City Research Online

Abstract

This thesis was designed to examine two non-rational decision approaches in individual and team decision making. In Chapter 2 (Paper 1), a normative theory about how people should use intuition in making complex decisions is proposed. I draw from extant literature to derive why allowing intuition to interrupt analysis is beneficial to complex decision processes. In Chapter 3 (Paper 2), the theory of intuitive interruptions is applied to the entrepreneurial context. I argue that allowing intuitions to interrupt analysis helps entrepreneurs navigate the ambiguous environment in which they often find themselves. Chapter 4 (Paper 3) documents findings on the phenomenon of teams’ escalation of commitment and the effect of hope. According to the results, when faced with continuous negative feedback, teams that remain hopeful persist in the face of mounting costs. In Chapter 5 (Paper 4), changes in self-efficacy and team efficacy beliefs as responses to performance feedback were examined. The results indicated that the relationship between negative feedback and a decrease in efficacy beliefs is mediated by depressive realism—the negative yet realistic expectations of future outcomes. In summary, this thesis finds that non-rational approaches facilitate decision making by filling in the gaps, colouring the tone and changing the course of thinking where exhaustive information processing (i.e., full analysis) is not possible. Employing non-rational approaches can either be a deliberate choice or a reaction of human nature. Employing non-rational approaches does not necessarily yield favourable or unfavourable results. However, the analysis confirms that non-rational approaches are largely involved in complex decision making. Findings from this thesis add to our knowledge about how complex decisions are made by individuals and teams.

Publication Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School > Management Doctoral Theses Bayes Business School > Bayes Business School Doctoral Theses

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