![]() ![]() This paper reports a critical analysis of BE in all fields of IS based on an intensive investigation of quality IS research using bibliometric content analysis. Surprisingly, and despite calls for greater use of BE in IS research, it seems that IS has been slow to adopt contemporary BE as reference theory. Therefore, it is logical that IS research that involves decision making should consider BE as foundation or reference theory. ![]() The discipline of behavioral economics (BE) provides the dominant contemporary approach for understanding human decision-making. Theories of decision-making have long been important foundations for information systems (IS) research and much of IS is concerned with information processing for decision making. Weber and Borcherding (1993) studied behavioural influences on eliciting weights in several weighting methods including SMART (simple multi-attribute rating technique) (Edwards, 1977), Swing (Von Winterfeldt & Edwards, 1986), and Tradeoff (Keeney & Raiffa, 1976) and argued that the attributes weights could be influenced by the choice of weighting method, the hierarchical structure of the problem, and the reference points. Whereas cognitive biases have been discussed extensively in the areas of psychology (Gigerenzer, 1991 Hilbert, 2012 Kahneman et al., 1982 West et al., 2008), marketing (Fisher & Statman, 2000 Thomas et al., 2007), healthcare (Phillips-Wren et al., 2019), organisational studies (Das & Teng, 1999 Schwenk, 1984 Tetlock, 2000), business intelligence (Ni et al., 2019), and political science (Arceneaux, 2012 Rouhana et al., 1997), surprisingly enough, as also acknowledged by Montibeller and Von Winterfeldt (2015), we were able to identify only a few number of studies in the area of multi-attribute decision-making, most of which are theoretical. Those errors are called cognitive biases and they lead to biased decisions. ![]() Originality/value – This study proposes a theoretical model of actual BI systems use from an individual user perspective that increases our understanding of both the complexity of BI usage and the complementary organisational resources that drive both actual BI systems use and the impacts of BI systems. Decision-makingĪutonomy, data-based culture, BI system dependence, and BI system infusion are significant contributors to achieving decision-making performance. Specifically, a data-based culture and the quality of data in source systems are found to significantly enhance BI system dependence and BI system infusion. The partial least square (PLS), a structural equational modelling (SEM) technique, was employed to analyse the survey data.įindings – The survey findings attest to the influence of key complementary organisational resources (i.e., data-based culture, quality of data in source systems, and decision-making autonomy) on employees’ actual BI use (comprising BI system dependence and BI system infusion) and on their decision-making performance. This study aims to develop and test a model of the impact of key complementary organisational resources on employees’ actual BI systems use behaviours and their decision-making performance.ĭesign/methodology/approach – To test the research model, a cross-sectional survey of 437 North American employees, who described themselves as using a BI system to make decisions, was conducted. Purpose – Although we understand much about Business Intelligence (BI) technology adoption, we know less about the complementary organisational resources that drive the actual use of BI systems and impacts of BI systems at an individual employee level. ![]()
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