Making the cut: occupation-specific factors influencing employers in their recruitment and selection of immigrant professionals in the information technology and accounting occupations in regional Australia

Exploring employer attitude towards migrant workers

Purpose

Migrants play an essential role in economic and societal outcomes of the host society, both as members of the workforce and as citizens. However, integration and finding employment after migration remain critical issues. The purpose of this paper is to employ an evidence-based quantitative approach to identify migrant workers’ most important qualifications from an employer perspective and to explore factors that influence employer perception of migrants.

Design/methodology/approach

This study uses European Social Survey data that contain responses from managers in European countries in 2014 (n=2,828) and 2016 (n=3,014). Confirmatory factor analysis and structural equation modelling are used to analyse the data.

Findings

For managers, migrants’ commitment to the host country’s way of life is more important than their job skills, educational level and language proficiency. The effects of managers’ individual characteristics, including demographics, expectancies and personal values, on their general attitude towards migrants are also quantified.

Practical implications

The study’s outcomes can assist migrants to develop the qualifications most valued by employers, and allow policymakers to integrate the organizational perspective into policies and initiatives for integration of migrant labour.

Originality/value

Through HR practices, organizations significantly affect migrants’ career outcomes. Yet research on migrant workers from an organizational and managerial perspective is limited. This study identifies migrant workers’ most important qualifications from an employer perspective. It also explores which individual characteristics most influence organizational decision-makers’ perception. Utilizing a cross-cultural and longitudinal data set provides a unique opportunity to generate generalizable findings.