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.
Knowledge Management
Mostafa Pahlevanzadeh; Nadjla Hariri; Dariush Matlabi; Fahimeh Babalhavaeji
Abstract
Introduction The purpose of the current research is to design a knowledge management system performance evaluation model in the software industry using a neural network. Based on the collected data, a quantitative study was conducted to confirm the findings obtained from the qualitative stage. For exploratory ...
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Introduction The purpose of the current research is to design a knowledge management system performance evaluation model in the software industry using a neural network. Based on the collected data, a quantitative study was conducted to confirm the findings obtained from the qualitative stage. For exploratory study and extraction of categories related to evaluation factors, the meta-combination method (Sandelowski and Barroso model) was used. The research method in the quantitative part is descriptive survey. The statistical population of the research was made up of all software developers and software experts in universities and companies. Findings: 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 innovation of the research is building a model using neural network algorithms that have the ability to predict the performance evaluation index of the knowledge management system and the impact of each of the indicators using a neural network in the field of software. Conclusion: The results obtained from the questionnaire have been used for the input of the network model, the results showed that components such as technology infrastructure factors and functional factors have a greater impact on the evaluation of knowledge management performance in software development.Literature ReviewIn a research, they evaluated the performance of the knowledge management system in Iranian software companies. The results showed that the knowledge management system consists of 4 processes of identifying and creating, recording and maintaining, sharing and applying and internalizing knowledge. In a research, they designed a fuzzy inference system to evaluate the performance of the knowledge management system in the software development industry. The use of neural networks in evaluating the key factors of the knowledge management system in Iranian companies based in Alborz province was investigated. A research modeled an organizational knowledge management system based on artificial intelligence. Fuzzy theory was used to create knowledge extraction mechanism and reference model library from project model to dedicated reference model.MethodologyThe method used in this research is a mixed research method of exploratory type with a qualitative approach and meta-composite and Delphi methods. In the first stage, the meta-composite method was used to identify the main and sub-categories of the indicators, and then the validation and presentation of the final indicators were done with the fuzzy Delphi method. The current research method is practical in terms of purpose. The sample size was selected by simple random sampling method with Cochran's formula of 186 people. In the meta-combination method of the research, library sources and documents including articles, reliable and referable internet sources, as well as domestic and foreign scientific reports were used. For exploratory study and extraction of categories related to evaluation factors, the meta-combination method (Sandelowski and Barroso model) was used. Factors and dimensions of knowledge management system evaluation for which indicators are considered were provided to 20 members and experts. The implementation of the Delphi panel was carried out in two periods. Fuzzy Delphi method was used to screen and identify the final indicators and to answer the first and second questions of the research regarding the agreement of the experts of the research community regarding the obtained components, which includes software experts and knowledge management experts. 7 main categories including individual factors, organizational factors, technology and infrastructure, functional factors, knowledge management tools, economic factors, and 29 sub-categories were identified. In order to collect quantitative data, a researcher-made questionnaire (40 items) was used, the items of which were taken from the results of the meta-composite analysis in the first stage. In this research, in order to check the reliability of the research questionnaire, Cronbach's alpha coefficient was estimated at 0.89 for infrastructure factors and 0.88 for functional factors, respectively.Results In this research, the performance of the knowledge management system was evaluated with a neural network approach. Examining the results showed that the following components affect the evaluation of knowledge management performance in the software development industry. 1. Individual factors 2. Economic factors 3. Organizational factors 4. Knowledge management processes 5. Functional factors 6. Technological infrastructure factors 7. Knowledge management toolsDiscussionSolutions to improve the performance of knowledge management in the software development industry were presented: • Adjust the strategies in such a way that the creation of new knowledge, the application of new knowledge, its dissemination and sharing, and the storage and documentation of knowledge are explicitly considered. • Identifying influential people in the process of implementation and establishment of knowledge management, to improve the effective factors in the effective establishment of knowledge management more than in the past. • Developing procedures for documenting the experiences of experts in the software development industry on a continuous basis. • Managers and practitioners of the software industry should also consider parameters such as available budget, organizational culture, infrastructure, etc. • To provide the relevant managers and practitioners with a criterion for reviewing future policies and investments and help them make more appropriate decisions.ConclusionIn this research, 29 primary indicators have been identified based on the research literature, which include: • Organizational culture for sharing and using knowledge • Organizational Structure • The physical environment • Organization strategy • Support of senior managers such as motivation and commitment • Supporting innovations and digital technologies • Specialized knowledge of software development • General knowledge in software development • Involvement of developers • Education • Being up-to-date in the fields of specialized software • Knowledge and awareness of the knowledge management system • Correct understanding of system design requirements • Portals and portals of knowledge such as the Internet and email and social networks • MIS, Expert, DSS systems • Data warehouse - knowledge warehouse • Search and recovery tools and dashboard • Data security • The degree of integration of organizational systems • Quality of knowledge • Document management • Data management and workflow • Process Management • Creation and acquisition of knowledge, transfer and sharing of knowledge • Acquisition and use of knowledge • Operating cost of the software • Cost of software support.
