Determination of Influential Factors of Perceived Employability during High Unemployment

Abstract: Unemployment rates describe the possible employment situation and relative ease a person could find a job. When the unemployment rate is high, the opportunity to get a job is challenging as the availability and accessibility of employment is limited. The unemployment rate is measured by determining the number of people in the labor force who are unemployed. The labor force includes those who are employed or actively seeking work. In India, the unemployment rates for 2020 through 2022 have been, 14.7%, 10.9%, and 10.4%, respectively (Vyas, 2022).
Internal and external factors impact employability and an individual’s perceived employability. One’s capacity and skills comprise the internal dimension, whereas the external dimension is structural. Situations in the labor market, such as unemployment levels and opportunities for specific fields of study, are influential on perceived employability (Smaldone, 2022). A job seeker’s perceptions of the external market, real or imagined, influences the seeker’s perception of capabilities, capacities, and skills (Batistic & Tymon, 2017). Elements of career development learning, work and life experiences, subject knowledge, generic skills, and emotional intelligence have been posited as the key to employability for university students (Dacre Pool & Sewell, 2007).
Higher education institutions have responded to a sense of urgency to promote and embed employability skills in their graduates (Crayford et al., 2012). An expectation for universities to prepare students for the complex world of work and improve the employability of their graduates lies in a shift from vocational and professional preparation to ones of resources for personal and professional development over their entire lives (Garcia-Álvarez et al., 2022). Transversal competencies offer recent graduates a competitive advantage in the work environment through the development of systemic competencies such as how to deal critically with issues, foster self-reflection, and continuously improve performance (Belchior-Rocha et al., 2022).
Labor force dynamics, internal and external factors, and higher education programs and preparation have influenced the employability of students. The purpose of this study was to determine the influential factors of perceived employability during high unemployment in India using the Self Employability Scale (Rothwell & Arnold, 2007; Rothwell et al., 2008).
To conduct this study, management students in India completed the 37-item instrument. Response analysis determined which factors measured within the Self Employability Scale were most influential on self-perception of employability. Students from seven management schools in India participated in the study (141 males, 93 females).
Factor analysis was conducted to determine what underlying structures, if any, exist for the measure of perceived employability. The responses to the 37-item scale were assessed for means, standard deviations, and correlations. Principle components analysis was conducted using a varimax rotation. The initial analysis contained 12 components and accounted for less than 70% of the variability. Examination of the communalities revealed many of the scale responses were less than 0.7, thus, the eigenvalue criteria was questionable. A systematic process to remove one scale entry at a time from the factor analysis was conducted. The scale with the lowest value in the communalities table was removed from the model until all the scale values in the communalities table were 0.7 or higher; 23 scale entries were removed from the model. Four criteria were used to determine the appropriate number of components to retain the model: eigenvalue, variance, scree plot, communalities score. The result the principle component analysis was the retention of six components that accounted for 76.3% of the variance. The Barlett’s Test of Sphericity approximate Chi-Square was 595.96 with 78 degrees of freedom. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.663. The two values were determined to be statistically significant with a p-value less than .001. All of the loadings were between 0.792 and 0.876.
After rotation, the first component accounted for 17.3% and included questions related to connections and network development (Q32, 33, and 34). The second component included high demand for the field of study, 12.4%. Current education status and student status made up the third component, 12.1%. The fourth component included highest education of father and mother, 11.7%. Academic performance and having academic work as a priority made up the fifth component, 11.5%. Provision of resources by the university and using those resources was the sixth component, 11.3%.
Academic institutions and students can use this information to prioritize the development of professional networks, identify where employment opportunities exist for their field of study, select a field of study that is placed as a priority to attain high grades, and use available resources to help with career identification and placement.