE, Tu Y, et al. First-year donation patterns predict long-term commitment for first-time donors. Vox Sang. 2005;88(2):114–121. https://doi.org/10.1111/j.1423-0410.2005.00593.x

Published
2026-05-06
How to Cite
Jerusalem, M. A., Pertiwi, K. R., Subiantoro, A. W., Jayatri, U. F., & Mahmud, D. P. (2026). Behavioral Drivers of First-Time Blood Donor Retention in Yogyakarta, Indonesia. Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 9(5), 1057-1073. https://doi.org/10.56338/mppki.v9i5.9414
Issue
Vol. 9 No. 5 (2026): May 2026
Section
Article

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