Towards ESCO 4.0-Is the European classification of skills in line with Industry 4.0? A text mining approach

被引:39
作者
Chiarello, Filippo [1 ]
Fantoni, Gualtiero [1 ]
Hogarth, Terence [2 ,3 ]
Giordano, Vito [1 ]
Baltina, Liga [2 ]
Spada, Irene [1 ]
机构
[1] Univ Pisa, Sch Engn, Pisa, Italy
[2] Fdn Giacomo Brodolini, Rome, Italy
[3] Univ Warwick, Inst Employment Res, Coventry, W Midlands, England
关键词
Industry; 4.0; Technological change; Employment; Skill analysis; Text mining; TECHNOLOGIES; INNOVATION; EMPLOYMENT;
D O I
10.1016/j.techfore.2021.121177
中图分类号
F [经济];
学科分类号
02 ;
摘要
ESCO is a multilingual classification of Skills, Competences, Qualifications, and Occupations created by the European Commission to improve the supply of information on skills demand in the labour market. It is designed to assist individuals, employers, universities and training providers by giving them up to date and standardized information on skills. Rapid technological change means that ESCO needs to be updated in a timely manner. Evidence is presented here of how text-mining techniques can be applied to the analysis of data on emerging skill needs arising from Industry 4.0 to ensure that ESCO provides information which is current. The alignment between ESCO and Industry 4.0 technological trends is analysed. Using text mining techniques, information is extracted on Industry 4.0 technologies from: (i) two versions of ESCO (v1.0 - v1.1.); and (ii) from the 4.0 related scientific literature. These are then compared to identify potential data gaps in ESCO. The findings demonstrate that text mining applied on scientific literature to extract technology trends, can help policy makers to provide more up-to-date labour market intelligence.
引用
收藏
页数:18
相关论文
共 92 条
[1]   An intelligent framework using disruptive technologies for COVID-19 analysis [J].
Abdel-Basset, Mohamed ;
Chang, Victor ;
Nabeeh, Nada A. .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2021, 163
[2]   Robots and Jobs: Evidence from US Labor Markets [J].
Acemoglu, Daron ;
Restrepo, Pascual .
JOURNAL OF POLITICAL ECONOMY, 2020, 128 (06) :2188-2244
[3]  
Acemoglu D, 2011, HBK ECON, V4, P1043, DOI 10.1016/S0169-7218(11)02410-5
[4]   Technology forecasting: A case study of computational technologies [J].
Adamuthe, Amol C. ;
Thampi, Gopakumaran T. .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2019, 143 :181-189
[5]  
Ahrens Volker, 2012, Productivity Management, V17, P30
[6]   Ontology-based approach to extract product's design features from online customers' reviews [J].
Ali, Munira Mohd ;
Doumbouya, Mamadou Bilo ;
Louge, Thierry ;
Rai, Rahul ;
Karray, Mohamed Hedi .
COMPUTERS IN INDUSTRY, 2020, 116
[7]  
[Anonymous], 2018, ARXIV PREPRINT ARXIV
[8]  
Azimi S., 2020, IEEE T ENG MANAGE
[9]   Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities [J].
Bag, Surajit ;
Pretorius, Jan Ham Christiaan ;
Gupta, Shivam ;
Dwivedi, Yogesh K. .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2021, 163
[10]   Estimating the burden of United States workers exposed to infection or disease: A key factor in containing risk of COVID-19 infection [J].
Baker, Marissa G. ;
Peckham, Trevor K. ;
Seixas, Noah S. .
PLOS ONE, 2020, 15 (04)