Understanding Demand for Project Manager Competences in the Construction Industry: Data Mining Approach

Zheng, Junping; Wen, Quan; Qiang, Maoshan

doi:10.1061/(asce)co.1943-7862.0001865

Cited by 31 publications

(17 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As different types of documents are generated during the life cycle of construction project, TM techniques have a wide range of applications in AEC domains. In addition to the above areas, TM techniques have also been used in defect analysis [ 118 , 119 ], material management [ 120 ], workforce planning [ 121 ], facility management [ 87 , 122 , 123 ], complaints management [ 124 ], contract management [ 113 , 117 ], field inspection [ 125 ], product search [ 126 , 127 ], post-project reviews [ 128 , 129 , 130 , 131 ], cost prediction [ 132 ], quality management [ 68 ], job advertisements [ 133 , 134 ], legal decision support [ 135 ], building renovation [ 136 ], feasibility study [ 137 ], patent analysis [ 138 ], schedule delay [ 139 ], and incident duration prediction [ 140 ].…”

Section: Application Areas Of Tmmentioning

confidence: 99%

Overview and analysis of the text mining applications in the construction industry

Yan

et al. 2022

Heliyon

View full text Add to dashboard Cite

Section: Application Areas Of Tmmentioning

confidence: 99%

Overview and analysis of the text mining applications in the construction industry

Yan

et al. 2022

Heliyon

View full text Add to dashboard Cite

“…Место профессиональных стандартов на рынке труда и процесс устаревания ЗУН ◆ Анализ кейсов [Петерсен, Йепсен, 2015;Спивак, 2017;Дудырев и др., 2019;Tūtlys, Spoettl, 2017;Lester, Koniotaki, Religa, 2018;Chulanova, 2019;Sochneva, Isner, Chervyakov, 2019;Lee, Jacobs, 2020;Azman Shah, Haron, Mahrin, 2021] Трансформация ЗУН и цифровизация экономики ◆ Классификация ◆ Тематическое моделирование ◆ Кластерный анализ [Wowczko, 2015;Föll, Thiesse, 2017;Boselli et al, 2018;De Mauro et al, 2018;Lovaglio et al, 2018;Dubey, Tiwari, 2020;Alekseeva et al, 2021] Спрос на ЗУН в сфере менеджмента ◆ Контент-анализ ◆ Тематическое моделирование ◆ Кластерный анализ [Litecky, Igou, Aken, 2012;Atolagbe, Floyd, 2020;Poba-Nzaou, Uwizeyemungu, Clarke, 2020;Zheng, Wen, Qiang, 2020;Roscher, 2021] Анализ спроса на основе данных об онлайн-вакансиях в России ◆ Классификация ◆ Кластерный анализ [Botov et al, 2019;Belov et al, 2020;Nikolaev, Ryazanov, Botov, 2020;Sozykin et al, 2021] Вестник СПбГУ. Менеджмент.…”

Section: тематика исследований методология источникunclassified

“…По итогам работы алгоритмов кластерного анализа эмпирически проверяется количество групп, обеспечивающее стабильное разбиение. Например, в смежных исследованиях, посвященных анализу рынка труда, итоговое количество групп в среднем варьируется от 5 до 8 [De Mauro et al, 2018;Nikolaev, Ryazanov, Botov, 2020;Poba-Nzaou, Uwizeyemungu, Clarke, 2020;Zheng, Wen, Qiang, 2020].…”

Section: данные и методология исследованияunclassified

“…В процессе кластерного анализа было протестировано различное количество групп (от 2 до 20). Итоговый выбор осуществлялся с учетом проверки кластеров на стабильность с применением часто используемых в литературе метрик уровня согласованности элементов внутри групп [De Mauro et al, 2018;Nikolaev, Ryazanov, Botovm, 2020;Poba-Nzaou, Uwizeyemungu, Clarke, 2020;Zheng, Wen, Qiang, 2020]. В результате было получено шесть кластеров, группирующих профстандарты.…”

Section: данные и методология исследованияunclassified

See 1 more Smart Citation

Professional standards and demand in the labor market in the sphere of higher management in Russia

Ternikov¹

2022

Vestnik SPbSU. Management

View full text Add to dashboard Cite

The paper discusses the problem of aligning the professional standards with a real demand in the labor market of top management. The study proposes approaches to the analysis of demand in the labor market and algorithms for quantification of qualitative information about relevant skills, as well as the methodology for grouping professional standards and job advertisements. The research methods of text mining, topic modeling and cluster analysis were applied for the analysis of databases of vacancies and the professional standards. As a result, data on the labor market were summarized as 30 professional groups; each group is characterized by a specific set of competencies. The conclusions present the correspondence between the classification of labor market and the current need in personnel and areas of their discrepancy with respect to the matrix of skills for subgroups of professional standards and vacancies of the HeadHunter portal in the “Higher management” category are identified. The proposed approaches and methods allow to flexibly classify various professions between official classifiers and the demand in the labor market. It helps to improve the coordination between educational organizations and professional communities in order to ensure consistency of requirements for workers.

show abstract

“…Besides, text mining technology [ 17 – 19 ] is also widely used to deeply dig the talent demand information in the job description text, to obtain the skills, technologies, or knowledge fields that the position needs to master. Among them, the semanteme-based subject model [ 20 – 23 ] is often used to mine job skills and requirements, extract the subject words of the job description, and then conduct manual induction, analysis, and summary of the subject words to further dig the potential demand information.…”

Section: Related Studiesmentioning

confidence: 99%

Analysis of big data job requirements based on K-means text clustering in China

Dai

Zhao

2021

PLoS ONE

View full text Add to dashboard Cite

This paper aims to understand the characteristics of domestic big data jobs requirements through k-means text clustering, help enterprises, and employees to identify big data talents, and promote the further development of big data-related research. Firstly, the crawler software is used to crawl the recruitment information about "big data" on the zhaopin.com recruitment website. Then, Jieba word segmentation and K-means text clustering are used to cluster big data recruitment positions, and the number of clustering was determined by the average sum of squares within the group. Finally, big data jobs are divided into 10 categories, and the urban distribution, salary level, education requirements, and experience requirements of big data jobs are discussed and analyzed from the perspectives of the overall data set and clustering results, to clarify the characteristics of big data job demands. The analysis results show that the job demands of big data are mainly distributed in first-tier cities and new first-tier cities. Enterprises are more inclined to job seekers with a college degree or bachelor’s degree and more than one year’s relevant experience. There are wage differences among different types of jobs. The higher the position, the higher the requirement for education and experience will be.

show abstract

Understanding Demand for Project Manager Competences in the Construction Industry: Data Mining Approach

Cited by 31 publications

References 66 publications

Overview and analysis of the text mining applications in the construction industry

Overview and analysis of the text mining applications in the construction industry

Professional standards and demand in the labor market in the sphere of higher management in Russia

Analysis of big data job requirements based on K-means text clustering in China

Contact Info

Product

Resources

About