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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.

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.

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.

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.

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.

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.

A model for an e‐business infrastructure that can be used to support supply chain activities in construction is proposed. A virtual network structure that acts as a value‐added component of an e‐business infrastructure is used to improve communication and coordination, and encourage the mutual sharing of inter‐organisational resources and competencies. The e‐business infrastructure used to support the proposed network structure and the human, organisational and cultural barriers that may be encountered are presented and discussed. It is proffered that the proposed e‐business model not only will be of benefit to those organisations which operate in the construction supply chain, but also may be fit for other types of business‐to‐business e‐commerce when cooperation between business partners is necessary to improve organisational performance and gain a competitive advantage

Multiple parties are involved in completing a construction project. Such parties possess different skills and each sets out to be self‐sufficient. However, it is accepted that communication between parties is critical to the success of an alliance. A supporting mechanism is developed, which determines the roles of inter‐ and intra‐organisational communication, and helps to achieve efficient and effective communication. Communication between construction alliance parties consists of several aspects. First, inter‐organisational communication should take place in the alliance team. Representatives from individual organisations take the role for communication in the team. Second, communication channels are created for either close contacts or distant connections. Finally, the choice of channels depends on the amount of information, how instant it needs to be, and the efficiency and effectiveness of communication.