Mixed Approach

Purpose: Considering the quantitative growth of university growth and technology centers in the country and the need for solutions to facilitate the success of these companies in global competition, this study identifies the indicators of innovation ecosystem of university growth and technology centers with the knowledge-based economy approach with data foundation approach. Methodology: This research was based on empirical philosophy and has been done with a deductive-inductive approach. From the perspective of the goal, it was a fundamental research that has been done with a mixed research method. The study population of this study includes entrepreneurship professors and managers who have work experience in knowledge-based companies. Sampling was done by non-probabilistic and purposeful sampling method. Data collection tools are interviews and questionnaires. In the analysis of the interviews, the data theory of the foundation (selective, open and pivotal) was used. Also, the identified indicators have been validated by fuzzy Delphi method. Finally, the final model of the research is presented. Research data analysis was performed in the qualitative phase with MAXQDA software and in the quantitative phase with Matlab software. Findings: To achieve the objectives of the research, by analyzing interviews with experts, a set of practical indicators of the innovation ecosystem were identified. In the open coding stage, 517 codes were identified, which with the observed observations, 9 main categories and 41 sub-categories were obtained. Conclusion: Based on the achievements of this study, issues such as organizational management, growth center rules and regulations, innovation ecosystem infrastructure, knowledge ecosystem, innovation ecosystem, ecosystem capital, economic ecosystem, innovation ecosystem challenges and key players are key elements in the success of these universities in knowledge-based economy was considered. These cases also interact with each other in 41 minor areas.

In recent decades, mentoring has been hailed as an important human resource management strategy, a career tool, and a learning activity in the workplace, and has played an important role in enhancing the positive outcomes of a high-performance work system. Therefore, the purpose of this study is to provide a model of mentoring system in the Ministry of Interior based on a mixed approach. First, based on the data theory of the foundation, 12 experts were interviewed. Sampling was done purposefully with a network of experts and continued until the theoretical saturation was reached. According to the research model, causal, pivotal, contextual, interventionist, strategic and consequences variables were identified. The results showed that mentoring will have consequences such as increasing knowledge sharing, organizational performance excellence, productivity, job satisfaction, psychological well-being, client satisfaction, increasing public trust in the organization, increasing trust in government and improving political understanding. For quantitative validation of the model and study of component relationships, the structural equation modeling approach was used using the partial least squares method using smart-PLS software. The statistical sample of this department was 237 managers and experts of the Ministry of Interior. The results of the quantitative step showed that the relationships in the model with appropriate impact coefficients were confirmed.

The automotive industry, as a job-creating and infrastructure industry, needs an executive model for success in the domestic and international markets. In this regard, the present study has been conducted with the aim of designing a model for implementing the fourth generation industry to achieve sustainable development goals in Iran Khodro Company. The study is an applied-developmental study in terms of purpose and cross-sectional survey research. Also, in this study, a mixed research method (qualitative-quantitative) was used. Content analysis method and MaxQDA software were used for data analysis in the qualitative section. Then, using Interpretive Structural Modeling (ISM) with MICMAC software, the initial pattern was drawn. In the quantitative section, one-sample t-test and SPSS software were used to measure the current situation. The research findings showed that the Collection and Analysis of Big Data affects the Simulation and Automatic Robots. These factors affect horizontally and vertically integration systems and thus lead to the Internet of Industrial Things, Augmented Reality and Cyber Security. Further, through the Cloud Computing system, Additive Manufacturing is affected and this Additive Manufacturing leads to Sustainable Development.

Purpose: The present study aims to provide a model for analyzing the brand-customer relationship on Instagram in the cosmetics industry. Method: In this study, qualitative and quantitative methods have been used to analyze the data. The statistical population in the qualitative phase includes brand marketing experts and in the quantitative phase consists of customers of the cosmetics industry. Sampling has been non-probabilistic and purposeful in the qualitative phase, and random using Cochrane formula in the quantitative phase. Interviews and questionnaires were utilized as data collection tools. Content analysis was used to analyze specialized interviews. Moreover, the identified indicators were validated by Fuzzy Delphi method. Then, using the structural-interpretive method, the initial model is designed and finally, using the partial least squares method, the final model of the research is presented. The analysis of research data in qualitative quantitative phases has been done with MaxQDA software and Smart PLS software, respectively. In order to achieve the objectives of the study, a set of practical indicators of brand-customer relationship were identified on Instagram through analyzing experts’ interviews. Findings: The findings reveal that brand performance influences the positioning and creating of brand and brand value. Brand awareness and brand personality are among the indicators that affect brand performance. Furthermore, marketing strategy affects the quality of services. Conclusion: It is recommended that various entertainment tools such as videos, applications, in cyberspace on Instagram be posted and create a happy and fun atmosphere in cyberspace along with humor, contest design, and puzzles related to the type of activity and brands.