International Journal of Metaverse & Virtual Transformation

Abstract This research was conducted with the objective of designing an intelligent implementation model for industrial policies based on Article 4 of the 7th Development Plan, specifically focusing on Megamotor Saipa. The primary problem involves transforming macro-level industrial policies into operational smart mechanisms at the organizational level by leveraging modern data-driven governance approaches and digital technologies, such as the industrial metaverse and digital twins. The methodology follows a mixed-methods (qualitative-quantitative) design, utilizing Grounded Theory in the qualitative phase and Structural Equation Modeling (SEM) in the quantitative phase. During the qualitative stage, data were collected through semi-structured interviews with automotive experts, senior managers, and technology specialists, which were then analyzed through rigorous open, axial, and selective coding. In the quantitative stage, the relationships between the model’s dimensions were tested using SEM and statistical analysis software, confirming the overall model fit and validity. The findings reveal that successful implementation is driven by causal factors including strategic leadership, sustainable financing, and governance mandates. Furthermore, contextual conditions such as digital infrastructure, policy stability, and organizational culture act as facilitators, while intervening conditions like international sanctions, employee resistance, and financial limitations function as deterrents. Executive strategies include digital twin development, metaverse-based training, reverse engineering, and industrial consortiums, leading to increased productivity, innovation, and enhanced organizational sustainability. Ultimately, the proposed model suggests that transitioning toward intelligent implementation requires a shift from traditional management to data-driven digital governance.

Abstract This study investigates the impact of financial market liberalization indices and intelligent digital financial services on total factor productivity (TFP), with particular emphasis on their dynamic effects across different time frequencies within the context of digital transformation. Initially, variable reliability was evaluated using the Hegyi and Phillips-Perron unit root tests; Subsequently, the Johansen-Juselius cointegration test confirmed a robust long-run equilibrium relationship among TFP, financial market liberalization, and intelligent digital financial services. The estimation results reveal that financial liberalization indicators—including stock market depth, foreign direct investment, financial depth, and trade openness—positively and significantly enhance TFP. Conversely, macroeconomic instability, reflected in exchange rate volatility and rising interest rates, reduces productivity performance. Credit market frictions, including non-performing loans, loan loss reserves, and government debt, also exert significant adverse effects on productivity growth. In contrast, intelligent digital financial services and smart financial infrastructure strengthen productivity by improving digital resource allocation, reducing information asymmetry, lowering transaction and information costs, enhancing financial transparency, and supporting more efficient digital financial intermediation. Overall, the findings indicate that the productivity gains generated by financial market liberalization are substantially reinforced when supported by digital financial transformation and intelligent financial infrastructure, whereas credit frictions and structural banking inefficiencies may offset these benefits. Accordingly, the study highlights that coherent fiscal and financial policies, together with strategic investment in digital financial technologies, fintech innovation, artificial intelligence-enabled financial services, and smart banking infrastructure, are essential for achieving sustainable improvements in total factor productivity and accelerating the transition toward a resilient digital economy.

Abstract This study examines the relationship between external debt and sustainable economic growth, considering institutional quality, smart governance, and digital transformation in Iran and selected Middle East oil countries. Using annual data for Middle East oil countries (2000–2023) and Iran (1991–2024), the study applies a Panel Smooth Transition Regression (PSTR) model for the panel analysis and a Logistic Smooth Transition Regression (LSTR) model for the country-specific time-series analysis to capture nonlinear relationships. The research is descriptive-correlational and applied. In the panel analysis, LLC and IPS unit root tests confirmed stationarity, and the Kao test established a long-term cointegrating relationship. Linearity tests supported the use of the PSTR model. In Iran’s time-series analysis, Phillips–Perron and Johansen tests confirmed long-run equilibrium, while linearity tests justified the application of the LSTR model. The LSTR estimates indicate that external debt negatively affects sustainable economic growth under nonlinear conditions, with the adverse effect strengthening after crossing the −0.459 threshold. Furthermore, institutional quality, regulatory quality, smart governance, digital transformation, financial development, trade openness, and the rule of law exhibit positive and significant effects, whereas inflation and budget deficits negatively affect growth. Diagnostic tests confirm coefficient stability and the absence of autocorrelation and heteroscedasticity. Overall, the findings suggest that strengthening smart governance, institutional quality, and digital transformation can mitigate the adverse effects of external debt, providing a robust framework for fiscal and debt policy formulation in Middle Eastern oil-producing countries and Iran.

Abstract To compete effectively in today's competitive environment and achieve long-term sustainability, rural entrepreneurial enterprises need to leverage modern digital interactions while strengthening private partnerships. As the smallest social unit, villages play a fundamental role in economic and social development, and rural entrepreneurship is recognized as one of the key drivers of sustainable rural development. In this context, modern digital interactions facilitate communication, knowledge sharing, collaborative networks, and coordinated decision-making, while active private-sector participation enhances investment, innovation, and long-term business resilience. Accordingly, the present study aimed to identify the sustainability themes of rural entrepreneurial enterprises based on modern digital interactions and private partnerships through a qualitative research strategy. The study employed thematic content analysis based on the Braun and Clarke (2006) model. Data were collected through semi-structured, in-depth interviews and analyzed using open and axial coding procedures. The research population consisted of 12 experts, including university professors, sustainability and entrepreneurship consultants, and managers of rural small and medium-sized entrepreneurial enterprises. Purposive sampling was applied until theoretical saturation was achieved. The findings revealed that the sustainability of rural entrepreneurial enterprises is shaped through the integration of modern digital interactions and private partnerships, resulting in a comprehensive thematic framework comprising ten overarching themes: economic sustainability, social sustainability, environmental sustainability, governance and leadership, private partnerships, innovation and entrepreneurship, digital transformation, organizational resilience, ESG and strategic sustainability, and infrastructure and institutional environment. These themes were further supported by 236 basic themes, collectively forming a comprehensive sustainability model for rural entrepreneurial enterprises.