Abstract
Purpose
The purpose of this paper is to report on a study of change management practices in private sector organisations in the small island economy of Mauritius.
Design/methodology/approach
Interviews were conducted with key decision makers and individuals who had experienced the organisational change process in three private organisations from different sectors in Mauritius: a bank, a hotel and a privatised state-owned enterprise. A grounded theory approach was employed to establish the key dimensions of organisational change in this setting.
Findings
Organisational change is a multi-dimensional, multi-directional and evolutionary process strongly influenced by the contextual and historical aspects of the country. The emerging key elements of change identified in the data confirmed a range of dimensions evident in the extant literature, but also identified a largely unacknowledged factor, considered to be central to the change process in Mauritian organisations. This emerging factor was identified as partisanship.
Originality/value
This study served to confirm six dimensions evident in the extant literature on organisational change: organisational structure, organisational culture, leadership processes, individuals, knowledge management and resistance to change. A seventh dimension, and heretofore largely unacknowledged factor, considered to be central to the change process in Mauritian organisations was also identified: partisanship. The study identified this emerging key dimension as having a pervasive influence. History, culture and context have served to embed this dimension in Mauritian organisations. Evidence is presented to illustrate how the process of organisational change is undertaken in Mauritius, and identify the role of partisanship. This has the potential to be applied to other small island economies with similar historical, cultural or contextual features.
Subject
Management of Technology and Innovation,Organizational Behavior and Human Resource Management,Strategy and Management,General Decision Sciences
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