Leveraging AI and Strategic Management for Sustainable Digital Transformation in the Iranian Telecom Industry

Abstract

Iran’s telecom industry is rapidly evolving as artificial intelligence (AI) becomes integrated into daily operations and service delivery. While many studies show that AI enhances performance, less is known about how AI capabilities and strategic management work together to drive sustainable digital transformation, particularly in emerging markets. This study addresses this gap by integrating four theoretical perspectives—the Technology Acceptance Model (TAM), Resource-Based View (RBV), Dynamic Capabilities (DC), and the Triple Bottom Line (TBL)—into a unified AI–Strategy–Sustainability framework.
A sequential explanatory mixed-methods design was adopted; however, this paper reports only the quantitative strand. Survey data were collected from 300 professionals at major Iranian operators (MCI, Irancell, Rightel) and analyzed using LISREL-based Structural Equation Modeling (SEM). The model demonstrated a good fit (χ²/df = 2.41, CFI = 0.94, RMSEA = 0.056). All hypotheses were supported: AI capabilities → digital transformation (β = 0.61, p < .01), strategic management → digital transformation (β = 0.55, p < .01), and AI capabilities → sustainability outcomes (β = 0.48, p < .05). The model explained a substantial proportion of variance (R²_DT = 0.52; R²_SUS = 0.23), indicating meaningful effect sizes.
Contribution: The findings demonstrate that AI capabilities and strategic management jointly explain R²_DT = 0.52 and R²_SUS = 0.23, extending the integration of TAM, RBV, and DC into a TBL outcome space. This shows that aligning AI adoption with strategic planning enhances both digital performance and sustainability outcomes in emerging-market telecom contexts.
Although the broader research explored additional analyses, including potential moderation and mediation effects, this paper focuses exclusively on the direct pathways tested in the conceptual model.