Authors: Elizabeth Norman
This study presents a complexity theory approach to decision systems analysis (DSA) through an examination of asymmetric algorithms for adoption and rejection of solar panels in a business-to-business context. DSA facilitates the representation of the complex and nonlinear nature of non-routine decision-making behaviour in case study research. This research assumes a non-reoccurring quality to non-routine decision making, however complexity theory suggests that a closer view of these non-linear outcomes may reveal adaptive nonlinear networks, which are governed by logical rules and give rise to patterns between similar interactive networks. This self- organizing, bottom up perspective on decision making processes reveals intricate systems that give rise to asymmetric rules. Through asymmetric algorithmic representation of causal factors, this research works towards developing an adaptive, relational, non-reductionist representation of factors present in the adoption of innovative technology in a business-to-business context. The study uses DSA to represent observable and unobservable factors in the innovation adoption process, which is supplemented with binary variables pertaining to the sentiments and beliefs of decision making managers. The intention of this exercise is to provide a basis for configurational analysis consistent with the principles of DSA, which can be used to test tenets of complexity theory. The research extends prior work by Woodside and Baxter (2013) in developing accurate, complex and generalizable methods for analysing business-to-business case study processes.
Journal: ( – )
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Publish Year: 2018
Conference: Marseille, France (2018)