Knowledge Management
Mostafa Pahlevanzadeh; Nadjla Hariri; Dariush Matlabi; Fahimeh Babalhavaeji
Abstract
The aim of the current research is to design a performance evaluation model of the knowledge management system in the software industry using a neural network. First of all, qualitative method using library studies and review of backgrounds in evaluating the performance of knowledge management has been ...
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The aim of the current research is to design a performance evaluation model of the knowledge management system in the software industry using a neural network. First of all, qualitative method using library studies and review of backgrounds in evaluating the performance of knowledge management has been discussed in relation to the extraction of various indicators and reviewed in past studies. In the second stage, based on the data collected from the qualitative stage, a quantitative study was conducted to confirm and confirm the findings obtained from the qualitative stage. For exploratory study and extraction of categories related to evaluation factors, meta combination method was used. With the fuzzy Delphi method, the validation and presentation of the final indicators were done with the help of a questionnaire and a survey of experts. 7 main categories including individual factors, organizational factors, technology and infrastructure, functional factors, knowledge management tools, economic factors, knowledge management tools and 29 sub-categories were identified. The sample size was selected by simple random sampling with Cochran's formula of 186 people. In order to collect quantitative data, a researcher-made questionnaire was used, the items of which were taken from the results of meta-composite analysis in the first stage. The innovation of the research is building a model using neural network algorithms, which is the ability to predict the performance evaluation index of the knowledge management system and the impact of each of the indicators using neural network in the field of software.
Knowledge Management
Mostafa Pahlevanzadeh; Nadjla Hariri; Dariush Matlabi; Fahimeh Babalhavaeji
Abstract
IntroductionNew software development models are emerging that help in software development by default. Secure software should be defined based on the fundamental framework of the organization and the fundamental framework of the organization's software, which means that the targeted level of security ...
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IntroductionNew software development models are emerging that help in software development by default. Secure software should be defined based on the fundamental framework of the organization and the fundamental framework of the organization's software, which means that the targeted level of security of various software of an organization depends on the business context and the degree of importance of information in that context. Proper support of the software industry requires a precise and appropriate understanding of the state of this industry as an ecosystem and knowledge of the software product. In addition to having technical complexities, the software industry follows certain economic structures and principles, which are very important in the analysis of the existing support regimes. Without having a secure software production line, the possibility of releasing secure software from this production cycle is impossible. Also, the competitive business environment of organizations depends on the software they have in this field; therefore, considering the level of vulnerabilities, it is reported that in the field of software, the existence of a secure software life cycle, which results in the production and development of secure software, is very necessary (Palumbo et al., 2020). Software companies need to develop knowledge in diverse domains. One of the industries that are very important in the transition from the oil economy due to its great potential in the country, and on the other hand, it realizes a part of the knowledge-based economy, is the software industry, which is struggling with many changes and problems. Due to the intensity of changes in technology and its highly competitive environment, software companies are facing many challenges and uncertainties in providing their products or services in the form of value for customers and also in making money from it. Therefore, it is necessary to provide a suitable solution for software companies to reach their position and the future that can be waiting for them. The present research tries to consider.What are the factors and performance indicators of the knowledge management system in the software development industry?Literature ReviewIn the research, Wang, Ding, and Ming Li (2017) presented a hybrid method for evaluating knowledge management performance based on triangular fuzzy numbers and group support systems. The results showed that the evaluation method has a strong practical and operational capability, and in addition, the evaluation is activated using a group support system. The systematic KMPE method based on an index system can improve the efficiency of organizations in the performance evaluation process. The review goes under these subheadings.Pojadi and Sardjono (2018) investigated "Evaluation of Knowledge Management System for Disaster Management Using Factor Analysis". The results showed that evaluation models can be built through the performance of agents, organizational culture, and information through the knowledge management system, management support and participation, access and updating, and information monitoring. Fu, Jiang, and Chen (2020) modeled an organizational knowledge management system based on artificial intelligence in a research. The key technologies that need to be solved to achieve knowledge integration were pointed out, including the integration of heterogeneous knowledge distributed between companies, the integration of correlation and the integration of knowledge and production processes. Fuzzy theory was used to create a knowledge extraction mechanism and reference model library from the project model to the dedicated reference model. Finally, a layered diffusion model was developed that matches the characteristics of online knowledge transfer. Greco et al. (2021) in their research examined "a method for evaluating knowledge management systems". The results show that WikiIEN is the solution most indicated for the larger public due to its more user-friendly interface and workflow, and sufficient feature set that does not depend on external software.MethodologyThis research is applied in terms of purpose, which was done with a documentary method, meta composite. The meta-composite approach is a type of qualitative study that examines the information and findings extracted from other qualitative studies with a similar and related topic. As a result, the desired sample for the meta-combination of selected qualitative studies is based on the relationship made with research questions (Lindgreen, Palmer, and Vanhamme, 2004). In this research, the combination method has been used in order to compare, interpret, transform, and combine different frameworks and models presented in the field of knowledge management system performance evaluation in the software development industry.ResultsThe purpose of this research is to provide a suitable conceptual framework for identifying the performance evaluation components of knowledge management system in the software development industry with a meta-composite approach. Due to the lack of comprehensive research in the field of identifying factors affecting the performance of the knowledge management system, the performance evaluation indicators of the knowledge management system were first extracted by extensive literature study and based on the frequency of evaluation indicators. The goal is to cover various dimensions of knowledge management system evaluation with a comprehensive study. Due to budget constraints and the economic era with uncertainty, knowledge management implementers need to be able to demonstrate the commercial value of knowledge distribution and reuse in the organization. There are two basic points that all organizations should consider when designing success measures. Therefore, based on the presented conceptual model, managers can evaluate the performance of knowledge management in the software development industry by using organizational factors, individual factors, technology infrastructure factors, knowledge management processes, and economic benefits/costs. DiscussionBased on the presented conceptual model, managers can evaluate the performance of knowledge management in the software development industry by using organizational factors, individual factors, technology infrastructure factors, knowledge management processes, and economic benefits/costs. ConclusionExamining the results showed that components such as organizational factors (culture, senior management support, organization strategy, organizational structure), individual factors (training, employee participation, knowledge and awareness of knowledge management systems, resistance to change); technology infrastructure factors (user-friendliness, data and information security, communication and cooperation methods, degree of integration of organizational systems, knowledge quality); knowledge management process (knowledge acquisition, knowledge transfer, knowledge creation); economic benefits/costs (capital cost, operating cost) affect the evaluation of knowledge management performance in the software development industry.
