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Top1. Introduction
Barry Richmond, the originator of the systems thinking term in 1987, defines systems thinking as the art and science of making reliable inferences about behaviour through the development of an increasingly deep understanding of underlying structure (Richmond, 1994). Many researchers have redefined the term afterwards. Hopper & Stave (2008) concluded that among the various definitions provided covering the vast arena of literature on the topic; common elements tend to include interconnections, the understanding of dynamic behaviour, systems structure as a cause of that behaviour, and the idea of viewing systems as wholes rather than parts.
Among multidisciplinary theories such as: control theory, decision theory, simulation technology and computer applied technology, System Dynamics (SD) has advantages on dealing with dynamic and complex problems in complex system (Sterman, 2000). Thus, SD is a discipline for seeing wholes, recognizing patterns and interrelationships, and learning how to format those interrelationships in more effective and efficient ways (Senge, 1990). SD has been widely used in macroscopic areas such as economic, social, and ecological and biological systems (Yujing et al., 2015). Recently, many researchers introduce SD to product development project management and use as a managerial tool to understand the behavior of organization and Micro and small enterprises (Mindila et al., 2014). The main advantage of system dynamics methodology is that the interrelationship of different variables of systems can be easily seen in terms of cause and effect, thus, the true cause of the behavior can be identified. The other advantage is that it is possible to investigate which parameters or structures need to be changed in order to improve behavior (Azar, 2012).
Pruyt (2016) asserted that there are interesting opportunities for SD in the era of ‘big data’. There are at least three ways in which bigger data and data science may play a role in SD: (1) to obtain useful inputs and information from bigger real data, (2) to infer plausible theories and model structures from bigger real data, and (3) to analyse and interpret large ensembles of simulation runs (i.e., bigger model generated data).
Systems thinking will be pursued in this research paper through the adoption of System Dynamics approach to develop a modelling tool for better (ICT) facilities management, portrayed in provided Library Service and Computer Labs Service facilities, and accordingly, enhanced perceived quality in universities. One public university and one private university would be taken as a case study and ultimately the application of the suggested tool could be extended in to all the Egyptian educational sector.
The coming sections begins with presenting the related research conducted in the same field then introduces the developed system dynamic model with a detailed explanation of causal loops and stock and flows diagrams and a clear demonstration of its application on both a public and a private university. Afterwards, a model merge of these service facilities is provided to end up with a summing conclusion.
TopSystem dynamics research has been introduced to some management fields including operations management, organizational behavior, project management, and market uncertainties research. Al-Kadeem et al. (2017) highlighted the use of system dynamics in modeling work system design of projects’ organizations seeking better projects behavior.
Knowledge is vital in achieving enterprise growth and development. Mindil et al. (2014) presented a conceptual and system dynamic model that reveals the structural underpinnings of knowledge identification and management and in so doing makes clear influence points where interventions can be made. The paper provides a systematic strategy of employing Information and Communication Technologies (ICTs) as interventions in the structural underpinnings of knowledge identification and management and models them within the system dynamic model. The system dynamic model developed acts as a learning tool for researchers who can further modify it and apply in different scenarios.