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Give Advocacy a Chance: Social Workers’ Policy Practice in Nonprofit and Public Organizations in Italy

Published online by Cambridge University Press:  29 October 2026

Riccardo Guidi*
Affiliation:
Department of Political and Social Sciences, University of Florence , Italy
Lorenzo Maraviglia
Affiliation:
INVALSI, Istituto Nazionale per la Valutazione del Sistema Educativo di Istruzione , Italy
*
Corresponding author: Riccardo Guidi; Email: riccardo.guidi@unifi.it
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Abstract

Nonprofit organizations (NPOs) are often considered key actors in advocacy and social change, leading to the expectation that social workers employed in NPOs are more engaged in policy practice (PP) than their counterparts in public organizations. Drawing on the motivation–facilitation–opportunity framework proposed by Gal and Weiss-Gal, this article examines differences in social workers’ PP engagement across nonprofit and public organizational settings in Italy. Using survey data from 6,859 Italian social workers, the study distinguishes four forms of PP—internal informal, internal formal, external direct, and external indirect—and compares patterns of engagement across five organizational groups. Descriptive statistics and nested regression models examine the role of sociodemographic characteristics, political attitudes, occupational characteristics, and organizational support. The findings show that social workers employed by and working within NPOs exhibit higher engagement primarily in external–indirect PP, a form more closely associated with advocacy and collective mobilization. However, these differences are associated less with sectoral identity or organizational support for PP than with organizational opportunity structures, particularly hierarchical position and access to decision-making arenas. This article contributes to policy practice research and nonprofit studies by suggesting that the distinctiveness of NPOs in fostering advocacy-oriented engagement lies less in their mission and organizational culture than in the structural opportunities they provide practitioners. More broadly, the findings highlight the importance of organizational opportunity structures in shaping practitioners’ engagement in advocacy and policy practice and encourage future research to examine how the distribution of roles and resources within organizations interacts with welfare governance.

Information

Type
Research Paper
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press or the rights holder(s) must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of International Society for Third-Sector Research
Figure 0

Table 1. Descriptive statistics for the analytical sampleaTable 1. long description.

Figure 1

Fig. 1. Distribution of key characteristics across the five social workers’ groups included in the study. Stacked bars report percentages; organizational support for PP is presented as a standardized index.Fig. 1 long description.

Figure 2

Fig. 2. Empirical distribution of the standardized component indices measuring engagement in four types of PP. Higher values indicate higher levels of engagement.Fig. 2 long description.

Figure 3

Fig. 3. Unadjusted contrasts in PP engagement between “core” NPO social workers and other groups. Positive values indicate higher engagement among “core” NPO workers; the dashed line indicates no difference.Fig. 3 long description.

Figure 4

Fig. 4. Contrasts in PP engagement between “core” NPO social workers and other groups. Rows report unadjusted and sequentially adjusted models.Fig. 4 long description.

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