Institutional Logics and Accountability: Advancing an Integrated Framework in Nonprofit–Public Partnerships


  • Kate Albrecht North Carolina State University



Nonprofit—Public Partnership, Institutional Logics, Accountability, Collaborative Governance


Public and nonprofit management literature has focused more on formal accountability and less on emerging informal structures that are present in the pilot stages of partnerships. This study uses a phenomenological approach to examine the institutional logics of partner organizations and offers an integrated framework for how these logics may translate into accountability structures in a nonprofit—public partnership (NPPP). This framework advances a basis for the mechanisms present when an individual organization’s or agency’s institutional logics must be reconciled in the context of accountability. The analysis points to emerging challenges and cross pressures within the NPPP that are driving a need for comprehensive evaluation measures, established processes for business planning, and written agreements such as memorandums of understanding to provide clear definitions of partnership roles. Public managers designing or joining pilot partnerships need to be aware that mismatched institutional logics and perceptions of accountability can occur, and these dynamics may lead to a variety of hybrid measures to ensure future sustainability of interorganizational relationships.

Author Biography

Kate Albrecht, North Carolina State University

Kate Albrechtis a Ph.D. candidate in public administration in the School of Public and International Affairs at North Carolina State University. Her research agenda focuses on aspects of nonprofit and public agencies engaging in boundary management by pursuing research questions for understanding collaborative governance while advancing methods to inform and expand organizational and network theories. She also examines organizations as actors within broader institutional and community environments with a secondary focus on research methods, particularly methodologies capable of handling longitudinal, multilevel, dynamic, and interdependent data structures.






Research Articles