Linking Digital Intelligence to Sustainable SME Performance through Green Innovation

This study adopts a quantitative research design to examine the relationships among Digital Intelligence, Green Innovation, and Sustainable SME Performance. The target population consists of small and medium enterprises operating in West Java, Indonesia. SMEs were selected because they represent a dominant portion of economic activity and are increasingly undergoing digital transformation while facing growing sustainability challenges. Data were collected using a structured questionnaire distributed to SME owners or managers, who are considered the most knowledgeable informants regarding digital capability, innovation activities, and sustainability performance.
The study employs a reflective measurement model, in which all constructs are conceptualized as latent variables and measured by multiple indicators. Digital Intelligence was measured using five indicators reflecting digital capability, data utilization, technology integration, digital decision support, and responsiveness to digital information. Green Innovation was measured using five indicators representing environmentally friendly innovation activities, including eco-process innovation, waste reduction, energy efficiency, cleaner production, and environmentally oriented product or process improvement. Sustainable SME Performance was measured using five indicators capturing long-term sustainability outcomes, including resource efficiency, environmental performance, operational sustainability, long-term competitiveness, and sustainable production practices. The measurement items were adapted from prior validated studies in digital capability, eco-innovaton, and sustainability literature.
Data were analyzed using Partial Least Squares Structural Equation Modeling, a variance-based structural equation modeling technique suitable for prediction-oriented research, complex models, and studies involving SMEs or emerging economy contexts. The analysis followed two stages. First, the measurement model was evaluated to assess reliability and validity. Convergent validity was assessed using outer loadings and Average Variance Extracted, with recommended thresholds of 0.70 and 0.50, respectively. Internal consistency reliability was evaluated using Composite Reliability and Cronbach’s Alpha, with acceptable values of 0.70 or higher. Discriminant validity was examined using the Heterotrait-Monotrait ratio to ensure construct distinctiveness