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    Analysis of the Organizational Knowledge Management System of Nurse Education Centers with Hybrid Fuzzy DEMATEL-Network DEA Method
    (IOS Press BV, 2023-12-27)
    On-the-job training is important for the growth of organizational human capital. The present paper evaluates the efficiency of on-the-job training using the DEA model and knowledge management system to illustrate the interactions of organizational knowledge management concepts for measuring the efficiency of on-the-job training courses for employees within the organizational knowledge management systems. In this research, the DEA method was used to evaluate the efficiency of knowledge management system and analyze the interdependencies of the system. Also, the EU Knowledge and Innovation Management Measurement and the Kirkpatrick models were employed to identify the indicators. This study was conducted on 27 education centers in Iran. The results of evaluating the effectiveness of the knowledge management system show that the factor of “educational resources and facilities” is the most affecting variable and the most affected variable is “educational effectiveness”. Then, based on the DEA method, the weights of the sub-factors of the model were determined, and the efficiency of each step and as a result the efficiency of the whole system was obtained. Inefficient units were discarded and finally the efficient units were ranked. This study can help the organizations to identify the factors affecting the effectiveness of knowledge management programs.
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    Examining operational efficiency with prudent risks of Covid-19: a contextual DEA analysis with an undesirable intermediate measure
    (Springer, 2026-05-01)
    In the wake of the losses of human lives and disruption to the world economy caused by the spread of the COVID-19 pandemic, it has become imperative to assess the effectiveness of containment strategies adopted by countries. The success of any containment strategy of achieving low mortality and high recovery rate depends on the efficient utilization of available but limited resources, such as number of hospital beds and healthcare workers. While the spreading pattern of the pandemic has been researched heavily, there is limited research that comprehensively focuses on the efficient utilization of available resources to achieve the desired aims of low mortality and high recovery. In order to close this research gap, we employ a two-stage network data envelopment analysis (DEA) to identify the inefficiency in the process and resolve the resource constraints by considering medical and non-medical (administrative) interventions as two serial stages. The number of infected people is treated as the intermediate product, which is an undesirable output of the first stage and subsequently enters the second stage as an input. This network DEA model successfully addresses the conflict between the two stages over the handling of infected people and assesses the vulnerabilities of the countries against the transmission rates of the disease in the respective countries. Thus, the objective of this study is to develop a well-coordinated plan for different government agencies to jointly mitigate the risk under constrained resources. The findings reveal that almost 60% of the Organization for Economic Cooperation and Development (OECD) countries have used their resources suboptimally and are producing, on average, almost half the amount of the maximum possible outputs. As a sizeable amount of inefficiency can be explained by varying economic and demographic factors, such as health expenditure and the proportion of the aged population, the efficiency evaluation has been revisited with adjustments for unfavorable externalities. The analysis and its implications can help policymakers formulate optimal resource plans and identify potential areas for improvement.
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