The Decision Making Process
Name
DDBA 8560 - Seminar in Healthcare Managerial Decision Making
Walden University
2022
Origin of Decision Making
Buchanan and O’Connell, (2006) posit that the term decision making was first introduced by
Chester Barnard who imported the term from the lexicon of public administration into the
business world. Buchanan and O’Connell attribute that the intention of this was to replace
narrower descriptions such as “resource allocation” and “policy making”. The authors further
note that the introduction of the phrase changed how managers thought about what they did.
2
The Decision Making Process
The process of making a decision may be intuitive for most people, but management decisions
require the decision maker to take into consideration a variety of factors. Decision making in the
medical field is particularly complex, as the decisions made may impact upon the life, health and
wellbeing of numerous people. Therefore, managerial decision making in hospitals has to be
based on a rational framework, rather than subjective intuition. Thus it relies on decision theory,
which is concerned with goal-directed behaviour in the presence of options (Harris & Shimizu,
2004).
Decision-making in the fields of health, safety and environment is predominantly based on
assessments of risk to the environment and health. Typical risk measures, on which subsequent
decision-making is based, are individual risk, societal risk and the risk of having specific adverse
or irreversible consequences. Thus hospital managers have to consider these factors when
making decisions on the supply of drugs to hospitals. Thus it may seem that the manager’s
decision making process is simple, namely to make the decision which best promotes the
wellbeing of the greatest number of people. However, there are factors which are inevitably
beyond the manager’s control, and these factors introduce an element of uncertainty into the
decision making process. Uncertainty is “the difference between the amount of information
required to perform the task and the amount of information already possessed by the
organization. This suggests that to make a good decision, the decision maker needs as many
accurate sources of information as possible (Hermalin et al., 2003).
As initially outlined by von Neumann and Morgenstern (1947) a variety of methods have been
developed for making optimal decisions. However, the method to be followed in making a
decision depends on the degree of uncertainty involved in making the decision. The greater the
uncertainty, the greater the risk, therefore decisions with certain outcomes are less risky than
decisions with uncertain outcomes.
Two approaches towards decision making in situations with certain outcomes are Multi-Attribute
Utility and Linear Models. Multi-Attribute Utility (MAU) is used to make decisions that have
relatively certain outcomes. Gardiner and Edwards (1975) state that MAU requires the decision
maker to obtain a utility value for each decision alternative and then to choose the alternative
with the highest value. The effectiveness of a selected alternative is determined by a weighted
sum of separate part utilities for various attributes of the choice. Thus the MAU method is a
straightforward determination of the best choice among many. It is used to make decisions such
as the location of new production plants and human resource recruitment. It may not be suitable
for managing the shortage of drugs as drug supply decisions involve many complex interacting
variables (Huse et al., 2009).
Linear Models of decision are used both to guide and to explain decisions with certain outcomes.
One contentious issue with linear models is the relevance of the weights assigned to the
significant and dependent factors. Research has shown that assigning equal weights, or even
random weights to factors in a decision, do as well as optimal weights in many settings. This
suggests that the weighting of factors may be arbitrary, as the level of uncertainty is low.
Nevertheless, the reliability of linear models of decision making explains their use in tasks, such
as graduate school admissions (Goldberg, 1970). However, because of their simplicity, and
because the level of uncertainty is low, linear models are inappropriate for decision making in
hospital drug shortages.
The decision-making models that are more useful in handling drug shortages are those designed
for uncertain outcomes, such as Decision-Tree Analysis. “A decision tree is a graphical model
that displays the sequence of decisions and the events that comprise a (risky) sequential
decision” (Huber, 1980, p. 118). It involves drawing a diagram of choice alternatives, uncertain
events, and outcome utilities as a series of branches. For each alternative, an expected value (EV)
is calculated as the average outcome value over all possible events. The optimal choice is then
the alternative with the highest EV. Decision trees are used to guide risky decisions in marketing
strategy, plant expansion, and public policy planning. For these reasons, decision tree is the ideal
decision making model for handling drug shortages in hospitals, as it takes all the factors into
consideration, and assesses them relative to each other to come up with the best solution in the
current circumstances. However, it faces the major drawback that no manager is prescient
enough to account for all the factors that influence a decision. There will always be factors
outside a manager’s knowledge, and thus incapable of inclusion in a decision tree. These
unforeseen factors can have a significant impact on whether the decision is optimal or not
(Ingram et al., 2016).
It is indisputable that a hospital is a complex environment. It also goes without saying that
complexity requires good organization and management (Breese & Heckerman, 1999). Therefore
decision makers in hospitals need to apply various management including planning, organizing,
defining roles, creating processes and incentives, ensuring accountability, and hiring and
motivating staff. The execution of each of these functions requires the making of decisions, and
the quality of management hinges upon the quality of the decisions made.
Decision making in Kenyan hospitals is the responsibility of a network of governance structures,
rather than just an individual. With variations depending on the level of hospital, the head of a
medical facility is the Office of the Medical Superintendent, who works with the Health Facility
Management Board and the Hospital Management Team (HMT). These institutions work in
concordance with the District Health Management Team (DHMT). However, in provincial
institutions, there are several committee units based at the departmental level which vary in
numbers depending on the number of departments functional at the facility level (Solberg, 2008).
References
Abdullah, S. N., Ismail, K. N. I. K., & Nachum, L. (2016). Does having women on boards create
value? The impact of societal perceptions and corporate governance in emerging markets.
Strategic Management Journal, 37(3), 466-476.
Aguilera, R. V., Filatotchev, I., Gospel, H., & Jackson, G. (2008). An organizational approach to
comparative corporate governance: Costs, contingencies, and complementarities. Organization
science, 19(3), 475-492.
Bhagat, S., & Black, B. (2001). The non-correlation between board independence and long- term
firm performance. J. CorP. l., 27, 231.
Boivie, S., Bednar, M., Aguilera R. & Andrus, J. (2016). Are boards designed to fail? The
implausibility of effective board monitoring. The Academy of Management Annals, 10(1), 319-
407.
Chen, G., Crossland, C., & Huang, S. (2016). Female board representation and corporate
acquisition intensity. Strategic Management Journal, 37(2), 303-313.
Dalton, D. R., Daily, C. M., Ellstrand, A. E., & Johnson, J. L. (1998). Board composition,
leadership structure, and financial performance: Meta-analytic reviews and research agenda.
Strategic Management Journal, 19: 269-290.
Dang, R., Nguyen, D. K., & Vo, L. C. (2014). Does the glass ceiling exist? A longitudinal study
of women's progress on french corporate boards. Journal of Applied Business Research, 30(3),
909.
De Dreu, C. K. W., & Weingart, L. R. (2003). Task versus relationship conflict, team
performance, and team member satisfaction: a meta-analysis. The Journal of Applied
Psychology, 88(4), 741–749.
De Massis, A., Frattini, F., & Lichtenthaler, U. (2013). Research on technological innovation in
family firms: Present debates and future directions. Family Business Review, 26(1), 10-31.
Duran, P., Kammerlander, N., Van Essen, M., & Zellweger, T. (2016). Doing more with less:
Innovation input and output in family firms. Academy of Management Journal, 59(4), 1224-
1264.
Ferreira, D. (2015). Board diversity: Should we trust research to inform policy?. Corporate
Governance: An International Review, 23(2), 108-111.
Fich, E. M., & Shivdasani, A. (2006). Are busy boards effective monitors? Journal of Finance,
61, 689–724.
Fiss, P. C., & Zajac, E. J. 2006. The symbolic management of strategic change: Sensegiving via
framing and decoupling. Academy of Management Journal, 49: 1173–1193.
Gabaldon, P., Anca, C., Mateos de Cabo, R., & Gimeno, R. (2016). Searching for women on
boards: An analysis from the supply and demand perspective. Corporate Governance: An
International Review, 24(3), 371-85.
183
Gabrielsson, J., & Huse, M. (2004). Context, behavior, and evolution: Challenges in research on
boards and governance. International Studies of Management & Organization, 34(2), 11-36.
Golden, B. R., and Zajac, E. J. (2001) When will boards influence strategy? Inclination× power=
strategic change. Strategic Management Journal. 22: 1087-1111.
Harris, I. C., & Shimizu, K. (2004). Too busy to serve? An examination of the influence of
overboarded directors. Journal of Management Studies, 41, 775–798.
Haynes, & Hillman. (2010). The effect of board capital and CEO power on strategic change.
Strategic Management Journal, 31(11), 1145–1163.
Hermalin, Benjamin E., and and Michael S. Weisbach. (2003). “Boards of Directors as an
Endogenously Determined Institution: A Survey of the Economic Literature,” Economic Policy
Review, 9 (1), 7–26.
Homan, A. C., Van Knippenberg, D., Van Kleef, G. A., & De Dreu, C. K. (2007). Bridging
faultlines by valuing diversity: diversity beliefs, information elaboration, and performance in
diverse work groups. Journal of applied psychology, 92(5), 1189.
Huse, M., Nielsen, S. T., & Hagen, I. M. (2009). Women and employee-elected board members,
and their contributions to board control tasks. Journal of Business Ethics, 89(4), 581-597.
Ingley, C. B., & Van der Walt, N. T. (2001). The strategic board: The changing role of directors
in developing and maintaining corporate capability. Corporate Governance: An International
Review, 9(3), 174-185.
Ingram, A. E., Lewis, M. W., Barton, S., & Gartner, W. B. (2016). Paradoxes and innovation in
family firms: The role of paradoxical thinking. Entrepreneurship Theory and Practice, 40(1),
161-176.
Joseph, J., Ocasio, W., & McDonnell, M. H. (2014). The structural elaboration of board
independence: Executive power, institutional logics, and the adoption of CEO-only board
structures in US corporate governance. Academy of Management Journal, 57(6), 1834- 1858.
Kaczmarek, S., Kimino, S., & Pye, A. (2012). Board task‐related faultlines and firm
performance: A decade of evidence. Corporate Governance: An International Review, 20(4),
337-351.
Karra, N., Tracey, P., & Phillips, N. (2006). Altruism and agency in the family firm: Exploring
the role of family, kinship, and ethnicity. Entrepreneurship: Theory and Practice, 30(6), 861–877.
Khanna, P., Jones, C. D., & Boivie, S. (2014). Director human capital, information processing
demands, and board effectiveness. Journal of Management, 40(2), 557-585.
Kirsch, A. (2017). The gender composition of corporate boards: A review and research agenda.
The Leadership Quarterly. https://doi.org/10.1016/j.leaqua.2017.06.001
Leblanc, R. (2005). Assessing board leadership. Corporate Governance: An International
Review, 13(5), 654-666.
Matsa, D. A., & Miller, A. R. (2013). A female style in corporate leadership? Evidence from
quotas. American Economic Journal: Applied Economics, 5(3), 136-69.
