Identification and analysis of employee branding typology using fuzzy c-means clustering
Abstract
Purpose
The purpose of this paper is to identify and analyse the typology of employee branding in an airline company using fuzzy c-means (FCM) clustering to improve the quality of employee brand (EB).
Design/methodology/approach
Data were collected from employees of Air India, Chennai division, using a questionnaire and analysed using FCM to find the optimum cluster number. The nature of each cluster was analysed to know its type.
Findings
The results prove the presence of four types of EB, namely, all-stars, injured reserves, rookies and strike-out kings in the aviation company. It is proven that employees in all-star have high level of knowledge of the desired brand (KDB) and psychological contract (PC), those in injured reserves have high KDB and low PC, rookies have low KDB and high PC and strike-out kings have low KDB and PC.
Research limitations/implications
The results of this study are limited to the Air India employees. This study contributes to employee branding by empirically substantiating the proposed typology using FCM. It proposes the need to analyse organisations individually before comparisons.
Practical implications
The management must focus on the quality of training and development programmes to enhance the position of rookies and strike-out kings. It must also receive regular feedback from injured reserves and strike-out kings to evaluate their perception of PC.
Originality/value
This is the first paper to empirically prove the typology of employee branding and to implement FCM in clustering employees for enhancing the EB’s quality.
Keywords
Citation
Natarajan, T., Periaiya, S., Balasubramaniam, S.A. and Srinivasan, T. (2017), "Identification and analysis of employee branding typology using fuzzy c-means clustering", Benchmarking: An International Journal, Vol. 24 No. 5, pp. 1253-1268. https://doi.org/10.1108/BIJ-01-2016-0010
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited