Ranking mechanisms for assessing the competitiveness of listed companies in the Philippines: A comparative analysis
Wann‐Yih Wu et al.
What the paper says
This study evaluates the competitiveness of 269 listed firms in the Philippines using an integrated framework that combines multi-criteria analysis, two-stage Data Envelopment Analysis (DEA), and Back Propagation Neural Network (BPNN). Drawing on the Resource-Based View (RBV) and Dynamic Capability Theory (DCT), the research investigates how functional capabilities in marketing, operations, and human resources influence both profitability-based and market value-based performance. The Enterprise Competitiveness Index (ECI) synthesizes performance (productivity and profitability) and potential (market image, stability, and growth), offering a comprehensive and standardized basis for ranking firms. Results reveal significant misalignments: some firms rank high in short-term performance but low in long-term potential, while others show strong future competitiveness despite current inefficiencies. These insights emphasize the importance of integrated assessments rather than relying solely on financial metrics. Two-stage DEA results further show that many firms convert capabilities into profitability efficiently but struggle to transform that profitability into market value. The DEA–BPNN framework achieves over 90 % predictive accuracy when DEA efficiency scores are included, demonstrating the model's ability to capture non-linear relationships and provide reliable forecasting. This study contributes a novel, theory-driven and empirically validated approach to measuring and predicting firm competitiveness. By integrating ranking, benchmarking, and predictive modeling, it offers a valuable tool for strategic decision-making and performance management in emerging markets.
2 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.25 × 0.4 = 0.10 |
| M · momentum | 0.55 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.