## 3 Research Methods
### Evaluation of the Application Maturity of Enterprise AI Technology  
#### 3.1.1 Construction of the Evaluation Index System  
An evaluation index system to measure the application maturity of enterprise AI technology was constructed via grounded theory. The data, which were analyzed using grounded theory, came from expert interviews and enterprise AI-related data collected from various channels. Grounded theory is different not only from the single top-down logical deduction of quantitative empirical research but also from the inductive method usually used in qualitative research. In grounded theory, induction and deduction are used interchangeably. In the process of induction, researchers collect a large amount of data through participatory observation and in-depth interviews and then continuously analyze, compare, classify, conceptualize, and categorize the data, thus gradually forming a theoretical framework. After summarizing and absorbing the content of the data, this study deduced the possible connections between categories according to the existing data, and then deduced the preliminary concepts, which is the process of deduction. Previous studies rarely describe the structural dimension of the application maturity of AI technology, and the application content of AI technology in enterprises is diverse and complex. Therefore, this study adopted grounded theory to explore the composite dimensions of the application maturity of enterprise AI technology. In accordance with the operating principles proposed by grounded theory, this study encoded the data systematically according to grounded theory operational procedures and encoded the data step-by-step using NVivo 11 software. Step-by-step coding can be divided into open coding, spindle coding, and selective coding. To check the validity of the results, a quarter of the sample was reserved for a theoretical saturation test before the study, which was again incorporated into the grounded theory analysis process to verify the extent of coverage of the categories constructed by the previous theoretical model. After the test, there were no new important concepts or categories that affect the original core category, and there were no new relationship structures between categories, indicating that the theoretical saturation test of the data was passed. Finally, this study extracted 67 initial concepts, 24 initial categories, 8 main categories, and 4 core categories after classifying and summarizing the original data. The specific meanings of the main categories are shown in Table 1.  
Through grounded theory analyses, and taking into account the basic principles of constructing indexes, 4 first-level indexes, 8 second-level indexes, and 24 third-level indexes were selected to construct an initial evaluation index system for the application maturity of the AI technology of film and television enterprises, which allows for the evaluation of the application maturity of enterprise AI technology at all levels. On the basis of the results of the expert interviews, the evaluation indexes were screened, and by deleting, merging, and adding indexes, an evaluation index system for the application maturity of the AI technology of film and television enterprises, which consisted of 4 first-level indexes, 7 second-level indexes, and 21 third-level indexes, was finally determined, as shown in Table 2. Taking film and television enterprises as examples, from the perspective of maturity composition, the application maturity of enterprise AI technology was assessed mainly based on the aspects of intelligence strategy, intelligence technology, intelligent business operations, and intelligent organizational management. The maturity of the intelligence strategies reflects the importance that enterprises attach to the application of AI technology, the intelligent layout, and the implementation of intelligence strategies from a global perspective. Intelligence technology maturity reflects the enterprise's intelligent technologylevel and the integration levels of the business processes.