{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T15:29:39Z","timestamp":1787239779608,"version":"build-2736575974"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:00:00Z","timestamp":1735776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This study investigates the sector-specific economic impacts of robot density across countries with varying levels of technological adoption. The analysis focuses on three key sectors\u2014manufacturing, industry (excluding construction), and construction\u2014using panel data from 12 European Union countries between 2016 and 2022. To explore these relationships, the study employs the Method of Moments Quantile Regression (MMQR) methodology, which enables the assessment of the effects of robot density across different levels of sectoral performance while accounting for cross-country variations and heterogeneity. The findings reveal that robot density significantly enhances economic performance in the manufacturing sector, while its effects are smaller but still positive in the industrial and construction sectors. These results highlight the varying capacity of sectors and countries to integrate and benefit from automation technologies. The study concludes by emphasizing the importance of tailored automation strategies and policy interventions to maximize the economic benefits of robotics across diverse national and sectoral contexts. These insights contribute to understanding the role of automation in driving industrial transformation and economic growth.<\/jats:p>","DOI":"10.3390\/systems13010026","type":"journal-article","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T07:44:53Z","timestamp":1735803893000},"page":"26","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automation Systems Implications on Economic Performance of Industrial Sectors in Selected European Union Countries"],"prefix":"10.3390","volume":"13","author":[{"given":"Nicoleta Mihaela","family":"Doran","sequence":"first","affiliation":[{"name":"Department of Finance, Banking and Economic Analysis, Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7413-8121","authenticated-orcid":false,"given":"Gabriela","family":"Badareu","sequence":"additional","affiliation":[{"name":"Department of Finance, Banking and Economic Analysis, Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3967-3726","authenticated-orcid":false,"given":"Silvia","family":"Puiu","sequence":"additional","affiliation":[{"name":"Department of Marketing, Management and Business Administration, Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,2]]},"reference":[{"key":"ref_1","first-page":"99","article-title":"Information and communication technology capabilities and business performance: The case of differences in the Czech financial sector and lessons from robotic process automation between 2015 and 2020","volume":"7","author":"Zelenka","year":"2021","journal-title":"Rev. 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