This paper aims to propose a modeling and decision-making framework for organizational sustainability excellence of construction firms. This research aims to find how construction organizations can achieve excellence in terms of corporate sustainability.
This paper first reviews the literature of organizational sustainability maturity, and then differentiates its approach by focusing on organizational sustainability excellence. Organizational maturity and organizational excellence in sustainability are two approaches to organizational performance management that aim to improve organizational sustainability performance.
After a detailed model design and development process, models were run and sensitivity analysis was performed. After running various scenarios, it was shown that both workforce management and knowledge management are key components of People Capability, and they play crucial roles in the viability and sustainability performance of construction firms. Therefore, human resource development and training affect all capability areas of construction organizations without which no capability-building programs can be planned and implemented effectively.
Organizational excellence focuses on organizational resources, capabilities and knowledge management to determine what is driving the long-term success of organizations, whereas the organizational maturity focuses on organizational processes. This paper presents a modeling approach that can facilitate the process of policy verifications in organizations.
Organizations may have various options in choosing different policies, and those policies can be planned and expressed in different manners and along different scales. How can an organization determine which scenarios end up producing the desired performance results? The proposed framework presents a practical methodology that can result in the assessment of organizational excellence methodologies.
Terouhid, S. and Ries, R. (2016), "Organizational sustainability excellence of construction firms – a framework", Journal of Modelling in Management, Vol. 11 No. 4, pp. 911-931. https://doi.org/10.1108/JM2-06-2014-0055Download as .RIS
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