ارزیابی به اشتراک گذاری دانش :رویکرد تحلیل پوششی داده ها بر اساس سیستم کلونی مورچه
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7875||2013||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 8, 15 June 2013, Pages 3137–3144
Knowledge sharing as one of the most crucial processes in knowledge management, operates in a dynamic environment. Dedicated tools to measure its performance under such an environment are not found in the literature. This paper aims to fill this void by proposing a hybrid model based on Data Envelopment Analysis (DEA). Monte Carlo simulation is incorporated into the model to handle stochastic data. In addition, to improve the model’s accuracy, the Ant Colony System (ACS) metaheuristic is blended with Monte Carlo simulation and DEA. The model is named ACS-DEA and is found to be able to increase the accuracy and reliability of the results. Although this model aims to assess knowledge sharing performance, it could also be used in other relevant fields in dynamic settings.
In this rapidly changing world, knowledge has become the most powerful leverage for an organization to achieve competitive advantages. It is therefore crucial for an organization to effectively manage its knowledge. Knowledge Management (KM) can be broken down into a few sub-processes such as knowledge creation, knowledge storing, knowledge sharing, and knowledge utilization. Indeed, all these processes play important roles in forming a successful KM program. Particularly, knowledge sharing is well-recognized as the main element for KM to thrive. For many organizations, getting workers to share and contribute knowledge is the emphasis of their KM initiatives. However, as revealed by past research, most existing frameworks and assessment tools broadly cover the area of KM, and only few are targeted specifically at knowledge sharing (Liebowitz and Chen, 2003 and Small and Sage, 2006). Effectively managing and evaluating knowledge sharing performance have emerged to become a critical research subject (Liu & Tsai, 2008). Through performance assessment, organizations could measure how well they are performing in knowledge sharing and then determine the appropriate improvement strategies and resource allocations for their projects. Recognizing the needs, this paper proposes the use of Data Envelopment Analysis (DEA), integrated with Ant Colony System (ACS) and Monte Carlo simulation, to devise a knowledge sharing assessment model. DEA, proposed by Charnes, Cooper, and Rhodes (1978) is a methodology to measure the efficiencies of a group of homogenous organizations without involving much subjective judgments. Since knowledge sharing is stochastic in nature, Monte Carlo simulation is utilized to introduce stochasticity into the DEA model. ACS is used to further enhance the accuracy of the model. Following this introduction, a review on knowledge sharing and existing evaluation models will be presented. Next, the developed knowledge sharing assessment model will be explained. Then, to demonstrate the applicability of the model, a real world application will be presented. Finally, the paper concludes by giving a summary of the work and some future research directions.
نتیجه گیری انگلیسی
This paper has proposed a new method for assessing knowledge sharing of a group of organizations. A set of proxy measures has been established to be evaluated using DEA. Since the knowledge sharing process itself is stochastic in nature, its data are non-deterministic as well. Therefore, MC-DEA has been proposed as it is capable of dealing with stochastic data. Stochastic models are preferable due to their capability of handling stochastic data and they produce more reliable and informative outcomes. However, the reliability of the analysis using limited data sets is a primary concern because the results may be subjected to too many uncertainties and become unreliable. In addition, organizations often have a limit or budget on how many data to be collected. Therefore, ACS-DEA has been devised to design an efficient data collection plan that can optimize the accuracy of the results. Based on a real-life application, it is observed that ACS-DEA outperforms MC-DEA by granting a higher level of accuracy to the results. With the data collection plan from ACS-DEA, managers would know how to effectively allocate the budget for data collection in order to maximize the accuracy of the efficiency scores. For future work, researchers can use this model to conduct longitudinal studies that measure knowledge sharing performance before and after certain knowledge management initiatives or interventions. The proposed ACS-DEA model can also be used as an internal evaluation tool to assess knowledge sharing in different departments in one organization. However, it should be noted that the measures proposed in this paper are by no means to be an absolute set of items to evaluate knowledge sharing in every industry. Researchers could apply the model in other industries, but the measures could be modified or revised to suit a particular industry. Another issue is that only quantitative measures are used in this research. For future work, it could be more informative to include a few qualitative measures to identify the specific organizational cultures and actions that can enhance knowledge sharing performance. Such future studies are promising in providing useful insights into how organizations can influence employees’ trust to increase knowledge sharing. Finally, since knowledge sharing does not only happen within a single organizational level and could occur between hierarchies, examination across different levels could be done to capture the complexity of knowledge sharing (Klein & Kozlowski, 2000). A direction for research would be using multi-level or hierarchical DEA models to appropriately examine the dynamics of knowledge sharing across multiple organizational levels.