How successfully can decision-making style predict the orientation toward well-or ill-structured decision-making problems

Abstract

A decision-making style is the manner through which a manager acquires, processes and uses information in the decision-making process. It is a set of qualitative indicators which within the decision-making process manifest themselves in the form of a decision-making style and is a typical method of implementing a decision. Knowledge of decision-making styles and their active use in a management structure contributes directly to building the competitiveness of the business. Identifying decision-making styles and delegating the right decision tasks to the right employee who possesses the fundamental knowledge to address a particular type of decision-making problem (well or poorly structured) is the essence of competent decision-making. This research paper deals with the relationship between the decision-making style of a manager in terms of the type of decision-making problems. Binary logistic regression was used to predict the relationship between the type of decision problem (binary dependent variable) and the overall score in the decision-making style (cardinal independent variable) through which the probability of the occurrence of the dependent value was calculated. Based on these results, we assert that as the unit score in the intuitive decision-making style increases, the odds ratio in a group of ill-structured decision-making problems increases as well. The prediction for the growth of the Sensing and Thinking odds ratio has not been confirmed. Results of non-parametric testing using the Mann Whitney U test confirmed that the preference for the type of decision-making problem has a statistically significant effect on the score achieved in the Sensing and Intuiting decision-making style. The effect size for the Mann Whitney U test was then calculated. To analyze the dependence between the nominal data, the non-parametric Chi Square Test of Independence was used, and Cramer's V was used to determine the strength of association between the variables.