Abstract
In cases where a trial-and-error experiment is costly or impossible, especially in healthcare industry, researchers have used simulation modeling to avoid the risk caused by a trial-and-error experiment. In healthcare industry, blood supply plays an important role because the shortage of blood could make people’s life at risk. In most countries in Southeast Asia, including Indonesia, blood services have not been considered as an essential service for healthcare support program. Moreover, blood supply chain in low-and-middle-income countries has different characteristics and challenges compared to the high-income countries. For developing countries, one of the important factors is the number of donors. This research conducted to see how the agent makes decision about donating their blood. Finding?
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References
Rytila, J. S., & Spens, K. M. (2006). Using simulation to increase efficiency in blood supply chains. Management Research News, 29(12), 801–819.
Yuzgec, E., Han, Y., Nagarur, N. (2013). A simulation model for blood supply chain systems. Proceedings of The 2013 industrial and systems engineering research conference.
Onggo, B. S. (2014). Elements of a hybrid simulation model: A case study of the blood supply chain in low-and middle-income countries. Proceedings of the 2014 winter simulation conference.
Soedarmono, Y. S. M. (2010). Donor issue in Indonesia: A developing country in South East Asia. Biologicals, 380, 43–46.
Holdershaw, J., & Gendall, P. (2011). Predicting blood donation behaviour: Further application of the theory of planned bahaviour. Journal of Social Marketing, 1, 120–132.
Ferguson, E., & Bibby, P. A. (2002). Predicting future blood donor returns: Past behavior, intentions, and observer effects. Health Psychology, 21(5), 513–518.
Davis, C. A., et al. (2015). A role for network science in social norms intervention. Procedia Computer Science, 51, 2217–2226.
Fishbein, M., & Ajzen, I. (2010). Predictiong and changing behavior: The reasoned action approach. New York: Psychology Press.
Tscheulin, D. K., & Lindenmeier, J. (2005). The willingness to donate blood: An empirical analysis of socio-demographic and motivation-related determinants. Health Services Management Research, 18(3), 165.
Guiddi, P., et al. (2015). New donors, loyal donors, and regular donors: Which motivations sustain blood donation? Transfusion and Apheresis Science, 52, 339.
Karacan, E., et al. (2013). Blood donors and factors impacting the blood donation decision: Motives for donating blood in Turkish sample. Transfusion and Apheresis Science, 49, 468–473.
Zhou, Y., Poon, P., & Yu, C. (2012). Segmenting blood donors in developing countries. Marketing Intelligence & Planning, 30(5), 535–552.
Burdit, C., et al. (2009). Motivation for blood donation among African Americans: developing measures for stage of change, decisional balance, and self-efficacy construct. Journal of Behavioral Medicine, 32, 429–442.
Borshchev, A., & Filippov, A. (2004, July 25–29). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. The 22nd international conference of the system dynamics society. Oxford, England.
Suwardie, A. W., Sopha, B. M., & Herliansyah, M. K. (2013). A simulation model of blood supply chain at Indonesian regional red-cross. Proceedings of The 2013 international conference on logistics and maritime systems.
Chahal, K., & Eldabi, T. (2011). Hybrid simulation and modes of governance in UK healthcare. Transforming Government: People, Process and Policy, 5(2), 143–154.
Samuel, C., et al. (2010). Supply chain dynamics in healthcare services. International Journal of Health Care Quality Assurance, 23(7), 631–642.
Young, M. (1989). The technical writer’s handbook. Mill Valley: University Science.
Afshar, J., et al. (2014). System dynamic analysis of a blood supply chain system. Applied Mechanics and Materials, 510, 150–155.
Rusman, M., & Rapi, A. (2014). Blood banks location model for blood distribution planning in Makassar city. Proceedings of the Asia Pacific industrial engineering & management systems conference 2014.
Delen, D., et al. (2011). Better management of blood supply-chain with GIS-based analytics. Annals of Operations Research, 185, 181–193.
Katsaliaki, K., Mustafee, N., & Kumar, S. (2014). A game-based approach towards facilitating decision making for perishable products: An example of blood supply chain. Expert Systems with Application, 41, 4043–4059.
Gillet, P., et al. (2015). First-time whole blood donation: A critical step for donor safety and retention on first three donations. Transfusion Clinique et Biologique, 22, 312.
Scherer, C. W., & Cho, H. (2003). A social network contagion theory of risk perception. Risk Analysis, 23, 2.
Godin, G., et al. (2005). Factors explaining the intention to give blood among the general population. Vox Sanguinis, 89, 140–149.
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Dwiartika, D.N., Putro, U.S., Siallagan, M., Onggo, B.S. (2017). People’s Willingness to Donate Blood: Agent-Based Approach. In: Putro, U., Ichikawa, M., Siallagan, M. (eds) Agent-Based Approaches in Economics and Social Complex Systems IX. Agent-Based Social Systems, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-3662-0_2
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