Competitive Landscape Analysis of International Construction Industry Using Natural Language Processing

Abstract

This study presents an automated text-based information extraction model for analyzing the competitive landscape of the international construction industry. The proposed model comprises named entity recognition (NER) and relation extraction (RE) models. From news articles, the NER model identifies major participants in the international construction industry, whereas the RE model automatically extracts their relationships, particularly in the bids and awards stage. Based on the extracted information, bids and awards networks are generated. This study presents an illustrative competitive landscape analysis focusing on the Middle East construction market using social network analysis. Furthermore, this study introduces the construction contractor activation index (CCAI), which is a metric designed to quantify the engagement level of each construction participant in bidding activities. The correlation coefficient between the CCAI and the revenue of following year of Korean contractors was 0.490 at a significance level of 0.001, confirming the potential application of the CCAI as a leading indicator in predicting a contractor’s revenue. The proposed model is expected to facilitate data-driven decision-making for improved market entry and bidding strategies, benefiting policymakers and practitioners, specifically those aiming to expand internationally but lacking experience and reliable information. This study introduced a natural language processing (NLP)-powered information extraction model. The proposed model is expected to help practitioners automate the review of documents during in-house investigations, saving a considerable amount of time. In addition, this study presented a competitive landscape analysis of the international construction industry focusing on the bidding stage via social network analysis (SNA) based on the extracted information. An international contractor often becomes a new entrant to the target market when prompted by a new project. The findings are useful for analyzing market dynamics and thus developing national policies and a firm’s strategies from a macro perspective. Policymakers and practitioners can utilize the results of this study to determine the niche market and recognize new entrants within rapidly changing circumstances based on up-to-date news articles. In particular, the CCAI, which represents the influence of construction participants based on the bidding activities, is expected to be used as a leading indicator for revenue forecasting.