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    Modeling influence of change readiness on knowledge acquisition process: A case study
    (Elsevier, 2020-06-01)
    Change readiness (CR) has important impact on the success of knowledge acquisition (KA). So it is necessary for managers to know how KA is shaped by CR elements. Review of the extant literature shows a paucity in this regard specially regarding intra-organizational rather than inter-organizational level. Therefore, to bridge this gap, this study aims to present a fuzzy cognitive map (FCM) model in which interactions among CR elements for KA are identified. To do so, first, elements for measuring CR for KA were extracted from the relevant literature. Then, the identified elements were screened through distributing the first designed questionnaire among the select sample of survey organization. Then, the second questionnaire was used to measure the select elements and find the relation between them, using FCM and Mental Modeler Software. To improve CR for KA, some scenarios are suggested by the managers and the impact of each scenario on the whole CR elements is identified using sensitive analysis of the FCM model. After comparing the scenarios, it is concluded improving more elements in one scenario does not necessarily result in better impact on the whole CR for KA. This is due to the interaction among the elements which sometimes could be negative. The proposed model might help managers of the survey organization evaluate their CR improvement plans for KA before taking any actions. In addition, this study can give an idea to other organizations and industries to apply this approach according to their own relevant criteria. This study is among the first in its kind which presents a model using FCM method in which the interactions among the CR elements influencing KA are considered.
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    Transportation energy demand forecasting in Taiwan based on metaheuristic algorithms
    (Taylor and Francis Ltd., 2022-01-01)
    A new methodology is suggested in this study to provide optimum forecasting of the future transportation energy demand in Taiwan. The paper introduces a new improved version of Emperor Penguin Optimizer (IEPO) to provide an optimal and suitable forecasting model. The forecasting was based on three different models including linear, exponential, and quadratic where their coefficients have been optimized using the suggested IEPO algorithm which is based on considering the population, the GDP growth rate, and the total annual vehicle-km. The study considers two different scenarios based on curve fitting and projection data. The results indicate that the RMS value for the TED forecasting based on the proposed IEPO algorithm applied to the linear, exponential, and Quadratic for training are 0.0452, 0.0461, and 0.0492, respectively and for testing are 0.0456, 0.0596, and 0.0642, respectively. This shows better results of the optimized exponential method’s efficiency. Simulation results showed high efficiency for the proposed IEPO-based transportation energy demand forecasting based on all of the employed models for decision-making in ROC.