Decision making has usually being a lengthy process in the past days. The process however, have been simplified by the availability of telecommunication equipment like mobile phones, PCs and PDAs (Arnott, & Pervan, 2005, p. 8). The process now, is far much simpler that the past. Mobile decision support system (Gao, 2013, p. 12) has helped eased the process of decision making given that there has been a lot of mobility of the people (Anokwa, Ribeka, Parikh,2012, p. 4). On the other hand, the tool will help the multi-national businesses make decisions with ease and without much movements. This article states the model that should be taken to implement a working mobile decision support system (Perez, Cabrerizo, Herrera-Viedma, 2010, p. 4). The first step is to ensure a uniformed process. This is allowing the people to use different systems to pass their preference. Secondly, aggregation and exploitation phase which includes selecting the alternatives (Nicholas, Heiby & Hatzell, 1991, p. 150). After selection of alternatives, a step to sort different preference representation. Herrera-Viedma, Herrera, Chiclana, (2002) suggests that this is best done by consensus. The process is then followed by selection of the best alternative. The choice will depend on the sector where the system is being used. The framework is then considered fully functional with the availability of a feedback process.
The review of mobile decision support system
The MDSS will be of a great advantage in the different fields. The system will ease movements of the people since they can participate in crucial decision making process processes from where they are. The MDSS system can be adopted at any industry including the health and security fields.
Challenges of MDSS
The MDSS faces a very a very big difficulty in extrapolating the information to get the usable information (Elsevier, 2014, p.70). The information received can be very massive in size and therefore pose a hard task for the analysers (Simonic, Holzinger, Bloice, et. al. 2011). Also, the process of sorting the information from different devices will be very hard since it involves complicated procedures (Van De Belt, Engelen, Berben, and Schoonhoven, 2012).
Anokwa, Y., Ribeka, N., Parikh. T. (2012). Design Of A Phone-Based Clinical Decision Support System For Resource-Limited Settings. Proceedings Of The Fifth International Conference On Information And Communication Technologies And Development Design Of A Phone-Based Clinical D. 5(2) p. 3-12
Arnott, D., & Pervan, G.P. (2005). A critical analysis of decision support systems research. Journal of Information Technology, 20, 6787.
Gao, S. (2013). Mobile decision support systems research: a literature analysis. Journal Of Decision Systems, 22(1), 10-27. http://dx.doi.org/10.1080/12460125.2012.760268Elsevier, B. V. (2014). A model for improving quality of decisions in mobile health Upkar Varshney. Decision Support Systems. 62. 66-77
Nicholas, D. D., Heiby, J.R., and Hatzell. T.A. (1991). The Quality Assurance Project: introducing quality improvement to primary health care in less developed countries. Quality Assurance in Health Care, 3(3):147-165
Herrera-Viedma, E., Herrera, F., Chiclana, F. (2002). A consensus model for a multipersonal decision making with different preference structures. IEEE Transactions on Systems, Man, And Cybernetics. Part A: Systems and humans, 32(3). 394-402
Perez, J., Cabrerizo, F. J., Herrera-Viedma, E. (2010). A Mobile Decision Making Support System for Gr Dynamic Group Decision Making Process. . IEEE Transactions on Systems, Man, And Cybernetics. Part A: Systems and humans
Pervasive Computing Technologies for Healthcare, PervasiveHealth.
Van De Belt, T. H., Engelen, L. J., Berben, S. A., and Schoonhoven, L.(2012). Definition of Health 2.0 andMedicine 2.0: A systematic review. J Med Internet Res 2010:12(2).
Simonic, K., Holzinger, A., Bloice, M., et. al. (2011). Optimizing long-term treatment of rheumatoid arthritis with systematic documentation, Proc. of 5th Inter. Conference on
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