ABSTRACT
Civil servants are increasingly expected to behave proactively at work, being confronted with workplace changes that require increased autonomy and responsibility. However, proactivity is a resource-intense behaviour that may become increasingly difficult in a demanding work context characterized by frequent change. We examine the impact of organizational change on civil servant proactivity, taking into account civil servants’ past change experiences. Analysis of two survey waves in a Belgian government agency reveal that civil servants who experienced many changes in the past behave less proactively in response to new changes compared to their colleagues who only experienced few past changes.
Disclosure statement
No potential conflict of interest was reported by the authors.
Ethics declaration
Reference to ethical committee approval: Social and Human Sciences Ethics Advisory Committee, decision on file SHW_19_08, University of Antwerp.
Notes
1. A common approach to increase representativeness of a sample exists in calculating and using weights based on specific population characteristics. Yet, when estimating causal effects (in our case the effect of perceived changes on proactivity), the question whether to weigh data is extremely complex (see also Solon, Haider, Wooldridge, Citation2015). The use of weights is strongly supported when the sampling probabilities vary endogenously (sampling probabilities are correlated with the error term in the regression). However, if they vary exogenously, and thus purely on explanatory variables, weighting is unnecessary and even harmful for precision (Wooldridge, Citation1999; Solon, Haider, Wooldridge, Citation2015). We notice variations in our explanatory variables but are unable to detect if the sampling probabilities also vary endogenously. Moreover, if we would add weights, we are only able to construct weights based on official statistics which relate to observable characteristics (e.g. gender, age, education…). Even if the sampling probabilities vary endogenously, the weights would not be able to solve this issue (see for more information regarding this topic; Wooldridge, Citation2002 and Fitzgerald, Gottschalk, and Moffitt, Citation1998). Consequently, the use of weights would, in our case, only offer a false sense of representativeness. Due to the limited representativeness of our sample, caution is needed when drawing causal inferences.
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Notes on contributors
Stéphanie Verlinden
Stéphanie Verlinden is a doctoral student at the Department of Management, Faculty of Business and Economics, University of Antwerp and research group Politics & Public Governance. Her research focuses on the impact of organizational change on organizational decision-making.
Tobias Bach
Tobias Bach is a professor at the Department of Political Science and Senior Researcher at the ARENA Centre for European Studies, at the University of Oslo, Norway. His research focuses on politics-administration relations and bureaucratic politics in an internationally comparative perspective.
Jan Wynen
Jan Wynen is a professor at the Department of Management, Faculty of Business and Economics, University of Antwerp and research group Politics & Public Governance. His research focuses on organizational responses and adaptions to changing environments.
Bjorn Kleizen
Bjorn Kleizen is a postdoctoral researcher at the University of Antwerp. His research focuses on the effects of reforms and digitalization in the public sector, with a particular focus on change- and digitalization-related psychosocial effects.
Koen Verhoest
Koen Verhoest is a professor and spokesperson of the Politics & Public Governance Research Group (Faculty of Social Sciences) and GOVTRUST Centre of Excellence, University of Antwerp.