IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v173y2021ics0040162521006107.html
   My bibliography  Save this article

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

Author

Listed:
  • Chiarello, Filippo
  • Fantoni, Gualtiero
  • Hogarth, Terence
  • Giordano, Vito
  • Baltina, Liga
  • Spada, Irene

Abstract

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.

Suggested Citation

  • Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521006107
    DOI: 10.1016/j.techfore.2021.121177
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521006107
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2021.121177?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang Lu, 2019. "Artificial intelligence: a survey on evolution, models, applications and future trends," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(1), pages 1-29, January.
    2. Nam, Taewoo, 2019. "Technology usage, expected job sustainability, and perceived job insecurity," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 155-165.
    3. James Heckman, 2011. "Policies to foster human capital," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 3, pages 73-137.
    4. Dingel, Jonathan I. & Neiman, Brent, 2020. "How many jobs can be done at home?," Journal of Public Economics, Elsevier, vol. 189(C).
    5. Müller, Julian Marius & Buliga, Oana & Voigt, Kai-Ingo, 2018. "Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 2-17.
    6. Dionysis Bochtis & Lefteris Benos & Maria Lampridi & Vasso Marinoudi & Simon Pearson & Claus G. Sørensen, 2020. "Agricultural Workforce Crisis in Light of the COVID-19 Pandemic," Sustainability, MDPI, vol. 12(19), pages 1-13, October.
    7. Penmetsa, Praveena & Adanu, Emmanuel Kofi & Wood, Dustin & Wang, Teng & Jones, Steven L., 2019. "Perceptions and expectations of autonomous vehicles – A snapshot of vulnerable road user opinion," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 9-13.
    8. Tortorella, Guilherme Luz & Saurin, Tarcísio Abreu & Fogliatto, Flavio S. & Rosa, Valentina M. & Tonetto, Leandro M & Magrabi, Farah, 2021. "Impacts of Healthcare 4.0 digital technologies on the resilience of hospitals," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Reischauer, Georg, 2018. "Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 26-33.
    10. Baumann, Manuel & Domnik, Tobias & Haase, Martina & Wulf, Christina & Emmerich, Philip & Rösch, Christine & Zapp, Petra & Naegler, Tobias & Weil, Marcel, 2021. "Comparative patent analysis for the identification of global research trends for the case of battery storage, hydrogen and bioenergy," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    11. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    12. Jabbour, Charbel Jose Chiappetta & Jabbour, Ana Beatriz Lopes de Sousa & Sarkis, Joseph & Filho, Moacir Godinho, 2019. "Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 546-552.
    13. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    14. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    15. Poonam Garg & Bhumika Gupta & Ajay Kumar Chauhan & Uthayasankar Sivarajah & Shivam Gupta & Sachin Modgil, 2021. "Measuring the perceived benefits of implementing blockchain technology in the banking sector," Post-Print hal-03145195, HAL.
    16. PIAAC Numeracy Expert Group, 2009. "PIAAC Numeracy: A Conceptual Framework," OECD Education Working Papers 35, OECD Publishing.
    17. Wang, Lu & Luo, Gong-li & Sari, Arif & Shao, Xue-Feng, 2020. "What nurtures fourth industrial revolution? An investigation of economic and social determinants of technological innovation in advanced economies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    18. Garg, Poonam & Gupta, Bhumika & Chauhan, Ajay Kumar & Sivarajah, Uthayasankar & Gupta, Shivam & Modgil, Sachin, 2021. "Measuring the perceived benefits of implementing blockchain technology in the banking sector," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    19. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    20. Colombo, Emilio & Mercorio, Fabio & Mezzanzanica, Mario, 2019. "AI meets labor market: Exploring the link between automation and skills," Information Economics and Policy, Elsevier, vol. 47(C), pages 27-37.
    21. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    22. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    23. Scuotto, Veronica & Beatrice, Orlando & Valentina, Cillo & Nicotra, Melita & Di Gioia, Leonardo & Farina Briamonte, Massimiliano, 2020. "Uncovering the micro-foundations of knowledge sharing in open innovation partnerships: An intention-based perspective of technology transfer," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    24. Handel, Michael J., 2016. "The O-NET content model: strengths and limitations," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 49(2), pages 157-176.
    25. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    26. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    27. Luca Bonacini & Giovanni Gallo & Sergio Scicchitano, 2021. "Working from home and income inequality: risks of a ‘new normal’ with COVID-19," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 303-360, January.
    28. Mangla, Sachin Kumar & Kazancoglu, Yigit & Ekinci, Esra & Liu, Mengqi & Özbiltekin, Melisa & Sezer, Muruvvet Deniz, 2021. "Using system dynamics to analyze the societal impacts of blockchain technology in milk supply chainsrefer," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    29. Edward Felten & Manav Raj & Robert Seamans, 2021. "Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses," Strategic Management Journal, Wiley Blackwell, vol. 42(12), pages 2195-2217, December.
    30. Adamuthe, Amol C. & Thampi, Gopakumaran T., 2019. "Technology forecasting: A case study of computational technologies," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 181-189.
    31. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    32. Shakina, Elena & Parshakov, Petr & Alsufiev, Artem, 2021. "Rethinking the corporate digital divide: The complementarity of technologies and the demand for digital skills," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    33. Liu, Xia & Shin, Hyunju & Burns, Alvin C., 2021. "Examining the impact of luxury brand's social media marketing on customer engagement​: Using big data analytics and natural language processing," Journal of Business Research, Elsevier, vol. 125(C), pages 815-826.
    34. Handel, Michael J., 2016. "The O-NET content model: strengths and limitations," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 49(2), pages 157-176.
    35. Abdel-Basset, Mohamed & Chang, Victor & Nabeeh, Nada A., 2021. "An intelligent framework using disruptive technologies for COVID-19 analysis," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    36. Kai Hu & Huayi Wu & Kunlun Qi & Jingmin Yu & Siluo Yang & Tianxing Yu & Jie Zheng & Bo Liu, 2018. "A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1031-1068, March.
    37. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    38. Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    39. Benzidia, Smail & Makaoui, Naouel & Bentahar, Omar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    40. Trappey, Amy & Trappey, Charles V. & Hsieh, Alex, 2021. "An intelligent patent recommender adopting machine learning approach for natural language processing: A case study for smart machinery technology mining," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    41. Frank, Alejandro G. & Mendes, Glauco H.S. & Ayala, Néstor F. & Ghezzi, Antonio, 2019. "Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 341-351.
    42. Jia-Yen Huang & Hung-Tu Hsu, 2017. "Technology–function matrix based network analysis of cloud computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 17-44, October.
    43. Smaïl Benzidia & Naouel Makaoui & Omar Bentahar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Post-Print hal-03028127, HAL.
    44. Lee, Kyungpyo & Lee, Sungjoo, 2013. "Patterns of technological innovation and evolution in the energy sector: A patent-based approach," Energy Policy, Elsevier, vol. 59(C), pages 415-432.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Delke, Vincent & Schiele, Holger & Buchholz, Wolfgang & Kelly, Stephen, 2023. "Implementing Industry 4.0 technologies: Future roles in purchasing and supply management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    2. Fareri, Silvia & Apreda, Riccardo & Mulas, Valentina & Alonso, Ruben, 2023. "The worker profiler: Assessing the digital skill gaps for enhancing energy efficiency in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    3. Spada, Irene & Chiarello, Filippo & Barandoni, Simone & Ruggi, Gianluca & Martini, Antonella & Fantoni, Gualtiero, 2022. "Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing courses in Italy," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    2. Caselli, Mauro & Fracasso, Andrea & Traverso, Silvio, 2021. "Robots and risk of COVID-19 workplace contagion: Evidence from Italy," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Chatterjee, Sheshadri & Rana, Nripendra P. & Dwivedi, Yogesh K. & Baabdullah, Abdullah M., 2021. "Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    4. Caselli, Mauro & Fracasso, Andrea & Marcolin, Arianna & Scicchitano, Sergio, 2021. "The reassuring effect of firms' technological innovations on workers' job insecurity," GLO Discussion Paper Series 938, Global Labor Organization (GLO).
    5. Caselli, Mauro & Fracasso, Andrea, 2021. "Covid-19 and Technology," GLO Discussion Paper Series 1001, Global Labor Organization (GLO).
    6. Chigusa Okamoto, 2019. "The effect of automation levels on US interstate migration," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 63(3), pages 519-539, December.
    7. Carbonero, Francesco & Scicchitano, Sergio, 2021. "Labour and technology at the time of Covid-19. Can artificial intelligence mitigate the need for proximity?," GLO Discussion Paper Series 765, Global Labor Organization (GLO).
    8. Fabio Montobbio & Jacopo Staccioli & Maria Enrica Virgillito & Marco Vivarelli, 2021. "Labour-saving automation and occupational exposure: a text-similarity measure," DISCE - Quaderni del Dipartimento di Politica Economica dipe0021, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    9. Schiavone, Francesco & Leone, Daniele & Caporuscio, Andrea & Lan, Sai, 2022. "Digital servitization and new sustainable configurations of manufacturing systems," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    10. Delke, Vincent & Schiele, Holger & Buchholz, Wolfgang & Kelly, Stephen, 2023. "Implementing Industry 4.0 technologies: Future roles in purchasing and supply management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    11. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio & Traverso, Silvio & Tundis, Enrico, 2021. "Stop worrying and love the robot: An activity-based approach to assess the impact of robotization on employment dynamics," GLO Discussion Paper Series 802, Global Labor Organization (GLO).
    12. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    13. Fruehwirt, Wolfgang & Duckworth, Paul, 2021. "Towards better healthcare: What could and should be automated?," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    14. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    15. Eleni Giouli & Pisinas Yorgos & Anna-Maria Kanzola, 2021. "Human Capital and Production Structure: Evidence from Greece," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 7, January -.
    16. Alex Chernoff & Casey Warman, 2023. "COVID-19 and implications for automation," Applied Economics, Taylor & Francis Journals, vol. 55(17), pages 1939-1957, April.
    17. Gries, Thomas & Naudé, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," IZA Discussion Papers 13606, Institute of Labor Economics (IZA).
    18. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    19. Ilona Pavlenkova & Luca Alfieri & Jaan Masso, 2021. "Effects Of Automation On The Gender Pay Gap: The Case Of Estonia," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 131, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    20. Huang, Xu & Hu, Yan & Dong, Zhiqiang, 2019. "The macroeconomic consequences of artificial intelligence: A theoretical framework," Economics Discussion Papers 2019-48, Kiel Institute for the World Economy (IfW Kiel).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521006107. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.