SUNConferences, Southern African Institute of Industrial Engineering 2013

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Industrial Engineering Career Paths : Can We learn anything from LinkedIn?
Cobus Roux, Liezl Van Dyk

Last modified: 2013-06-20

Abstract


This paper considers the information that can be derived from the professional social network, LinkedIn, with respect to the career paths of South African Industrial Engineering graduates. A sample size of 93 people, whom recently graduated with a Bachelors degree in Industrial Engineering, has been selected. The data collected from theses LinkedIn accounts include Alma Mater, employer sequence, job titles, employment periods and “expertise and endorsements”. The data is mined from LinkedIn manually and stored in an Excel datasheet. The data is then analysed using Markov chains and Pareto Analysis. The results include identifiable trends and frequencies determined using the Pareto Analysis. Markov properties such as the accessibility of certain states (career position such as analyst, consultant, executive etc.) and determining the presence of other states such as transient states, recurrent states and absorbing states is also determined. This also includes the determination of the first passage time from a certain state (such as new graduate) to another state (such as manager). A discussion on the findings and caveats of this analysis is then provided, including recommendations for future investigation.




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