Decision Making Techniques

Decision Making Techniques

Delphi Technique

The Delphi technique was initiated as a means to forecast technological trends and has evolved since the 1950s. Delphi methods include qualitative and quantitative components to gather data from expert opinions. Goodarzi et al. (2018) recommend a sample size ranging from 10-20 in a panel of experts, three rounds, and one open-ended question in the first round. Because panel experts often do not agree Hasson et al. (2000) recommends a statistical aggregation of responses scaled to mean, median, and mode for selected index per round. In IT Service Management (ITSM) activities the Delphi technique can be useful. Santiago’s (2017) study showed in project management, information security, and configuration management the Delphi observability into the decision-making process with subject-matter experts impacted decision-making cost control and complexity for small and medium-sized businesses.

Consensus Technique

The consensus decision-making process involves checking the individual responses for consistency then aligning the solution to sufficiently support the consensus. Finally, the consensus decision-making process ranks the options from best to worst alternatives. A consensus model can help when large differences among judgments or decision makers have developed. Lu’s (2018) research discourages the implementation consensus technique when a group is unanimous. The researcher also showed the use of consensus technique with a triangular fuzzy weight computation formula worked best in star hotels wishing to associate with green tourism. Consensus technique is typically viewed as fair and likely to satisfy decision makers with open communication, agreeable, conscientious, non-neurotic communication (Sager, 2006).

Compare and contrast

The Delphi technique uses subject matter experts in qualitative and quantitative methods. The Delphi questioning is typically several rounds; in comparison, the consensus uses open communication to discuss issues and priorities. The Delphi is a more formal method of decision making where the consensus is more liberal, and with the use of open communication, decision makers may be enlightened to other angles or perspectives from other panel members that were not previously considered, therefore potentially changing their recommendation. For this reason, I would lean toward open communication and the consensus technique. Evidence-based management (EBM) promotes evidence-based decision making in times of uncertainty will increase the odds of success (Rousseau, 2018). Leaders can often be rapid and biased (Baron, 2008; Kahneman, 2011) and lead to negative outcomes. However, when EBM seeks to discover the root cause of problems with rational and intuitive solutions decision-making success will increase (Ayad, 2013).

Decision making also includes the non-quantitative insight that is needed to create a competitive advantage and involves the unconscious and intuitive side of experienced leadership (Enriquez de la O, 2015). The experience of an expert panel in the Delphi technique will give decision makers that type of intuitive experience. When compared to the consensus, the pervasiveness, miscommunication, misunderstanding of the root cause, or knowledgeable insight to the problem or solution from any particular member may be a weak link. Szymaniec-Mlicka (2014) research showed that in private organizations the use of SME’s (subject matter experts) was more prevalent than in public organizations.

In conclusion, I would have to recommend the Delphi technique over the consensus technique for decision making because decision making is both an art and a science (Etzioni, 1981). Root cause analysis is also a major factor in decision-making, therefore, giving the edge to Delphi with the combined experiences of the panel to impact the decision making for more positive and sustainable outcomes. 

References

Ayad, A., Rahim, E. (2013). Toward a theory for management success: the role of evidence-based management in the retail industry. Int. J. of Project Organization and Management, Vol.5, No.3, pp.199

Baron, J. (2008). Thinking and Deciding. Cambridge University Press, NY.

Cavaliere, D., Morente-Molinera, J. A., Loia, V., Senatore, S., & Herrera-Viedma, E. (2019). Collective scenario understanding in a multi-vehicle system by consensus decision making. IEEE Transactions on Fuzzy Systems. doi: 10.1109/TFUZZ.2019.2928787

Enriquez de la O, J. F. (2015). Individual decision-making by top executives as a valuable resource for strategic management–A resource-based view and dynamic capability approach. Vezetéstudomány-Budapest Management Review, 46(11), 2-14.

Etzioni, A. (1989): Humble Decision-making. Harvard Business Review, July-Aug: p. 122–126.

Goodarzi, Z., Abbasi, E., & Farhadian, H. (2018). Achieving consensus deal with methodological issues in the Delphi technique. International Journal of Agricultural Management and Development, 8(2), 219-230. Retrieved from http://ijamad.iaurasht.ac.ir/article_540498_d4bd6133361312bb4c273242368de1ee.pdf

Hasson, F., Keeney, S. & McKenna, H. (2000). Research Guidelines for the Delphi Survey Technique. Journal of advanced Nursing, 32(4): 1008-1015. doi:10.1046/j.1365-2648.2000.t01-1-01567.x

Kahneman, D. (2011). Thinking fast and slow. New York: Farrar, Straus, and Giroux.

Lu, P., Yang, X., & Zhou-Jing, W. (2018). Fuzzy group consensus decision making and its use in selecting energy-saving and low-carbon technology schemes in star hotels. International Journal of Environmental Research and Public Health, 15(9) doi:http://dx.doi.org.proxy.cecybrary.com/10.3390/ijerph15092057

Rousseau, D. M. (2018). Making evidence-based organizational decisions in an uncertain world. Organizational Dynamics, 47(3), 135-146. doi:10.1016/j.orgdyn.2018.05.001

Santiago, J. R. (2017). Observability and the decision-making process in information technology service management: A delphi study (Order No. 10615673). Available from ProQuest Dissertations & Theses Global. (1958951575). Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1958951575?accountid=26967

Sager, K. L., & Gastil, J. (2006). The origins and consequences of consensus decision making: A test of the social consensus model. The Southern Communication Journal, 71(1), 1-24. Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/226917248?accountid=144789

Szymaniec-Mlicka, K. (2014). Resource-based view in strategic management of public organizations - a review of the literature. Management (1429-9321), 18(2), 19–30. https://doi-org.proxy.cecybrary.com/10.2478/manment-2014-0039