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 Review

In 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.

Methodology

The 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 tools

Discussion

Solutions 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.

Conclusion

In 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.