An integrated p,q-quasirung orthopair fuzzy decision-making approach for strategic selection of competitive intelligence platforms

In an increasingly globalized world, Competitive Intelligence (CI) plays a vital role for export-oriented businesses aiming to maintain a competitive advantage and identify opportunities in international markets. Accurate, timely, and comprehensive information is essential for understanding market dynamics, evaluating competitors, and analysing customer behaviour. However, selecting reliable commercial intelligence websites presents challenges, such as issues of data quality, pricing, usability, and coverage. This study addresses these challenges by introducing a scientific decision-making framework using a fuzzy Multi-Criteria Decision-Making (MCDM) approach to handle the uncertainty in the selection process. The research proposes a novel decision-making model based on p,q-Quasirung Orthopair Fuzzy Numbers (p,q-QOFNs), applying p,q-quasirung operators to calculate expert weights. It integrates the Simple Weight Calculation (SIWEC) and Multi-Attributive Border Approximation Area Comparison (MABAC) methods, called “p,q-quasirung-SIWEC-MABAC”, to determine criteria weights and rank website alternatives. This model enhances the integration of subjective evaluations, improving both robustness and efficiency in decision-making. A case study validates the model's practical application in evaluating CI websites, supported by sensitivity and comparative analyses confirming the model's reliability across diverse scenarios. Findings highlight that data security and reliability are the most critical factors in CI website selection. Among the evaluated platforms, A3 emerges as the top choice due to its detailed insights into textile import trends and supplier analysis. This research contributes a unique methodology to CI literature by enhancing export decision-making processes through advanced fuzzy logic techniques, ultimately helping businesses navigate the complexities of global trade more effectively.