Beyond managerial talent: ‘key group’ identification and differential compensation practices in multinational companies

Anthony McDonnell, Patrick Gunnigle, Jonathan Lavelle, Ryan Lamare

Research output: Contribution to journalArticle

14 Citations (Scopus)
166 Downloads (Pure)

Abstract

With the maturation of strategic human resource management scholarship, there appears to be a greater call to move from monolithic workforce management to a more strategic and differentiated emphasis on employees with the greatest capacity to enhance competitive advantage. There has been little consideration in the literature as to whether organizations formally identify key groups of employees based on their impact on organizational learning and core competences. Using survey evidence from 260 multinational companies (MNCs), this paper explores the extent to which key groups of employees are formally recognized and whether they are subject to differential compensation practices. The results demonstrate that just in excess of half of these MNCs identify a key group. There was considerable differentiation in the compensation practices between these key groups, managers and the largest occupational group in the workforce. The results give rise to questions worthy of future investigation, namely whether the differentiated approaches used lead to improved performance outcomes.
Original languageEnglish
Number of pages20
JournalInternational Journal of Human Resource Management
Early online date22 Oct 2015
DOIs
Publication statusPublished - 2016

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Personnel
Human resource management
Industry
Managers
Compensation and Redress
Multinational companies
Employees
Workforce
Competitive advantage
Maturation
Core competence
Strategic human resource management
Organizational learning

Cite this

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Beyond managerial talent: ‘key group’ identification and differential compensation practices in multinational companies. / McDonnell, Anthony; Gunnigle, Patrick; Lavelle, Jonathan; Lamare, Ryan .

In: International Journal of Human Resource Management, 2016.

Research output: Contribution to journalArticle

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