Designing a Business Intelligence Model Based on Systems Thinking

Abstract

The purpose of this study is to design and validate a business intelligence model grounded in systems thinking for small and medium-sized manufacturing enterprises, particularly within the oil and gas industry. The model aims to elucidate the dynamic interaction among technology, human resources, and processes, thereby facilitating improvements in organizational performance and agility. This research is applied in purpose and employs a mixed-methods approach with a predominance of qualitative analysis. In the qualitative phase, using comparative content analysis and semi-structured interviews with experts and managers in business intelligence and information technology, the components and dimensions of the model were extracted and enriched. Sampling was purposive and continued until theoretical saturation was achieved. In the quantitative phase, the conceptual model derived from the qualitative analysis was tested using a researcher-made questionnaire among managers and specialists of small and medium-sized manufacturing enterprises in the oil and gas sector. Data were analyzed through confirmatory factor analysis and structural equation modeling using LISREL software, and the model’s validity and reliability were assessed. Qualitative findings indicated that business intelligence is a multidimensional and systematic phenomenon, not limited solely to technology, but emerging from the interaction of human, technological, and knowledge factors. Accordingly, eleven principal dimensions were identified, including reporting technologies, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and multidimensional analysis. Quantitative results demonstrated that the proposed model possesses appropriate validity, reliability, and fit, with all model paths exerting significant positive effects on business intelligence. Among these, benchmarking, complex event processing, and analytics contributed most substantially to explaining business intelligence. The findings suggest that business intelligence represents a dynamic organizational capability based on systems thinking, which can enhance decision-making, organizational agility, and performance through the synergy of data analysis, organizational learning, and data-driven culture. The presented model can serve as a practical framework for implementing, evaluating, and developing business intelligence in small and medium-sized enterprises for managers and policymakers.