Optimizing Performance and Increasing the Reliability of Small and Medium Enterprises Using a Modified Hybrid Artificial Intelligence Model
الموضوعات : Artificial Intelligence Tools in Software and Data EngineeringGohar Varamini 1 , Dr. hassan Soltani 2
1 - Department of Electrical Engineering, Bey. C., Islamic Azad University, Beyza, Iran
2 - Department of Management,Shiraz Branch,Islamic Azad university, Shiraz,Iran
الکلمات المفتاحية: Artificial Intelligence, Modified Multi-Medium Models, Small and Medium Enterprises, Optimization,
ملخص المقالة :
Extended Abstract
Article type: Research Article
Article history: Received 13 May 2025, Revised 13 July, 2025, Accepted 24 Oct. 2025 Published 30 Oct. 2025
Introduction & Problem Statement: In the modern business era, SMEs face increasing pressure to meet customer expectations through rapid and accurate intelligent response systems. While artificial intelligence (AI) and blockchain are recognized as essential catalysts for productivity, empirical research on how AI acquisition specifically improves customer acquisition and corporate performance remains in its infancy. A critical gap exists in understanding the intermediary layers—such as data privacy, decentralized data heterogeneity, and the lack of specialized expertise that influence the relationship between AI uptake and operational success, particularly within the context of emerging economies.
Research Objective & Hypotheses: This study aims to provide a comprehensive understanding of the relationship between AI absorption and the optimal performance of SMEs using a dynamic capability perspective framework. The primary hypothesis suggests that the AI attraction hypothesis has a positive effect on absorption capacity (AC). Additionally, the research explores how a modified parallel multi-mediary model can optimize the reliability factor and strategic asset utilization.
Methodology: The study employs a modified hybrid AI model, incorporating deep learning (DL) and federated learning to enhance data privacy. Statistical analysis was conducted using SPSS for descriptive statistics and AMOS for confirmatory factor analysis (CFA). A multi-factor estimation (EFA) was utilized, identifying five distinct factors explaining 67.221% of the total variance. The model also utilizes LSTM structures and ConvNet algorithms to process input data with high accuracy.
Findings: Results indicate that AI absorption has a direct positive impact on customer acquisition (CA) and firm performance. Organizational agility (OA) and absorption capacity (AC) were found to be critical mediating factors that complement the ability of a firm to extract value from AI technologies. The study also highlights that AI-powered personalization, similar to models used by Amazon and Netflix, significantly boosts customer satisfaction and sales.
Conclusion: The research concludes that SMEs can only realize the full value of AI by integrating it into end-to-end processes and investing in complementary capabilities like digital literacy and IT infrastructure. Managers are encouraged to focus on customer needs and the integration of externally acquired knowledge to adapt to volatile market conditions. Ultimately, combining AI with human expertise remains the cornerstone for maintaining a competitive advantage in developing markets.
Keywords: Artificial Intelligence, Modified Multi-Medium Models, Small and Medium Enterprises, Optimization, Learning Agility.
Cite this article: Gohar Varamini, Hassan Soltani. (2025). Optimizing Performance and Increasing the Reliability of Small and Medium Enterprises Using a Modified Hybrid Artificial Intelligence Model. Journal of Artificial Intelligence Tools in Software and Data Engineering (AITSDE), 3 (2), pages.
© G. Varamini, H. Soltani. Publisher: Yazd Campus (Ya.C.), Islamic Azad University
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