Applying a fuzzy‐morphological approach to complexity within management decision making

Abstract

PurposeNoting the scarcity of complexity techniques applied to modelling social systems, this paper attempts to formulate a conceptual model of decision‐making behaviour within the information systems evaluation (ISE) task, against the backdrop of complexity theory.Design/methodology/approachComplexity theory places an emphasis on addressing how dynamic non‐linear systems can be represented and modelled utilising computational tools and techniques to draw out inherent system dynamics. In doing so, the use of fuzzy cognitive mapping (FCM) and morphological analysis (MA) (hence a fuzzy‐morphological approach), is applied to empirical case study data, to elucidate the inherent behavioural and systems issues involved in ISE decision making within a British manufacturing organisation.FindingsThe paper presents results of applying a combined FCM and MA approach to modelling complexity within management decision making in the ISE task: both in terms of a cognitive map of the key decision criteria; a matrix of constraint criteria; and a synthesised model that provides an indication of the linkages between technology management factors and organisational imperatives and goals. These findings show the usefulness of viewing the topic in complexity science terms (emergent behaviour, non‐linearity and chaotic response).Research limitations/implicationsThis research is limited in applying the given technique to a single case study organisation in the UK manufacturing sector, where the sample size is limited. Since this is the first time that such a combined MA‐FCM technique has been used in this field known to the authors, future research needs to validate and explore the implications of this approach in a wider context (multiple organisations and viewpoints).Practical implicationsThe paper highlights the need for those involved in analysing managerial decision making to include aspects of complexity theory in their evaluations – namely uncovering inherent inter‐relationships that may exist between stakeholders, processes and systems. In doing so, expanding the manager's understanding of how to achieve congruence between driving forces and factors, which may exhibit non‐linear, chaotic or feedback behaviour.Originality/valueThe given research brings together both artificial intelligence and operational research techniques, applied in the socio‐technical milieu of information systems evaluation, within the context of complexity theory, in order to describe the rich detail within the ISE decision‐making task.