Revisiting organizational age, inertia, and adaptability: developing and testing a multi-stage model in the nonprofit sector

Purpose – The literature of organizational change hints that adaptability and inertia not only counterbalance but also reinforce each other, and the inertia-adaptability balance over time is nonlinear. The author aims to address this view more clearly by presenting a multi-stage conceptual model tha...

Full description

Saved in:
Bibliographic Details
Main Author: Chen, Chung-An
Other Authors: School of Humanities and Social Sciences
Format: Article
Language:English
Published: 2016
Subjects:
Online Access:https://hdl.handle.net/10356/80602
http://hdl.handle.net/10220/40572
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Purpose – The literature of organizational change hints that adaptability and inertia not only counterbalance but also reinforce each other, and the inertia-adaptability balance over time is nonlinear. The author aims to address this view more clearly by presenting a multi-stage conceptual model that delineates how adaptability and inertia take turns to override each other. In addition, data collected from over 400 nonprofit organizations within the USA were used to test this model. Design/methodology/approach – This study uses polynomial regression to examine the multi-stage conceptual model. More precisely, it tests how organizational age influences an organization's innovativeness, managerial risk aversion, and red tape. Findings – The findings support the multi-stage conceptual model. The results imply that organizational ecology and rational adaptation are mutually compatible perspectives in explaining organizational age dynamics. Originality/value – This study introduces a multi-stage model that more clearly examines how adaptability and inertia counterbalance and reinforce over time. More importantly, the author empirically examines the nonlinear organizational age dynamics using quantitative data.