Title: Identification of the clusters of employee brand using FIMIX-PLS and FCM

Authors: N. Thamaraiselvan; P. Sridevi; B. Senthil Arasu; Thushara Srinivasan

Addresses: Department of Management Studies, National Institute of Technology, Tiruchirappalli – 620015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli – 620015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli – 620015, India ' Department of Management Studies, National Institute of Technology, Tiruchirappalli – 620015, India

Abstract: Ensuring sustenance of service brand using functional difference is arduous in this competitive era. Such brand difference is significantly based on service employees' interaction with customers. A favourable employee brand presented by employees to customers affords service organisations with competitive advantage. This study attempts to identify the optimum number and types of clusters in employee brand of Air India using two modern data mining techniques, viz., finite mixture partial least squares (FIMIX-PLS) and fuzzy c-means (FCM) clustering for decision making. Employees of Air India, Chennai Division were surveyed and four optimum numbers of clusters of employee brand were identified by both FIMIX-PLS and FCM. It was identified that the employees' knowledge of the desired brand (KDB) their satisfaction in terms of psychological contract (PC) varied across clusters. Quality training, developmental programs, internal communication and feedback systems must be focused and enhanced to increase the employees' KDB and PC.

Keywords: employee brand; typology; employee branding; knowledge of the desired brand; KDB; psychological contract; service branding; service employees; brand image; finite mixture partial least squares; FIMIX-PLS; fuzzy c-means; FCM.

DOI: 10.1504/IJBFMI.2017.084054

International Journal of Business Forecasting and Marketing Intelligence, 2017 Vol.3 No.2, pp.165 - 184

Received: 29 Dec 2016
Accepted: 13 Jan 2017
Published online: 08 May 2017 *

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