Career adaptability as a strategic competence for career development: An exploratory study of its key predictors

Federica Bocciardi, Andrea Caputo, Chiara Fregonese, Viviana Langher, Riccardo Sartori

In the current labour market, the competence to adapt is becoming significantly relevant for career development and career success. The construct of career adaptability, i.e. the capability to adapt to changing career-related circumstances and predict advancement in career development, seems to provide a fruitful scientific base for successful career intervention. The purpose of the study is exploratory, with the aim of providing new findings about the key predictors of this meta-competence that are relevant for career development.
Through a web-based survey, a convenience sample of 230 working participants completed an online questionnaire, including socio-demographic characteristics (age, gender, education), professional status (role seniority, sector of employment, professional role), professional development-related features (training, new professional assignments, financial incentives) and psychological factors (work self-efficacy, search for work self-efficacy and job satisfaction). Four-step hierarchical multiple regression analyses were conducted to understand which of these factors account for the most career adaptability variance.
Results highlight that work self-efficacy, search for work self-efficacy and education play a significant role in predicting career adaptability. Surprisingly, professional development-related features and professional status do not seem to have a relevant influence.
Practical implications
Training and career-development professionals can improve their understanding of which career-related skills and attitudes can increase one’s capability to cope with sudden changes and instability of the current labour market.
This study supports previous research, addressing the importance of career adaptability in times of dramatic change. It also provides some insight into the factors that could predict it.

ISSN: 2046-9012

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