Presenting an Open Data Innovation and Value Creation Model and Ranking the Challenges and Obstacles of Open Data in Knowledge‐Based Companies

The main goal of this research is to present a model of innovation and value creation of open data and to rank the challenges and obstacles of open data in knowledge‐based companies. The research method was a combination (qualitative‐quantitative), and the data collection tools were interviews and questionnaires. The qualitative part was done using grounded theory. A questionnaire was developed based on the results of the qualitative section and theoretical foundations. In the quantitative section, descriptive statistics and inferential statistics (structural equation modeling and non‐parametric test (Chi‐square)) were used to analyze the findings using SPSS and smart‐pls3 software. The fuzzy TOPSIS method was used to rank the challenges and barriers of open data. The findings of the qualitative section include causal conditions, strategies, intervening and contextual conditions, and their consequences. The quantitative results showed that the dimensions of facilities and infrastructure, cost and product, skills, licenses, and cultural and organizational factors significantly affect innovation in open data. In addition, these mentioned dimensions significantly affect value creation in open data. The results also showed that the large number of actors in the open data process, the lack of open data tools and infrastructure, and the lack of resources are among the most important challenges and obstacles to open data. This research aims to focus on open data's value‐creation capacity and the necessity of using the open data approach to develop data‐oriented and knowledge‐based businesses.