Career Challenges Model Among Female Engineers: PLS-Sem Analysis

Abstract

Women are likely to leave the job sector as a result of the crisis between their commitments to the career with the household interest. In response to this issue, this study aims to build a career challenge model that caters to the demand among women in this century. Hence, this study has identified the key factors to the challenges faced by female engineers in pursuing their career as an engineer through the Delphi Modified Technique. The result shows this study looking into the relationship between four independent constructs namely, life balance, childcare, leaves and gender discrimination. Meanwhile, the dependent construct of this study is career challenges faced by women. The scope of the study comprises female engineers with families and 211 respondents were selected to answer the questionnaire. The data obtained were analysed using the PLS-SEM 2.0 software via the algorithm, bootstrapping and blindfolding method. The construction process of this model involves two tests including the construction of the measurement model and the structure model. Testing the measurement model involves internal consistency namely (a) convergent validity and (b) discriminant validity in which these two validities have six analyses; (i) external loading, (ii) composite reliability, (iii) average variance extracted (AVE), (iv) Fornell-Larcker, (v) cross loading, and (vi) Heterotrait-Monotrait Ratio (HTMT). Meanwhile, the structural model testing involves the analysis of (i) Multicollinearity (Inner VIF), (ii) Path Coefficient, (iii) R square (R2), (iv) size effect (f2), and (v) Predictive Relevance (Q2). The findings indicate that gender discrimination and life balance have significant relationships in influencing career challenges. Hence, this model is expected to contribute to the literature of Human Resource Management.

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“…We observed that correlations among the variables are mostly consistent with the prediction direction of previous studies. Some of the latent constructs in the study may have a high correlation; thus, the heterotrait–monotrait ratio (HTMT) should be adopted for detecting multicollinearity (Sakinah et al , 2020). The study conducted AMOS 26.0 to calculate the HTMT and STATA 15.0 to estimate the variance inflation factor (VIF).…”