Reforms as reputational signals: An examination of the longitudinal relation between organizational reputation and structural reform intensity

Paper submitted for panel "The Politics of Bureaucratic Reputation" Reputation serves as a crucial information shortcut for various actors to process information in the evaluation of the performance of public organizations (Bovens & Hart, 2016). An organization’s reputation refers to the perception that stakeholders have about the organization’s unique character, activities and results (Carpenter, 2001; 2010). The assumption that favorable reputations insulate public organizations from political interventions formed the starting point of the reputational research agenda in political science (Carpenter, 2001). Curiously though, no study to date has directly tested whether and under which conditions reputations are causally related to structural reforms on a larger N sample. This paper aims to fill this gap in the literature by studying the longitudinal relation between the reputation of public organizations and the structural reform intensity they experience. At their core, structural reforms have symbolic value. Reforms are signals to stakeholders and society that issues with the performance of organizations are perceived and acted upon. A large variety exists in the strength and content of structural reforms. Some reforms might be beneficial for organizations – for instance, when they aim to bring more coherence in an organization’s task profile (Wilson, 1989) – and might actually be wanted by these organizations. This project deals with this nuance by exploiting the variance in structural reforms that public organizations experience. Instead of seeing structural reforms as discrete instances, structural reform intensity is defined by a higher frequency and strength of structural reforms through time (Wynen, Verhoest, & Kleizen, 2017). Our study design offers an ideal and unique setting to examine this question. First, we can rely on the Belgian State Administration Database to gain an insight in all structural reforms all Flemish public sector organizations experienced between 1985 and 2020. Second, the paper makes use of automated text analysis techniques, more specifically supervised machine learning, and sentiment analysis, based on natural language processing (Anastasopoulos and Whitford, 2018) References Anastasopoulos, L. J., & Whitford, A. B. (2018). Machine Learning for Public Administration Research, With Application to Organizational Reputation. Journal of Public Administration Research and Theory. https://doi.org/10.1093/jopart/muy060 Bovens, M., & Hart, P. ‘t. (2016). Revisiting the study of policy failures. Journal of European Public Policy, 23(5), 653–666. Carpenter, D. (2001). The Forging of Bureaucratic Autonomy: Reputations, Networks, and Policy Innovation in Executive Agencies 1862-1928. Princeton: Princeton University Press. Carpenter, D. (2010). Reputation and power: Organizational image and pharmaceutical regulation at the FDA. Princeton, NJ: Princeton Univ. Press. MacCarthaigh, M., & Roness, P. G. (2012). Analyzing Longitudinal Continuity and Change in Public Sector Organizations. International Journal of Public Administration, 35(12), 773–782. Wynen, J., Verhoest, K., & Kleizen, B. (2017). More reforms, less innovation? The impact of structural reform histories on innovation-oriented cultures in public organizations. Public Management Review, 19(8), 1142–1164.