Strategic decision-making process (SDMP) in times of crisis:evidence from Greek banks

This paper investigates the strategic decision making process (SDMP) of Greek banks’ top management in the context of profound organisational changes introduced in 2012 due to the aftermath of the 2008 global financial crisis. It focuses on the impact of three key dimensions of the SDMP, namely, rationality, intuition and political behaviour, relating to four changes introduced, namely, mergers and acquisitions, branch network rationalisation, integration of information technology (IT) and downsizing of operations and personnel. A survey questionnaire was conducted, targeting Greek banks’ top management. Out of 140 questionnaires, 78 were returned, a 55.71% response rate. Data was analysed using structural equation modelling. Research findings identify rationality as a key dimension of SDMP for all organisational changes, as there was high focus on identifying and analysing all required information, use of external financial advisors, and reliance on multiple methods of information gathering. Decision-makers used their intuition in the form of past experience when making acquisition decisions, whilst their personal judgment and “inner voice” were neglected.Finally, political behaviour was not displayed during this process, as decision-makers were open with each other about their interests and preferences, and there was no bargaining, negotiation or use of power amongst them. One limitation was that of not considering all the factors that might help measure SDMP. Also, this study was conducted in a period of political and financial uncertainty for Greek banks, as well as for the Greek economy in general, so findings may not be generalizable to other industries and countries. Conducting interviews could have offered deeper insight as well. This study’s value lies in the fact that the organisational changes were determined by Greece’s leaders, and thus the Greek banks had to operate under a dynamic, inflexible and non-autonomous environment. Also, this study extends prior SDMP research by examining the impact of the three key SDMP dimensions on four types of organisational change

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management

Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy