{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:48:02Z","timestamp":1754156882228,"version":"3.41.2"},"reference-count":38,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2025,1,14]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>This study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>The building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>This study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>As a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-04-2023-0132","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T09:23:02Z","timestamp":1722590582000},"page":"61-81","source":"Crossref","is-referenced-by-count":0,"title":["Analysis of CEO career patterns using machine learning: taking US university graduates as an example"],"prefix":"10.1108","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4849-363X","authenticated-orcid":false,"given":"Chia Yu","family":"Hung","sequence":"first","affiliation":[]},{"given":"Eddie","family":"Jeng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8640-5049","authenticated-orcid":false,"given":"Li Chen","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"issue":"4","key":"key2025011207574458400_ref001","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1080\/01615440.1983.10594107","article-title":"Sequences of social events: concepts and methods for the analysis of order in social processes","volume":"16","year":"1983","journal-title":"Historical Methods: A Journal of Quantitative and Interdisciplinary History"},{"issue":"1","key":"key2025011207574458400_ref002","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1146\/annurev.economics.050708.143301","article-title":"CEOs","volume":"1","year":"2009","journal-title":"Annual Review of Economics"},{"issue":"1","key":"key2025011207574458400_ref003","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1177\/1094428113499408","article-title":"Analyzing sequence data: optimal matching in management research","volume":"17","year":"2014","journal-title":"Organizational Research Methods"},{"issue":"2","key":"key2025011207574458400_ref004","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.jvb.2012.06.003","article-title":"Career patterns: a twenty-year panel study","volume":"81","year":"2012","journal-title":"Journal of Vocational Behavior"},{"issue":"6","key":"key2025011207574458400_ref005","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1108\/oir-09-2012-0159","article-title":"Corporate LinkedIn practices of Eurozone companies","volume":"37","year":"2013","journal-title":"Online Information Review"},{"issue":"2","key":"key2025011207574458400_ref006","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10869-015-9416-7","article-title":"Distinguishing CEOs from top level management: a profile analysis of individual differences, career paths and demographics","volume":"31","year":"2016","journal-title":"Journal of Business and Psychology"},{"issue":"2","key":"key2025011207574458400_ref007","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1080\/14616696.2013.821620","article-title":"The measurement of school-to-work transitions as processes: about events and sequences","volume":"16","year":"2014","journal-title":"European Societies"},{"year":"2017","key":"key2025011207574458400_ref008","article-title":"Data mining on LinkedIn data to define professional profile via MineraSkill methodology"},{"issue":"1","key":"key2025011207574458400_ref009","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrmr.2019.100698","article-title":"A critical review of algorithms in HRM: definition, theory, and practice","volume":"31","year":"2019","journal-title":"Human Resource Management Review"},{"issue":"3","key":"key2025011207574458400_ref010","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.jvb.2007.09.002","article-title":"Executive career management: switching organizations and the boundaryless career","volume":"71","year":"2007","journal-title":"Journal of Vocational Behavior"},{"year":"2023","key":"key2025011207574458400_ref011","article-title":"Diversifying society's leaders? 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