Career adaptability predicts subjective career success above and beyond personality traits and core self-evaluations

The Career Adapt-Abilities Scale (CAAS) measures career adaptability as a higher-order construct that integrates four psychosocial resources of employees for managing their career development: concern, control, curiosity, and confidence. The goal of the present study was to investigate the validity of the CAAS with regard to its effects on two indicators of subjective career success (career satisfaction and self-rated career performance) above and beyond the effects of employees' Big Five personality traits and core self-evaluations. Data came from a large and heterogeneous sample of employees in Australia (N=1723). Results showed that overall career adaptability positively predicted career satisfaction and self-rated career performance above and beyond the Big Five personality traits and core self-evaluations. In addition, concern and confidence positively predicted the two indicators of subjective career success. The findings provide further support for the incremental validity of the CAAS.

Career adaptability and career entrenchment

Career adaptability constitutes a resource that can help employees to effectively manage career changes and challenges. The goal of this study was to investigate the relationship between the two higher-order constructs of career adaptability and career entrenchment (i.e., the perceived inability and/or unwillingness to pursue new career opportunities), as well as relationships among the dimensions of career adaptability and career entrenchment. We hypothesized a negative relationship between overall career adaptability and career entrenchment, and more differentiated associations among their dimensions. Data for this study came from 404 employees in Brazil. Results of structural equation modeling showed that overall career adaptability weakly negatively predicted overall career entrenchment (standardized effect = − .13), after controlling for age, gender, education, and job tenure. More differentiated findings emerged at the dimension level. Future research should examine the mechanisms and boundary conditions of the relationship between career adaptability and career entrenchment.

Career Adapt-Abilities Scale—Iran Form: Psychometric properties and relationships with career satisfaction and entrepreneurial intentions

This study examined the psychometric properties of a Persian translation of the Career Adapt-Abilities Scale (CAAS—Iran Form) and its relationships with career satisfaction, business opportunity identification, and entrepreneurial intentions. It was hypothesized that career adaptability relates positively to these three outcomes, even when controlling for demographic and employment characteristics. Data were provided by 204 workers from Iran. Results showed that the overall CAAS score and sub-dimension scores (concern, control, curiosity, and confidence) were highly reliable. Moreover, confirmatory factor analyses indicated that the CAAS—Iran Form measures four distinct dimensions that can be combined into a higher-order career adaptability factor. Findings also demonstrated criterion-related validity of the scale with regard to career satisfaction and entrepreneurial intentions. In contrast, overall career adaptability was not significantly related to opportunity identification, while concern related positively, and control related negatively to opportunity identification. Overall, the CAAS—Iran Form has very good psychometric properties and predicts important career outcomes, suggesting that it can be used for career counseling and future research with Persian-speaking workers.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.