Understanding Current Demand for BIM Professionals in China through Recruitment Data MiningSource: Journal of Management in Engineering:;2024:;Volume ( 040 ):;issue: 003::page 04024011-1DOI: 10.1061/JMENEA.MEENG-5439Publisher: ASCE
Abstract: Building information modeling (BIM) is critical to the digital transformation and upgrading of the construction industry. With its continuous promotion, the demand for BIM professionals is increasing, which can be seen from the large number of job recruitment ads on the Internet. Mining the massive recruitment information is conducive to understanding current demand for BIM professionals. After 5,033 pieces of BIM-related recruitment information in China being collected and preprocessed, statistical analysis was applied to reveal the demand for BIM professionals at the macro level. Then cluster analysis was carried out to classify different posts, with their corresponding required skills being visualized through a word cloud. Subsequently, based on the extracted keywords, an index system including 11 first-level indicators and 61 second-level indicators was constructed to comprehensively evaluate the competencies of BIM professionals. Finally, correlation analysis was introduced to quantify the relationships between different skills and posts. Through the recruitment data mining, it is possible to understand the overall demand for BIM professionals, available BIM posts, and their requirements which can provide reference for both BIM professionals training and BIM job hunting. Furthermore, it sheds light on the application of text mining in construction industry. This study utilized text mining to extract information from a large quantity of online BIM recruitment data, in order to analyze the current demand for BIM professionals in China. The research findings provide valuable insights into three main subjects. First, for BIM practitioners it provides valuable insights into the current market trends for BIM positions and the essential skills needed, enabling them to engage in targeted learning efforts. Second, for employers it enhances recruitment efficiency by offering a clear picture of the requirement for different positions. Third, for industry and educational institutions the competency index system facilitates the development of well-founded BIM professionals cultivation programs, promoting systematic training for aspiring BIM professionals.
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contributor author | Simin Zhou | |
contributor author | Rui Yu | |
contributor author | Min Pan | |
contributor author | Jian Zuo | |
contributor author | Bocun Tu | |
contributor author | Na Dong | |
date accessioned | 2024-04-27T22:23:17Z | |
date available | 2024-04-27T22:23:17Z | |
date issued | 2024/05/01 | |
identifier other | 10.1061-JMENEA.MEENG-5439.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4296541 | |
description abstract | Building information modeling (BIM) is critical to the digital transformation and upgrading of the construction industry. With its continuous promotion, the demand for BIM professionals is increasing, which can be seen from the large number of job recruitment ads on the Internet. Mining the massive recruitment information is conducive to understanding current demand for BIM professionals. After 5,033 pieces of BIM-related recruitment information in China being collected and preprocessed, statistical analysis was applied to reveal the demand for BIM professionals at the macro level. Then cluster analysis was carried out to classify different posts, with their corresponding required skills being visualized through a word cloud. Subsequently, based on the extracted keywords, an index system including 11 first-level indicators and 61 second-level indicators was constructed to comprehensively evaluate the competencies of BIM professionals. Finally, correlation analysis was introduced to quantify the relationships between different skills and posts. Through the recruitment data mining, it is possible to understand the overall demand for BIM professionals, available BIM posts, and their requirements which can provide reference for both BIM professionals training and BIM job hunting. Furthermore, it sheds light on the application of text mining in construction industry. This study utilized text mining to extract information from a large quantity of online BIM recruitment data, in order to analyze the current demand for BIM professionals in China. The research findings provide valuable insights into three main subjects. First, for BIM practitioners it provides valuable insights into the current market trends for BIM positions and the essential skills needed, enabling them to engage in targeted learning efforts. Second, for employers it enhances recruitment efficiency by offering a clear picture of the requirement for different positions. Third, for industry and educational institutions the competency index system facilitates the development of well-founded BIM professionals cultivation programs, promoting systematic training for aspiring BIM professionals. | |
publisher | ASCE | |
title | Understanding Current Demand for BIM Professionals in China through Recruitment Data Mining | |
type | Journal Article | |
journal volume | 40 | |
journal issue | 3 | |
journal title | Journal of Management in Engineering | |
identifier doi | 10.1061/JMENEA.MEENG-5439 | |
journal fristpage | 04024011-1 | |
journal lastpage | 04024011-12 | |
page | 12 | |
tree | Journal of Management in Engineering:;2024:;Volume ( 040 ):;issue: 003 | |
contenttype | Fulltext |