Activation of Artificial Intelligence Technology Capacities in Expanding Economic Strategies and National Macroeconomic Policies with a Piloting Approach
Keywords:
Artificial Intelligence Technology, Economic Strategies, Macroeconomic Policies, Piloting ApproachAbstract
The present study aimed to activate the technological capacities of artificial intelligence in expanding economic strategies and national macroeconomic policies. This research, conducted with a piloting approach to improve the research process through identifying weaknesses, refining tools and methods, and preventing issues that may arise on a larger scale, is applied in terms of its purpose and qualitative in nature, using a data-driven and exploratory approach. It was carried out in two phases. In the first phase, the participants were experts from the industrial and academic sectors (managers of knowledge-based companies in the field of information technology and university professors with a Ph.D. in economics and technology). These participants were selected using purposive sampling based on the principle of theoretical saturation, and 18 individuals were chosen as the sample. The data collection tool was semi-structured and in-depth interviews with participants. Initially, the data obtained from the interviews were implemented based on the systematic approach of Strauss and Corbin, and analyzed in three stages: open coding, axial coding, and selective coding. The validity and analyses conducted were confirmed by the interviewees. In the second phase, the results of the analysis (from phase one) were formulated into a questionnaire and distributed in three rounds using the Delphi method among 42 managers (from startups, organizations, and governmental institutions). In the first round of Delphi, the questionnaire was distributed among 15 experts, and after calculating the Kendall's concordance coefficient of 0.564, 25 components were eliminated. In the second and third rounds of Delphi, the questionnaire was distributed among 15 and 12 individuals, respectively. Since the significance level for all components was less than 5%, no components were eliminated in the second and third rounds, and the Kendall's concordance coefficient was calculated as 0.600 and 0.788, respectively, indicating strong consensus among the members. Finally, the research model was developed with 6 main categories and 55 subcategories.
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