Research Paper Title
Investigating the Impact of Training Influence on Employee Retention in Small and Medium Enterprises: A Regression-type Classification and Ranking Believe Simplex Analysis on Sparse Data.
This study investigates the impact of available training alternatives (TAs) on employee retention in small and medium enterprises (SMEs).
A noticeable problem with this research issue is that individual SMEs may utilise different combination of TAs. The considered survey questionnaire allowed respondent SME owners/managers the option to gauge the level of satisfaction of a TA or to indicate that they did not use it.
It follows, therefore, that the survey-based data set is sparse, in the sense that the ‘did not use’ option infers that a form of missing value is present (Likert-scale-based satisfaction value present if a TA was used). To facilitate an effective analysis of the considered sparse data set, because the missing values have meaning, the nascent regression-type classification and ranking believe simplex (RCaRBS) technique is employed.
As a development of the CaRBS technique, this technique is able to undertake multivariate regression-type analysis on sparse data, without the need to manage the missing values in any way. Results are presented from the RCaRBS analyses relating to SME owner/managers’ satisfactions with TAs and their impact on two employee retention facets, namely greater employee loyalty and, conversely, losing an employee to a competitor. Emphasis here is on the graphical elucidation of findings in regard to model fit and TA contribution.
The pertinence of the study is the inclusiveness of the data considered (a novel approach to analysing sparse data), and the comparisons between these associated issues of TA satisfaction and employee retention.
Beynon, M.J., Jones, P., Pickernell, D. & Packham, G> (2015) Investigating the Impact of Training Influence on Employee Retention in Small and Medium Enterprises: A Regression-type Classification and Ranking Believe Simplex Analysis on Sparse Data. Expert Systems. 32(1), pp.141-154.