![]() Participants were sampled from user departments (Human Resources and Information Technology) and cut across all ranks and categories. The study adopted a cross-sectional survey approach which was both descriptive and analytical in nature. The specific objectives of the study included determining factors that affect adoption and implementation of the e-recruitment system in Bank of Uganda, development of a model for adoption and implementation of e-recruitment projects using the factors as well as validation of the model with the major users. A study was conducted on “an adoption and implementation model for e-recruitment system in a developing country’s public sector: a case of Bank of Uganda”. There was therefore a need to develop a model for adoption and implementation in order to ensure value for money for e-recruitment projects. The ‘business case’ model currently being used has weaknesses like lack of emphasis on user training. There is a no comprehensive model for Bank of Uganda to follow so as to ensure success of e-recruitment projects. We develop a simple abstract framework to show how this education policy could help reduce STEM shortages in both STEM and non-STEM occupations. Hence, a more efficient way of satisfying the STEM demand in non-STEM occupations could be to teach more STEM in non-STEM disciplines. Although our results indicate that the STEM pipeline breakdown may be less problematic than typically thought, we also find that many of the STEM requirements of “non-STEM” jobs could be acquired with STEM training that is less advanced than a full time STEM education. Moreover, STEM jobs are associated with higher wages within both STEM and non-STEM occupations, even after controlling for detailed occupations, education, experience requirements, employers, etc. 35% of all STEM jobs belong to non-STEM occupations and 15% of all postings in non-STEM occupations are STEM. We design and evaluate machine learning algorithms that classify thousands of keywords collected from job adverts and millions of vacancies into STEM and non-STEM. non-STEM and measures STEM requirements at the level of jobs by examining the universe of UK online vacancy postings between 20. To address it, this paper goes beyond the discrete divide of occupations into STEM vs. “Microsoft C#”, “Systems Engineering”) and not simply for their problem solving and analytical abilities? This is an important question in the UK where less than half of STEM graduates work in STEM occupations and where this apparent leakage from the “STEM pipeline” is often considered as a wastage of resources. Graphic Designers, Economists) seek to hire STEM (Science, Technology, Engineering, and Mathematics) graduates with a higher probability than non-STEM ones for knowledge and skills that they have acquired through their STEM education (e.g. A flow diagram for e-recruiting was developed by the authors for use by employers.ĭo employers in “non-STEM” occupations (e.g. The recommendations suggest that the HR objectives have to align with the organisational objectives to ensure that they recruit, select and employ the right candidate for the right job. Some implications for managers are pointed out such as the need to be trained for these changes it can save a lot of money on advertisements the savings could be used to develop careers or training for employees. E-recruitment is revolutionising the way employers hire employees. Research done in Auckland’s Rosebank Business Precinct (New Zealand) revealed that for certain job categories e-recruitment is effective. A 36- question questionnaire was used and we employed random stratified cluster sampling. Face-to-face interviews were conducted with 102 companies within that population. This article focuses on the background of the ‘conventional’ or ‘old’ way of recruiting, it reviews different ‘new’ ways e-recruiting and its effectiveness advantages such as accessibility and disadvantages such as transgression of some legislation in e-recruiting and the impact it has on management.
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