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Predicting Organizational Change in Health Institutions through Management Communication: A Multiple Regression Analysis

Author(s): Ruffa Mae S. Olbes and Ronel G. Dagohoy

Published: 16 Sep 2022
Volume: 26 (2022) Issue 1, Page: 16, Category: Natural Sciences,


The successful application of change plays a significant role in communication as it is used to announce, explain, or prepare individuals for change. However, researchers, unfortunately, cannot find any research studies studying the impact of organizational change in health institutions in the local context. The main objective of this study is to determine if there is a model that could significantly predict organizational change in health institutions. This study utilized a descriptive-predictive research method to determine the possible optimal outcomes of the organizations using the gathered data. The researchers used the Slovin's Formula to calculate the appropriate sample size resulting to 114 respondents of the study. To get the primary information; first – hand survey was conducted using adopted questionnaires – Development of a Measure to Assess Organizational Change by Szamozi and Duxbury (2002) and Validity and Reliability of the Management Communication Style Scale by Rozilah, Muhammad and Kalamuddin (2013). In predicting the organizational change, multiple regression analysis was utilized to create a model. It was revealed that the level of management communication is high while for organizational change is very high. Also, it was discovered that management communication had a positive significant relationship on organizational change. Moreover, management communication significantly influences the implementation of changes in the organization. Furthermore, the study revealed a model which describes the significant prediction of management communication towards organizational change in health institutions. This paper provided essential information for health institutions in implementing future changes in their organizations

Keyword(s): Management Communication Organizational Change Prediction Health Institutions

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