Analysis of CEO career patterns using machine learning: taking US university graduates as an example
| Date | 02 August 2024 |
| Pages | 61-81 |
| DOI | https://doi.org/10.1108/DTA-04-2023-0132 |
| Published date | 02 August 2024 |
| Author | Chia Yu Hung,Eddie Jeng,Li Chen Cheng |
Analysis of CEO career patterns
using machine learning: taking US
university graduates as an example
Chia Yu Hung
College of Management, National Taipei University of Technology,
Taipei, Taiwan, and
Eddie Jeng and Li Chen Cheng
Department of Information and Finance Management,
National Taipei University of Technology, Taipei, Taiwan
Abstract
Purpose –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.
Design/methodology/approach –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.
Findings –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.
Research limitations/implications –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.
Practical implications –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 theyremain at
show patterns in making them who they are today.
Originality/value –As a matter of fact, nearly all CEOs had previous experienceworking 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.
Keywords LinkedIn, Career pattern, Optimal matching, Hierarchical clustering, Machine learning,
Human resource management, CEO
Paper type Case study
Data
Technologies and
Applications
61
The authors are very grateful to the anonymous referees and the editor for their helpful comments and
valuable suggestions for improving the earlier version of the paper. This work was supported by
Ministry of Science and Technology (MOST), Taiwan, under 109-2410-H-027 -009 -MY2 and 111-2410-H-
027 -011 -MY3.
Ethical statement: This manuscript has not been published or presented elsewhere in part or in
entirely and is not under consideration by another journal. We have read and understood your journal’s
policies, and we believe that neither the manuscript nor the study violates any of these. There are no
conflicts of interest to declare.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-9288.htm
Received 19 April 2023
Revised 31 January 2024
Accepted 4 May 2024
Data Technologies and
Applications
Vol. 59 No. 1, 2025
pp. 61-81
© Emerald Publishing Limited
2514-9288
DOI 10.1108/DTA-04-2023-0132
1. Introduction
The global economy is expanding, technology is advancing rapidly, and corporate structures
are evolving. Consequently, job relationships have become more flexible, and career
trajectories are less predictable. It is widely recognized that a career pathway plays a crucial
role in an individual’s professional success throughout their employment. A thorough
understanding of the sequence of different positions a person holds over their lifetime could
illuminate the key to career success. The career pattern analysis of Chief Executive Officers
(CEOs) has been a particularly fascinating subject of study, as they epitomize the core of
organizations. CEOs are likely the most extensively researched group within business
organizations, as researchers aim to understand how individuals can ascend to the apex of
the corporate hierarchy (Bertrand, 2009).
A career pattern is best defined as the sequence of positions an individual occupies in
competition with other professionals. Career patterns, derived from the panel data of targeted
subjects, reveal job-related trajectories that inform us about individuals’occupational
mobility. Traditional human capital research represents a CEO’s knowledge, skills, and
abilities through their education level. However, research on career patterns has surged with
the increasing availability of rich resources from the internet.
Researchers are interested in examining the job trajectories of CEOs, as these reveal the
strategies employed to ascend to the highest-ranking positions (Koch et al., 2017). While most
previous research has focused on deciphering the distinct paths and career moves of CEOs,
earlier studies have typically concentrated on professionals within a specific industry or have
overlooked those in managerial roles. This study provides an original contribution to the field
of career pattern analysis by identifying distinct career path groups of CEOs using a
comprehensive LinkedIn online dataset. Notably, our CEO profiles are not confined to any
industry or to specific career paths leading to CEO positions. Furthermore, numerous studies
have confirmed the importance of patterns based on professional experiences. This study
addresses a gap in the literature by incorporating additional information to analyze the career
patterns of CEOs, including LinkedIn data for U.S. university graduates, multi-dimensional
sequence analysis, optimal matching, and the application of machinelearning (Dai
et al., 2015).
Traditional research has faced technical challenges in automating the clustering of career
patterns for two primary reasons. First, traditional research datasets often lack sufficient
diversity in individual profiles. The findings from traditional studies are suboptimal, as they
are primarily based on a limited number of questionnaire responses or rely solely on manual
data annotation to build datasets. Second, previous research has utilized one-dimensional
coded sequences of career data due to the original form of the optimal matching method.
However, career trajectory information comprises multi-dimensional longitudinal data, and
incorporating more attributes significantly enhances the clustering process (Bons
on and
Bedn
arov
a, 2013). CEO career information, including both company name and job title, is
coded for sequence analysis and optimal matching (Biemann and Datta, 2014).
To address the gap in the literature, this study proposes a novel framework for analyzing
the career patterns of CEOs using data crawled from LinkedIn. To the best of our knowledge,
our investigation is among the first to conduct such research in the HR area. The
contributions of this study are as follows:
(1)The study collected a large-scale dataset from LinkedIn to examine the career paths of
various CEOs. By leveraging LinkedIn, the most widely used professional social
network, the study obtained data on CEO career experiences. The usefulness of the
findings is enhanced by including graduates from selected public and private US
universities, ensuring fair representation in our sample pool of LinkedIn public
profiles.
DTA
59,1
62
Get this document and AI-powered insights with a free trial of vLex and Vincent AI
Get Started for FreeStart Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting
Start Your Free Trial of vLex and Vincent AI, Your Precision-Engineered Legal Assistant
-
Access comprehensive legal content with no limitations across vLex's unparalleled global legal database
-
Build stronger arguments with verified citations and CERT citator that tracks case history and precedential strength
-
Transform your legal research from hours to minutes with Vincent AI's intelligent search and analysis capabilities
-
Elevate your practice by focusing your expertise where it matters most while Vincent handles the heavy lifting