{"title":"利用机器学习分析首席执行官的职业模式:以美国大学毕业生为例","authors":"Chia Yu Hung, Eddie Jeng, Li Chen Cheng","doi":"10.1108/dta-04-2023-0132","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<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.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<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.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<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.</p><!--/ Abstract__block -->\n<h3>Research limitations/implications</h3>\n<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.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<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. 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引用次数: 0
摘要
目的 本研究探讨了首席执行官(CEO)的职业轨迹,揭示了有助于他们成功的独特特征。本研究利用网络搜索和机器学习技术,分析了 LinkedIn 上两千多名首席执行官的个人资料,以了解他们的职业道路模式。与以往研究中主要采用的定性研究方法相比,本研究提供了另一种方法。设计/方法/途径本研究提出了一个分析 CEO 职业模式的框架。职称和公司信息采用标准职业分类(SOC)方案进行编码。研究采用Needleman-Wunsch最优匹配算法和聚类方法来构建距离矩阵,并对CEO的职业路径进行聚类。研究结果本研究通过LinkedIn收集了美国几所著名公立和私立大学毕业生的职业转换过程数据。利用机器学习技术,分析揭示了多样化的职业轨迹。研究局限/意义建立一个考虑到年数的职业序列需要整数。为了便于优化匹配过程,非整数的数字被四舍五入,但这种方法无法完全准确地反映工作时间。实际意义本研究利用丰富的 LinkedIn 在线数据集,揭示了 CEO 的不同职业路径群体,为职业模式分析领域做出了原创性贡献。请注意,我们的首席执行官档案并不局限于任何行业或成为首席执行官的特定职业道路。鉴于担任首席执行官职位的人通常被社会视为成功人士,我们有兴趣找到他们成功背后的特征,以及所担任的头衔或所待的公司是否显示出使他们成为今天这样的人的模式。在那些曾在《财富》公司工作过的首席执行官中,曾在《财富》500 强企业工作过的人数超过了曾在《财富》1000 强企业工作过的人数。
Analysis of CEO career patterns using machine learning: taking US university graduates as an example
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 they remain at show patterns in making them who they are today.
Originality/value
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.