A comparative sequence analysis of career paths among knowledge workers in a multinational bank

Paul Squires, Harold Kaufman, Julian Togelius, Catalina M. Jaramillo

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This study examined two metrics for measuring the distance between sequences (Euclid and OMSpell) and creating distance matrices combined with two types of clustering methods (AGNES and PAM) to analyze the career path clusters of knowledge workers. A regression tree of covariates and career path clusters was used to predict advancement rates. The results indicated that the metric which focused on subsequences (OMSpell) worked best for both clustering methods. Less time as a knowledge worker was associated with greater advancement. Implications for boundaryless careers and social capital formation are discussed.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3604-3612
    Number of pages9
    Volume2018-January
    ISBN (Electronic)9781538627143
    DOIs
    StatePublished - Jan 12 2018
    Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
    Duration: Dec 11 2017Dec 14 2017

    Other

    Other5th IEEE International Conference on Big Data, Big Data 2017
    CountryUnited States
    CityBoston
    Period12/11/1712/14/17

    Fingerprint

    Pulse amplitude modulation
    Sequence Analysis
    Clustering Methods
    Comparative Analysis
    Euclid
    Regression Tree
    Metric
    Path
    Distance Matrix
    Subsequence
    Covariates
    Predict
    Knowledge
    Banks
    Clustering
    Knowledge workers
    Multinational banks
    Sequence analysis
    Career paths
    Boundaryless career

    Keywords

    • career paths
    • clustering
    • knowledge worker
    • optimal matching
    • regression tree
    • sequence analysis

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Hardware and Architecture
    • Information Systems
    • Information Systems and Management
    • Control and Optimization

    Cite this

    Squires, P., Kaufman, H., Togelius, J., & Jaramillo, C. M. (2018). A comparative sequence analysis of career paths among knowledge workers in a multinational bank. In Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (Vol. 2018-January, pp. 3604-3612). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258354

    A comparative sequence analysis of career paths among knowledge workers in a multinational bank. / Squires, Paul; Kaufman, Harold; Togelius, Julian; Jaramillo, Catalina M.

    Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 3604-3612.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Squires, P, Kaufman, H, Togelius, J & Jaramillo, CM 2018, A comparative sequence analysis of career paths among knowledge workers in a multinational bank. in Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 3604-3612, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 12/11/17. https://doi.org/10.1109/BigData.2017.8258354
    Squires P, Kaufman H, Togelius J, Jaramillo CM. A comparative sequence analysis of career paths among knowledge workers in a multinational bank. In Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3604-3612 https://doi.org/10.1109/BigData.2017.8258354
    Squires, Paul ; Kaufman, Harold ; Togelius, Julian ; Jaramillo, Catalina M. / A comparative sequence analysis of career paths among knowledge workers in a multinational bank. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3604-3612
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