درخت حوزه‌های تخصصی

Today, the sports industry is known through modern technology, especially in the field of manufacturing sports tools and equipment. The present study uses correlation and applied methodology, which was done in the field using Structural Equations Modeling (SEM). The statistical population included producers of sports products, of which 320 producers from Iran were considered through available sampling. Morris (2002) Standard Entrepreneurial Marketing Questionnaire, Moghimi Market Orientation (2011), Noh and Fitzsimmons Information Technology (1999), and Azizi (2011) Financial Performance Questionnaire were used to collect data. Finally, 292 questionnaires were received physically and electronically. The results showed that entrepreneurial marketing and market orientation had a direct and indirect effect (mediated by technology) on financial performance and they significantly affected the financial performance. Finally, the model had the goodness of fit. In general, manufacturers who adopted a combination of entrepreneurial marketing strategy and information technology had better and more rational financial performance by taking advantage of attractive entrepreneurial opportunities. According to the research results, it is suggested that manufacturers provide the consumers with superior value by paying attention to the market orientation and innovation, to enable the company to achieve a competitive advantage.

The main purpose of this study was to provide a framework of effective components in Iranian championship sports with a data mining approach. The research method was qualitative. The advanced search was performed in the general framework of factors affecting the sports industry and development and integration with data-driven technologies. Based on the literature review, 15 frameworks in the field of sports and 13 frameworks for the development and integration of the sports industry with technology were identified. After the data analysis, a researcher-developed framework for the championship sports industry for the use of data-driven technologies and data mining was presented in three parts. In the first part, nine influential factors including athletes/champion teams, leagues/clubs, stadiums/sports venues, fans/spectators, brands, media, government, academic/research institutions, and technology companies, were identified. In the second part, a strategic plan based on the development and integration of sports industry with data-driven technologies and data mining was presented, which includes four stages: Identifying and selecting talents, pre-game and match preparation, in-game and match activities, and post-match and match analysis. All data-driven activities and data mining in the first and second sections were performed by the IBM data science analysis methodology presented in the third section. Then, a conceptual framework was provided to 7 experts and their opinions were collected through semi-structured interviews and focus group methods. This conceptual framework enables sports managers to plan for their organization and adopt appropriate strategies using data-driven technologies and data mining.