تحلیل عوامل موثر بر طراحی مدل اقتصاد پلتفرمی در ایران متاثر ازهوش مصنوعی

Abstract
Objective
This research investigates the factors influencing the design of a platform economy model in Iran, with a particular focus on the transformative impact of Artificial Intelligence (AI).
Research Methodology
The study employs a mixed-methods exploratory approach, combining qualitative and quantitative research techniques to provide a comprehensive understanding of the subject. The qualitative phase of the research is designed to achieve an in-depth and holistic understanding of the factors affecting platform design. This phase involves several stages. First, the structure and operational mechanisms of successful platform businesses, specifically Alibaba and Amazon, are examined through thematic analysis. This initial analysis provides a foundational understanding of established platform models and their key success factors.
Following the analysis of existing platforms, a purposive sampling strategy is employed to select key informants. Twelve experts are chosen based on their expertise and experience in digital economics, platform design, and AI. This panel includes content production specialists, sales and marketing managers, decision-makers from chambers of commerce and relevant ministries, academic experts, business model designers, managers of successful Iranian economic platforms, and designers of AI-based platforms. The selection criterion prioritizes individuals with demonstrable experience and knowledge in the relevant domains. Semi-structured interviews are conducted with these experts, continuing until theoretical saturation is reached, ensuring that no new insights are emerging from subsequent interviews.
The interview questions are structured based on the thematic analysis of successful platform models and comprise twelve key questions. These questions focus on identifying the transformations resulting from the integration of AI, specifically concerning its impact on audience behavior analysis, brand enhancement, the development of technology-driven infrastructures, and adaptation to evolving legal frameworks. The questions aim to uncover both the critical success factors and the challenges associated with platform design in the Iranian context.
The data obtained from the interviews are analyzed using grounded theory methodology, involving open, axial, and selective coding. This rigorous coding process facilitates the extraction of the core components of the platform model. The grounded theory approach allows for the development of a theory that is grounded in the data, rather than imposed upon it, ensuring that the resulting model is reflective of the realities of platform design in Iran.
In addition to the interviews, a thorough review of national and international commercial and legal documents and regulations is conducted, with a specific focus on AI and digital platforms. The thematic analysis of these documents and regulations helps to identify the legal and regulatory components that significantly influence platform implementation. This ensures that the proposed platform model is not only economically viable but also legally compliant.
Finally, the findings from the interviews and the thematic analysis of documents and regulations are integrated using the grounded theory approach. This integration leads to the identification of the factors influencing the implementation and success of a joint commercial platform. A paradigmatic model of the research is then designed, outlining the causal, contextual, intervening, and strategic conditions, as well as the consequences of platform implementation. This model provides a comprehensive framework for understanding the dynamics of platform development in Iran.
The validity of the model is ensured through data triangulation, using multiple sources of data to corroborate the findings. Rich and detailed descriptions are provided to enhance the robustness of the research, and meticulous documentation of all stages of the research process ensures its reliability and replicability.
The quantitative phase builds upon the paradigmatic model developed in the qualitative phase. A quantitative instrument, specifically a questionnaire, is designed based on the qualitative findings. The purpose of this phase is to assess the validity and generalizability of the findings obtained through qualitative data by using quantitative data.
The researcher-developed questionnaire, based on the theoretical model from the qualitative phase, includes 69 questions using a Likert scale. This scale allows for the collection of expert opinions regarding the relationships and impact of the variables within the model. Prior to widespread distribution, the validity and reliability of the data collection instrument (questionnaire) are examined and refined through preliminary studies. This ensures that the questionnaire accurately measures the constructs of interest and that the responses are consistent and reliable.
The statistical population for the quantitative phase consists of experts active in digital economics and AI who are currently working on media and economic platforms. Given the unlimited access to the complete list of experts, purposive sampling is used. Therefore, only individuals with in-depth experience in e-commerce, platforms, AI, and related laws are included in the sample. This ensures that the participants have the necessary expertise to provide informed opinions on the research topic.
Ultimately, the questionnaire is sent to 221 experts. The sample size is determined using Cohen's sample size calculation software, based on structural equation modeling. Data collection is carried out through field methods, including direct visits to platforms and questionnaire distribution. The data obtained from the questionnaires are analyzed using statistical analysis of structural equations and PLS software. Structural equation modeling is used to test the relationships between the variables in the model, and PLS software is used to estimate the parameters of the model.
Findings
In the qualitative part of the research, the components extracted from the results of interviews with experts, using the grounded theory method (including open, axial and selective coding), along with the findings obtained from careful monitoring and thematic analysis of documents and legal documents related to trade and cyberspace, entered the process of compiling the basic theory. This approach led to the identification and determination of causal, pivotal, strategic, contextual and intervening factors, as well as the identification of related factors and consequences. These analyses were integrated within the framework of the Strauss and Corbin grounded theory model and transformed into a coherent theoretical model. The research findings include 35 main categories and 120 sub-categories. These categories represent the key themes and concepts that emerged from the data.
In the quantitative part of the research, the main components of the model were identified and the relationships between the components were evaluated and their significance was confirmed with a confidence level of 95%. This confirms the statistical significance of the relationships between the variables in the model, providing further support for the findings of the qualitative phase. The integration of the qualitative and quantitative findings provides a robust and comprehensive understanding of the factors influencing the design of an AI-driven platform economy model in Iran.
Discussion & Conclusion
Based on the results, audience behavior and the development of smart services have the greatest impact on the development of support strategies, branding, and effective interaction with customers. Support and branding strategies also play an important role in amending existing laws and protecting authors' rights. Developing and strengthening user infrastructure and reviewing the legislative system are prerequisites for the success and sustainability of the platform economy model; in that legal and regulatory policies directly affect the improvement of institutional and strategic outcomes and are a key factor in increasing public trust and market stability, and emphasize the importance of promoting support policies, standardizing services, and redesigning laws by considering developments in artificial intelligence. Finally, this study highlights the constructive interaction between user behavior, practical policymaking, and legal infrastructure as key factors for the success of Iran's platform economy model, and explains domestic infrastructure gaps in more detail and by suggesting practical solutions.