Abstract
Purpose
– Change excessiveness is argued as a critical contextual aspect of change management. The purpose of this paper is to identify three major dimensions to change excessiveness: change frequency, extent, and impact. A three-factor structure is proposed to broaden the emerging study on the contextual aspects of change. Its pertinence is proposed in addressing healthcare employees’ exhaustion, change-related uncertainty, and support for change.
Design/methodology/approach
– Using questionnaires, a first pilot sample (n=131) was recruited to test the psychometric properties and validity of the three-factor structure, while controlling for affectivity. Structural equation modeling techniques following a two-step approach were used on a second sample (n=363). First a confirmatory assessment of the three-factor structure of excessive change is tested. Second, a full mediation effect of excessive change, as a second-order latent factor, regrouping change frequency, impact and extent as first-order factors, was modeled to predict a tripartite conception of change-related reactions: exhaustion, uncertainty, and support for change.
Findings
– The excessive change three-factor structure is validated, while showing its superiority over alternative models. The fully mediated model is confirmed. Therefore, the significant added effects of change frequency, impact, and extent are positively related to emotional exhaustion and cognitive uncertainty, while negatively related with behavioral support for change.
Originality/value
– This study contributes by proposing a three-factor structure to excessive change assessment based on previous and independent findings in the literature. It also contributes in modeling the added effect of change frequency, extent, and impact in the full mediation relationship of change excessiveness on a tripartite reactions to change in healthcare management settings.
Subject
Management of Technology and Innovation,Organizational Behavior and Human Resource Management,Strategy and Management,General Decision Sciences
Cited by
22 articles.
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