IoT adoption for sustainable performance in SMEs using an SEM-ANN approach

This research examines the adoption of the Internet of Things (IoT) by small and medium enterprises (SMEs) in Bangladesh using the Technology-Organization-Environment (TOE) model and the Resource-Based View (RBV) theory. This study integrates these frameworks to assess the factors influencing IoT adoption and its impact on firm performance. Furthermore, it seeks to gauge the economic dimensions of IoT-integrated SMEs in developing countries and investigates the effects of IoT adoption on organizational and environmental sustainability. A sample of 149 Bangladeshi professionals from agro-processing, consumer products, e-commerce, leather, and other IoT-based SME sectors was analyzed. A hybrid Structural Equation Modeling–Artificial Neural Network approach was employed to capture complex linear and non-linear relationships among variables. To ensure robustness, the Partial Least Squares model was applied, followed by the bootstrapping method for resampling. The findings indicate that complexity, competitive pressure, top management support, external vendor support, and regulatory frameworks significantly drive IoT adoption in SMEs. Moreover, IoT adoption positively influences both financial and environmental performance, fostering sustainability within SMEs. Hence, this study offers valuable insights for policymakers and researchers regarding the key factors necessary for the successful adoption of IoT in SMEs, particularly in developing economies. The findings also highlight how IoT technologies enable end-to-end monitoring and optimization of environmental performance, improving resource efficiency, reducing operational costs, and enhancing corporate reputation among consumers and stakeholders. Thus, IoT adoption not only aligns with global environmental protection initiatives but also spawns economic benefits.

Text 123337.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) Official URL or Download Paper: https://link.springer.com/article/10.1007/s43926-0...

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