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Title: Investigating the naturalistic decision making role of business intelligence in the oil and gas industry
Other Titles: Naturalistic decision making and business intelligence in oil & gas
Authors: Swedi, Ahmed N
Advisors: Lycett, M
De Cesare, S
Keywords: BI;NDM;business information technology;situational awareness;cognitive mapping
Issue Date: 2016
Citation: Brunel University London
Abstract: This study aims to investigate the naturalistic decision-making role of business intelligence (BI) in the oil and gas (O&G) industry. O&G organisations spend a lot of time, effort and resources in maximising their operations to gain a competitive advantage. With the introduction of technological solutions, BI provides organisations with the ability to collect, sort, analyse and transform data into timely intelligence. However, the industry is characteristically competitive, difficult to predict and continuously changing with decision-makers sometimes faced with naturalistic decisions necessitating quick decisions under pressure, strict timeframes and with incomplete data. Literature on the role BI in the O&G industry has been minimal, with the focus being on how BI is used to assist rational decision-making. This study relies on data collected from two O&G organisations operating in different streams of the industry. Using a dynamic model of situated cognition, this study employs an interpretive, qualitative approach to data analysis in an attempt to fill the gap in the literature and determine whether BI plays any role in facilitating the decision-making process in response to naturalistic decisions. A dynamic model of situated cognition has been employed because of its strong correlation with naturalistic decision-making (NDM). The findings of this study indicate that different naturalistic decisions exist in the two streams of the industry and these decisions vary in their levels of complexity and domains. Furthermore, the findings show that while BI plays a major role NDM, this role is mitigated by the cognitive capabilities of individual decision-makers and their areas of expertise.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London
Appears in Collections:Computer Science
Dept of Computer Science Theses

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