Abstract
PurposeLinkedIn is a popular and maybe the best job-oriented social networking site (SNS) around the world. Numerous individuals (mostly, jobseekers), as well as the companies (mostly, hiring organizations) have LinkedIn accounts. This study intends to reveal the roles of LinkedIn-based skill endorsements (LSEs) as well as LinkedIn-based hiring recommendations (LHRs) on the hiring preferences (HPs) of Bangladeshi employers.Design/methodology/approachThe authors purposefully selected 388 valid respondents (employers) from different Bangladeshi job sectors. Based on their responses, the authors applied SPSS 25 for explanatory statistics and a “Covariance-based Structural Equation Modeling (CB-SEM)” (with AMOS 25) to measure the hypothesized associations.FindingsAfter appropriate analysis, the authors revealed a momentous positive role of LHR on HP, whereas LSE was identified to have an inconsiderable role on HP.Originality/valueIn this empirical study, the authors tried to highlight the relatively unattended area of Bangladeshi employers' HPs that can be affected by LSEs and LHRs. The authors believe that this study will encourage the researchers to unveil the impacts of LinkedIn-based profile characteristics on job candidates' employability. This empirical study can also aid the employers and policymakers to look for a new avenue for hiring competent talents.
Reference47 articles.
1. Detecting common method bias: performance of the Harman's single-factor test;The Database for Advances in Information Systems,2019
2. The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis;Psychometrika,1984
3. Comparative fit indexes in structural models;Psychological Bulletin,1990
4. Browne, M.W. and Cudeck, R. (1993), “Alternative ways of assessing model fit”, Sage Focuses Editions, Vol. 154, pp. 126-136.