Career Dynamics

This article proposes a perspective on careers that recognizes the interdependencies between work and home over the life course and is particularly suitable to contemporary careers. We first discuss the meaning of a work-home (WH) perspective and elaborate on the economic, organizational, and workforce changes that have affected contemporary careers. We then illustrate the implications of adopting a WH perspective for four streams of scholarship relevant to contemporary careers (career self-management, career success, global careers, and sustainable careers), suggest directions for future research in each area, and discuss the practical implications of adopting a WH perspective. We conclude that contemporary careers can be better understood by considering how employees' home lives influence and are influenced by career processes and that the adoption of a WH perspective requires understanding the role of gender norms in prescribing and sanctioning women's and men's participation in the work and home domains in a given culture.

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The Contemporary Career: A Work–Home Perspective

Greenhaus

Kossek

2014

Annu. Rev. Organ. Psychol. Organ. Behav.

291

283

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Analysis of Soft Skills and Job Level with Data Science: A Case for Graduates of a Private University

2023

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This study shows the significant features predicting graduates’ job levels, particularly high-level positions. Moreover, it shows that data science methodologies can accurately predict graduate outcomes. The dataset used to analyze graduate outcomes was derived from a private educational institution survey. The original dataset contains information on 17,898 graduates and approximately 148 features. Three machine learning algorithms, namely, decision trees, random forest, and gradient boosting, were used for data analysis. These three machine learning models were compared with ordinal regression. The results indicate that gradient boosting is the best predictive model, which is 6% higher than the ordinal regression accuracy. The SHapley Additive exPlanations (SHAP), a novel methodology to extract the significant features of different machine learning algorithms, was then used to extract the most important features of the gradient boosting model. Current salary is the most important feature in predicting job levels. Interestingly, graduates who realized the importance of communication skills and teamwork to be good leaders also had higher job positions. Finally, general relevant features to predict job levels include the number of people directly in charge, company size, seniority, and satisfaction with income.

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Career Dynamics

Cited by 2 publications

References 201 publications

The Contemporary Career: A Work–Home Perspective

The Contemporary Career: A Work–Home Perspective

Analysis of Soft Skills and Job Level with Data Science: A Case for Graduates of a Private University

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