Enhancing Performance Regimes to Enable Outcome-based Policy Analysis in Cross-boundary Settings: A Dynamic Performance Management Approach (System Dynamics for Performance Management & Governance, 6) 3031070739, 9783031070730

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System Dynamics for Performance Management & Governance 6

Vincenzo Vignieri

Enhancing Performance Regimes to Enable Outcome-based Policy Analysis in Cross-boundary Settings A Dynamic Performance Management Approach

System Dynamics for Performance Management & Governance Volume 6

Series Editor Carmine Bianchi, University of Palermo, Palermo, Italy Scientific Committee for System Dynamics for Performance Management & Governance Luca Anselmi, University of Pisa, Italy—Professor of Public Administration David Birdsell, Baruch College/CUNY, USA—Dean, School of Public Affairs Elio Borgonovi, Bocconi University, Milan, Italy—Professor of Economics and Management of Public Administration Tony Bovaird, University of Birmingham, UK—Professor of Public Management and Policy John Bryson, University of Minnesota, USA—McKnight Presidential Professor Emeritus, Hubert H. Humphrey School of Public Affairs Dario Cavenago, Bicocca University, Milan, Italy—Professor of Public Management Denita Cepiku, University of Rome Tor Vergata, Italy—Associate Professor of Public Management Lino Cinquini, Scuola Superiore Sant’Anna, Pisa, Italy—Professor of Business Administration Paal I. Davidsen, University of Bergen, Norway—Professor of System Dynamics, Chair of the System Dynamics Group Scott Douglas, Utrecht School of Governance, The Netherlands—Associate Professor Giuseppe Grossi, Kristianstad University (Sweden) and Nord University (Norway)—Professor of Public Management & Accounting Jeremy L. Hall, University of Central Florida (USA)—Professor of Public Administration and Director of the Ph.D. Program in Public Affairs John Hallighan, University of Canberra, Australia—Emeritus Professor of Public Administration and Governance Roger E. Hartley, University of Baltimore (USA)—Dean, College of Public Affairs David Lane, Henley Business School, UK—Professor of Informatics Manuel London, State, University of New York at Stony Brook, USA—Distinguished Professor of Management

Roula Masou, ESSCA School of Management, France—Associate Professor of Performance Management Luciano Marchi, University of Pisa, Italy—Professor of Planning & Control Systems Marco Meneguzzo, Università della Svizzera Italiana, Lugano, Switzerland—University Tor Vergata, Rome, Italy—Professor of Public Management Donald P. Moynihan, McCourt Chair at the McCourt School of Public Policy Georgetown University (USA) Riccardo Mussari, University of Siena, Italy—Professor of Public Management Stephen P. Osborne, University of Edinburgh Business School—Scotland, Director of the Centre for Service Excellence (CenSE), Chair of International Public Management Guy Peters, University of Pittsburgh, USA—Maurice Falk Professor of American Government, Department of Political Science, President of the International Public Policy Association Angelo Riccaboni, University of Siena, Italy—Professor of Planning & Control Systems William C. Rivenbark, University of North Carolina at Chapel Hill, USA, School of Government—Professor of Public Administration and Government Etienne Rouwette, Nijmegen School of Management, The Netherlands—Associate Professor of Research Methodology and System Dynamics Khalid Saeed, Worcester Polytechnic Institute, USA—Professor of System Dynamics Markus Schwaninger, University of St Gallen, Switzerland—Professor of Management Carlo Sorci, University of Palermo, Italy—Professor of Business Management Jürgen Strohhecker, Frankfurt School of Finance & Management, Germany— Professor for Business Administration, Operations and Cost Management Jarmo Vakkuri, University of Tampere, Finland—Professor of Local Government Accounting & Finance Wouter Van Dooren, University of Antwerp, Belgium—Associate Professor of Public Management David Wheat, University of Bergen, Norway—Professor in System Dynamics Jiannan Wu, Shanghai Jiao Tong University, China—Dean of the School of International and Public Affairs, and Executive Vice Director of the China Institute for Urban Governance

Vincenzo Vignieri

Enhancing Performance Regimes to Enable Outcome-based Policy Analysis in Cross-boundary Settings A Dynamic Performance Management Approach

Vincenzo Vignieri Department of Business and Law Studies University of Siena Siena, Italy

ISSN 2367-0940 ISSN 2367-0959 (electronic) System Dynamics for Performance Management & Governance ISBN 978-3-031-07073-0 ISBN 978-3-031-07074-7 (eBook) https://doi.org/10.1007/978-3-031-07074-7 © Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

A mio padre (to my father)

Preface

The origins of this book can be traced back to my Ph.D. studies when I began developing research in the field of public management. Since then, I have been maturing the belief that, over the last two decades, the field of public administration has witnessed theoretical and practical changes which have innovated the organizing principles of public sector organizations, the rationale of public policy design and implementation, and the aims and scope of performance management and governance. As it will be illustrated in this book, the bureaucratic approach of the old public administration (OPA) was not replaced by the managerialism of new public management (NPM), which was then supplanted by the stakeholder/networkoriented model of public governance. This idea has paved the way for crafting a transition perspective on the evolution of public administration. Rather than seeing the changes in the context for policy analysis and performance evaluation as a sequence of phases, the perspective adopted in this book frames the evolution of public administration as a layering process in which some aspects of each paradigm are coexisting and shaping the complex reality of contemporary public administration. In fact, although OPA, NPM, and public governance portray different configurations of institutional logics, policy design and implementation modes, and performance domains for control, their core characteristics can be regarded as the legacy of each paradigm for contemporary public administration, which gives a nuanced version of the different performance regimes that coexist in the current reality of public policy. The concept of performance regime comprises the collection of performance management routines used by different actors working together on a societal issue. Enhancing such routines is a crucial issue in contemporary public administration since the problems policy-makers and their organizations face are much more unpredictable and complex than two decades ago. Problems impacting on community’s quality of life, such as immigration, pandemics, societal aging, crime, unemployment, and financial crisis, cannot be easily solved by quick fixes that are focused only on a short-term and on a bounded vision of their own causes (i.e., organizational perspective). To deal with them, a systems approach is needed in vii

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policy analysis for framing dynamic complexity and affecting sustainable (community) outcomes. To enable outcome-based performance management, the approach advocated in this book is dynamic performance management (DPM). By applying DPM to different inter-institutional policy contexts, this book intends to provide scholars and practitioners with a piece of fabric for enhancing performance regimes to enable outcome-based policy analysis in cross-boundary settings. A novel approach in dealing with wicked problems that come with empirical research proving its effectiveness may provide policy-makers and their stakeholders with a good methodological support in such an endeavor. To bring such a method under effective use in various policy contexts, the first part of the book (i.e., Chaps. 1 and 2) sets out both the theoretical and the methodological perspectives that guide the empirical research developed in the second part of the book (i.e., Chaps. 3 and 4). Chapter 1 frames the complexity of contemporary public administration. In doing this, it will analyze the changes in public administration’s institutional, political, and managerial aspects to identify what core aspects of “old public administration” (OPA), “new public management” (NPM), and “public governance” have shaped coexisting performance regimes in contemporary public administration. Chapter 2 explores the dynamic complexity of governing local area performance by discussing how the changes in the governance structure affect local area performance development over time. As a result, the chapter advocates DPM as a methodological framework to enable outcome-based policy analysis in cross-boundary settings. Also, it suggests embracing an “outside-in” perspective of stakeholders’ collaboration to properly frame the multiple dimensions of local area performance through DPM. In response to the need for enhancing performance regimes, Chap. 3 discusses how learning through DPM may foster a dialogic form of policy-making and performance evaluation. To this end, the chapter illustrates how the use of DPM can foster policy learning so as to help decision-makers to get a grip on policymaking failures. Chapter 4 shows how DPM may provide the actors in a public value-driven performance regime with the methodological framework to implement policy learning for assessing community outcomes. As illustrated by different examples, it discusses how DPM may support local area policy-makers and their stakeholders in governing the achievement of the specific purposes that have triggered the formation of the collaborative arrangements under investigation. Writing a book makes the author wishful to contribute to the field in which the research is grounded. Equally, it makes the author grateful to the people who have made this possible, though he feels that giving recognition to some of them implies the risk of disregarding others. I am particularly indebted to a group of faculty members at the Department of Political Sciences and International Relations at the University of Palermo (Italy), who has encouraged me since the beginning of my academic pathway. I owe the most significant debt to Carmine Bianchi for his generous mentorship and his reliable ethical and scientific guidance, which have thoroughly inspired me

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as a person and as a scholar. Over the last eight years, we have built a genuine friendship, and I hope it will continue in the future since it has made all the work we have done together worthwhile. I am grateful to Enzo Bivona and Federico Cosenz, as they have strongly nurtured my interest in studying business and public management – since I was an undergraduate student. Their encouragement, friendships, and professional support have significantly impacted my academic and personal life. A special thank belongs to Antonio Perrone, who – over the last five years – has generously involved me in fruitful research and teaching experiences. Also, I would like to thank Daniele Malfitana. My experience as a post-doctoral researcher at the Institute for Cultural and Monumental Heritage of the National (Italian) Research Council in Catania (Italy) has enabled me to advance my research in business and public management with a keen interest in cultural heritage. Merit goes to Marzia – none of this would have been possible without her emotional intelligence. She has always stood me back up from the feeling of discomfort brought along by the challenging task of writing a book. Finally, my heartfelt gratitude goes to my family members – mom, dad, and my sister Angela – as their care and encouragement have never been lacking. Among them, the utmost appreciation goes to my father. Had my father not worked so hard to improve our means to ensure my education, would I have had the privilege to write this work? This book is dedicated to him. Siena, Italy

Vincenzo Vignieri

Contents

Part I 1

2

Dynamic Performance Management as a Methodological Framework to Enable Outcome-based Policy Analysis

Framing the Institutional Complexity of Public Administration: Coexisting Performance Regimes in Contemporary Governance . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 A Three-Dimensional Framework to Outline the Complexity of Public Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Integrating Institutional Logics in Public Administration: Different Purposes and Roles of Public Sector Organizations from OPA Through NPM to Public Governance . . . . . . . . . . 3.2 Describing Policy-Making in Public Administration: Modes of Policy Design and Implementation from OPA Through NPM to Public Governance . . . . . . . . . . . . . . . . . . 3.3 Placing Policy Outcomes at the Core of Contemporary Public Administration: The Scope of Performance Domain for Control from OPA Through NPM to Public Governance . . . . 4 The Legacy of OPA, NPM, and Public Governance for Contemporary Public Administration . . . . . . . . . . . . . . . . . . . . 4.1 Articulating the Characteristics of Coexisting Performance Regimes in Contemporary Public Administration . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Dynamic Performance Management: A Methodological Framework to Enhance Public Value-Driven Performance Regimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Framing the Dynamic Complexity of Governing Local Area Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2.1 2.2

A Systems Approach to Local Area Governance . . . . . . . . . . . A Dynamic View of the Relationships Between Governance Structure and Local Area Performance . . . . . . . . . . . . . . . . . . 3 The Need for Dynamic Performance Management to Implement Inter-institutional Performance Management Routines . . . . . . . . . . . 4 Framing Local Area Performance Through an “Outside-In” Perspective of Stakeholders’ Collaboration . . . . . . . . . . . . . . . . . . . 4.1 The “Depth” of Performance: Combining Institutional and Inter-institutional Levels to Generate Public Value in Local Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Pairing “Depth” with “Span” Dimensions of Local Area Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Integrating “Depth” and “Span” Through the “Time” Dimension of Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 A Multidimensional View of Local Area Performance to Frame Community Outcomes . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part II 3

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Applying Dynamic Performance Management to Cross-Boundary Settings

Fostering Policy Learning in Public Value-Driven Performance Regimes Through Dynamic Performance Management . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Overcoming the “Missing Link” Between Policy Design and Implementation to Integrate Policy-Making and Performance Evaluation Through Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Enhancing Learning Forums Through Dynamic Performance Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Applying Dynamic Performance Management to Support Tourism Planning and Development in a Local Area: Learning In and About the Complexity of Destination Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applying Dynamic Performance Management to Implement Policy Learning for Assessing Community Outcomes . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Challenges in Implementing Outcome-Based Performance Assessment in Public Value-Driven Performance Regime . . . . . . . 2.1 Performance Causality: Dealing with Attribution Problem . . . 2.2 Framing the Effects of a Policy Program on the Socio-economic Context . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Using Outcome Measures to Support Learning and Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Considering Potential Side Effects of Performance Measurement: The Relevance of Extending the Boundaries in Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Assessing Community Outcome Through DPM in Public Value-Driven Performance Regimes . . . . . . . . . . . . . . . . . . . . . . . 3.1 Applying DPM to Support a Policy Network in the Pursuit of Urban Brownfield Regeneration Policy Outcomes . . . . . . . 3.2 Improving Destination Image Through Outcome-Based DPM Insight Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Framing Public Services Co-production Community Outcomes Through Dynamic Performance Management . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part I

Dynamic Performance Management as a Methodological Framework to Enable Outcome-based Policy Analysis

Chapter 1

Framing the Institutional Complexity of Public Administration: Coexisting Performance Regimes in Contemporary Governance

1 Introduction The environment in which public and private organizations operate has become increasingly complex and highly unpredictable. Though social issues arise in a policy field, they are likely to escalate, possibly through a pattern affected by delays. These complex features characterize “wicked” community problems. The term “wicked” describes problems showing a high level of uncertainty on dealing with them effectively (Head & Alford, 2015; Lægreid & Rykkja, 2014; Peters, 2017). Community problems like social exclusion, elder health care, rural area depopulation, and neighborhood blight—just to mention a few—are considered “wicked” due to the dynamic and complex nature of their underlying cause-andeffect relationships. Wicked problems require that different organizations (e.g., public agencies, businesses, and other stakeholders) embark on collaborative initiatives cutting across different policy fields. Collaborative policy design and implementation imply dealing with some challenges. Differences in stakeholders’ values, political priorities, and logics of action may cause goal vagueness and actors’ disengagement and encourage opportunistic behavior, since a satisfactory answer to specific community needs does not emerge from a predetermined idea. Discord and conflicts among stakeholders may idle relational resources (e.g., trust, active citizenship, or social capital). This condition may downgrade partners’ commitment to the collaborative undertakings leading to fragmented and inconsistent policy responses (Innes & Booher, 2018). To address such challenges, scholars have suggested actively engaging partners in learning forums (Gerlak & Heikkila, 2011) and performance dialogues (Rajala et al., 2020) through which develop a “shared theory of change” (Emerson & Nabatchi, 2015a, p. 63) that might include a feasible response to a wicked issue (Kania & Kramer, 2011). In this perspective, collaborative performance regimes may lead “actors working on the same societal issue develop collaborative routines © Springer Nature Switzerland AG 2022 V. Vignieri, Enhancing Performance Regimes to Enable Outcome-based Policy Analysis in Cross-boundary Settings, System Dynamics for Performance Management & Governance 6, https://doi.org/10.1007/978-3-031-07074-7_1

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1 Framing the Institutional Complexity of Public Administration:. . .

to achieve shared ambitions” (Douglas & Ansell, 2021, p. 954). This requires methods to properly implement collaborative inter-institutional performance management routines to support policy-makers and their stakeholder to deliver public value (Moynihan et al., 2011). This book aims at illustrating how the Dynamic Performance Management framework may enhance performance regimes by fostering policy learning and supporting policy-makers and their stakeholder in assessing community outcomes. To this end, this chapter will frame public administration complexity. In doing this, it will analyze the changes in public administration’s institutional, political, and managerial aspects through an integrated framework (Borgonovi, 2002). As a result, the analysis will identify the core aspects of “old public administration” (OPA), “New Public Management” (NPM), and “public governance” that permeated the contemporary public administration (Osborne, 2006). In this perspective, contemporary public administration can be regarded as a complex institutional reality (Kristiansen et al., 2019) in which three performance regimes coexist. Such regimes provide the methodological context for illustrating how Dynamic Performance Management may foster policy learning and support policy-makers and their stakeholder in assessing community outcomes.

2 The Structure of the Book Research and education should provide “intellectual control” (Barzelay & Thompson, 2010, p. 295) over the challenge to innovate contemporary public administration (Kettl, 2015; Kroll & Moynihan, 2018). Innovating contemporary public administration entails facing a multi-level and multi-actor policy context (Peters & Pierre, 2004) within a plural and pluralist state (Osborne, 2010a). Also, it requires dealing with trade-offs in time and space in different policy fields (Bianchi, 2016) and policy resistance (Sterman, 2000) that are hard to tame because of the delays and non-linearities involving cause-and-effect relationships structuring complex social systems (Forrester, 1969, 1971, 1987). “To deal with them, a systems perspective is needed in policy design, for framing dynamic complexity and affecting sustainable (community) outcomes” (Bianchi, 2021, p. 333). Implementing a systems approach in public administration implies innovating research and education to encourage new practices. A novel methodological perspective on how to deal with “traditional” public administration challenges that come with empirical research proving its effectiveness might be a good support in such an endeavor. This book intends to provide scholars and practitioners with a piece of fabric to design innovation mechanisms in public administration through the Dynamic Performance Management framework (Bianchi, 2016, 2021; Bianchi et al., 2021). To bring such a method under effective use in different policy contexts, the first part of the book (i.e., Chaps. 1 and 2) sets out both the theoretical and the

2 The Structure of the Book

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Fig. 1.1 The overall research design and book structure

methodological perspectives that guide the empirical research developed in the second part of the book (i.e., Chaps. 3 and 4). Figure 1.1 outlines the structure of the book. Chapter 1 frames public administration institutional complexity. Chapter 2 profiles the dynamic complexity of governing local area performance to discuss the need for Dynamic Performance Management to enhance the governance of such context. Chapters 3 and 4 are devoted to illustrating how implementing outcomebased performance assessment through Dynamic Performance Management may foster policy-makers learning and improvement. As Fig. 1.1 shows, step 1 discusses how changes and innovation in public policy and service delivery have crafted public administration (Christensen & Lægreid,

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1 Framing the Institutional Complexity of Public Administration:. . .

2010; Peters, 2016). In doing this, an introductory framework outlining institutional complexity will be illustrated. The analytical framework will outline the complexity of contemporary public administration by describing the relationships between performance management and public administration. To this end, three paradigms—ranging from bureaucracy through business-like management to inter-organizational/network-based public administration (Klijn & Koppenjan, 2015; Provan & Kenis, 2007)—will be analyzed along three dimensions, i.e., public administration’s institutional logic, policy design and implementation mode, and performance domain for control. As it will be explained in the following sections, such dimensions come close to what Talbot (2010, p. 81) defines as “the institutional context” and “the nature of actual performance intervention.” The framework sketched in step 1 will allow one to understand how todays’ public administration complexity conflates the legacy of OPA, NPM, and public governance. With step 2, public administration changes are framed as an evolution process that had has passed through three dominant stages (Osborne, 2006) in which some features of the former stage are melted with the emerging “new” ones (Frederickson, 1999; Iacovino et al., 2015; Osborne, 2010a). In fact, the contemporary reality of public administration has inherited core aspects of OPA, NPM, and public governance, whose legacy has shaped performance regimes1 in contemporary public administration. Such construct is crucial in this book because it captures the characteristics of coexisting inter-institutional “performance management routines” (Moynihan et al., 2011, p. 141) that local actors may use to “review their joint performance” (Douglas & Ansell, 2021, p. 951). The coexistence of performance regimes in contemporary public administration reveals the high level of complexity that features performance management and governance in contemporary public administration (Bowman & Parsons, 2013; Moynihan et al., 2020). As a result of step 3, a particular type of performance regime provides the methodological research directions for the second part of the book as the implementation of such inter-institutional routines in dynamic and complex governance settings requires proper support through robust methods and tools. In response to such methodological needs, Chap. 2 introduces Dynamic Performance Management as a framework to enhance performance regimes. In doing this, the characteristics of dynamic and complex systems will be highlighted (i.e., step 4) to position the relationship that links governance structure to policy outputs and outcomes at the core of the methodological framework (i.e., step 5). Based on this, we will advocate DPM as a methodological framework to implement interinstitutional performance management routines in cross-boundary settings. Such a

The term regime was introduced in the international organization literature to define “principles, norms, rules, and decision-making procedures around which actor expectations converge in a given issue-area” (Krasner, 1982, p. 187). In the context of this book, it refers to the collection of performance management routines, as defined later in this chapter. 1

3 A Three-Dimensional Framework to Outline the Complexity of. . .

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Fig. 1.2 A three-dimensional space to frame the public administration complexity

position will allow us to embrace an “outside-in” perspective of stakeholders’ collaboration to properly frame the multiple dimensions of local area performance through DPM. Then, in the second part of the book, we will apply DPM to different policy contexts to illustrate how such a framework may enhance performance regimes by fostering policy learning (i.e., Chap. 3) and implementing outcome-based performance assessment (i.e., Chap. 4).

3 A Three-Dimensional Framework to Outline the Complexity of Public Administration The complexity of public administration stems from the relationships involving (1) institutional logic (Peters, 2010, 2011; Talbot, 2005; Thornton & Ocasio, 2008, (2) policy design and implementation mode (Bardach, 1977; Mazmanian & Sabatier, 1983; Moore, 1995; O’Toole, 2000; O’Toole et al., 2005; Pressman & Wildavsky, 1973), and (3) performance domain for control (Bouckaert & Halligan, 2008; Hatry, 1999). This conceptual structure configures a three-dimensional space that is portrayed in Fig. 1.2. The goal of the following sections is to analyze such space. Firstly, each dimension is individually illustrated by drawing the reader’s attention to the factors that have led to changes in each of the analyzed viewpoints. Once these aspects are clarified, we will bring together these three dimensions to frame public administration complexity.

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1 Framing the Institutional Complexity of Public Administration:. . .

Integrating Institutional Logics in Public Administration: Different Purposes and Roles of Public Sector Organizations from OPA Through NPM to Public Governance

Human beings deliberately establish institutions such as families, businesses, and public and not-for-profit organizations, characterized by the production and consumption of goods. Besides organizations, people create other “institutions” such as education, religion, language, and politics. Thus, such institutions as “human societies” are established to pursue the specific goals set by their founders and societal members. Institutions “represent the interaction of structures and processes for governing” (Peters, 2011, p. 81). They are characterized by “a set of material practices and symbolic constructions—which constitutes its organizing principles” (Friedland & Alford, 1991, p. 248). These principles refer to the institutional logic establishing “the implicit relationships between means and goal that is assumed by organizational actors” (Bacharach & Mundell, 1993, p. 423). “Institutions are historically contingent” (Thornton et al., 2012, p. 12; Thornton & Ocasio, 2008), but as organizations, they are “made to last” (Coda, 2010, p. 16). This tension implies that institutional logic must change to make “the conditions of existence and the manifestation of life” of an organization (Zappa, 1927) compatible with its dynamic institutional environment. Changes in sociodemographic conditions, shifts in regulatory systems, political turnover (Scott et al., 2000), value conflicts, and competition (Townley, 2002) challenge currently adopted institutional logics. Thus, to properly govern an organization toward the achievement of its purposes, the logic of action pro tempore adopted must fit with the dynamic nature of the socio-economic context in which the organization operates, i.e., the environment. The concept of institutional logic may explain how the shifts in the basis of the legitimacy of public administration have determined changes in the purposes and role of public sector organizations (Llewellyn, 2004; Thomas & Davies, 2005). This kind of analysis sheds light on how the “organizing principles” (Friedland & Alford, 1991, p. 248) of public administration have evolved from OPA—through NPM—to public governance (Christensen & Lægreid, 2010; Meyer et al., 2014). As Fig. 1.3 shows, three main institutional logics for public administration can be schematically positioned along a continuum from OPA through NPM to public governance. These will be individually analyzed in the following section.

3.1.1

Changes in the Institutional Logic of Public Administration

The administration is the core of a modern form of government. Under the authority of an administrative chief or an elected official, a technical body of public officials serves democratic governments “in the accomplishment of the purposes of the State”

3 A Three-Dimensional Framework to Outline the Complexity of. . .

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Fig. 1.3 Institutional logics in public administration: an integrative perspective

(Wilson, 1887, p. 6). This role implies that a democratic constitution should hold public administration accountable (Waldo, 1952) in a relationship between “people and those who use power to govern on their behalf” (Warren, 2014, p. 40). Democratic accountability was a primary issue for the OPA (Karl, 1963), and the development of administrative law in democratic states is the imprint of such concern. The administrative law comprises “mechanisms that operate as constraints on the set of actions, or policy choices, that a bureau or individual can take” (Bertelli, 2005, p. 133). Such prescriptive logic posits that “the rule of law” is the fundamental principle of democratic governance which “commit(s) public administration to fulfill its constitutional role” (Lynn, 2009, p. 803). “A commitment to the rule of law is an acceptance of a requirement to respect the rules and put them into fully effect” (Ingram, 1985, p. 359). This principle implies that the rule of law is not simply an alternative to “the rule of men” (Dicey, 1979, p. 184), but it means that the prescription it contains stands above men. Coherent with the logic of a legislation-driven system, a full-time bureaucracy operating within rigid and hierarchical large public bodies was perceived as reliable means to ensure equity, transparency, and cost-effectiveness in public policy and service delivery. As a result, much public administration rationality was rendered as a technical or legal matter (Pollitt & Bouckaert, 2011), with many public agencies funneling citizens throughout the public service realm. In this context, “a dense ‘grid’ of rules” (Dunleavy & Hood, 1994, p. 9) prescribed public administration ends, power, and role of politicians and bureaucrats, as well as the rights and duties of citizens in front of the public administration. The hegemony of such institutional logic lasted for more than 100 years—from the end of the nineteenth century to the late 1970s (Pollitt & Bouckaert, 2011). By that time, rather than considering the law as a guiding principle for enhancing public administration performance and accountability, a set of emerging ideas were pointing at law as constraint, as “an obstacle on the way” (Lynn, 2009, p. 808) to improve public sector organization efficiency. In the early 1980s, due to a blend of economic, social, political, and technological factors, a group of Western economies, pioneered by the Anglo-Saxon countries (Pollitt, 1990), promoted a set of reforms that changed the institutional logic of public administration (Bovaird & Löffler, 2009b; Larbi, 1999). Labeled as NPM

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(Hood, 1991)—the “entrepreneurial government” movement (Osborne & Gaebler, 1993) contended that the injection of business sector management style into the public sector would have recovered public administration efficiency (Pollitt & Bouckaert, 2011, p. 6). The core components of NPM came from a “marriage of opposites” (Hood, 1991, p. 5): the business-type managerialism (Pollitt, 1990) and the new institutional economics (Niskanen, 1971; Ostrom, 1974). Such components have been associated with several administrative practices (Ferlie et al., 1996; Hood, 1991, 1995, 2001; Osborne, 2006), as reported in Table 1.1. They may provide an empirical meaning to the different “doctrinal components” (Hood, 1991, p. 4) underpinning the ascent of the “managerial state” (Clarke & Newman, 1997). As remarked by several scholars, NPM was a wide stream of thoughts (Frederickson et al., 2016) that grew as “a chameleon-like and paradoxical creature” (Pollitt et al., 2007, p. 4) “not owning universal characteristics” (Ferlie & Geraghty, 2005, p. 431). Although different interpretations of the nature and implications of NPM exist, reforms produced similar administrative trends (Hood, 1991). NPM reforms assumed that “the state was too large and too costly, and that centralized, or rule-oriented solutions are part of the problem” (MacLauchlan, 1997, p. 118). Reforms attempted to reverse a costly hierarchical and centralized service delivery structure (Dunsire & Hood, 1989) into a less expensive market-oriented approach (Flynn, 2002; Mascarenhas, 1993; Stewart & Walsh, 1992; Walsh, 1995) by placing a growing “emphasis on the subsidiarity principle” (Hood, 1991, p. 3). Particularly at the local level, reforms established autonomous “public” agencies (i.e., state-owned companies) to deliver public services in different policy fields (e.g., health-care education, social services, and utilities). Also, contracting out and outsourcing were suggested as cost-effective organizational strategies because contracts distinguished the purchaser from the service provider (Pollitt, 1990), set roles and responsibilities of the parties, and made the cost of the service controllable. “Government by Contract” (Vincent-jones, 2016, p. 611) and “Management in Government” (Hood, 1991, p. 3) mechanisms changed the prescriptive logic of the OPA by “an expansion of the reach of administrative law to the contractee-state” (Bertelli, 2005, p. 148). In the pursuit of governmental efficiency, contracts would have allowed a better control/discretionary power of public managers over “the instrument by which the goods and services required by government departments are procured from the private sector” (Turpin, 1989, p. ix). Such “new” institutional logic set the framework for developing hands-on professional management practices in the functioning of government. However, the injection of such practices provoked a substantial change in the ethos of public administration. Traditional values of OPA, such as universalism and fairness, conflicted with those of individualism, efficiency, and productivity (Dunleavy & Hood, 1994, p. 10), underlying NPM. The ascent of these principles diminished the dominance of “the rule of law” in favor of market-based mechanisms, causing a conflict between the preexistent perspective of legalism (Goodnow, 1886) and managerialism (White, 1926). The first approach “relies on law-based priorities and processes to balance discretion/innovation and accountability,” while the second

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Table 1.1 NPM institutional logic: clustering several administrative practices from “managerialism” to “new institutional economics” Authors • Hood (1991) • Dunleavy and Hood (1994)

• Pollit (1993, 1994)

• Ferlie et al. (1996)

• Borins (1998)

Administrative practices of New Public Management • Hands-on professional management shift to disaggregation of units into quasi-contractual or quasi-market forms • Shift to greater competition and mixed provision, contracting relationship in the public sector; opening up provider role to competition • Stress on private sector styles of management practice • Greater emphasis on output controls explicit standards and measures of performance • Stress on greater discipline and parsimony in resource use • Reworking budgets to be transparent in accounting terms • Decentralizing management authority within public services • Breaking up traditional monolithic bureaucracies into separate agencies • Introducing market-type mechanisms with clearer separation between the purchaser and provider function • Stress on quality, responsiveness to customers • Performance targets for managers • Capping/fixed budgets • Changing employment relations • Decentralization of services • Unbundling; new forms of corporate governance • Split between strategic core and large operational periphery • Develop quasi-markets as mechanisms for allocating resources • Split between public funding and independent service provision • A major concern with service quality • More transparent methods to review performance • Strong concern with value-for-money and efficiency gains • Downsizing Deregulation of the labor market • Increased autonomy, particularly from central agency controls • Receptiveness to competition and an open-minded attitude on the way the public sector should perform public activities • Providing high-quality services that citizens value: service users as customers • Organizational and individual performance-based reward systems • Performance-based budgeting

Managerialism

(continued)

1 Framing the Institutional Complexity of Public Administration:. . .

12 Table 1.1 (continued) Authors • Osborne and Gaebler (1992)

Administrative practices of New Public Management • Decentralization; flattened and flexible form of government • Catalytic government: steering, not rowing • Competition even within public services • Driven by mission, not rules • Result-oriented government: funding outputs, not inputs • Enterprising government: earning not spending

New institutional economics

“relies on innovation and efficiency to do the same” (Christensen et al., 2017, p. 635). Such tension has shaped the intellectual foundation of contemporary public administration. Managerialism supporters argued that a “rules-based management” (Kassel, 2008, p. 241) approach would have made it difficult to ensure performance. Legalism proponents warned that without the law, there is a risk that democratic values such as “political responsiveness, representativeness, and accountability become subordinate concerns” (Rosenbloom & O’Leary, 1997, p. 2) in public administration. That is because the rule of law, though inconvenient, guides policy implementation (Bertelli & Lynn, 2006; Gilmour & Jensen, 1998; Kettl & Fesler, 1996) and protects public rights in the functioning of government. To overcome the apparent theoretical irreconcilability between law and management in public administration, scholars and practitioners have reinvented the government by suggesting that contemporary public management is significantly more than the sum of those two elements (Christensen et al., 2017). A symbiotic relationship between law and public management shapes the meaning of the rule of law in contemporary public administration (Lynn, 2009). The “reconciliation between law and management” (Christensen et al., 2017) implies a new institutional logic for public administration because the law may offer “a set of tools for management” (Cooper, 1997, p. 104). Public managers may instrumentally use rulemaking, judgment, agreements, and contracts to pursue public ends efficiently and effectively (DeLeon & Denhardt, 2000). This innovation requires abandoning the “public administration orthodoxy” (Rosenbloom, 1993) that “constrains the private role in public governance” (Freeman, 2000, p. 1289) due to the “overly legalistic conception of public law” (Christensen et al., 2017, p. i132) that underestimates the social impact of an active engagement of the private sector. By embracing an institutional logic that “facilitate(s) and direct(s)” (Freeman, 2000, p. 1289), civil society, businesses, and not-for-profit organizations may enlighten what contribution the private sector may provide in the pursuit of the public good. In this new institutional logic, “law not only constrains but also enables” (Christensen et al., 2017, p. i131) citizens and society to enhance “the work of administration” (Lynn, 2009, p. 804) through “supervising mechanisms” (Shapiro, 1994, p. 502) that are the rules. Lawmaking and law enforcement should facilitate citizen participation in the administrative process to build collaborative relationships

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with stakeholders and ensure value plurality. Particularly at the local level, the local area governance implies that public administration should develop the capacity to empower citizens and enable community development by implementing public policies and service delivery. For example, such new logic offered an alternative dispute resolution method for environmental conflicts (Rosenbloom & O’Leary, 1997). Federal statutory rules enabled a negotiation process that involved multiple parties in “decision-making forums as public/political arenas for problem-solving in which address multiple issues under technical complexity conditions, scientific uncertainty and unequal power and resources distribution” (Rosenbloom & O’Leary, 1997, p. 102). In this case, participation in public governance affairs has enabled “stakeholders in a dispute to reach a mutually satisfactory agreement on their own terms” (O’Leary & Bingham, 2003, p. 1). An enabling/empowering logic for public administration can accommodate the pursuit of government effectiveness with the protection of democratic values. This adaptation is possible because, under complex conditions, public administration legitimacy stems from the aptitude of politics and administration to sustain community-building processes (Nalbandian, 1991, 1999) through strategies aimed at empowering vulnerable groups and underrepresented voices (Ansell & Gash, 2007; Schuckman, 2001). Based on such institutional logic, regulation should aim at “empowering all stakeholders—regulated entities, administrators, and intended beneficiaries alike—in a collaborative regulatory endeavor” (Seidenfeld, 2000, p. 411). Therefore, public administration may reduce power-resource asymmetries and mitigate value conflicts to create incentives or remove constraints on participation. In this perspective, the law becomes an instrument to trigger the starting conditions for collaboration within the scheme of public governance (Ansell & Gash, 2007). The term governance originates from the ancient Greek verb κυβερνάω (kubernáo), which means “to steer.” In this book, public governance is intended as “the way in which stakeholders interact with each other in order to influence the outcomes of policies” (Bovaird & Löffler, 2009a, p. 7). Such a way is neither a variation of government nor an alternative: it is a comprehensive and inclusive method that sets new logic for government in contemporary society. In this sense, the “formal authority” of public administration is not used to steer society, as it was under the OPA, but as a means to enable/empower public policy-makers and other societal actors “to row” together toward the achievement of “shared goals” (Rosenau, 1992). As Bovaird and Löffler (2009b, p. 19) remarked, “the most important question is not whether the state will remain more powerful than other players, but which set of formal (legal) and informal rules, structures and processes will be needed so that the state, the private and voluntary sector, citizens and other important stakeholders can exercise power over the decisions by other stakeholders so as to create win-win situations for all parties concerned.” To this end, the enabling/empowering logic should leverage collaboration in the decision-making process to guide stakeholders’ actions toward achieving policy

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outcomes through institutional design and facilitative leadership (Ansell & Gash, 2007). This implies that public administration retrieves the “authoritative allocation of values” (Easton, 1965, p. 3) by placing the political nature of the policy-making process at its roots. Stakeholders’ representation, community participation, and partnership commitment are fundamental to ensure consistency between policy design and implementation in the complex policy context of public governance.

3.2

Describing Policy-Making in Public Administration: Modes of Policy Design and Implementation from OPA Through NPM to Public Governance

As the second dimension of the framework, the changes in policy design and implementation mode are paired with the integration process involving public administration institutional logics. Policy-making comprises two strictly related phases: policy design and implementation (Pressman & Wildavsky, 1973). Policy design means stating goals regarding specific policy so that strategies to attain them can be carried out through policy implementation (Howlett, 2019). This activity is inherently political because it prioritizes community issues, based on which politicians may cascade envisioned courses of action into implementation plans. These plans convert a policy goal into an action program that outlines how an expected chain of events will achieve a set of desired policy outcomes (Mazmanian & Sabatier, 1983). As two specular phases, policy design and implementation are crucial to change the state of the political system. In fact, the way in which a policy is carried out is not extraneous to public administration’s aptitude to satisfy political demands (Easton, 1953) because it cuts across the institutional, political, and managerial aspects of public administration. In fact, the ability to perceive and change the state of societal problems depends on how powers, roles, and prerogatives are balanced with political priorities within representative bodies and available organizational means (Borgonovi, 2002). As Fig. 1.4 portrays, each mode’s label describes the main characteristics of the policy-making process within the OPA, NPM, and public governance. The “transition perspective” on the evolution of public administration2 also influences the understanding of the changes in policy design and implementation modes, as it will be discussed in the following section. 2

This perspective explains the changes in public administration as a layering process that has been innovating the context for public policy design and implementation (Bovaird & Löffler, 2009b; Osborne, 2010c) and the logic of service delivery (Osborne, 2020). Consequently, contemporary public administration is a complex reality in which a variety of institutional configurations for managing public sector performance coexists (Jakobsen et al., 2018; Moynihan et al., 2011; Talbot, 2010). This implies that the linear and competitive modes are still existent in contemporary public administration, though less influent, than in the OPA and NPM, respectively.

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Fig. 1.4 Different modes of policy design and implementation from OPA, through NPM, to public governance

3.2.1

Changes in Policy Design and Implementation Mode

The OPA paradigm was governed by a drastic separation of politics from administration (Osborne, 2006), with elected officials establishing long-term political goals and bureaucrats identifying appropriate technical means. Such separation is firmly rooted in the prescriptive logic (i.e., legal-mandated) of the OPA3: a statute states policy goals and “the implementation will follow in a fairly linear fashion from this” (Schofield, 2001, p. 250). In this context, the hierarchy among different jurisdictional levels of government ensures the coordination between policy design and implementation. In fact, the law attributed competences, duties, and administrative tasks to local, regional, and national level. In this way, policy-making unfolds as a linear process, with policy design and implementation regarded as two distinct phases, indeed reflecting the principle of the politics/administration divide. A liner mode of policy design and implementation is the hallmark of the rational decision-making approaches4 that influenced the bureaucratic paradigm. The strict procedures of administration, mechanistic control, and hierarchical relationships among levels of government were regarded as means for a “successful

The prescriptive logic implies the strict commitment of public administration to “the rule of law” (see Sect. 4). 4 The rationality in decision-making is a contended argument by scholars of organizations. All scholars agree on the fact that the rationality is a crucial element that informs decisions, but some disagree on the existence of an absolute rationality (Rugiadini, 1979, p. 153). Two approaches characterize the decision-making within organizations. On the one side is the “economic rationality” of the homo economicus: a figurative human being characterized by the infinite ability to make rational decision to maximize personal utility (Gulick & Urwick, 1937; Miller & Starr, 1967; Taylor, 1914). On the other side is the belief that “the capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problems whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality” (Simon, 1957). Under these conditions, the decision is rational because human beings select alternatives that aim at achieving satisfactory results, rather than optimal solution that maximizes the personal utility, as the “economic rationality” would argue. 3

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implementation by public managers of policies decided ‘up stream’ in this system by democratically elected (and, it is implicitly assumed, accountable) politicians” (Osborne, 2010b, p. 8). Scholars of policy design and implementation (Sabatier & Mazmanian, 1979; Van Meter & Van Horn, 1975) described this linear mode of policy-making as a “top-down” (Sabatier & Mazmanian, 1979) perspective.5 In their studies, they investigated “a policy decision (usually a statute) and examined the extent to which its legally-mandated objectives were achieved over time and why” (Sabatier, 1986, p. 22). Such works attributed the success or failure of policy implementation to inconsistent legislation or imperfect bureaucratic control over its implementation (Sarbaugh-Thompson & Zald, 1995), a finding which is consistent with the prescriptive institutional logic of the OPA—as discussed in the previous section. Lately, from the 1970s to the 1980s, a “bottom-up” perspective of policy-making commenced recognizing the importance of the implementation structures as a new unit of administrative analysis (Hjern, 1982; Hjern & Porter, 1981). Several studies (Elmore, 1979; Ingram, 1977; Lipsky, 1971) found that “local actors often deflect centrally-mandated programs toward their own end” (Sabatier, 1986, p. 22). This behavior may influence policy implementation due to the adverse interpretative power of the bureaucrats (Lipsky, 1971, 1980; Prottas, 1979). Lipsky’s “streetlevel bureaucracy” theory explained the role of bureaucratic discretion within policy implementation. “In the execution of their tasks, street-level bureaucrats establish routines to cope with uncertainties and work pressures. Often such routines effectively become the public policies they carry out” (Lipsky, 1980, p. xiii). To avoid that street-level bureaucrats could “divert the true policy” (Sabatier, 1986, p. 30), bottom-up scholars advised to incorporate the discretionary power into a model of implementation. By acting as “interpreters of the central policy” (Schofield, 2001, p. 251), street-level bureaucrats may suggest adaptive forms of implementation (Thompson, 1982) and innovative programs (Maynard-Moody et al., 1990). Within the framework of the OPA, the “street-level bureaucracy” theory sought to bridge the politics-administration divide (Osborne, 2010b, p. 8) by integrating policy-makers, bureaucrats, and citizens within an implementation structure based on power, dependency, exchange, and negotiation (Bardach, 1977; Barrett & Hill, 1984; Sabatier, 1991). The acknowledgment of a context of bargaining relationships and potential adverse behaviors at level of policy implementation uncovered the weaknesses of a linear mode of policy-making. Influenced by a neo-classical economics perspective, the development of the principal-agent theory and the public choice theory drove a reform movement which advanced a market-based view of policy design and

5 The “top-down” perspective to policy design and implementation peaked from the 1950s to the late 1960s. Firstly, it sustained the achievement of the long-term goals of the “reconstruction”— after World War II—and then it underpinned the building of the social protection of the welfare state (Frederickson, 1999).

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implementation (Osborne, 2010b). NPM ideas re-shaped the bureaucracy into a market and a quasi-market system for the provision of public service (Ewan Ferlie, 1992) in which “semi-autonomous and single-purpose arm’s length agencies” (Verschuere & Vancoppenolle, 2012, p. 249) were competing. The role of agencies in policy implementation (Bach et al., 2012) was predominant, with the State playing the pivotal role of the regulator (Osborne, 2010b). Through constitutional reforms, statutes, and administrative practices, the “authority and responsibility are delegated or transferred to lower levels, organizations or positions in the civil service” (Christensen & Lægreid, 2007b, p. 18). A disarticulated State favored competition among a plethora of autonomous agencies. Such competitive policy design and implementation mode found in the market a means “to increase efficiency and effectiveness, enhance the autonomy of managers, place services closer to citizens, reduce political meddling and enable ministers to concentrate on the big policy issues” (Pollitt et al., 2005, p. 3). In this context, manager’s focus was on intra-organizational aspects of public policy and service delivery as crucial areas to recover process efficiency and improve the quality of public provisions. In fact, market-based mechanisms were used to allocate public budgets; contracts were widely adopted not only to regulate the activities of specialized agencies but also to exert control over policy implementation and service delivery. Contracts with agency played a role from policy formulation through implementation to evaluation (Egeberg, 1995; Pollitt & Talbot, 2004; Yesilkagit & van Thiel, 2008). Ideally, NPM reforms aimed to pursuing efficiency gains and service quality by separating the political design from the technical implementation. However, the pressure on agency specialization decoupled the policy-making process (Bouckaert et al., 2010, p. 9). A lack of coordination revealed the weaknesses of such competitive mode within a multi-agent policy implementation context (Bardach, 1998). In this regard, empirical studies have found that executive agencies have influenced the policy-making process (Verschuere & Bach, 2012) and its outcomes (Gains, 2003)—besides what was established in the formal regulation among them (i.e., contract). This evidence recognizes how distant was the ideal type of NPM—with the government exerting its political prerogatives and executive arm’s length agencies as a surrogate of the large bureaucratic organizations, entirely committed for policy implementation—from the reality of public administration. Scholars found reasons for such discrepancy in the “plural and pluralist6” (Osborne, 2010b) nature of policy-making in contemporary society (Bovaird & Löffler, 2009b). This view of the policy context posits that public administration reality is increasingly complex as it involves a plurality of stakeholders within multiactor relationships (Hjern et al., 1979; O’Toole, 1986, 2000; Parson, 1995). Within this network, governmental bodies (e.g., ministers and levels of government),

Public governance “posits both plural state, where multiple interdependent actors contribute to the delivery of public services, and a pluralist state, where multiple processes inform the policy-making system” (Osborne, 2010b, p. 9).

6

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1 Framing the Institutional Complexity of Public Administration:. . .

independent agencies (e.g., audit agencies and mandated agencies), and service delivery units (e.g., hospital wards or schools) may partner to deal with increasingly wicked community issues. Problems like urbanization, migration, decay of production industry, climate change, disaster, and recovery are difficult to tame by individual organizations acting alone. Such problems deeply affect citizens’ needs (e.g., social inclusion, equality, safety, health care, access to culture, and education), and their governance is relevant for community quality of life. The complexity of such community problems entangled requires a holistic response from public administration. Public governance emerged as “both a product of and a response to the increasingly complex, plural and fragmented nature of public policy implementation and service delivery in the twenty-first century” (Osborne, 2010b, p. 9). Such response draws from institutional and network theory (Ouchi, 1979; Powell, 1990; Powell & DiMaggio, 1991; Scharpf, 1978). It suggests developing new governance arrangements for public service delivery and policy outcomes evaluation (Bovaird & Loeffler, 2012; Chaebo & Medeiros, 2017) based on collaborative relationships among stakeholders (Christensen & Lægreid, 2007a; Lægreid & Rykkja, 2015). The institutional logic of public governance has innovated the purposes and role of the State in contemporary society (Peters & Pierre, 2001). Consequently, the law and regulation may be regarded as instruments to empower or enable public sector organizations, citizens, businesses, and the community to achieve their institutional goals within a broader scheme of societal aim. Also, collaboration in policy design and implementation may guide policy-makers and their stakeholders to ensure consistency and generate public value. To this end, the relationships among partners should be horizontal and relational, rather than hierarchical or transactional—as it was under the OPA and NPM, respectively. Public governance demands strategies to leverage a collaborative approach within network (Klijn & Koppenjan, 2000). This is a hard task for policy-makers (Hill & Lynn, 2003) if there is no prior collaboration experience (Ansell & Gash, 2007). In this case, the policy context requires an active public leadership that involves relevant stakeholders in the policy-making process to effectively contribute to public value generation (Torfing & Ansell, 2017). Also, within the logic of public governance, a collaborative mode of policy design and implementation may encourage patterns of interactions in which policy designers, implementers, and involved stakeholders may develop performance dialogues (Rajala et al., 2018, 2020) and assessment forums (Emerson & Nabatchi, 2015b; Ouchi, 1980; Page et al., 2015). In this sense, collaboration in policy design and implementation integrates the “upward” and “downward” movements, of the linear and competitive modes of the OPA and NPM, with the “outward” direction of public governance (Moore, 1995; O’Toole et al., 2005, p. 46). The perspective of public value in policy design and implementation promotes cross-sector collaboration among policy-makers and their stakeholders (Ansell et al., 2017; Page et al., 2015). Such an innovative perspective of public governance transcends the traditional dichotomy of “politics-administration” by drawing policy-makers’ attention to policy outcomes (Emerson & Nabatchi, 2015b; Halligan

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et al., 2012; Uusikylä & Valovirta, 2007). This new focus implies extending the span of results in contemporary public administration “to handle the complexity of public sector performance” (Bouckaert & Halligan, 2008, p. 15). “Performance domain for control” in public administration is the third dimension of the framework.

3.3

Placing Policy Outcomes at the Core of Contemporary Public Administration: The Scope of Performance Domain for Control from OPA Through NPM to Public Governance

Performance has been a debating theme “as long as the government itself exists” (Talbot, 2005, p. 493). This discussion has positioned performance at “the core of public management” (Bouckaert & Halligan, 2008, p. 13) because it focuses on the relationship between public sector organizations’ political promises and their actual delivery. Consequently, approaches and methods to improve public sector results characterize much of management studies in public administration (Ammons, 2001; Behn, 1995; Hood, 1991; Hood & Peters, 2004; Moore, 1995; Moynihan, 2008; Talbot, 2005). Over the last three decades, across the social sciences, various interpretations and definitions7 of performance have been emerging, as evidence that “performance is not a unitary concept, with an unambiguous meaning” (Bovaird, 1996, p. 147). In this book, performance is regarded “as a set of information about achievements of varying significance to different stakeholders” (Bovaird, 1996, p. 147). Such achievements represent “performance in the domain of the organization, in the domain of the service system and in the domain of the communities to which these services are intended to be delivered” (Bovaird, 2008, p. 185). This definition posits for a contemporary view of managing public sector results because it implies maintaining a relationship with internal and external stakeholders. This perspective widens a generic definition of performance as a “concept to define results and bottom lines” (Bouckaert & Halligan, 2008, p. 15). In fact, to report material information, performance management systems should be designed in a way that a wide set of results are gauged. These may include accountability, user choice, customer service, efficiency, effectiveness, outcomes, and public value (Ammons, 2001; Bouckaert & Halligan, 2008; Hatry, 1999; Talbot, 2005, p. 496). The extension of the meaning of performance is a further shred of evidence that “old” and “new” paradigms have influenced the relevance of performance

7

For an overview of how performance has become a dominant theme in probably the majority of OECD countries, see Talbot, C. (1999). Public Performance—towards a new model? Public Policy and Administration, 14(3), 15–34. https://doi.org/10.1177/095207679901400302.

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Fig. 1.5 Performance domain for control from OPA through NPM to public governance

management8 (Bouckaert & Halligan, 2008; Moynihan, 2008; Walker et al., 2010) within public administration (Christensen & Lægreid, 2007a, 2010; Osborne, 2010a; Peters, 2011). In fact, “the expected improvements in performance within the public sector” (Karen & Ogden, 2009, p. 478; Maurel et al., 2014; Siegel & Summermatter, 2008) have impacted the scope of performance management “over time and space” (Summermatter & Siegel, 2009, p. 1). The “dynamics of performance management” (Moynihan, 2008) can be linked with the extent of “performance domain for control.” This dimension captures the scope of the result area over which public policy-makers carry out improvement initiatives. OPA, NPM, and public governance, though different, can be put over a continuum. In this perspective, three nested performance domains for control can be identified: bureaucratic compliance and organizational economy, managerial efficiency and public service quality, and policy outcomes and relational resources can be identified as three nested performance domains for control (Fig. 1.5). While OPA and NPM mainly adopt an intra-organizational focus (i.e., compliance, economy, efficiency, and service quality) (Barzelay, 1992; Dunleavy & Hood, 1994; Pollitt et al., 2007), public governance takes an inter-organizational emphasis (i.e., public value, policy outcomes, stakeholder engagement, trust, and social capital) (Bovaird & Löffler, 2009a; Klijn & Koppenjan, 2015; Moore, 1995; Osborne, 2010a, 2020). Such outward movement has widened the scope of the investigated performance domain for control in public administration from an organizational to a community perspective.

3.3.1

Changes in the Scope of Performance Domain for Control in Public Administration

Under the OPA, government emphasis was on “getting and spending” (Pliatzky, 1982) public resources to develop statewide policies. The focus of control was intra-

8

The role of performance management for policy design and implementation in the context of local area governance is discussed in Chap. 2.

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organizational with a high emphasis on “administering due processes” (Bouckaert & Halligan, 2008, p. 78) and a low concern for results (Moynihan, 2006).9 Grounded on the principles of scientific management (Taylor, 1914) which postulates a valuefree administration (Bouckaert, 1990), the OPA found in the separation of politics from administration the solution for enacting a “performing administration” (Goodnow, 1900). Also, the administrative management (Gulick & Urwick, 1937) and the bureaucracy (Weber, 1952), with their tension for economy and certainty in public administration, promoted a “closed-system strategy” (Thompson, 1967, p. 4) for organizational development and performance improvement. A strategy centered on internal processes framed public administration as a “black box,” in which all variables are stable, quality is constant, and policy-makers deal with a determinate system. In this system, the relationship between input and output is controllable because few variables linearly interact. If observed through a closed-system organizational perspective, long-term planning and control process would have enabled performance improvement. In addition, the pursuit of organizational efficiency would have fostered democratic accountability and transparency, provided that politicians and civil servants would have published annual budgets with details about the use of public resources (Day & Klein, 1987). As Bouckaert and Halligan (2008, p. 75) have remarked, such “static and micro” perspective of “performance administration” was limited to bureaucratic compliance and public administration legitimacy, while the emphasis of the control function was on the internal economy (Ewalt, 2001; Iacovino et al., 2015), i.e., “the ratio of resources expected to be consumed on the right things to resources actually consumed” (Sink et al., 1984, p. 267). The prescriptive logic of the OPA was influential in determining the focus of such control. The “thick” net of rules governing public policy and service delivery demanded a detailed recording system to keep track of personnel presences, absences, and working hours, sources of public revenues and motivation for expenditures, and above all the volume of files flowing in and out of the organization. To control the economy of the organization, the administrative requirements for public procurement were placed under the spotlight of a detailed accounting system. Besides directors and executives, such information nurtured bureaucratic compliance control activities carried out by external bodies. In this system, a technically trained bureaucracy with specialized skills would have ensured internal compliance with administrative requirements, while a hierarchical chain of control bodies would have acted as a coordination mechanism among public sector organizations. As a result, specialization and hierarchy would have flattened uncertainties and discretion in public resource use so that master plan goals could have been economically achieved (Thompson, 1967, p. 6).

9

Such orientation of control in the public sector is generally acknowledged in the European countries, while different orientations exist for the USA (Moynihan, 2006; Williams, 2003).

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The “closed-system” assumptions of the OPA were found unrealistic by NPM.10 Such group of managerial ideas contended the hegemony of “the rule of law” within bureaucratic processes (Mishra, 1984) by promoting market and competition as a means to improve public performance. The ambition of NPM was “unpacking the black box” by deepening the intra-organizational scope of performance domain for control. In fact, as the NPM came into play, administrative reforms of Western countries borrowed management methods from the business context to improve managerial efficiency and service quality (Ongaro, 2009; Pollitt & Bouckaert, 2011). Drawing from big companies literature (Porter, 1985), NPM provided a transactional explanation of the intra-organizational aspects of public policy and public service delivery. A production-oriented focus of NPM set the performance domain for control in the search for managerial efficiency. In this sense, to control inputoutput relationships behind public service delivery, public managers sought to distinguish transaction costs from production costs. Based on such view, they found a key to improve managerial efficiency in controlling information exchange, including coordination and monitoring costs. In this context, market conditions were used by public sector organizations to indirectly coordinate service providers and service producers through contracts. In a NPM perspective, market was considered as a more efficient alternative to hierarchy and staffing. As Osborne (2020, p. 6) clearly remarked, “the NPM [. . .] predicated upon the efficiency of the market as opposed to the bureaucracy, and the importation of competition and markets/quasimarkets into public service delivery in order to allocate scarce public resources.” Such doctrinal approach to public management was not entirely focused on efficiency. Inspired by the quality movement (Peters & Waterman, 1982), NPM extended the scope of performance domain for control by including service delivery attributes such as users’ satisfaction (Pirie, 1991) and quality (Morgan & Murgatroyd, 1994; Pollitt & Bouckaert, 1995). In modern times, citizens’ rights make public service organizations responsible for “the levels of service they intend to supply, in terms of timeliness, accessibility, and quality” (Talbot, 2005, p. 498). In fact, under the push of globalization, our societies have become complex, connected, and populated by citizens showing diversity in culture and personal conditions. In front of such rising complexity, the service delivery model of the OPA with a traditional “one-size-fits-all” response was found inadequate compared to the flexibility and support expected by citizens and businesses, as the end-1990s was approaching. This transformation required “a reformulation of the place of public service users in public service delivery from ‘clients’ to ‘customer’ or ‘consumers” (Osborne, 2020, p. 6). A customer orientation led public administration to mitigate the excess of fragmentation (Haveri, 2006) in public service delivery—mainly driven by the

10

NPM was remarkably influenced by the public choice theory (Buchanan & Tullock, 1962) and competitive advantage (Porter, 1985).

4 The Legacy of OPA, NPM, and Public Governance for Contemporary. . .

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specialization mantra of the early NPM reforms (Christensen & Lægreid, 2007a)— through responsive public service organizations (Linden, 1994).11 The concern for service quality has revealed that a mere search for efficiency is deceptive if disjointed from delivering effective public services to the benefit of citizens and the community. Working on such relationships was found critical to leverage the capacity of public and private actors to generate public value (Bryson et al., 2014; Crosby & Bryson, 2010; Moore, 1995). In this sense, public sector organizations are called to generate value through public policy and public service delivery within the broader scheme of “open innovation” (Ansell & Torfing, 2014; Chesbrough, 2003; Sørensen & Torfing, 2011). This perspective implies that concern for efficiency and service quality were subsumed within a broad performance domain of control, i.e., improving public value to develop shared resources across the network, such as trust and social capital (Bianchi et al., 2021; Douglas & Ansell, 2021; Emerson & Nabatchi, 2015b; Provan & Milward, 2001). An effective method for performance management and governance may support policy-makers and their stakeholders in generating public value. The analysis developed in this section has set the basis for identifying how OPA, NPM, and public governance have contributed to shape public policy and public service delivery. Such contribution can be regarded as the legacy of each paradigm for contemporary public administration. That is the object of the next section.

4 The Legacy of OPA, NPM, and Public Governance for Contemporary Public Administration The analysis developed in the previous section has investigated the complex reality of public administration through the framework depicted in Fig. 1.2. Such analysis has considered the process of change of public administration along three dimensions. The “institutional logic” dimension concerns public sector organizations’ purposes and roles within society. The “policy design and implementation mode” dimension describes the initiatives undertaken by public sector organizations to ensure that policy goals and program aims are achieved through performance management and governance routines. The “performance domain for control” dimension focuses on the improvement areas where the interests of politics, administration, and society would converge. The three framework dimensions may provide a comprehensive view of how OPA, NPM, and public governance have shaped the complex reality of

Signs of this attempt were the spread of “one-stop shop” and the development of citizen service chart. With the first term, public sector organizations describe the facility they offer for the citizens and businesses to discuss and arrange the full delivery of services, without the need of driving all over the town to attain related services at different store. The second refers to the minimum standards in public service delivery stated by each public service organization.

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Table 1.2 A comprehensive view of how OPA, NPM, and public governance have layered contemporary public administration Framework dimensions Public administration paradigms OPA

Institutional logic Prescriptive

Policy design and implementation mode Linear

NPM

Managerialism

Competitive

Public governance

Enabling/ empowering

Collaborative

Performance domain for control Bureaucratic compliance and organizational economy Managerial efficiency and public service quality Policy outcomes and relational resources

Table 1.3 The legacy of OPA, NPM, and public governance for contemporary administration

Legacy for contemporary public administration

Public administration paradigms OPA NPM Public governance Focus Emphasis on Concerns for stakeholders’ collabon law management oration and public value

contemporary public administration. To this end, we could adopt a “transition perspective” on the evolution of public administration that layers the three paradigms. This means reading Table 1.2 along each row. The OPA prescriptive logic posited “the rule of law” as the organizing principle of hierarchical governance. Such a logic has informed public sector organizations’ role and purposes and the way to achieve them through a bureaucratic administration. NPM ideas turned the legally mandated logic of the OPA into a managerial one by implementing market-led policies in search of public service efficiency and quality. Public governance set out an enabling/empowering institutional logic which regards the law as an instrument to leverage the role of the private sector to operate in the public domain—through collaborative networks—to affect community outcomes. Though each paradigm portrays different configurations of institutional logics, policy design and implementation modes, and performance domains for control, some fundamental aspects of OPA, NPM, and public governance have “invariably overlapped and percolated” (Osborne, 2020, p. 5) the current reality of public policy and public service delivery. Such aspects can be regarded as the legacy of OPA, NPM, and public governance for contemporary public administration. This may provide an explanation of the need for different performance regimes12 in contemporary public administration. As Table 1.3 shows, contemporary public administration has inherited a focus on law, an emphasis on management, and concerns for stakeholders’ collaboration and public value from OPA, NPM, and public governance, respectively. Such legacy

A performance regime “conveys a combination of the institutional context of performance steering [. . .] and the nature of actual performance intervention”(Talbot, 2010, p. 81).

12

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stems from a comprehensive view of the influence of OPA, NPM, and public governance on the complex reality of contemporary public administration. Framing the rising institutional complexity13 of public administration entails relating the different configurations of performance management routines (Talbot, 2008) “not just to the practices of measuring and managing performance indicators but also to capture the embedded nature of these practices in almost all aspects of contemporary governance” (Moynihan et al., 2011, p. 141). Such understanding may help articulate the characteristics of coexisting performance regimes in contemporary public administration.

4.1

Articulating the Characteristics of Coexisting Performance Regimes in Contemporary Public Administration

The term “performance regime” has recently gained scholarly attention (Jakobsen et al., 2018; Moynihan et al., 2011; Talbot, 2010; Walshe & Harvey, 2010) because it positions “the collection of routines used by actors working together on a societal issue” (Douglas & Ansell, 2021, p. 1) “amidst governance complexity” (Moynihan et al., 2011, p. i142). “Performance regime” is a construct composed of two concepts: “performance” and “regime.” In this book, “performance” has been defined as relevant information on public sector achievements in the organizational, public service, and community domains (Bovaird, 2008, p. 185). The term “regime” refers to “principles, norms, rules, and decision-making procedures” (Krasner, 1982, p. 185) underpinning a set of governing arrangements “that regularize behavior and control its effects” (Keohane & Nye, 1977, p. 19). The information concerning policy effects and implications for involved actors circulate among decision-makers via performance management routines. Such routines “are among the most important tools by which governments structure relationships, state values, and allocate resources with employees, thirdparty providers, and the public” (Moynihan et al., 2011, p. 141). Also, performance regimes help in “getting a grip on the performance of collaborations” (Douglas & Ansell, 2021, p. 1) because they may enable public policy-makers and their stakeholders “to explicate their goals, exchange performance information, examine progress, and explore performance improvement actions” (Douglas & Ansell, 2021, p. 1). In this sense, performance regimes can be regarded as methodological patterns

These complex institutional contexts include “the whole-of-government initiatives” (Christensen & Lægreid, 2007a, p. 1059), network governance arrangements (Edelenbos & van Meerkerk, 2016; Klijn, 2008), cross-sectoral relationships (Bryson et al., 2006), collaboratives (Ansell & Gash, 2007; Emerson et al., 2012; Vangen et al., 2015), policy networks, and service programs (Bouckaert & Halligan, 2008; Klijn & Koppenjan, 2000). 13

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for governing public policy and service delivery performance.14 Such patterns refer to “the overall structure and approach to performance management” (Douglas & Ansell, 2021, p. 1), which are mostly adopted at institutional level (Bouckaert & Halligan, 2008) to support performance improvement through evaluation. Performance management15 can be defined as “a system that generates performance information through strategic planning and performance management routines and that connects this information to decision venues, where, ideally, the information influences a range of possible decisions” (Moynihan, 2008, p. 5). Such definition emphasizes the role of performance management as a methodological process to evaluate the sustainability of adopted policy through planning and control systems. The essence of performance management can be regarded as the combination of two structural components, i.e., the organizational structure and measurement framework, linked by a feedback process, i.e., the reporting process (Brunetti, 1979; Maciariello, 1984). The organizational structure configures the formal relationships between individuals and groups through hierarchies and functions. It coordinates tasks, goals, resources, and working methods for each team member and team to pursue organizational aims properly. The measurement framework (i.e., the technical structure) gauges financial and non-financial information concerning performance at budget and closure values. The process bridges the organizational structure with the measurement framework because it channels performance information throughout planning and control activities. In doing this, it balances the demand and supply of information concerning performance as it unfolds from “measurement through incorporation to the use of performance information” (Bouckaert & Halligan, 2008, p. 37). If the measurement system provides intelligent measures, then the measurement activity will allow policy-makers to pursue goals and objectives. Else, the control over the adopted policies turns deceptive, misleading, and potentially harmful for the organization (Drucker, 1954). In this sense, performance measurement should supply decision-makers with relevant information in relation to their functions and levels (Ammons, 2001). This implies a mutual interaction between the methodological process and the structural components of performance management in a way that policy-makers may focus on “the extent to which the outcomes sought are being achieved” (Hatry, 1999, p. 55). It means that the process should align the informative need at the strategic, managerial, and operative control levels (Anthony, 1965) with the design of reliable, valid, and functional performance measures (Bouckaert, 1993; Brunetti, 1979) to feed such need. 14 Jakobsen et al. (2018, p. 127) identify performance regime as “the turn to performance measurement practices in the governance of public services” and Moynihan et al. (2011, p. 141) as “the practices of measuring and managing performance indicators but also to capture the embedded nature of these practices in almost all aspects of contemporary governance.” 15 Performance management is a debated theme in the scientific domain of public management at both theoretical (Bouckaert & Halligan, 2008; Moynihan, 2008; Radin, 2006; Talbot, 2010) and empirical levels (OECD, 2009, 2017).

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The increasing complexity involving policy design and service delivery in the context of public governance entails a proper configuration of performance management routines (Halligan et al., 2012) “in ways that will fundamentally affect both performance regimes and governance” (Moynihan et al., 2011, p. 151). As the complexity of societal challenges increases, the “cultural/institutional tensions, task complexity, causal uncertainties, and goal conflict” (Moynihan et al., 2011, p. i152) may escalate. This implies that conventional configurations of performance management are proven inadequate due to the cultural diversity among the involved stakeholders that lead to different causal explanations of the relationship involving undertaken actions and achieved outcomes. Also, the discrepancies in stakeholders’ institutional logic misplace the focus of performance management routines that are at play in and across involved organizations. This means that traditional performance measures (e.g., economy, efficiency, and effectiveness) may provide only a bounded explanation of a broad dynamic and complex phenomenon underlying a social issue. On such concerns, some caveats have been noted (Bianchi, 2021). A first issue regards the role of performance management to support inter-institutional policy coordination and consistency under dynamic complexity conditions. At the institutional level, the organizational structure acts as a coordination mechanism to ensure that performance information will support decision-making through evaluation. At the inter-institutional level, different organizations from the public and private domains interact with each other in order to influence the outcomes of public policies (Bovaird & Löffler, 2009c). In this policy context, each partner has its own organizational structure, which manages to achieve policy goals through performance routines. The lack of coordination mechanisms leads to a second issue, which concerns the role of routines in balancing the stakeholder system with the inter-institutional system, i.e., the rules and power relationships among involved actors. At the interinstitutional level, the authority is dispersed, and “responsibility is diffuse” (Moynihan et al., 2011, p. 144) among institutional leaders. Based on stakeholder information on organizational performance, institutional leaders with other relevant actors in the network may set performance evaluation venues (Moynihan, 2008). To foster stakeholder engagement and communication in such venues, the institutional leaders may leverage the power of adhocracy16 (Mintzberg, 1983), i.e., “any form of organization that cuts across normal bureaucratic lines to capture opportunities, solve problems, and get results” (Waterman, 1990, p. 37). The relational nature of adhocracy may blend the cultural differences within and beyond organizations (Rajala et al., 2020) into a “social process” (Rajala et al., 2018, p. 117) of informal control (Dekker, 2004; Ouchi, 1979). In this way, different stakeholders may engage themselves in inter-organizational routines “to explicate

Adhocracy is a portmanteau word that blends the Latin ad hoc, which means “for the purpose,” and the suffix -cracy, which originates from the ancient Greek verb kratein (κρατεῖν), i.e., “to govern” (Travica, 1999).

16

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their goals, exchange and examine performance information, and explore actions” (Douglas & Ansell, 2021, p. 1). As performance management routines are shaped by the “legacy” OPA, NPM, and public governance (Table 1.3), a focus on law, an emphasis on management, and a rising concern for stakeholders’ collaboration and public value may give a nuanced version of the performance regimes that coexist in contemporary public administration. Such nuances help us in identifying three main recurring performances in contemporary public administration: (1) “regulation-driven performance regime,” (2) “management-driven performance regime,” and (3) “public value-driven performance regime” (Table 1.4). To frame the differences among such performance regimes, two main dimensions could be adopted: (1) the characteristics of performance management and governance routines (Douglas & Ansell, 2021) and (2) the level of knowledge sharing and convergence among involved stakeholders (Morton et al., 2015). The first dimension contains a set of sub-items through which we may outline how the actors in the regime use performance information to support evaluation and decision-making. The second dimension captures the knowledge integration patterns among the stakeholders dealing with complex societal issues. In the next sections, for each performance regime, examples will be provided to illustrate how the institutional context shapes the characteristics of performance management routines and the level of knowledge convergence among stakeholders.

4.1.1

Regulation-Driven Performance Regimes

Regulation-driven performance regimes are characterized by the presence of actors from different jurisdictions (e.g., national and local levels), systems (e.g., political and judicial), and agencies (e.g., independent administrative bodies and supranational agencies). As Table 1.4 shows, performance assessment focuses on compliance with legally mandated standards because the actors involved in such regime tend to standardize roles, actions, and expected performance to legally mandated aspects, despite the complexity of the issue. An example of a workers’ health and safety protection policy network may illustrate the characteristics of a regulation-driven performance regime. Such network involves the national workers’ protection institute, unions, and employers’ associations to implement initiatives to improve workers’ health and safety conditions in the workplace. Performance evaluation is based on the volume17 of law enforcement initiatives, such as controlling and monitoring the level of employers’ compliance with legally set standards carried out by the institute in a year. The underlying assumption is that improving the level of compliance with legally

17

To this end, the institute aggregates data and information from several jurisdictional levels (i.e., local, regional, and national levels).

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Table 1.4 The characteristics of coexisting performance regimes in contemporary public administration

Dimensions 1. Characteris- 1.1. Actors in tics of perforperformance mance manage- regime ment routines (Douglas & Ansell, 2021) 1.2 Performance goals

1.3 Performance information

1.4 Performance assessment

1.5 Performance actions

2. Level of knowledge convergence (Morton et al., 2015)

2.1. Stakeholders’ convergence and information sharing

Regulation driven performance regime Actors from different jurisdictions, systems (e.g., political and judicial), and agencies Ensuring compliance on legally mandated standards (e.g., on transparency, integrity, social cohesion, human and civil rights, accountability, democracy) Focus on compliance with the regulation prescriptions

Assessment is based on the compliance with standards and publicly reported Passive use of performance information mainly for external reporting to citizens and other stakeholders Low convergence and information sharing from a single perspective

Performance Regime Management driven performance regime Actors from the political and managerial levels

Public valuedriven performance regime Networked actors from different organizations

Improving economy, efficiency, service volumes, and quality standards

Pursuing quality of life and other community outcomes

Focus on resource use efficiency, public service volumes, and user satisfaction

Focus on sharing qualitative and quantitative information among network stakeholders Collaborative performance forums to assess stakeholders’ contribution Collaborative initiatives to support public value generation, learning, and institutional adaptation

Comparison between actual and expected results through benchmarks Managerial actions aimed at improving efficiency and user satisfaction

Mid convergence and information sharing from different perspectives toward a common domain of interest

High convergence and high information sharing from multiple perspectives to pursue a common community outcome

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1 Framing the Institutional Complexity of Public Administration:. . .

mandated standards would prevent accidents in the workplace (i.e., death and injuries). This example has illustrated how a formalistic culture of the actors involved in the regime may lead them to a passive use of performance information, mainly based on input measures for external reporting to citizens and other stakeholders. To evaluate the effectiveness of such complex network initiatives, the scope of measurement should include changes in conditions or altered behaviors that could affect policy outcomes. In doing this, a causation analysis may support the actors in the regime to understand the underlying critical factors behind performance. In such regime, actors show a low knowledge sharing and convergence that is limited to a single discipline perspective (i.e., the legal)—as Table 1.4 displays.

4.1.2

Management-Driven Performance Regimes

The management-driven performance regime involves actors at the political and managerial levels from the public and private domains to improve the economy, efficiency, volumes, and quality standards of the services delivered. As Table 1.4 shows, performance measurement focuses on resource use, efficiency, public service volumes, and user satisfaction. Such information supports benchmarking activities that compare actual performance vs. expected results to support the implementation of managerial initiatives to improve service efficiency and user satisfaction based on such analysis. An example of a management-driven performance regime may be illustrated by describing how the actors involved in a health-care network commit themselves to improve network performance in treating complex and acute illnesses. Such network involves the regional administration (i.e., service provider), a group of hospitals (i.e., service producer), and patients’ associations (i.e., service users). As managerial and clinical performance measures, the network adopts the average length of stay and service quality (e.g., the mortality rate), respectively. The underlying assumption is that an improvement in efficiency, i.e., reducing the average length of stay, determines an improvement in service effectiveness measured by service volumes, i.e., more patients treated per year. Also, an improvement in service effectiveness—all other conditions being equal—affects service quality as it implies that clinical treatments have been delivered accurately18 (Vignieri et al., 2019). This example has illustrated how the managerial culture of the actors involved in the regime may lead them to identify potential areas of improvement (i.e., service efficiency, effectiveness, and service quality) and associated leverage points (e.g., sharing capacity across the network, training, developing common organizational routines).

18

The implicit assumption here is that if the clinical treatments are not delivered to patients properly, some complications will occur with unavoidable effects on the average length of the stay in hospital or mortality rate.

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To properly frame clinical outcomes well beyond service quality at the point of delivery, the social fabric of the context in which the health-care network operates should be considered. In fact, long-term clinical outcomes are influenced by several factors outside the inner health-care setting (i.e., the hospital ward and the adopted care protocols). Change in patients’ quality of life after treatment, rehospitalization rate, and mortality rate are influenced by patients’ access to care, income levels, and community arrangements for social care. As Table 1.4 displays, by engaging other stakeholders in the regimes, decision-makers at both the managerial and political levels may extend the ambition of the performance regime toward improving community outcomes. Such perspective is a prerequisite to generate public value for the local community.

4.1.3

Public Value-Driven Performance Regimes

The public value-driven performance regime captures an increasing commitment of both public and private organizations to pursue quality of life and other community outcomes. Such regime may apply to performance review initiatives that focus on both global- and local-level policy program outcomes. Global-scale issues, such as human rights, climate changes, gender equality, and pandemics, are widely discussed in a plurality of contexts witnessing the participation of organizations from both the public and private domains. Actors participating in such forums are willing to share their knowledge and experience in dealing with the issues in a specific field. The goal of such regimes is exchanging practices and networking to develop more effective interventions at the local scale and put pressure on globalscale politics. As Table 1.4 shows, the actors in the regime may share qualitative and quantitative information to frame public value, support learning, and institutional adaptation. At the local level, a public value-driven performance regime may cover the assessment of different initiatives including social inclusion programs, urban regeneration actions, migrant integration policies, and local transportation plans. The goal of such initiatives is open-ended with a variety of partners interested in being part of the discussion because the potential effects of the initiatives will impact their areas of interest. The results of such initiatives slowly materialize on the ground as much of stakeholders’ initial efforts are devoted to designing an inclusive institutional setting, identifying a leadership, and promoting a facilitative consultation process. An example of a policy program aiming at reducing and preventing alcohol abuse among youngsters may illustrate the characteristics of such regime. The regional department of health care, local health-care districts, family physicians, families, schools, municipalities, and community associations set the goal of reducing alcohol abuse among youngsters. To this end, they design a policy program to subsidize youngsters to practice sport and cultural activities while provide funds schools and community associations to develop/improve infrastructures and services related to sports and arts, mostly in high-risk city neighborhoods. The underlying assumption

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is that promoting sport and arts reduces youngsters’ boredom and in turn alcohol abuse. If the actors implement such policy by issuing the call for subsidies and for building sports facilities, the intermediate policy outcomes, such as the change in sport and cultural facilities capacity, the change in sport and cultural facilities density and variety, and the change in youngsters participating to sports and art projects funded by the policy, will be improved over time. Also, on a longer time horizon, such intermediate outcomes will lead the network to achieve final outcomes, such as the % reduction in youngsters abusing alcohol, the % reduction in car accidents due to alcohol abuse, and the % change in youngsters still practicing sport or cultural activities within a year after the end of the policy program. Eventually, such outcomes will reveal to what extent the policy has addressed youngsters’ bad behaviors. This example has illustrated how a public value-driven performance regime may support policy-makers and their stakeholders to pursue community outcomes by reviewing their policy actions in light of the desired policy outcomes. To this end, actors in the regime should develop a holistic understanding of the policy issue so that they will develop causal analysis grounded on public value drivers. Such analysis set the basis to implement a public value-driven performance regime through Dynamic Performance Management and Governance.

5 Conclusion Since the early 1980s, public administration has been changing through innovation by experience and design (Kettl, 2015; Riccucci, 2001). This change means that there have been practical and theoretical forces that have pushed public administration well beyond the bureaucratic approach of public policy and service delivery. The evolving process of public administration has been layering the context of public policy design and implementation (Bovaird & Löffler, 2009b; Osborne, 2010c) and the logic of service delivery (Osborne, 2020). Such seamless development has transformed public administration into a complex reality in which a variety of institutional configurations for managing public sector performance coexist (Jakobsen et al., 2018; Moynihan et al., 2011; Talbot, 2010). In fact, this chapter has shown how the bureaucratic approach of the OPA was not replaced by the managerialism of NPM, which was then supplanted by the stakeholder/network-oriented model of public governance. Albeit with different intensities, aspects of the OPA, NPM, and public governance—what in this chapter has been called the legacy for contemporary public administration—are “actually existing” (Osborne, 2010c, p. 414) and shaping the complex reality of public policy and service delivery. Contemporary public administration has a relational nature which makes the traditional distinction between the public and private domains increasingly blurred (Alford, 2001; Cepiku et al., 2019), with public sector organizations designing innovative logics to integrate public offering with users’ contribution (Alford, 2002; Bovaird et al., 2016; Osborne, 2020; Osborne et al., 2012; Ostrom et al.,

References

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1978; Parks et al., 1981). Also, policy design and implementation are involved within a feedback relationship (Majone & Wildavsky, 1979; Scharpf, 1978; Winter, 1990) which takes place in different complex inter-organizational contexts, such as “the whole-of-government initiatives” (Christensen & Lægreid, 2007a, p. 1059), network governance arrangements (Edelenbos & van Meerkerk, 2016; Klijn, 2008), cross-sectoral relationships (Bryson et al., 2006), collaboratives (Ansell & Gash, 2007; Emerson et al., 2012; Vangen et al., 2015), policy networks, and service programs (Bouckaert & Halligan, 2008; Klijn & Koppenjan, 2000). The analysis of such institutional complexity has shown that a variety of performance regimes coexist in contemporary public administration. Among them, a public value-driven performance regime provides the methodological research context in which to implement learning-oriented performance management routines. Proper implementation of such routines may support a plurality of actors to develop a systems understanding of how to improve community outcomes. To this end, in the next chapter, we will introduce Dynamic Performance Management as a methodological framework to enhance performance regimes.

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Peters, G., & Pierre, J. (2004). Multi-level governance and democracy: A Faustian bargain? In I. Bache & M. Flinders (Eds.), Multi-level governance (pp. 75–90). Oxford University Press. https://doi.org/10.1093/0199259259.003.0005 Peters, T., & Waterman, R. (1982). In search of excellence: Lessons from Americas best run companies. Harper and Row. Pirie, M. (1991). The citizens’ charter. Adam Smith Institute. Pliatzky, L. (1982). Getting and spending: Public expenditure, employment, and inflation. Blackwell. Pollitt, C. (1990). Managerialism and the public services: The Anglo-American experience. Basil Blackwell. https://books.google.it/books?id¼0oB9QgAACAAJ Pollitt, C. (1993). Managerialism and public services. Blackwell. Pollit, C. (1994). The Citizen’s charter: A preliminary analysis. Public Money and Management, 14(2), 9–14. Pollitt, C., & Bouckaert, G. (1995). Quality improvement in European public services. Sage. https:// uk.sagepub.com/en-gb/eur/quality-improvement-in-european-public-services/book204670 Pollitt, C., & Bouckaert, G. (2011). Public management reform: A comparative analysis. Oxford University Press. https://books.google.it/books?id¼E0fdN3KiPmgC Pollitt, C., & Talbot, C. (2004). Unbundled government: A critical analysis of the global trend to agencies, quangos and contractualisation. Routledge. Pollitt, C., Talbot, C., Caulfield, J., & Smullen, A. (2005). Agencies: How governments do things through semi-autonomous organizations. Palgrave Macmillan. https://doi.org/10.1057/ 9780230504868 Pollitt, C., Van Thiel, S., & Homburg, V. (2007). New public management in Europe: Adaptation and alternatives. Palgrave Macmillan. https://books.google.it/books?id¼UxiFQgAACAAJ Porter, M. E. (1985). The competitive advantage: Creating and sustaining superior performance. The Free Press. Powell, W. (1990). Neither market nor hierarchy. Research in Organizational Behavior, 12, 295–336. Powell, W., & DiMaggio, P. (1991). The new institutionalism in organizational analysis. University of Chicago Press. Pressman, J., & Wildavsky, A. (1973). Implementation. University of California Press. Prottas, J. M. (1979). People-processing: The street-level bureaucrat in public service bureaucracies. Lexington Books. Provan, K. G., & Kenis, P. (2007). Modes of network governance: Structure, management, and effectiveness. Journal of Public Administration Research and Theory, 18(2), 229–252. https:// doi.org/10.1093/jopart/mum015 Provan, K. G., & Milward, H. B. (2001). Do networks really work? A framework for evaluating public-sector organizational networks. Public Administration Review, 61(4), 414–423. http:// www.jstor.org/stable/977503 Radin, B. A. (2006). Challenging the performance movement: Accountability, complexity, and democratic values. Georgetown University Press. Rajala, T., Laihonen, H., & Haapala, P. (2018). Why is dialogue on performance challenging in the public sector? Measuring Business Excellence, 22(2), 117–129. https://doi.org/10.1108/MBE06-2017-0032 Rajala, T., Laihonen, H., & Vakkuri, J. (2020). Exploring challenges of boundary-crossing performance dialogues in hybrids. Journal of Management and Governance, 24(3), 799–820. https:// doi.org/10.1007/s10997-019-09485-x Riccucci, N. M. (2001). The “old” public management versus the “new” public management: Where does public administration fit in? Public Administration Review, 61(2), 172–175. https:// doi.org/10.1111/0033-3352.00019 Rosenau, J. N. (1992). In J. N. Rosenau & E.-O. Czempiel (Eds.), Governance, order, and change in world politics (pp. 1–29). Cambridge University Press. https://doi.org/10.1017/ CBO9780511521775.003

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Rosenbloom, D. H. (1993). Editorial: Have an administrative Rx? Don’t forget the politics! Public Administration Review, 53(6), 503. https://doi.org/10.2307/977359 Rosenbloom, D. H., & O’Leary, R. (1997). Public administration and law. Marcel Dekker. Rugiadini, A. (1979). Organizzazione d’Impresa. Giuffrè. Sabatier, P. (1986). Top-down and bottom-up approaches to implementation research: A critical analysis and suggested synthesis. Journal of Public Policy, 6(1), 21–48. http://www.jstor.org/ stable/3998354 Sabatier, P. (1991). Two decades of implementation research: From control to guidance and learning. In F.-X. Kaufman (Ed.), The public sector: Challenge for co-ordination and learning (pp. 257–270). De Gruyter. Sabatier, P., & Mazmanian, D. (1979). The conditions of effective implementation: A guide to accomplishing policy objectives. Policy Analysis, 5(4), 481–504. Sarbaugh-Thompson, M., & Zald, M. N. (1995). Child labor laws: A historical case of public policy implementation. Administration and Society, 27(1), 25–53. https://doi.org/10.1177/ 009539979502700102 Scharpf, F. W. (1978). Interorganizational policy studies: Issues, concepts and perspectives. In K. Hanf & F. W. Scharpf (Eds.), Interorganizational policy making: Limits to coordination and central control (pp. 345–370). Sage. https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp? itemId¼item_2522353_2&view¼EXPORT Schofield, J. (2001). Time for a revival? Public policy implementation: A review of the literature and an agenda for future research. International Journal of Management Reviews, 3(3), 245–263. Schuckman, M. (2001). Making hard choices: A collaborative governance model for the biodiversity context. Washington University Law Review, 79(1), 343–365. Scott, W. R., Ruef, M., Mendel, P. J., & Caronna, C. A. (2000). Institutional change and healthcare organizations: From professional dominance to managed care. University of Chicago Press. https://books.google.it/books?id¼vomaHpFVcOAC Seidenfeld, M. (2000). Empowering stakeholders: Limits on collaboration for flexible regulation. William and Mary Law Review, 41, 411–501. Shapiro, M. (1994). Discretion. In D. H. Rosenbloom & D. Schwartz (Eds.), Handbook of regulation and administrative law (pp. 501–517). Marcel Dekker. Siegel, J., & Summermatter, L. (2008). Defining performance in public management: A survey of academic journals. In Permanent Study Group on Performance in the Public Sector European Group of Public Administration Conference 2008, Rotterdam, The Netherlands. Simon, H. A. (1957). Models of man: Social and rational. Wiley. Sink, D. S., Tuttle, T. C., & Devries, S. J. (1984). Productivity measurement and evaluation: What is available? National Productivity Review, 3(3), 265–287. https://doi.org/10.1002/npr. 4040030305 Sørensen, E., & Torfing, J. (2011). Enhancing collaborative innovation in the public sector. Administration and Society, 43(8), 842–868. https://doi.org/10.1177/0095399711418768 Sterman, J. (2000). Business dynamics: Systems thinking and modeling for a complex world. Irwin/ McGraw-Hill. https://books.google.it/books?id¼CCKCQgAACAAJ Stewart, J., & Walsh, K. (1992). Change in the management of public services. Public Administration, 70(4), 499–518. https://doi.org/10.1111/j.1467-9299.1992.tb00952.x Summermatter, L., & Siegel, J. (2009). Defining performance in public management: Variations over time and space. Paper for IRSPM XXIII,. Talbot, C. (2005). Performance management. In E. Ferlie, L. E. Lynn, & C. Pollitt (Eds.), The Oxford handbook of public management. Oxford University Press. https://doi.org/10.1093/ oxfordhb/9780199226443.003.0022 Talbot, C. (2008). Performance regimes—The institutional context of performance policies. International Journal of Public Administration, 31(14), 1569–1591. https://doi.org/10.1080/ 01900690802199437

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Chapter 2

Dynamic Performance Management: A Methodological Framework to Enhance Public Value-Driven Performance Regimes

1 Introduction Public value-driven performance regimes capture the increasing commitment of a plurality of actors to pursue quality-of-life and other community outcomes. At the local level, such regimes concern the governance of various public programs (e.g., social inclusion, health care, housing, urban regeneration, culture, and tourism). In such governance settings, a plurality of actors is called to collaborate to generate public value for the local community. This entails supporting local area policymakers and their stakeholders with inter-institutional performance management routines that may help them to develop a systems understanding of how to improve community outcomes. In response to the need for the implementation of properly designed interinstitutional performance management routines, we introduce Dynamic Performance Management as a method to enhance public value-driven performance regimes. In this perspective, this chapter prepares the field to illustrate how Dynamic Performance Management (DPM) may support local area policy-makers and their stakeholder to generate public value by fostering policy learning and implementing outcome-based performance assessment in such regimes. To this end, this chapter frames the dynamic complexity of governing local area performance. As a result, to advocate DPM as a methodological framework to implement inter-institutional performance management routines in cross-boundary settings, we will discuss the relationship that links governance structure to policy outputs and outcomes. Then, we will suggest embracing an “outside-in” perspective of stakeholders’ collaboration to properly frame the multiple dimensions of local area performance through DPM.

© Springer Nature Switzerland AG 2022 V. Vignieri, Enhancing Performance Regimes to Enable Outcome-based Policy Analysis in Cross-boundary Settings, System Dynamics for Performance Management & Governance 6, https://doi.org/10.1007/978-3-031-07074-7_2

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2 Framing the Dynamic Complexity of Governing Local Area Performance A local area configures a social system in which a plurality of actors interacts. It embodies a patterned network of relationships among individuals, groups, and institutions to form a unified whole. Such systems involve relevant system variables within information feedback loops characterized by non-linear relationships and delays between causes and effects. The emerging structure provides an endogenous explanation of the deep causes behind a problem affecting social systems’ behaviors over time. This means that a change in a point of the system, sooner or later, will have implications in another part of the same system (von Bertalanffy, 1968). Such effects may be sometimes unexpected, rarely obvious, and frequently counterintuitive (Cronin et al., 2009; Forrester, 1971; Morecroft & Sterman, 2000; Moxnes, 2004, 2012; Sterman, 1989; Sterman et al., 2007). Under dynamic and complex conditions,1 system structure drives behavior over time. In local areas, the decision of an actor is tied to that of another actor. Such conditions require proper lenses to conceptualize how a change in the system structure will affect local area behavior. A system dynamics approach may help policy-makers and stakeholders frame how the flow of their decisions will impact local area performance over time (Ghaffarzadegan et al., 2011; Kim et al., 2013).

2.1

A Systems Approach to Local Area Governance

In system dynamics,2 the feedback loop concept is crucial as it captures the essence of the causal relationship that links problem structure with system behavior. In fact, “complex behaviors usually arise from the interactions (feedbacks) among the components of the system, not from the complexity of the components themselves” (Sterman, 2000, p. 12). A feedback loop exists when information that results from an action goes through the system structure and eventually returns to its point of origin, influencing future courses of action (Richardson, 1997). Such influence may be positive if the loop describes a tension to reinforce the cause or negative when the 1

Different from detailed complexity, dynamic complexity depends neither on the extent of system components nor on their possible combinations. Rather, the dynamic of a system stems from the magnitude of delays and non-linear relationships involved by causal feedback mechanisms (Morecroft, 2015; Sterman, 2000). 2 System dynamics is a methodology developed at MIT (Cambridge, USA) by Jay W. Forrester. System dynamics bridges two branches that have been traditionally kept separated: on the one side, the formal quantitative-mathematic approach aimed at finding optimal solutions to business problems and on the other side, the management experience-based point of view (Bianchi, 2016). Relevant works in the field of system dynamics include Forrester (1961, 1969), Roberts (1978), Richardson and Pugh (1981), Wolstenholme (1990), Vennix (1996), Sterman (2000), Morecroft and Sterman (2000), Warren (2007), Morecroft (2015), and Bianchi (2016).

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causal chain limits the initial action. The multiplication of the signs characterizing the relationships among the involved variable determines whether the loop is positive or negative. A positive loop portrays a source of exponential growth or collapse over time, i.e., the continuous line in quadrants 1-a and 1-b of Fig. 2.1. A negative loop generates a goal-seeking behavior toward a point of equilibrium or an inertial decay toward zero, i.e., the continuous line in quadrants 1-c and 1-d of Fig. 2.1. The interplay between reinforcing (R) and balancing (B) loops can generate all kinds of behavioral patterns (Sterman, 2000, p. 12). In Fig. 2.1, a matrix combines feedback loop dominance and local area strategic perspective. “The concept of feedback-loop dominance is central to the system dynamics paradigm. In complex systems—high-order, multiloop nonlinear feedback systems—behavior over time depends on which of the many feedback processes in the system dominates. At any moment in the evolution of the system, some feedback loops will be highly influential and others will be inactive” (Richardson, 1995, p. 67). The matrix provides a basis for understanding how the structure of a problem influences local area performance over time through feedback loop dominance. In fact, each quadrant associates a causal loop diagram with a corresponding behavior mode based on the shift in loop dominance. In this sense, each graph showing a different behavioral pattern for local area performance configures a different strategic perspective for the local governance. Since each strategic perspective configures a different challenge for the local policy-makers, the benefits of applying a systems approach to local area governance can be discussed. The causal loop diagram in the top-left quadrant of Fig. 2.1 describes how policymakers may implement urban renovation policies by encouraging real estate investments. The associated graph portrays a “crisis prevention” scenario in which the early-stage exponential growth, driven by two reinforcing loops (i.e., loop R1 and loop R2), is then limited by two balancing loops (i.e., loop B1 and loop B2). The causal loop diagram shows how the investments in the real estate industry renovate the urban profile of a city, which attracts residents and businesses and, in turn, provides returns to real estate companies. This process generates exponential effects that reinforce the renovation policy (loop R1). However, an increase in the number of residents and businesses may saturate public service capacity, with potential negative impacts on the profitability of real estate investments (loop B1). Local area policy-makers should invest an increasing fraction of municipal revenues in expanding public service capacity (loop B2). This public initiative will prevent or counteract the adverse effects of public service saturation on the feasibility of the urban renovation project. The expansion of public service capacity decreases public service saturation, letting real estate investments grow further (loop R2). Such crisis prevention policy will allow local area governance to implement the urban renovation project sustainably. The quadrant 1-b of Fig. 2.1 emphasizes how a clientelistic culture of local area policy-makers may lead to a collapse in public funds and trust in government. The graph in the same quadrant portrays a restructuring performance scenario. A collapse in current local area performance (i.e., loop R1) may be faced by local actors through

Fig. 2.1 A systems view of the causal structure underlying local areas performance to identify dynamic and complex challenges associated with the corresponding strategic perspective [Adapted from Bianchi (2016, p. 43)]

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a policy (i.e., loop B1) that firstly slows down the “negative spiral” and then will drive system performance upward (i.e., loop R2), if it is sustained over time. The causal loop diagram describes a typical situation of backward communities in which elected officials adopt a clientelistic approach to public service delivery to boost political consensus. The practice of contracting out public service delivery increases the external staff size which boosts political consensus. This undesirable process reinforces a “clientelistic culture” in the local area (loop R1). As the graph shows, such a reinforcing loop drives public funds to downfall because the rising expenditures caused by the increase in external staff are not sustainable with current tax revenues and other State-level contributions. Suppose public decision-makers, such as a municipal manager or the management control unit director, aim to contain the collapse in the public fund and limit such bad culture. In that case, they may set a threshold for external staff by weighting the actual external staff level vs. the number of internal employees. Such performance standard obliges politicians to reduce current external staff and prevent further outsourcing of public service delivery (loop B1). Also, using performance indicators in social reporting may promote a good political culture based on accountability that may contribute to building trust in government. Such a positive outcome positions the gaining of political consensus in the long term on reducing waste of public funds (loop R2). The example in quadrant 1-c of Fig. 2.1 illustrates how information delays in performance management may affect quality and timeliness of decision-making. In this case, the causal loop diagram describes a policy-making process to maintain a standard quality of public service delivery by avoiding service capacity saturation (e.g., availability and frequency of public transportation means).3 As the graph in quadrant 1-c of Fig. 2.1 shows, current performance oscillates around the desired level until it finds a stable equilibrium point (i.e., loop B1). Such oscillations are caused by the multiple delays that influence the adequacy and intensiveness of an adjustment policy (Sterman, 1989). The balancing loop (loop B1) aims to control public service capacity saturation by adjusting the level of public service capacity based on a desired public service capacity saturation. This means that whenever there is a gap in public service capacity saturation, public policy-makers will invest in expanding the actual service capacity to reduce the saturation and keep the quality-of-service delivery at the desired level. However, a “stabilization” scenario is affected by peculiar dynamic complexity elements, such as information and material delays. In fact, the information about the actual level of public service capacity saturation is not immediately available to policy-makers. At best, it reflects a past observation because the measurement and reporting of information concerning potential undesired service bottlenecks take time. Also, local organizations cannot expand public service capacity right away due to administrative delays, which involve projects and funding availability and the time to set out an appropriate organizational strategy to implement the plan. Besides information delays, material

This example may be considered a further analysis of the balancing loop “B2” of the diagram displayed in the quadrant 1-a of Fig. 2.1 “crisis prevention.”

3

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delays required by specific construction time may affect this kind of adjustment policy. The effects of such delays on performance are hard to tame since they are embedded in the structure of a multi-actor policy-making process. The causal loop diagram in the bottom-right quadrant of Fig. 2.1 describes the potential shortfall associated with an output-based incentive distribution mechanism operating within a health-care network. As the graph in the same quadrant shows, such a revitalization scenario (loop R1) follows a slow decay process (loop B1) which has drained both the incentive budget and health-care network performance. A performance incentive distribution mechanism that allocates funding among partners based on service volumes (e.g., number of patients treated per quarter) will deplete the available incentive budget over time. Such a mechanism encourages emergency units and other hospital wards to treat incoming patients, regardless of the clinical conditions (loop B1). Also, it slackens network emphasis on service outcomes (e.g., quality of care) with evident negative consequences on performance. To revitalize the described condition, using effectiveness standards based on outcome measures in performance evaluation may stimulate a tension toward service quality improvement and the practice of learning through evaluation (loop R2). These examples have shown how “the shifting in dominance from one (loop) to the other gives the complex system much of its character” (Forrester, 1969, p. 108). As the behavioral analysis of local area performance has revealed, the shift in loop dominance is generated by the non-linearities and delays featuring the relationships among the variables involved in a feedback loop. Also, a feedback loop diagram can be adopted to understand and communicate model-based insights (Ghaffarzadegan et al., 2011; Wolstenholme, 2017). From a policy-making perspective, a causal analysis that infers the sources of behavior from the system’s structure may support policy-makers in identifying effective leverage points to improve local area performance. Such a systems approach underpins the DPM method because it captures the causal structure that characterizes many real-world problems4 to reproduce relevant variables behavior over time through models. In this sense, system dynamics provides the domain of DPM because it frames problems structure as closed boundaries5 feedback loops capturing the causal relationships between the fundamental components of a social system: levels or stocks (i.e., structural resources) and flow variables (i.e., performance) (Forrester, 1961).

4

Groundbreaking studies in system dynamics include Forrester (1968, 1969) and Meadows et al. (1972). 5 The causally closed-system boundary means that the researcher/modeler/decision-maker point of view is endogenous in a way that the formal structure (i.e., the model) is able to reproduce the observed behavior that characterizes the dynamic problem under investigation without relying upon external explanation (e.g., external shocks) (Forrester, 1969).

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2.2

51

A Dynamic View of the Relationships Between Governance Structure and Local Area Performance

The causality that links a flow to a stock is relevant in a discussion on the governance of local area performance because it describes how policy-making converts the continuous flow of information into actions. In a plural and pluralist society, such actions stem from a “modulating stream of decisions” (Forrester, 1992; Morecroft, 1983) influenced by stakeholders’ values, expected goals, delays in perceived and reported information, and discrepancies between desired and current conditions. Based on this information, actors take actions to change system structure in an attempt to improve its behaviors. Governing performance requires that policy-makers understand the fundamental structure of local area governance because adopted policy and results inherently relate to each other (Davidsen, 1991). The structure of a social system can be represented by levels (i.e., stocks) and flows (i.e., rates). As Fig. 2.2 displays, flows (i.e., inflow and outflow) change a resource (i.e., the structural condition) over time, while a stock that measures the size of a resource at a given point in time integrates the value of the rates flowing in and out. The graphical representation of stock and flows shown in Fig. 2.1 implies a mathematical relationship. The integral Eq. (2.1) describes the accumulation process which occurs within the level, where the Stock(t) is the result of Stock(t0) plus the net sum of all Inflow(s) subtracted by all the Outflow(s) between the time interval from t0 to t. Z

t

½inflowðsÞ  OutflowðsÞds þ Stockðt0Þ

ð2:1Þ

t0

The differential Eq. (2.2) describes the net rate of the change in the stock over the same time period, which is the difference between all the Inflow(s) subtracted by all the Outflow(s). dðstockÞ ¼ Net Change Rate ¼ Inflow ðt Þ  Outflow ðt Þ dt

ð2:2Þ

Both representations frame the same structure, which can fit many “real-world” examples in which a stock accumulates the net change between its in-and-out flows. Examples include population, with in-migration and out-migration flowing in and out from the stock, or service capacity, where the inflow captures the capacity Fig. 2.2 A dynamic view of the relationship between system structure and performance

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expansion process and the outflow the obsolescence rate. Flows and stocks influence each other. The relationship between stocks and flows is the source of dynamics because accumulation processes “give systems inertia and provide structure with memory” (Sterman, 2000, p. 192). From a dynamic perspective, system performance grasps the effects of decisions on the flows that change structural conditions over time (Forrester, 1992; Morecroft, 2015). In fact, local area performance can be framed as an inflow (e.g., business opening rate) or an outflow (e.g., reported crime reduction rate) that changes the corresponding stock (e.g., business population or reported crimes). Such resource endowments are the potential to affect future performance. In a local area, the complementary relationship between system structure and behavior (Davidsen, 1991) may be found in the effects of governance and managerial decisions on performance (Bianchi et al., 2021; Bianchi & Vignieri, 2020). “Robust cause-and-effect models” (Bovaird, 2014, p. 2) based on a systems methodological framework may provide a structural description of the system nature and a behavioral representation of its dynamic character. This is a prerequisite for effective policy design and implementation. A performance management method that frames the system’s structure and behavior may make policy-makers the cognitive a leap (Otley, 2012) to understand the causality behind dynamic and complex issues. That is the domain of DPM.

3 The Need for Dynamic Performance Management to Implement Inter-institutional Performance Management Routines DPM is a methodological framework that may enhance the governance of local areas as it provides policy-makers with a key to understanding the relationship between the inter-institutional network structure and inter-institutional performance.6 As a systems method to performance management, it helps local area policymakers to go beyond a static view of the system and short-termism. The need to

6

Understanding the relationships between structure and behavior is a timeless concern for public administration scholars. “Organizational structures provide the pervasive foundation for achieving coordination and control within an organization, influencing and being influenced by a host of different inputs, outputs and outcomes. One of the key functions for public managers is therefore the creation and maintenance of activities that can provide structural support for a host of other characteristics that are central to the pursuit of organizational goals, such as values and routines. Indeed, some might say it constitutes the foremost object of the theory and practice of bureaucracy. Woodrow Wilson (1887, p. 213) claimed that ‘philosophically viewed’ public administration was chiefly concerned with ‘the study of the proper distribution of constitutional authority’. Classical organizational theorists regarded bureaucratic structure, and its relationship with decision making and behaviour, as key to understanding organizational effectiveness (Gulick & Urwick, 1937; Simon, 1979; Weber, 1952)” (Andrews, 2010, p. 89).

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overcome such a bounded view is particularly relevant when local area policymakers face wicked problems affecting communities (Lægreid & Rykkja, 2014). Such problems cannot be tackled by a conventional approach, mostly based on an organizational perspective that attempts linear explanations to design solutions to cope with them. Wicked problems are complex issues that require engaging a plurality of stakeholders to face them (Roberts, 2000). Under such conditions, if policy-makers disregard a systems view of the problem, even well-designed policies are bound to fail (OECD, 2017). In fact, it is not uncommon that the misperception of the system structure explains policy failures (Forrester, 1969; Moxnes & Saysel, 2009; Sterman, 1989) or policy resistance and unexpected outcomes (Sterman, 2000). To face such risk, DPM supports decision-makers in (1) outlining the expected end results, (2) causally relating the corresponding performance drivers, and (3) setting different policies that local area policy-makers would adopt to build up and deploy the strategic resources required to affect such driver. As Fig. 2.3 shows, DPM encloses the causal structure in three layers: end results, performance drivers, and strategic resources. DPM identifies three levels of end results—outputs and intermediate and final outcomes—as shown in the bottom layer of Fig. 2.3. The identification of end-end results allows policy-makers to detect performance drivers, which can be described as the critical success factors that impact the end results. Performance drivers are gauged as ratios comparing a strategic resource endowment to either an internal or an external benchmark. An internal benchmark refers to standard operating conditions7 inside an area based on which a policy is implemented. An external benchmark takes a specific constrain as a point of reference for a ratio, such as stakeholder expectations, law prescriptions, or physical limits (e.g., the carrying capacity of a system). These are external as they come from outside the boundaries of the policy-making process. Such ratios play a crucial role in DPM analysis as they causally link the expected end results with a set of resources that are considered strategic for generating the expected policy effects. An example may clarify this concept. Suppose that policymakers aim at improving the quality of local area mobility services. In doing this, they identify the fit between service attributes and community needs is a relevant factor to improve. To gauge such relationship, the corresponding performance driver might compare current service attributes (e.g., the frequency of routes, ticket price, or the average age of the fleet) to perceived community needs concerning the same service attributes. That is the current level of a strategic resource vs. an external benchmark (e.g., average age of fleet/desired average age of fleet). If properly designed, performance drivers may be sensitive enough to capture subtle variations in performance to provide information that allows policy-makers to design policies

The concept of “standard operating condition” was adopted by Coda (1970) to describe a point of reference for the activity carried out inside an organization or within its unit. Such concept is further explained in this chapter, Sect. 4.3.1.

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Fig. 2.3 The Dynamic Performance Management framework to enhance the governance of local areas [Adapted from Bianchi (2021, p. 338)]

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to influence the desired end results. End results feedback on the strategic resource layer (Bianchi, 2016). In a DPM chart, strategic resources are modeled as stocks measuring the current endowments of local area tangible and intangible assets. From a policy design perspective, decision-makers aim at improving such assets to affect performance drivers and, eventually, the end results. As Fig. 2.3 displays, strategic resources are changed by inflows and outflows. Some strategic resources (e.g., service capacity or equipment) can be directly purchased on the market, as represented by the flow “direct acquisition” that changes the “strategic resource 1.” Besides direct acquisition, outputs and intermediate and final outcomes change local area strategic resources. In all these cases, the flows changing such resources are generated by governance and management routines (Morecroft et al., 2002). For instance, a policy that aims to improve the community quality of life in a neighborhood—as a final outcome—is affected by a set of intermediate outcomes, such as a “change in the quality of education of local schools” or the “change in reported crime by the police.” Such results are affected by policy outputs, such as the “change in youngster enrolled in the neighborhood schools” or the “change in the number of police staff patrolling the area.” Intermediate outcomes change the shared strategic resource endowments in the neighborhood, e.g., “quality of education” and “crime cases to be solved.” These resources cannot be purchased on the market because they are the preconditions for achieving the final outcomes, e.g., “change in community quality of life.” Eventually, the final outcomes will change its corresponding strategic resource, e.g., “community quality of life.” For local area governance, strategic resources are shared assets (e.g., the image of the place, natural resources, transportation infrastructures, public service capacity, cultural heritage, and community quality of life). This means that such resources pertain to the local area and their endowments profile the main attributes of a place. As the example has shown, shared resources can be boosted or depleted through intermediate and final policy outcomes if local area decision-makers consistently leverage shared resources with their organizational assets. This implies linking performance management with performance governance to design and implement consistent policies to combine the pursuit of community outcomes with organizational goals. To this end, public organizations, businesses, community associations, and other actors in a local area must adopt an “outside-in” perspective of stakeholders’ collaboration (Bianchi, 2021; Bianchi & Vignieri, 2020, p. 620). Such a view may help them in balancing the multiple dimension of local performance through collaboration.

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4 Framing Local Area Performance Through an “Outside-In” Perspective of Stakeholders’ Collaboration The “outside-in” perspective of stakeholders’ collaboration turns the conventional point of view of policy-making and performance evaluation, which usually sets performance goals from an organizational perspective to an inter-institutional one. An “outside-in” point of view of policy-making implies that the local area stakeholders should frame desired community outcomes from the outside of their organization so as to embody inter-institutional performance within their organizational policies. Not the other way round. If policy-makers embark on collaborative initiatives, they will be able to design and implement consistent policies that may nurture local area shared resources (Ansell & Gash, 2007; Emerson & Nabatchi, 2015b; Imperial, 2005; Torfing et al., 2020). An example may contribute to clarify this point (Box 2.1). Box 2.1 Collaborative Policy Design in Barolo Area to Protect Wine Quality, Reputation, and the Image of the Area Barolo is a well-known wine-producing area of 5.6 km2, located nearby Cuneo, in Italian Piedmont. The area is known for its “Barolo wine8” produced and sold by the many wineries having vineyards there. The local area stakeholders, such as the wine consortium, the wineries, and the municipalities involved in the local governance, have actively promoted many technical, financial, and social aspects of wine production in the Langhe region. They have defined the wine’s area of origin, grape varieties, and characteristics to protect the wines from counterfeit copies, adulteration, and unfair competition. Such initiatives aim at maintaining qualities of productions, wine’s reputation, and image of the area, i.e., three shared resources that local wineries build up and deploy to produce and sell their products at profitable prices. Also, other producers are not allowed to sell “Barolo,” unless they respect the technical requirements established by the local governance. At the same time, local wineries cannot prioritize organizational goals, such as improving business profitability, by deliberately increasing their production rate or disregarding the traditional production method. As the example in Box 2.1 has illustrated, for the “outside-in” perspective, policy design is first at the local area level and then at the organizational level (Bianchi, 2021). In this view, collaboration is not an end, but a strategy to generate “better

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Barolo is made from the Nebbiolo grapes, and it is often described as one of Italy’s greatest wines.

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organizational performance” (Agranoff, 2007, p. 157) and public value for the local community (Bryson et al., 2014). As a governance strategy, a leading organization should leverage cross-sectoral relationships (Crosby & Bryson, 2010) to set out collaborative arrangements (e.g., the Barolo wine consortium) in which a plurality of local stakeholders can be engaged. Stakeholders’ active participation is crucial to improve desired community outcomes (Bovaird, 2007; Bovaird et al., 2015) or nurture the common good (Bryson et al., 2015), provided that they will endow local area governance with additional resources (e.g., knowledge, social capital, reputation, and “time”) that can be deployed for pursuing collective purposes (Peters & Pierre, 1998). In this way, collaborative governance initiatives will develop a “shared capacity to joint actions” (Emerson & Nabatchi, 2015a, p. 57), which is a prerequisite to sustain future performance. To implement the “outside-in” stakeholder collaboration perspective, the role of performance management and governance is crucial (Bouckaert & Halligan, 2008; Halligan et al., 2012). If thoroughly implemented, policy-makers will focus on local area shared resources as the key to combine the pursuit of inter-institutional and institutional performance consistently.

4.1

The “Depth” of Performance: Combining Institutional and Inter-institutional Levels to Generate Public Value in Local Areas

Institutional and inter-institutional performance are complementary to each other. As shown in Fig. 2.4, inter-institutional performance affects the results of the organizations embedded in a given geographic area, and institutional performance, in turn, feedbacks on the wide system performance. Such instrumental relationships will generate public value for the local community if the adopted policies consistently combine the two levels of performance. The lack of consistency among organizational targets and desired community goals threatens the pursuit of community outcomes because organizational development is exploiting local area shared resource endowments. The inherent interdependence between inter-institutional and institutional levels is the “depth” dimension of local area performance. To clarify such dimension, an example of how policy inconsistency disconnects the two performance levels is provided in Box 2.2.

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Fig. 2.4 The “depth” of performance: combining institutional and inter-institutional levels to generate public value in local areas

Box 2.2 An Example of Policy Inconsistency: Will Menfishire Deploy Its Fertile Soil for High-Quality Wine Production or Improve Tourism Potential? Menfishire is a well-known local area, famous for its high-quality wine production and the beauty of its coastline, where the fruitful countryside is seamlessly mixed with vast sand shores. Wine production has been increasing over the past few years due to a rise in foreign demand for wine. To fulfill export, Menfishire local wineries set out a strategy to provide the required financial support to local farms to rent the vast area approaching the coastline to cultivate a variety of grapes in such fertile soils. According to the municipal plan, such zoning implements a strategic goal: improving place attractiveness to exploit local tourism potential. Based on urban regulation, this vast area should host second houses and small hospitality structures. Such prescription has encouraged landowners to sell most plots to real estate companies rather than to local farmers due to the price of the land. (continued)

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Box 2.2 (continued) In fact, the high price of the lots makes it convenient to exploit the proximity to the coastline for tourism instead of using the soils for cultivating grapes. This conflicting situation uncovers trade-offs in time and space because sustaining farmers in developing their production capacity is not compatible with tourism development. Though tourism development will fuel municipal financial performance, it will also demand public services, implying investments for the municipality. Also, denying the requests of a flourishing industry, such as a high-quality winery production, may slacken social capital and the image of the area at the detriment of business continuity and local area sustainable socio-economic development in the long run. As the example has illustrated, in complex policy contexts, values differences, power imbalances, and misalignment in stakeholders’ mental models are likely to jeopardize inter-institutional performance due to resource scarcity and policy inconsistencies. Under complex conditions, governance effectiveness is rooted in the actors’ aptitude to consistently build up and deploy local area shared assets (i.e., social resources, financial resources, and public service capacity resources) that may, in turn, affect inter-institutional performance. In this sense, policy-makers and their stakeholders should leverage collaborative relationships (Crosby & Bryson, 2010) to fuel their capacity to achieve specific goals that “could not have been attained by any of the organizations acting alone” (Huxham, 2003, p. 403). This capacity implies framing how stakeholders’ contributions can be systemically aligned toward public value generation through collaboration (Ansell, 2012; Bryson et al., 2014; Emerson et al., 2012; Moore, 1995). The lack of cohesion among local actors may entangle policy-makers into a challenging situational decision known as trade-offs, which unfolds as follows. If they tend to increase one aspect, another must decrease. These aspects might include organizational vs. local area, short term vs. long term, and financial vs. social performance vs. public service quality. Such trade-offs stem from a myopic view of policy-making underlying a static evaluation of the effects of current decisions on local area performance (Bianchi et al., 2021). Also, trade-offs might emerge due to the linear thinking underlying a decisionmaking logic, which tend to see “problem as event and solution as fix” (Morecroft, 2015, p. 32). Also, as discussed in Chap. 1, policy-makers have bounded rationality9 (Simon, 1947; Sterman, 1989) limiting people’s cognitive capabilities. In fact, in front of a decision, people may give weight to certain available information while discarding other evidence that is considered irrelevant or ignored because not yet available. As the example in Box 2.2 has illustrated, some stakeholders set forth their The concept of bounded rationality is alternative to the “rational economic man,” which is dominant in the traditional microeconomics literature. Motivated by a full rationality, decisionmaking is a discrete event that aims at optimizing available resource returns.

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priorities in an attempt to satisfy their needs, regardless of other local actors’ values. Also, if opportunistic decisions may potentially reward their short-term interests, they will make such choices, despite the potential adverse effects for the whole local area in the long run. In fact, such narrow perspective does not allow decision-makers and their stakeholders to frame the potential interplay that may occur over time (i.e., short term vs. long term) and between different performance measures (i.e., outputs vs. outcomes) pertaining to several policy fields. These two interrelated aspects profile the “span” and “time” dimensions of local area performance, which are discussed in the following sections.

4.2

Pairing “Depth” with “Span” Dimensions of Local Area Performance

Local area performance cannot be influenced by single organization policies— though an organization may have a leading role in the local context (e.g., a municipality or a development agency). Effective political and managerial responses must consistently combine the pursuit of institutional and inter-institutional goals to generate community outcomes. As discussed in the previous section, performance “depth” identifies two sequential levels for performance management and governance: institutional and inter-institutional. In the context of a local area, institutional performance may be associated with the outputs and outcomes achieved by a local organization (e.g., a municipality or a business located in the area). Inter-institutional performance refers to outcomes as the effects generated by the local stakeholders’ policies that change local area shared strategic resource endowments. The “span” dimension embodies both output and outcome measures. Outputs concern the volume of the service provided by an organization within a period of time (Ammons, 2001; Bouckaert & Halligan, 2008; Hatry, 1999). Examples of output measures may include “users served per year,” “km or roads maintained per year,” or “students passing the exam in a quarter.” Such measures can be used to assess the efficiency and effectiveness of a policy. Outcomes are “the events, occurrences, or changes in conditions or behavior, or attitude that indicates progress towards the achievement of the mission and objectives of the program” (Hatry, 1999, p. 15). They refer to the long-term effects of a public policy on the socio-economic context. In this sense, outcome measures gauge the impact of delivered services on community needs (Smith, 2013), including “side effects whether intended or not and whether beneficial or detrimental” (Hatry, 1999, p. 15). Outcome measures are relevant for public sector performance management because they capture how local area governance is able to generate public value for the local community. The literature in public administration and performance

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management offers different perspectives to describe the features and limitations of performance outcomes.

4.2.1

Defining Performance Outcomes: Main Viewpoints from the Literature

Several perspectives may contribute to define performance “outcome.” This section discusses four main viewpoints, as reported in Table 2.1. Easton (1953) initially framed the concept of “outcome” as a feedback mechanism that impacts the state of the system, changing the political “demand or support” (Easton, 1957, p. 384). In a similar fashion, von Bertalanffy (1968, p. 161) stated that “the basic model is a circular process where part of the output is monitored back, as information on the preliminary outcome of the response, into the input, thus making the system self-regulating; be it in the sense of maintenance of certain variables or steering toward a desired goal.” Based on the general systems theory of the political system, outcomes are policy effects that may restore an equilibrium condition whenever they satisfy the community needs. A second view identifies outcomes as the consequences of outputs. They “are the ‘so what’ of politics. [. . .] If outputs are what governments produce, outcomes are the grand design that citizens see behind those outputs” (Levy et al., 1975, p. 1). In this perspective, the social context enables sustainable policy implementation (Pawson & Tilley, 1997, p. 57). An example may clarify the role of context for policy outcomes. Suppose that the national government set out public incentives to sustain those businesses in expanding their research and development units. Such policy is likely to be properly implemented in some contexts with a strong entrepreneurial culture, regardless of the number of firms located in the area. Differences in contextual attributes are key to explain variations in the policy effectiveness (Forsythe, 2000, p. 18). A third perspective connects outputs with outcomes. Outputs gauge the quantity of public service delivery in a given time span (e.g., the number of construction permits issued by a municipality in a year). Outcomes capture the impact of the quality of such outputs (e.g., the fraction of that paperwork timely and correctly issued over the same period) to evaluate the worth of what has been delivered by Table 2.1 Defining performance outcomes: main viewpoints from the literature Definitions of outcome A feedback mechanism that regulates the political system The consequences of outputs which are influenced by the attributes of the policy implementation context The quality (i.e., worth) of what has been delivered by public sector organizations The long-term effects of public policies

Authors Easton (1953, 1957) and von Bertalanffy (1968) Levy et al. (1975), Pawson and Tilley (1997), and Forsythe (2000) Smith (2013) Ammons (2001) and Afonso et al. (2010)

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public sector organizations. In this perspective, the evaluation of outcomes requires measuring both tangible (i.e., service outputs) and intangible (i.e., service quality) aspects of any public initiatives (Smith, 2013, p. 2). In the attempt to define the outcome, the time horizon is a fourth relevant aspect to be considered. Outputs are measured in the short term. Outcomes usually occur in the long term. Such distinction is appropriate for analyzing public policy’s long-term impact on the system structure (Afonso et al., 2010; Ammons, 2001). The “time” dimension provides the other three perspectives with different time frames over which public policy and service delivery can be evaluated at the institutional domain, which also include the public service delivery ecosystem (Osborne, 2020), and the community domain (Bovaird, 2008, p. 185). In this sense, the “time” dimension integrates the “depth” and “span” of performance.

4.3

Integrating “Depth” and “Span” Through the “Time” Dimension of Performance

The “time” dimension covers performance over the short and long term by linking such time frame with measures of outputs and intermediate and final outcomes. Information on outputs circulates within organizational boundaries to indicate the volume of work accomplished within a certain period of time (e.g., a month, a quarter, or a year) (Ammons, 2001). As internal measures, outputs track “what is expected to lead to desired outcomes” (Hatry, 1999, p. 14), which is not the public value that has been delivered to the users or the community through the adopted policies. Gauging such value requires external measures to capture public policy and service delivery’s effectiveness in (1) changing users’ behavior, (2) improving users’ personal conditions, or (3) achieving broad community goals, such as nurturing local area shared resources. The example in Box 2.3 may provide further insights on the causality that links outputs through intermediate to final outcomes. The example concerns a high school service program that aims at improving the homework autonomy of disabled students. Box 2.3 An Example of a School Service Program to Improve the Homework Autonomy of Disabled Students The administrative board of a high school located in a middle-class neighborhood of a medium-sized US city has recently announced a new program to improve the homework autonomy of physically disabled students through assistive technologies and devices. They include “voice recognition programs, screen readers, screen enlargement applications, automatic page-turners, book (continued)

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Box 2.3 (continued) holders, and adapted pencil grips to help learners with disabilities participate in educational activities” (https://www.nih.gov). At the outset of the service program, the school administration provides students with such assistive tools. In addition, it sets out a training program for supporting the schoolteachers to conceive accessible educational content and students’ families for acquainting them on how to use such new technologies to help disabled students when doing homework. Based on the example illustrated in Box 2.3, Fig. 2.5 shows the logical sequence that leads from input—through outputs—to intermediate and final outcomes. The “number of students equipped with assistive tools,” the “amount of training hours,” and “teachers” are inputs of the service program because they represent the resources that the school administration may leverage to achieve the desired outcome. The “assistive device adoption rate” and “training class competition rate” are two service program outputs. The latter mainly depends on school efforts to encourage teachers and student family members to attend specific training courses. On the one hand, the more trained the family members, the higher the “change in their home support to disable students” will be; on the other hand, the more skilled the teachers, the higher the “change in the accessible educational content” will be. Both results are intermediate outcomes as they lead to the final outcomes, i.e., “change in homework autonomy of disabled students.” For instance, if few family members attend the training courses, most disabled students cannot effectively use the assistive device to do their homework, regardless they have adopted a device. To this end, the school

Fig. 2.5 Framing the causal chain from inputs through outputs to intermediate and final outcomes of a high school service program

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administrative board may strengthen the communication with parents to increase the “training class competition rate” that impacts the intermediate outcome “change in-home support to disable students,” improving the final outcome. As the example has illustrated, intermediate outcome measures are specific to the public policy implementation mode and service delivery logic. The causal chain from inputs through outputs to outcomes shows relevant information on how specific initiatives may contribute to the desired final outcomes. To understand how different initiatives contribute to generate policy outcomes, they should be monitored through performance indicators and periodically evaluated at political and managerial levels. If properly designed, performance management systems will enable effective measurement and evaluation. Relevant performance measures should target specific reference objects (e.g., societal needs, policy effects, service volumes, and resources) that fit with the context in which the information they produce can be functionally used. Such context is usually referred to the unit of analysis of performance management (Anthony, 1965), i.e., the “minimum functional entity” (Coda, 1970, p. 20), which adopts standards, goals, and performance indicators.

4.3.1

Goal, Standards, and Performance Indicators: Defining Performance Measures to Assess the Multiple Dimensions of Performance

Standards are points of reference for the activity carried by the unit under analysis. The “standard operating conditions” (Coda, 1970, pp. 37–40) configure appropriate solutions10 concerning services attributes, characteristics of materials, equipment’s features, operating needs, production cycles, and working procedures that apply to each responsibility center. Identifying the standard operating conditions is critical for performance evaluation as it provides the basis to assess the actual operating conditions of each center (Amigoni, 1988; Brunetti, 1979; Maciariello, 1984; Merchant, 1981, 1998). The use of standards in performance management allows discrepancy analysis between current performance and a benchmark. Policy goals and objectives identify a “succinct description of what the organization or the community should look like after its successful implementation” (Bryson, 1995, p. 155). Their definition must be specific, measurable, achievable, relevant, and timerelated. Otherwise, the measurement of results will lack relevant information indicating what a current condition is like and how it has evolved over time. Indicators may be single and ratio measures (Boyne et al., 2006; Hatry, 1977; Van Dooren et al., 2015). Single indicators are absolute measures, while ratios compare two values to provide intelligible fractional information suitable for comparison. Welldesigned performance indicators are crucial to gauge critical performance dimensions.

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What cannot be included in the standard operating conditions are non-controllable costs and production volumes because the first are out of the area’s chief control, while the second cannot be standardized (see Coda, 1970, p. 27).

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Table 2.2 Absolute and ratio indicators: different types of performance measures Absolute performance measures Type of Definition (based on the high school service indicator program example) Input – N. of students equipped with assistive tools – N. of training hours – N. of teachers involved in the projects – N. of families involved in the projects Output – N. of teachers completing training classes per month – N. of students adopting assistive devices per month Intermediate – Δ in the number of disabled students supported outcomes by their family members in interacting with the adopted assistive devices per month – Δ in the number of accessible educational content (e.g., videos, assignments, or teaching notes) designed explicitly for the adopted devices per quarter Final – Δ in the number of reported disabled students outcomes that have completed their homework per year Relative performance indicators Type of Definition indicator Efficiency Input costs/output Productivity Output/input Effectiveness Outcome/output CostInput/outcome effectiveness Goal Policy goals/socio-economic needs relevance Utility and Policy outcomes/socio-economic needs sustainability

Meaning What goes into the system? Which resources are used?

Which products and services are delivered?

What are the immediate impacts of the output?

What are the ultimate ends that the program aims to achieve? Time horizon Short term Short term Medium-long term Medium-long term Long term Long term

Adapted from van Dooren et al. (2015)

Indicators must be relevant, valid, actionable, and focused on a specific object without concealing potential constraints. Also, they should be time-oriented, periodically revised, sensitive enough to gauge subtle performance variations, and clearly defined with a proper unit that fits the phenomenon under analysis. This implies that indicators should avoid redundancy, overlaps, and fault measures to keep measurement lean, feasible, and sustainable. Also, good indicators should prevent dysfunctional behaviors such as “gaming the system” (Van Thiel & Leeuw, 2002). To this end, the literature suggests adopting outcome-based performance measures (Bianchi et al., 2010; Bianchi & Rua, 2020; Bianchi & Williams, 2015). Table 2.2 presents different measures in two broad categories: absolute and relative performance indicators. Definitions for absolute indicators are based on

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the example of the high school service program11 discussed in the previous section. Input indicators gauge resource consumption at a specific point in time. Output indicators are volume measures that capture the quantity of service delivered. Outcome measures assess the intermediate effects of outputs and the ultimate ends of the program. Such absolute measures source information to build short- and longterm ratio indicators. They include several measures. Efficiency measures gauge the ratio between the costs of the resources deployed to produce a volume of work and such workload. Productivity indicators relate service volumes to the resources used to generate such work. Effectiveness measures describe the attitude of adopted policies to achieve their ultimate ends by comparing outcome vs. output. Relevance measures capture the adequacy of a policy to meet the need of the socio-economic context. Utility and sustainability indicators cover the extent to which a policy has been effective in addressing the socio-economic needs of a community. High-quality measurement is the “lifeblood” (Moynihan, 2008b, p. 6) of a learning organization because it sources policy-makers with relevant information concerning goals, standards, and performance indicators. To this end, performance management should iterate through five steps: (1) planning goals, setting objectives, and budgeting (2) performance measurement (3) measurement of discrepancies between actual performance and planned objectives/standard operating conditions (4) performance evaluation (5) identification and implementation of corrective actions or changes in goals and objectives. Such performance management cycle connects measurement with the specific levels to which the information is directed (Hofstede, 1978, 1981). At the governance level, decision-making venues may be regarded as fertile learning forums where policy-makers and their stakeholders deliberately evaluate performance (Moynihan, 2005). The ambition of such evaluation forums (Koliba et al., 2011; Moynihan, 2008a) should be balancing the multiple dimensions of local area performance.

4.4

A Multidimensional View of Local Area Performance to Frame Community Outcomes

As discussed in the previous sections, the complexity of governing local area performance entails measuring short- and long-term policy effects, i.e., outputs and outcomes, on institutional and inter-institutional levels. Such policy effects cannot be clustered within a single field because they cut across the boundaries of several domains. A brief example may contribute to clarify this argument.

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The example of a high school service program to improve the homework autonomy of disabled students. See Sect. 3.3.

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Suppose that the municipality of a small town has adopted cutback policies to achieve a sustainable liquidity condition which may contribute to balance the overall financial equilibrium. Though the goal of “recovering the financial equilibrium of the municipal budget” can be framed as a “financial end” per se, its effects spread throughout the local government functions. That is because a sustainable liquidity condition is instrumental for the pursuit of other public “ends.” In fact, if not counterbalanced by specific projects—possibly supported by external funding schemes—the adoption of strict cutback policies will impact public service variety, coverage, and prices. Sooner or later, a reduction in public service quality will deplete community quality of life and place attractiveness (i.e., social outcomes). Such adverse outcomes will worsen the municipal liquidity condition (i.e., financial outcome) because a retrenchment in public service programs limits, for instance, business operation (i.e., financial outputs) that in turn will reduce municipal tax revenues (i.e., financial outputs). As the example has illustrated, the pressure on the financial domain should be associated with a tension on the quality of public service offering and social ends. Diverting public investments from service programs to “pure financial ends” would not be sustainable in the long run due to the rise of adverse social outcomes. Although a policy stems from the “financial,” its short- and long-term effects (i.e., outputs and outcomes) move from the “financial” through “quality of public services offering” to “social” policy domains involving both institutional and interinstitutional levels. To frame such effects, policy-makers should go beyond a “silo” thinking that frames a policy within its native domain. Also, this view may lead local area policy-makers to create “slabs” (Mintzberg, 2009, p. 169) that bound knowledge and information sharing along hierarchical levels. As Fig. 2.6 shows, the complexity of local area performance can be framed in relation to the interplay among “financial,” “social,” and “quality of public service offering” policy domains (Bianchi et al., 2021; Coda, 2010; Osborne, 2018, 2020; Walker et al., 2010). The effects of the interplay among these domains can be measured along three interrelated dimensions: “depth,” “span,” and “time.” The emerging multidimensional view may offer policy-makers a key to disentangle the complexity of governing local area performance. Under complex conditions, the governance of local area entails balancing the relationships among short- and long-term policy effects at different performance levels. Implementing such a view is crucial to enhance the governance of local area. This can be done through DPM.

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Fig. 2.6 A multidimensional view of local area performance [Adapted from Bianchi et al. (2021, p. 104)]

5 Conclusions This chapter has introduced Dynamic Performance Management as a method to enhance public value-driven performance regimes to provide a response to the need for the implementation of properly designed inter-institutional performance management routines. To this end, the chapter has framed the dynamic complexity of governing local area performance. This has enabled the chapter to discuss the benefit of adopting DPM as a methodological framework to implement inter-institutional performance management routines in collaborative settings. In doing this, the relationship that links governance structure to policy outputs and outcomes has been underlined.

References

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Lastly, the chapter has suggested embracing an “outside-in” perspective of stakeholders’ collaboration to properly frame the multiple dimensions of local area performance through DPM. By building on this perspective, this chapter has set the field to illustrate—in the following chapters—how DPM may support local area policy-makers and their stakeholder to generate public value by fostering policy learning (i.e., Chap. 3) and implementing outcome-based performance assessment (i.e., Chap. 4).

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Part II

Applying Dynamic Performance Management to Cross-Boundary Settings

Chapter 3

Fostering Policy Learning in Public Value-Driven Performance Regimes Through Dynamic Performance Management

1 Introduction For contemporary public administration, the aim of public sector organizations is delivering value to the administered community (Bryson et al., 2014; Moore, 1995; Moore & Hartley, 2009; Osborne, 2020). Performance regimes are collections of routines that—if properly designed—may enhance public sector organizations’ aptitude to accomplish such goal. However, in the increasingly complex and highly unpredictable governance context, it is hard for policy-makers and their stakeholders to implement performance regimes. As the complexity rises, policy-makers and their stakeholders struggle to frame the appropriate means-end relationship (Moynihan et al., 2011; Ouchi, 1979), ensuring that attained results will consistently reflect the underlying policy grand design (Levy et al., 1975). Such condition puzzles governance actors because it creates inconsistencies between designed initiatives and undertaken actions, with the risk of disengaging involved organizations (Innes & Booher, 2018) and other community stakeholders. If this happens, value conflicts and cultural tensions will emerge to the detriment of local “support for policy choices” (Head & Alford, 2015, p. 716). This will lead to a lack of consent on specific courses of action, creating fragmentation among the deliberations occurring in several—and often insulated— policy arenas. In the complex reality of contemporary public administration, “the policy implementation question” (Osborne, 2010b, p. 11) comprises more than limiting a potential risk of fragmentation. It calls for a shift in the root of policy-making from “control to guidance and learning” (Sabatier, 1991; Weible et al., 2011). To this end, public policy-makers and their stakeholders may harness the potential of learning to develop a shared understanding of the policy issues affecting their context (Bryson et al., 2014; Douglas & Ansell, 2021; McNamara, 2012). This is not common where no “prehistory of cooperation” (Ansell & Gash, 2007, p. 553) © Springer Nature Switzerland AG 2022 V. Vignieri, Enhancing Performance Regimes to Enable Outcome-based Policy Analysis in Cross-boundary Settings, System Dynamics for Performance Management & Governance 6, https://doi.org/10.1007/978-3-031-07074-7_3

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encourages collaboration (Gray, 1989) in policy-making and performance evaluation. Collaboration is needed for consistent planning and control because it removes “a general barrier” (Peters, 2017, p. 387) to pursue a successful policy implementation (Allen, 2001; Ansell & Gash, 2007; Bressers & Rosenbaum, 2000; Daniels et al., 2001; Daniels & Walker, 1996; Innes & Booher, 1999). To consistently plan under complexity conditions (Innes & Booher, 2018), local area stakeholders should leverage their learning capacity through performance management routines (Moynihan, 2005). Such routines may help local area stakeholders deal with the “substantive complexity” (Klijn & Koppenjan, 2015, p. 40), affecting performance evaluation. In doing this, a performance regime accommodates differences in stakeholders’ value basis, experiences, expectations, needs, and knowledge (Sørensen & Torfing, 2012, p. 1) by discussing policy goals and evaluating attained performance. This implies that involved actors from the local network actively participate in learning forums (Gerlak & Heikkila, 2011) and performance dialogues (Rajala et al., 2020). In response to the need for enhancing collaborative performance regimes, this chapter will illustrate how learning through DPM may foster a dialogic form of policy-making and performance evaluation. Before presenting the empirical work, the following sections will discuss how learning may help decision-makers to get a grip on policy-making failures.

2 Overcoming the “Missing Link” Between Policy Design and Implementation to Integrate Policy-Making and Performance Evaluation Through Learning The risk of policy failures has been traditionally framed as a “policy implementation gap” (Gunn, 1978) to stress the need of coupling the “missing link” (Hargrove, 1975) between policy design and implementation. As discussed in Chap. 1, in the traditional view, implementation follows policy design via a top-down (Bardach, 1977; Pressman & Wildavsky, 1973; Van Meter & Van Horn, 1975) or a bottom-up direction (Elmore, 1980; Hjern & Porter, 1981; Lipsky, 1971, 1980). From a top-down perspective, policy design is the goal-setting activity carried out at the center of the policy-making system. Implementation is the “interaction between the setting of goals and actions geared to achieve them” (Pressman & Wildavsky, 1973, p. xv) which unfolds under an accurate hierarchical control (Mazmanian & Sabatier, 1983; Van Meter & Van Horn, 1975). For bottomup scholars, the role of actors is crucial (Elmore, 1980; Hjern & Porter, 1981) to address real-world problems. Though a linear chain of control from the top to the bottom may exist, service delivery at the “street level” is influenced by the discretionary power of the bureaucrats (Lipsky, 1980).

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As the institutional complexity increases, top-down and bottom-up perspectives have revealed their limitations in dealing with multi-actor and multi-level policymaking contexts (O’Toole, 2000). Such flaws affect policy-making effectiveness (Peters, 2017) because the top-down perspective underplays the implementation phase, assuming that execution will linearly follow the design. The bottom-up view deems the bureaucratic aptitude to implement a policy at the core of the policy-making (Parson, 1995). Both perspectives recognize a divide between policy design and implementation as they leave implementation conditions, such as resources adequacy and organizational feasibility, undetected (Coda, 1970; Elmore, 1985). Such weaknesses constrain the learning potential of policy-making and performance evaluation (Sabatier & Jenkins-Smith, 1993). Learning is critical under complex conditions because it propels a constant revision (Sabatier, 1986) of goals and implementation actions (i.e., policy change) if the involved actors adopt a feedback view of the policy-making process (Majone & Wildavsky, 1979; Scharpf, 1978). This implies that relevant actors operate in a “cobweb of (social) interactions” (Heclo, 1974, p. 307) to form an “advocacy coalition” (Sabatier, 1988, p. 129) aimed at improving existing policy structures or developing new and innovative initiatives through learning. Policy learning1 induces “relatively enduring alterations of thought or behavioral intentions which result from experience, and which are concerned with the attainment (or revision) of policy objectives” (Heclo, 1974, p. 306). This may lead to “an improved understanding of causal relationships in the light of experience” (Meseguer, 2005, p. 71). Such understanding will develop a common interpretation of a problem and identify appropriate solutions (Sabatier, 1988, pp. 150–151) if the causation analysis fosters reflective thoughts (Mintzberg et al., 1976) that questions the decision-makers belief system2 (Sabatier & Jenkins-Smith, 1999), i.e., policymakers’ mental models. The actor’s beliefs affect planning (De Geus, 1988) because they influence the internal consistency of a decision-maker’s mental model. This influence means that the adopted decisions are logically drawn from assumptions about the real world, though the decision-maker may have minor evidence underpinning such premises. Such inference process (Argyris, 1990, p. 87)—portrayed in Fig. 3.1—reinforces the actor’s beliefs about reality, and thus, it influences data selection regarding the observed reality (Isaacs, 1992). As Fig. 3.1 shows, selected data are then filtered through cultural values, which give meanings to real-world events that—as evidence—support assumptions,

1

In public policy literature, policy learning offers an explanation of policy change (Heclo, 1974; Wlaker, 1974) which is alternative to social conflict (Nordlinger, 1981) and incrementalism (Quinn, 1980). This concept has been differently categorized, including social learning (Hall, 1993), political learning (Heclo, 1974), policy-oriented learning (De Geus, 1988; Sabatier & JenkinsSmith, 1993), and instrumental learning (May, 1992). 2 Sabatier organizes the actor belief system in three “tiers” (Freeman, 2008, p. 374): a deep core, i.e., the normative belief; a policy core, i.e., a commitment to a policy domain; and “secondary matter of details.”

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Fig. 3.1 The “ladder of inference” and the “reflexive loop” [Adapted from Senge (1990, p. 243)]

leading to conclusions that (quite often) confirm the actor’s beliefs on how realworld phenomena have unfolded. Based on such beliefs, a decision-maker takes actions without questioning the assumptions underpinning mental model validity. This is close to what Argyris and Schön (1978) termed as “single-loop” learning—a concept explaining that people’s experience mainly drives changes in their attitudes. Decision-makers’ experience is based on a static analysis of closing figures that usually follows strategy design and implementation (Mintzberg, 1990). In this linear process, adopted plans are mainly shaped by the decision-makers’ desired goals with a limited concern for the internal causes of policy success, failures, or resistance. This is a sign that the phases of planning and control are not aimed at developing a causation model to support policy-making and performance evaluation. The lack of a causal explanation of the system’s current state is risky in a context characterized by a high level of uncertainty because policy change mainly relies on experience rather than observation, reflection, problem diagnosis, and action. Though experience nurtures policy-makers’ attitudes, the understanding it provides is not sufficient to face the perils of dynamic complexity due to the limited

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aptitude of the human mind to deal with them (Forrester, 1992; Simon, 1947). Also, personal beliefs and biases may lead decision-makers to make inconsistent decisions as the contradictions between an espoused theory (i.e., the logic based on which an actor asserts to behave) and a theory-in-use (i.e., his or her actual behaviors) escalate due to the rising complexity of the system. To face complexity, it is necessary to leverage “double-loop learning3” (Argyris & Schön, 1978, p. 24) as it encourages inquiry into and promotes changes in actors underlying decision-making norms, policies, and objectives (Argyris, 1976; Sterman, 1994). Learning should be able to trigger continuous communication and reflection processes (Senge, 1990, p. 245) so as to enhance policy-making through performance evaluation (Schreyögg & Steinmann, 1987) and vice versa. To this end, they may use performance dialogues (Rajala et al., 2020) “specifically focused on solution seeking, where actors collectively examine information, consider its significance, and decide how it will affect action” (Moynihan, 2008, p. 167). Such dialogues may help local area stakeholders to implement double-loop learning within specifically designed forums or venues “which enhances a use of performance information, based on social interaction” (Laihonen & Mäntylä, 2017, p. 215). Learning in forums requires configuring “strategic planning routines, afteraction reviews, benchmarking processes, or other routines in which data is examined” (Moynihan & Landuyt, 2009, p. 1100). Such performance management routines are not limited to assessing performance information and prioritizing relevant measure as they may help decision-makers conceptualize real-world problems through discussion so as to promote innovation in policy-making. A DPM-based Interactive Learning Environment (ILE) may be a vehicle to elicit decision-maker’s mental model, if it is embodied in a setting, which is conducive for learning.

3 Enhancing Learning Forums Through Dynamic Performance Management Fostering changes in decision-maker’s mental models implies leveraging both actor’s experience and reflection through performance dialogue within specifically designed learning forums. Action research (Lewin, 1946) is an appropriate strategy to support local area stakeholders in implementing learning forums (Moynihan, 2005) because it aims to improve currently adopted routines (i.e., policy-making and performance evaluation) in the specific context (Argyris, 1976; Eden & Huxham, 1996). Such strategy embodies an iterative learning process that can be

Argyris and Schön (1978, p. 24) defined “double-loop learning” as “those sorts of organizational inquiry which resolve incompatible organizational norms by setting new priorities and weightings of norms, or by restructuring the norms themselves together with associated strategies and assumptions.”

3

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Fig. 3.2 Positioning action research enhanced by a DPM-based ILE at the core of double-loop learning [Adapted from Sterman (2000, p. 88)]

enhanced by a DPM-based ILE, i.e., a computer-based simulation model. Through a simulation model, decision-makers can test their policy assumptions. Simulation results may trigger double-loop learning because they provide a basis for eliciting decision-maker’s mental models’ underpinnings. A prerequisite of getting such information is the active participation of involved actors in a discussion concerning the causation linking policy assumption, adopted decisions, and simulation results (Argyris & Schön, 1978; Morecroft & Sterman, 2000; Sterman, 1994; Wolstenholme, 1990). As Fig. 3.2 portrays, double-loop learning has its core an action research process. If supported by a professional facilitator, such interventionalist approach may provide the context for leveraging “the capacity of humans to reflect, learn, and change” (Berg, 2001, p. 180).

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Within such real context, a DPM-based ILE may help decision-makers to theorize about “small-scale” problems (Reason & Bradbury, 2007). A DPM-based ILE is a simulation model which acts as a “transitional object” to support stakeholders’ imagination and learning. Since such virtual world is specifically designed to reproduce real policy-making challenges, it may allow a group of players to make decisions, simulate, and review their results under the guidance of a learning facilitator (Lane, 1992; Vennix, 1996). In doing this, the focus of actors’ reflection should be on policy goals, values, and associated decision-making structures rather than just emphasizing how effective a strategy has been to accomplish a particular goal. Learning limitations in computer-based environments stem from the so-called “video game” mentality, which may affect the way in which players interact with the business or management simulator, once in front of the screen. Such mentality may lead them to attempt different decisions until they improve the score (Bianchi, 2001). This logic is likely to happen when the simulation tool is not transparent. If the model is hidden inside a black box, namely, the embodied causation theory is not shared with players, this kind of tool will fail. This means that the simulation model does not trigger decision-maker’s reflections on how to achieve the desired outcomes and inquires on why such outcomes have been or not achieved (Isaacs & Senge, 1992; Kim, 1989; Kim et al., 2013; Senge, 1989). Computer-based learning environments, therefore, should lead the players to frame the gap between their intentions and simulated policy outcomes so as to make transparent that only changes in their underlying assumptions may allow them to understand the origin of recorded discrepancies. In this way, once in front of the screen, decision-makers can change the structure of system to influence its behavior toward the desired directions through reflections. In this perspective, an action research process may provide the setting for policymaking and performance evaluation through DPM. A learning facilitator may guide local area stakeholders to frame the underlying causal relationships of the problematic conditions affecting their context by helping them mature a dynamic hypothesis of the problem structure.4 The resulting DPM model can be used to test policy assumptions by evaluating simulation results through communication and reflection in the light of the shared causality linking actors’ decisions and attained simulation results. As Fig. 3.2 shows, double-loop learning implies that the perceived information feedback about the real world can be used by stakeholders as an input to design and use a protected “virtual world,” i.e., a model reproducing the problematic condition (Morecroft, 2015). In this way, through reflective iterations, policy-makers may evaluate the validity of their assumptions, i.e., the robustness of their mindset,

4

In this regard, see Chap. 2.

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against model behavior (Bianchi & Bivona, 2000). Since simulation results emerge from the model structure, the new information provided by the model will affect policy-makers mental models, which, in turn, influence adopted strategies, beliefs about problem structure, and decision-making rules. Such changes may determine innovative decisions on how to deal with real-world problems. In the next section, a fieldwork focused on tourism planning and development in local area will illustrate how the design of an action research enhanced by a DPM-based ILE may foster policy-maker’s learning.

3.1

Applying Dynamic Performance Management to Support Tourism Planning and Development in a Local Area: Learning In and About the Complexity of Destination Governance

This section will discuss the destination governance case to illustrate how action research enhanced by a DPM-based ILE may foster decision-maker’s learning in tourism planning and development. The fieldwork engaged the local policy-makers with the intent to address a specific governance challenge: increasing tourism presences while balancing financial vs. social vs. quality of public service performance over the short and long term (Vignieri, 2019). As Box 3.1 illustrates, the destination governance case (Vignieri et al., 2016) relates to the small town of Castelbuono—a niche destination in Sicily (Italy), where 10,000 inhabitants live. Box 3.1 Destination Governance: Tourism Planning and Development in Castelbuono Area The small town of Castelbuono is a niche destination in the province of Palermo, Sicily (Italy), where about 10,000 inhabitants live. Founded in the fourteenth century, it grew around a castle, from which the town’s name derives. Since the early 1990s, tourism has been the utmost economic sector of the town. Typical food production firms and restaurants have been starting their business. Hospitality and tourism facilities have been rising during the last two decades—though accommodation capacity is primarily composed of bed-and-breakfasts, agritourism, and home holidays. To foster such development, they have been improving cultural events and tourism attractions. (continued)

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Box 3.1 (continued) Such a destination is a classical model of a small area with a strong tourism potential due to its natural resources, culture, traditions, quality of food, and local products. Local area decision-makers are aware of the potential of the context, and they strive for improvement. Over the last decade, the municipality has run several projects aiming at marketing the destination to a wide array of tourists, while the civic museum has planned relevant exhibitions, and local restaurants have improved the quality of dining. Despite the tourism planning and development initiatives, destination performance has oscillated over the last 8 years. In this regard, Fig. 3.3 displays historical data of tourism arrivals, average holiday length, and the tickets sold by the local museum from 2007 to 2014. By looking at tourism arrivals and the tickets sold by the museum, it emerges that there is room for performance improvement. In fact, tourism presences (i.e., arrivals multiplied by the average holiday length) can be remarkably increased by attracting a high number of visitors at the current average holiday length. However, as shown in Fig. 3.3, from 2007 to 2014, tourism arrivals have oscillated (solid line in the left graph), and the number of museum tickets sold per year (solid line in the right graph) has slightly decreased. Though local area policy-makers are actively involved in the governance of the destination, a potential cause of such oscillations can be found in the lack of coordination among local actors’ policies. An inconsistent implementation at the organizational level may undermine local area performance. The case narrative portrays a context in which organizational performance and community outcomes influence each other. As illustrated in the next section, such causal relationship shaped the DPM model structure and the ILE interface design.

Fig. 3.3 Dynamics of tourism arrivals, average holiday length (left), and museum tickets sold (right) from 2007 to 2014 (Source: Municipality of Castelbuono and Regional Committee on Tourism, 2016)

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3.1.1

The Design of the Model Structure and the Interactive Learning Environment Interface

The ILE was designed in two layers: (1) at the ground layer, there is a system dynamics model; (2) on top of the model structure, the ILE is endowed with a friendly user interface that allows players to set their policy. Regarding the first layer, the ILE embodies a quantitative system dynamic model capable of simulating performance development over time (Morecroft, 1988). Specifically, the underlying model structure has reflected two perspectives: 1. A context perspective comprising (a) A subjective dimension that covers the main local actors having a stake in tourism governance (i.e., the mayor of the town, the director of the local museum, and a restaurant owner—as a meaningful sample of the entire hospitality industry). (b) A resource perspective that includes a set of shared assets pertaining to the local area, such as common good and relational resources (e.g., image of the town and service quality). Though the latter resources are not directly controlled by an actor, individual and collaborative policies impact on them. 2. A service perspective that associates each service to an actor Based on such views, four modules constitute the model structure: (1) “municipality,” (2) “museum,” (3) “restaurant,” and (4) “shared resources.” As Fig. 3.4 shows, though each module clusters the relevant variables pertaining to a specific domain, the model structure frames the causal relationships among such modules and their effects on local area performance. As discussed in Chap. 2, interinstitutional performance mainly consists of a change in shared strategic resource endowments gauged as community outcomes. Also, shared resources influence organizational performance, as represented by the arrows linking shared resources to the other modules in Fig. 3.4. The feedback relationship involving organizational and inter-institutional performance implies that an actor’s decision will influence the performance of another actor via the change in the shared strategic resource endowments. For instance, if the town mayor diverts financial resources from cultural events to road maintenance, the museum budget will drop, not only because of a direct funding relationship between these two organizations. Also, such adverse effect is due to a drop in ticket revenues because of a low number of events that will be hosted in the museum venue and a related loss in potential business sector sponsorships. Similarly, if the business sector’s financial contribution to local area marketing initiatives decreases, the municipality’s intensiveness of promotional activities will decline over time. To support policy-maker’s learning from facing the complex challenges associated with the governance of niche destination, the ILE allows them to test their assumption behind tourism planning and development initiatives within a protected

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Fig. 3.4 A pictorial representation of the relationships among modules

virtual environment. To this end, the players were provided with a control panel reflecting their real-world decision-making settings. Table 3.1 clusters the available policy levers for each decision-maker together with a succinct explanation. As illustrated in Table 3.1, user’s decisions may relate to an initial stock value, planned investment intensiveness, desired goals, service prices, or a time to perform specific actions (e.g., service delivery time or the time it takes to update information). Such individual decisions change the model’s structure, which, in turn, affects the future development of the simulation. Such new information is reported to each policy-maker by the graphs and displays positioned in the corresponding control panel, as shown in Fig. 3.5. For each policy-maker, the ILE interface layer prompts a control panel where specifically designed policy levers and graphs showing performance development over time are made available. By inferring the underlying cause-and-effect relationships between adopted policies and attained simulation results as displayed by the graphs, policy-makers can reflect on such causation in the light of their mental model as the iterative interaction with the ILE unfolds. To enable learning, an action research process supported by a learning facilitator conveys the virtual policymaking and dialogic nature of performance evaluation.

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Table 3.1 Decision-makers and associated available policy levers with keys Player Municipality

Policy lever (Unit of measure) Events (n. of events) AVG event contribution (euro/ event per year) Cleaning, urban space planning, and garbage collection (n. of people) Resources to museum (euro/ year) EU-based projects (n. of projects) Surplus allocation (%)

Museum

Exhibition (n. of exhibitions) Per-exhibition contribution (euro/exhibition per year) Concert (n. of concerts) Per-concert contribution (euro/ concerts per year) Networking expenses (euro/year) Surplus allocation (%)

Restaurants

Project with school (n. of projects) Unit price (euro/customer) Mark-up (dimensionless) Working days per year (days/ year) Networking expenses (euro/year) Personal income (euro/year) Maintenance reduction fraction (%) Fraction of bank account to invest (%) New investment switch

Key Number of cultural/touristic events hosted on average by the municipality The average supply of funds per event per year The level of services provided to keep the town clean, safe, and well organized The supply of funds to local museums The number of projects through which apply for EU call for tenders Fraction of cumulative surplus (if any) to current expenditure The number of exhibitions organized on average by the museum The average resources spent per exhibition per year The number of concerts organized on average by the museum The average resources spent per concert per year Resources invested in brochures, flyers, and projects with local partners Fraction of cumulative surplus (if any) to current expenditure The number of projects run by the museum The average price paid per customer The ratio between the price of and its cost The average number of working days in a year Resources invested in brochures, flyers, and projects with local partners The financial withdrawals per year as business’ owner personal income The percentage of obsolescence tolerated by the owner The fraction of new investment financed through restaurant funds (the rest fractioned through the back loan) Decision to invest in expanding capacity

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Fig. 3.5 The interface layer of the Interactive Learning Environment (Vignieri, 2019, p. 565)

3.1.2

The Action Research Process and the Learning Loop Therein

The learning intervention was delivered by a research team (Vignieri et al., 2016) in a 2-day workshop (8 h in total). It involved three main policy-makers operating in the destination, i.e., the mayor of Castelbuono, the director of the civic museum, and a restaurant owner, as representative of the business sector. To leverage the learning potential of action research, the research team designed an iterative process that begins with identifying a policy problem (i.e., improving tourism performance) pertaining to the specific context (Robson, 2002). Such process is portrayed in Fig. 3.6. As the action research process unfolds throughout a spiral (Saunders et al., 2007), knowledge matures from problem diagnosing—through planning initiatives and taking actions—to results evaluation. In doing this, participants may refine their mental models if they are engaged in debriefing session supported by a facilitator who guides them to thoroughly examine specific policy issues, share causal explanations, and discuss related effective policies. In the case of tourism planning and development in Castelbuono area (Box 3.1), the action research process went through three cycles of iteration structured in nine steps.

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Fig. 3.6 The iterative process of action research (Saunders et al., 2007, p. 141)

1. To frame specific policy problems, local actors were engaged in discussing local governance arrangements by sharing their views on the discrepancies between destination goals and current performance. To this end, the research team delivered the first survey to participants. This plenary discussion introduced the participants to the first round of decision-making and simulation. 2. In the first round, each policy-maker individually played the ILE, which was set in a non-collaborative mode. In this step, each actor took notes of each decision, the motivation, and the expected results. Such round of “virtual planning” led to three separated simulation runs. 3. As a result of the first round, three separate debriefing sessions were used to review each policy-maker’s runs individually. In this phase, each player was asked to evaluate simulation results in the light of the assumptions he/she took note of beforehand (see step 2). The research team delivered a second survey to participants to gauge the differences in how actors frame governance issues after the first round of simulation. Such activity led to a plenary discussion. 4. To foster comparative discussion on governance issue, individual simulations have been compared to each other. To this end, the learning facilitator guided policy-makers to develop a structure-and-behavior analysis in a plenary debriefing session. 5. Drawing on such causal analysis, the research team sketched a first DPM model, which was then shared with local area actors as a tool to diagnose governance issues in the next round of simulation. 6. In the second simulation round, the ILE was set in collaborative mode. This means that policy-makers were allowed to discuss their decisions before

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simulating their policies through the model. Also, they were asked to take note of adopted decisions and link them to the expected results. As outputs of the collaborative mode, a comprehensive simulation run was recorded by the ILE. 7. Such output was evaluated by the participants in the light of the DPM model sketched at the end of the first round of simulation. 8. To advance the first draft of a DPM model, the research team guided policymakers in a further modeling session, which led them to develop a causation tool for performance analysis to be used in the final debriefing. 9. Lastly, in the concluding plenary session, the DPM model was used to identify main local area policy outcomes and discuss critical performance drivers and the strategic resources impacting on them. After the discussion, the research team delivered the third survey on governance issues. The interaction of policy-makers with the ILE occurred in two rounds of simulation, i.e., non-collaborative and collaborative. Each round allowed policy-makers to set their policy in a time horizon of 12 years, divided into four intervals of 3 years, to consider the long-term effects of adopted policies. In the non-collaborative phase, each decision-maker was informed by the facilitator of the characteristics of the scenario; as expected by the study’s initial hypothesis, they experienced unexpected, poor results. Notwithstanding their strategic goals, in the first debriefing session, it emerged that each decision-maker was looking for the causes of poor results only within his/her organizational boundaries. Under the collaborative mode, decision-makers adopted prudential policies, which were intensified over time. After each time interval, results improved, and thus, they strengthened their policies gradually. Mainly, they discussed the interplays between different policy fields, including tourists’ reactions to events and exhibitions, customers’ behaviors to restaurant mark-up changes, citizens’ responses to the municipal financial shortage, and time delays in long-term investments. As a result of the collaborative running mode, policy-makers shared a causation model of tourism planning and development in the Castelbuono area. The use of an insight DPM model fostered double-loop learning as it enabled the participants to develop a holistic and dynamic perspective of local area performance.

3.1.3

Framing Policy Outcomes, Public Value Drivers, and the Strategic Resources Associated with Tourism Planning and Development Through Dynamic Performance Management

The DPM model offered policy-makers a selective key to frame the causation behind a set of policy outcomes associated with sustainable tourism planning and development in the Castelbuono area. The discussion on destination governance issues led the research team to identify two final outcomes, i.e., the change in town image and the change in tourism presences, which are affected by an intermediate outcome, i.e., the change in service quality—as shown in Fig. 3.7.

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Fig. 3.7 The causal sequence that links intermediate to final outcomes

Such causal sequence provides the basis for identifying the performance drivers and the associated strategic resources. In this regard, a DPM insight model5 may support destination governance as it provides a key for sustainable tourism planning and development in the local area. This implies that the model allows local area policy-makers to design policies to increase tourism presence while balancing financial vs. social vs. quality of public service performance over the short and long term (Vignieri, 2019). As Fig. 3.8 displays, the final outcome change in town image affects the shared strategic resource town image. The change in tourism presences updates the stock of average tourism presences in the local area over the last 3 years, which is another shared strategic resource for the destination. The intermediate outcome change in service quality affects the quality of services (e.g., quality of sanitation and transportation services) in the area. The three strategic shared assets for the local governance, i.e., service quality level, tourism presences, and town image, determine the performance driver destination attractiveness. As illustrated in Chap. 2, performance drivers represent critical success factors for the achievement of desired goals as they capture the contribution of a certain strategic resource endowments to performance. In fact, the performance driver destination attractiveness impacts the change in town image and change in tourism presences. These two final outcomes are influenced by word-of-mouth effects that capture tourists’ experiences at the destination. If such experience is positive, the effect will reinforce perceived town image leading to an increase in tourism presences over time.

5 The figure depicts outcomes also in the upper section of the DPM chart, which shows strategic resources. They are modeled (by using a “chessboard” symbol) as co-flows of the corresponding variables in the “end results” section. Also, some strategic resources are modeled as gray-filled boxes to distinguish local area “shared resources” from other strategic resources.

STRATEGIC RESOURCES

PERFORMANCE DRIVERS

Restaurants quality ratio

Change in Town Image

Cultural attractions and events ratio

Urban Sanitation Services

Change in Service Quality

Public service Restaurant equipment adequacy average age

Restaurant equipment investment

Cultural attractions and events

Change in Tourism presences

Population

Public Spending per /.000 residents

Municipal Budget

Change in Tourism presences

Destination attractiveness

Change in Town Image

Town Image

Change in Service Quality

Service Quality

Fig. 3.8 The DPM insight model illustrating main policy outcomes associated with tourism planning and development in Castelbuono area (Bianchi & Vignieri, 2020)

ENDRESULTS

Tourism Presences

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The intermediate outcome change in service quality is also influenced by two performance drivers, i.e., public service adequacy and public spending per .000 residents, as measures of public service quality in the local area. Public service adequacy is a ratio comparing the actual urban sanitation service capacity allocated by the municipality to the need of garbage collection that tourism implies. The performance driver public spending per .000 residents relate the municipal budget for critical public services (e.g., crime prevention, assistance to households, parks and roads maintenance) to local area residents. Such driver may help policy-makers compare the aptitude of their public service delivery to shape community quality of life as expected by local area resident. The final outcome change in town image is also affected by the quality and scope of the events hosted in the town through the performance driver cultural attractions and event ratio. Moreover, such final outcome is also affected by the performance driver restaurant quality ratio which depends on the restaurant equipment average age, as a measure of the level of obsolescence of business structures. Both drivers, i.e., cultural attractions and event ratio and restaurant quality ratio, represent two critical success factors for tourism planning and development in the local area as the financial sustainability of planned investments in cultural events and restaurant renovation is influenced by tourism presence spending.

3.1.4

What Can Policy-Makers Learn from Action Research Enhanced by a DPM-Based ILE?

The goal of the action research was to support policy-makers in designing policies aimed at increasing tourism presences in the small town while balancing financial vs. social vs. quality of public service performance in the short and long term (Vignieri, 2019). Such a dialogic process of policy-making and performance evaluation has leveraged causal modeling and simulation to foster decision-maker’s learning. To this end, the DPM-based ILE was used in two simulation rounds. Local actors played the first round of simulation under a non-collaborative mode. Simulating under such mode implies that each “real player” sets his/her policy individually. In contrast, for the other two players, the model itself loads a set of predefined “self-serving” decisions.6 Such “self-serving” decisions reflect the following institutional logics for the municipality, museum, and business sector, respectively. Driven by the need for voters’ consent, the town’s mayor diverts the budget for infrastructural projects to current expenditures such as events and attractions to boost destination attractiveness and get approval from local restaurant owners. Similarly, the museum director adopts a conservative approach to funding cultural production as his/her primary aim is to be appointed in the role given his/her

6

Such decisions were preset by the research to team. A button in the administration panel of the ILE was made available to load such policies, so as to switch from a collaborative to a non-collaborative mode.

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Fig. 3.9 Simulation results of the non-collaborative mode: three graphs that show local area performance as recorded by the ILE from the individual runs of municipality, museum, and business sector

capacity to maintain financial equilibrium. Lastly, restaurant owners take dividends from business annual profit as a source of personal budget, regardless of business sustainability and quality of provided services. Such selfish decisions emphasize the short-term needs of a single actor, at the detriment of destination performance. In this way, it is hard for the “real player” to improve local area performance due to the opportunistic decisions of the two “dummy players.” In fact, such myopic preset policies aim to boost actors’ short-term benefits, regardless of local area performance sustainability and the policies adopted by the “real player.” The results of the non-collaborative mode are displayed in Fig. 3.9. The figure portrays the result of the individual runs carried out by the mayor of the town for the municipality, the museum director for the museum, and a restaurant owner as representative of the business sector. For each run, a graph displays the behavior over time of three main shared strategic resources: (1) tourism presences (i.e., solid line), (2) service quality (i.e., dashed line), and (3) town image (i.e., dotted line). Such resources are regarded as the effect variables of the final and intermediate outcomes illustrated in Figs. 3.7 and 3.8. The three individual simulations presented by the graphs in Fig. 3.9 portray unsustainable behaviors of the three strategic resources associated with community outcomes. As assumed by the research, each player cannot achieve sustainable results in the long term by acting alone, despite the role and the intensiveness of individual efforts. In the non-collaborative simulation round, each player was informed by the research team that individual efforts would have only slightly mitigated performance downfall due to the adverse effects of “self-interested”

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Fig. 3.10 A pictorial representation of decision-makers perceived system boundaries: an insideout vs. outside-in perspective of local area performance

decisions. Nonetheless, as reported in the notepad actors used to frame the relationships between adopted policies and local area performance, policy-makers held organizational policies responsible for the unexpected poor results (see Fig. 3.9). This means that actors framed destination governance issues through an inside-out perspective which led them to draw perceived system boundaries around organizational borders. In doing this, they did not consider the effects of other local area organization policies on inter-institutional performance. Such view provided local area policy-makers with an “event-oriented thinking” (Morecroft, 2015, p. 32) that considered adopted policies as potential solutions to rising problems in their context. This linear mindset is portrayed in Fig. 3.10. If stuck in an “inside-out”7 perspective, local area decision-makers do not perceive that local area performance influences local area shared resource endowment, which, in turn, feedback on their organizational policies. Also, they remain unaware of the interdependencies between other local area organizational policies and local area performance. This means that an invisible system’s structure causes local area performance to rise and fall. Policy-makers can sense such a “hidden” structure by taking an “outside-in” perspective of local area performance, which allows them to frame destination governance issues from a systems perspective. As first-round debriefing, a DPM modeling session has helped local area actors extend their perceived system boundaries on the factors that impact public value

7

See Chap. 2.

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Fig. 3.11 Simulation results of the collaborative mode: local area performance as emerging from the interactions among the policies adopted by the three players

generation processes through communication and reflection. A sign of a shift from an “inside-out” to an “outside-in” perspective of local area performance was their rising concerns for policy collaboration as the action research was unfolding. Such a facilitated plenary modeling session led the players to sketch together a DPM model portraying the underlying causes of the unsustainable outcomes generated by non-collaborative simulation runs (i.e., steps from 3 to 5 of the action research process). By building on such modeling session, local actors were ready to play the second round of simulation under a collaborative mode. Simulating under such mode implies that policy-makers can discuss their decisions before simulating their policies. The simulation outputs of the collaborative rounds displayed in Fig. 3.11 result from the interactions among the policies adopted by the three players. The graph portrays the behavior over time of the three shared strategic resources (i.e., tourism presences, town image, and service quality) associated with the main local area outcomes. The graph illustrates how collaboration may lead policy-makers to generate sustainable community outcomes for the local area. As shown in Fig. 3.11, tourism presences and service quality decrease from year 1–5, while increase in the long run. Also, town image continuously increased over time. Tourism presences were driven by a rise in destination attractiveness as a result of balanced policies targeting service quality and town image. The causation analysis developed through the DPM model illustrates that an intensive policy of the municipality aimed at improving town image may increase tourism presences, which, in turn, will generate value for local area organizations. Such value supports the municipality and the museum in producing cultural events,

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while the restaurant improves its service quality. Such policies impact town image, which reinforces—ceteris paribus—tourism presence growth rate. However, an intensive policy focused on destination attractiveness may lead public policy-makers to face trade-offs in time and space. Diverting the municipal budget available for delivering community services to tourist attractions and events may cause a drop in public service quality in the long term. This implies balancing the investments in tourism attractions with an adequate expansion of public service capacity to prevent its saturation. Decision-makers invested in service capacity by prioritizing few cultural events. In this way, they were able to balance service quality, town image, with the need of improving tourism presences. The differences between the two simulation rounds reflect decision-maker’s perspective on destination governance issues and their role therein. A collaborative approach to policy-making and performance evaluation has helped them to gain a systems understanding (i.e., “outside-in”) of the cause-and-effect relationships underlying inter-institutional results. To this end, from an institutional perspective, the role of the DPM was crucial to frame policy outcomes, public value drivers, and the strategic resources needed to achieve planned results. Also, from an institutional perspective, DPM insight modeling has improved policy-makers’ awareness of how organizational policies may affect local area shared resources by extending the policy-makers’ focus on public value generation processes, rather than on organizational performance. In fact, in the final debriefing, the mayor said: “I found that tourism planning requires collaborative policies. So far, I haven’t considered the impact of tourism growth on sanitation services and how a decrease in service capacity will sooner or later feedback on tourism performance through attractiveness. Also, this may impact business profitability.” This case has illustrated how properly designed policy-making and performance evaluation routines may support destination governance. If enhanced through DPM insight modeling, such routines may help local area policy-makers to frame the policy issue affecting their context, share information across the network, and reflect on how to improve public value generation processes for the local area. This may contribute to support local area stakeholders in implementing a public value-driven performance regime.

3.1.5

How Learning Through DPM May Foster a Dialogic Form of Policy-Making and Performance Evaluation in Public Value-Driven Performance Regimes

The action research involved the key actors in a dialogic process of policy-making and performance evaluation through communication and reflection grounded on a DPM causation model. In doing this, the action research process enhanced by a DPM-based ILE has enabled the destination governance to put a public value-driven performance regime into full effect. As defined in Chap. 1, such a regime consists of a set of performance management and governance routines through which a network

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of actors uses performance information to support evaluation and decision-making by integrating the knowledge of involved stakeholders. The discussion developed in the previous section has shown how action research enhanced by a DPM-based ILE may support the actors in the regime to identify performance goals and share information about achievements to integrate such knowledge into a causal understanding of policy results. Such aspects may be associated with the characteristics of the routines’ profiling performance regimes. In this sense, Table 3.2 illustrates how learning through DPM may foster a dialogic form of policy-making and performance evaluation in public value-driven performance regimes. Through learning, local actors (i.e., item 1.1 in Table 3.2) may implement a dialogic form of policy-making and performance evaluation by leveraging communication and reflection within learning forums. Such forums are social processes (Rajala et al., 2020) in which the key stakeholders may develop a lesson learning capacity (Rose, 1993) that with “science and experts advice bring about change in public policy” (Gilardi & Radaelli, 2012, p. 155). The active involvement of a wide array of stakeholders is a prerequisite to support the key policy-makers in the area to develop a collective process of sensemaking (Weick, 1995) within and beyond the boundaries of each involved organization (Wenger, 2000). Such process may lead them to identify material community outcomes such as improving quality of life, place attractiveness, trust in government, and active citizen (i.e., item 1.2 in Table 3.2). From a policy implementation perspective, the identification of such performance goals implies developing a shared policy “interpretative framework” (Hall, 1993) providing a “common understanding of its goals and instruments as well as the nature of the problems to which policy is addressed” (Freeman, 2008, p. 374). The core of such framework is a system of absolute and relative performance measures, which causally link input—though output—to intermediate and final outcomes (i.e., item 1.3 in Table 3.2). Such links reveal the explanatory capacity of a causal model, which enhances policy-making and performance evaluation based on the identified material community outcomes (i.e., item 1.4 in Table 3.2). Such idea of learning underpins the concept of policy implementation “as exploration, or hypothesis testing” (Browne & Wildavsky, 1983, p. 254) (i.e., item 1.5 in Table 3.2) through modeling and simulating. DPM insight modeling (Bianchi et al., 2017; Ghaffarzadegan et al., 2011; Kim et al., 2013; Morecroft, 1988) may harness the information held by the plurality of actors involved in the regime because it requires eliciting their perspective on the issues at hand. In this way, policy-making and performance evaluation routines may integrate their different knowledge through communication, diagnosis, comprehension, reflexivity, innovation, deduction, assimilation, and deliberation (Gilardi & Radaelli, 2012; Scott, 2010). Such interdisciplinary perspective (i.e., item 2.1 in Table 3.2) positions the multiple facades of public value at the core of a performance regime.

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Table 3.2 The contribution of learning through DPM to foster a dialogic form of policy-making and performance evaluation in public value-driven performance regimes

Dimensions 1. Characteristics of performance management routines (Douglas & Ansell, 2021)

1.1 Actors in performance regime

The characteristics of a public value-driven performance regime Networked actors from different organizations

1.2 Performance goals

Pursuing community outcomes

1.3 Performance information

Focus on sharing qualitative and quantitative information among network stakeholders

1.4 Performance assessment

Collaborative performance forums to assess stakeholders’ contribution to community outcomes

How learning through DPM may foster a dialogic form of policymaking and performance evaluation in public value-driven performance regimes Networked actors from different organizations may involve facilitators, experts in the field, and practitioners to support communication and reflection within forums to develop a lesson learning capacity for policy change A collective process of sensemaking within and beyond the boundaries of each involved organization may lead the key policy-makers to identify material community outcomes, including improving quality of life, place attractiveness, trust in government, and citizen awareness Focus on sharing qualitative and quantitative information among network stakeholders through a system of absolute and relative performance measures, which causally link input—though output— to intermediate and final outcomes The explanatory capacity of a causal model to enhance policy-making and performance evaluation in collaborative forums based on the identified material community outcomes (continued)

4 Conclusion

101

Table 3.2 (continued)

Dimensions 1.5 Performance actions

2. Level of knowledge convergence (Morton et al., 2015)

2.1 Stakeholders’ convergence and information sharing

The characteristics of a public value-driven performance regime Collaborative initiatives to support public value generation, learning, and institutional adaptation

High convergence and high information sharing from multiple perspectives to pursue a common community outcomes

How learning through DPM may foster a dialogic form of policymaking and performance evaluation in public value-driven performance regimes Collaborative initiatives in which learning underpins policy implementation as exploration or hypothesis testing to support public value generation and institutional adaptation DPM insight modeling may harness the information held by the plurality of actors involved in policy-making and performance evaluation routines to integrate their different knowledge

4 Conclusion This chapter has illustrated how learning through DPM may foster a dialogic form of policy-making and performance evaluation (i.e., policy learning) in public valuedriven performance regimes. In doing this, the chapter has discussed the role of communication and reflection within learning forums through fieldwork. The case of destination governance has provided the empirical context for illustrating how to foster policy learning by involving local area decision-makers and their stakeholders in experimenting with collaborative communication and reflection processes. Particularly, the case has shown how action research enhanced by a DPM-based ILE may help local area actors understand their role in the local governance, share information across the network, and use collaborative forums to evaluate their contributions to inter-institutional performance. To this end, the chapter has advanced the idea that a dialogic process of knowledge sharing may help the local governance go beyond the policy implementation question (Osborne, 2010a) as it turns the root of policy-making and performance evaluation from control to guidance and learning (Sabatier, 1991; Weible et al., 2011). This shift makes policy learning a central issue for implementing public value-driven performance regimes. In fact, though planning and control have been traditionally considered as two distinct phases, policy learning permeates them to

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blend the several routines they comprise (i.e., goal settings, performance measurement, information reporting, causal analysis, and performance actions). This implies that policy-making underpins performance evaluation and vice versa (Schreyögg & Steinmann, 1987). From an integrative perspective of performance management, outcome-based performance assessment is the cornerstone of policy learning since it helps the actors in the regime to understand the interplays between organizational and interinstitutional policies. To this end, outcome-based DPM insight models may provide policy-makers and their stakeholders with relevant information for taking corrective implementation initiatives or changing policy program aims. Also, they may corroborate ongoing decisions and strategic guidance with feedforward control mechanisms and feedback policy analysis. The next chapter aims to illustrate how DPM may enable the actors in the regime to implement policy learning for assessing community outcome sustainability under dynamic complexity conditions.

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Chapter 4

Applying Dynamic Performance Management to Implement Policy Learning for Assessing Community Outcomes

1 Introduction This chapter will show how DPM may provide the actors in a public value-driven performance regime with the methodological framework to implement policy learning for assessing community outcomes, illustrated by different examples. Before presenting the empirical work, the following sections will discuss the main methodological challenges associated with outcome-based performance assessment to set the field for showing how DPM may implement policy learning in public valuedriven performance regimes.

2 Challenges in Implementing Outcome-Based Performance Assessment in Public Value-Driven Performance Regime “Evaluation is a systematic or careful assessment of the merit, worth, and value of administration, output, and outcome of government interventions, which is intended to play a role in future” (Vedung, 1997, p. 3). In this definition, performance evaluation has an explanatory tension as it aims to provide policy-makers with the knowledge to support judgments on current and prospective contributions to the public value of specific evaluands (i.e., an organization, a unit, or a policy program). Such explanatory capacity depends on the causal model that supports policy-making and performance evaluation (McGowan & Polster, 1985). From a causal perspective, specific desired community outcomes motivate adopting a particular policy program (i.e., the means) that might deliver public value (i.e., the end) to the local community through final outcomes (Alford & O’Flynn, 2009; Moore, 1995). In this perspective, “programs goals are the only © Springer Nature Switzerland AG 2022 V. Vignieri, Enhancing Performance Regimes to Enable Outcome-based Policy Analysis in Cross-boundary Settings, System Dynamics for Performance Management & Governance 6, https://doi.org/10.1007/978-3-031-07074-7_4

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way to understand a program” (Dahler-Larsen, 2005, p. 624) because they “constitute the only legitimate source of criteria for judging the program” (Weiss, 1998, p. 51) outcomes. The causation logic of performance evaluation demands grounding performance analysis on the “robust cause-and-effect models” (Bovaird, 2014, p. 2) that may frame the dynamic complexity affecting policy outcomes in the context of the public governance (Moynihan et al., 2011). As tools of performance management routines, such models must deliver “quantitative representation through measurement of the quality or quantity of input, output, and/or outcome of organizations or programs in its societal context” (Sterck et al., 2006, p. 5). Through models, the actors in the regime may take the cognitive leap (Otley, 2012) that enables them to assess community outcomes. However, designing outcome-based performance management routines entails some methodological issues (Borgonovi et al., 2018).

2.1

Performance Causality: Dealing with Attribution Problem

The first issue concerns performance causality. In the context of governance, outcomes refer to the effects of a policy program, which usually involves a wide range of institutions. “The findings on outcomes and the extent to which the program has been instrumental in producing the outcomes are likely to be important for judging the value of the current program” (Hatry, 1999, p. 196). Though it is relatively easy to understand that outcomes are the ultimate effects of a policy, identifying measures providing a causal relationship that links a policy to intermediate and final outcomes is comparatively challenging. Such a methodological issue “results in a major disconnect” (Bouckaert & Halligan, 2008, p. 17), influencing our ability to attribute “the causes of outcomes, as opposed to the factors which are merely associated with outcomes” (Bovaird, 2014, p. 4). To deal with the attribution problem, eminent scholars in public policy and strategy have suggested adopting cause-and-effect models (Ansoff, 1965; Chandler, 1962; Drucker, 1954; March & Simon, 1985; Simon, 1947). Such a recommendation that has led several authors to develop causation approaches and models to link policy program goals to policy outcomes (Barnabè et al., 2013; Eden & Ackermann, 2004; Kaplan & Norton, 1992; Rosemberg & Posner, 1979; Saaty, 1990; Sullivan & Stewart, 2006). From a methodological point of view, the attribution problem has implications for performance assessment and policy-making because a lack of causation analysis may turn out into a logic void in the causal chain that links inputs ! outputs ! outcomes. To establish a hierarchy among performance and determinants in interorganizational context (Mandell & Keast, 2008; Provan & Milward, 1995, 2001), scholars have envisaged the benefit of thought-provoking forums “to challenge” (Moynihan, 2005, p. 211) stakeholders’ logics (Rajala et al., 2018). This means

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unleashing the potential of learning routines in performance regimes so that involved actors may “get some insight into the crucial matter of what worked for which groups and why” (McDonald et al., 2003, p. 25). Also, a “relational and participative” (Agostino & Arnaboldi, 2015, p. 353) approach to performance management in hybrids may debunk superficial unanimity among stakeholders and eventually “add meaning to the strategy-making process” (Bovaird, 2014, p. 7). To get insights into the effects of a program, we suggest that performance management routines should provide the actors in the regimes with systemic measures to validate the hypothesized causal pathways. However, conventional methods may not be able to serve such a purpose under dynamic and complex conditions such as those involved by “super wicked problems” (Levin et al., 2012, p. 123). Difficulties in measuring policy outcomes (e.g., improved perceptions or altered behaviors) may restrain evaluation efforts leading policy analysts to adopt outputs/activity measures or break out available data into complex indicators. Such workarounds are deceptive because they ignore the full span and depth of public policy and public service delivery performance (Bouckaert & Halligan, 2008; Osborne, 2020). Also, pushed by the search for a shortcut to performance assessment, neither should policy-makers rely on a summative evaluation as “it provides no understanding of how” (Bovaird, 2014, p. 5) the policy program has produced the intended effects. In front of such methodological challenges, we suggest the beauty of simplicity. This means that a system of hierarchically ordered performance measures may help the actors in the regime trace the underlying casual relationships behind community outcomes. Such causation may guide them in performance evaluation and policy-making. A brief example may illustrate how reported data may help local government and municipal police managers shed light on validating the causal relationships between implemented actions and delivered community outcomes. For example, a municipal administration has implemented a policy program to improve pedestrian safety in the city center by renovating traffic signs, positioning acoustic signals at the intersections, and increasing traffic controls. Attributing attained outcomes to the implemented actions requires some evidence, which policy-makers might retrieve from reported accidents data (e.g., location, car’s speed, driver’s physical conditions, age, and condition of the car). Suppose they recorded a 15 % drop in serious traffic injuries in the city center. To link such outcome to adopted policy, they may use other measures, such as the reduction in the average car speed in the center, the increase in the number of alcohol tests performed each weekend, and the frequency of control of car’s working conditions. An improvement in such measures—all other conditions being equal—may underpin the proposed causality between implemented actions and policy outcomes. Also, they may help policy-makers to identify frequent causes of accidents and potential areas of improvement. This example has shown how identifying a causal pathway that links a policy to an attained outcome implies broadening the scope of analysis so that the existence of causation may be inferred from measuring causally related factors (i.e., performance determinants). Operationalizing such measures requires framing the effects of a

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policy program from a system-level perspective. This is a second methodological challenge associated with outcome-based performance assessment.

2.2

Framing the Effects of a Policy Program on the Socio-economic Context

In complex policy fields, such as education, crime, justice, social cohesion, or culture, framing the effects of a policy program and public service delivery entails measuring “the quality of life changes which they bring about for those affected by them, rather than the quality of the activities themselves” (Bovaird & Löffler, 2003, p. 317). An excess of focus on implementation actions may lead policy-makers to adopt a vague definition of public value. Examples of such ambiguity could include reform, advance, or “renewal, service improvement, higher quality” (Dahler-Larsen, 2005, p. 626). If desired community outcomes are not outlined, the emphasis is placed on intra-organizational aspects (Lusch & Vargo, 2013; Osborne, 2020) because they are easy to measure and control (Stake, 2004). Outcomes are “not what the program itself did, but the consequences of what a policy program did” (Hatry, 1999, p. 15). An example focused on an academic educational program may illustrate what keen enquiry is involved in the search for framing community outcomes. For instance, to communicate the effectiveness of a Master of Science in Public Administration, the service quality unit of a university department needs to develop some performance measures. Input measures, such as the quantity of provided teaching hours; volume measures, like the number of students graduated per year; or even efficiency measures, such as the average cost per student, can be operationalized relatively easily. Such measures are suitable for decision-making at the department level as they provide insights into managerial improvement areas. From an external perspective, is the number of students attending lectures a relevant measure of the quality of the educational content provided by the program? Is the number of students passing the exams a measure of the quality of teaching style and methods? Is the number of graduate students finding a job within 1 year a measure of the quality of the educational program? Such questions may help stakeholders understand how the program impacts students’ skills, knowledge, and potential carriers’ prospects because they compare program outcomes with the specific need of the community addressed by the master program (e.g., student and partner organizations).1

1

Such outcome measures can be adopted to design sustainability and utility indicators as they relate policy goals to community needs and policy effects to the socio-economic conditions of the context (Bouckaert & Halligan, 2008; Van Dooren et al., 2015). Though insightful for learning and improving, the use of contextual aspects in the design of performance standards may limit external benchmarking (Borgonovi & Maggi, 2008; Hinna & Monteduro, 2006; Molteni, 1997).

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This example has shown that a keen understanding of program outcomes should not disregard community needs in performance assessment as the effects of a policy also depend on the socio-economic context. Performance information of this kind may foster policy learning and tension for improvement at the governance level (Bianchi & Rivenbark, 2014; Kroll, 2015; Moynihan & Kroll, 2016).

2.3

Using Outcome Measures to Support Learning and Improvement

Rather than simply consisting in portraying numbers, performance measurement activities are oriented to deliver reliable information concerning performance (Amigoni, 1988; Ferrero, 1967). If properly designed, performance indicators may provide information to support ongoing control (de Haas & Kleingeld, 1999; Hofstede, 1978; Kloot, 1997; Otley, 1999) which “corroborate the traditional feedback mechanism” (Bianchi, 2016, p. 35). In this perspective, indicators are crucial for enabling goal-based learning and guidance (Moynihan, 2005) in performance regimes. Low-quality indicators may cause resistance to evaluation, waste of resources, non-use of performance information, and skepticism (Ammons, 2001). Good indicators provide valuable information that may get policy-makers out of the incremental trap (Lindblom, 1959; Quinn, 1980) and be helpful to design strategies for change goals, identify benchmarks, and implement actions (Cairney, 2016; Sabatier & Jenkins-Smith, 1993). Also, they may support the actors in the regime to capture key relationships between program effects and governance arrangement goals (Emerson & Nabatchi, 2015b; Koliba et al., 2011a; Moynihan, 2008b, 2009). To convey further information, the numerator and denominator of a ratio indicator must hold a valid unit of measure (e.g., people, euro, percentage, or users) (Ferrero, 1967, p. 100). In this way, by relating two measures with different units (e.g., euro/people; tons of waste/.000 people), the emerging information is understandable, consistent, and comparable with other representations (Barretta, 1999; Cattaneo, 1959; Masi, 1963). A brief example focusing on the measures adopted by a public works unit of a municipality may clarify how ratio indicators may foster learning and improvement by extending the scope of the provided information. Such unit adopts an output measure, such as the meters of road repaired and paved per year, and an outcome measure, that is, the fraction of renovated meters of road that has not been fixed in the last 3 years. The latter measure provides information on the unit’s capacity to ensure high-quality road maintenance by reducing the frequency (i.e., %) of required reworks. This is a ratio indicator that can be used to assess current performance vs. an internal (e.g., performance targets, standards, or past results) or an external (e.g., similar organizations’ performance or outcome measures of other municipal services) benchmark (Ammons, 2001; Hatry, 1999).

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This example has shown how comparing current performance against past performance may generate new information to support learning and improvement. To this end, a system of indicators may provide the actors in the regime with reliable information that corroborates program aptitude in attaining desired community outcomes. Also, a systemic measurement of outputs and intermediate and final outcomes is a further piece of evidence to detect potential side effects that might be generated by the policy program itself and prevent adverse behavior caused by misleading measurement. This is a fourth methodological challenge for implementing outcome-based performance assessment in public value-driven performance regimes.

2.4

Considering Potential Side Effects of Performance Measurement: The Relevance of Extending the Boundaries in Performance Evaluation

In the context of governance, the effects of a program most often cut across the traditional boundaries of a specific policy design and implementation field (e.g., education, health care, safety, and justice) (Christensen & Lægreid, 2007; Vangen et al., 2015). As a result, such phenomena are difficult to grasp with quantitative measures as they either slowly materialize on the ground or surge with non-linear and sometimes counterintuitive behaviors (Bianchi, 2016). Difficulties or improper measurement may lead policy-makers to focus on shorttime results. An excess of emphasis on output measures may appreciate specific performance dimensions in a relatively short time horizon, at the detriment of comprehensive societal outcomes. Such tension has implications for performance management, as shown by the evidence on this concern (Bevan & Hood, 2006; Bouckaert, 1993; Bouckaert & Balk, 1991; Vakkuri, 2003; Van Dooren, 2006). For instance, a school funding mechanism that associates a fraction of the school’s annual budget and teacher wages to students’ performance in standardized tests may lead schoolteachers to prepare students to perform well on such tests rather than providing a “holistic education” (Bianchi & Rua, 2020; Hamilton et al., 2002). Another example of behavioral distortion associated with a bounded performance measurement could be drawn from the health-care management literature. The goal of cutting expenses for medical devices may lead the hospital ward’s administration to suggest physicians adopt bare stents in coronary surgery treatments. Such a stent has a lower per-unit cost and a shorter lifecycle than the expensive covered stent. Though the use of bare stents may positively contribute to the short-term financial equilibrium of the hospital ward (i.e., output), in the long term, a misleading focus of performance measurement may generate counterproductive effects. In fact, the shorter lifecycle of the bare stent will negatively impact the financial equilibrium of the hospital ward and patients’ quality of life after treatment (i.e., final outcome)

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due to the increase in the patient’s retreatment rate (i.e., intermediate outcome) (Bianchi et al., 2010). These are examples of performance paradoxes (Henman & Gable, 2015; Van Thiel & Leeuw, 2002) as they describe unintended effects generated by a misleading focus of measurement (Dahler-Larsen, 2005). Also, side effects might emerge as an inertial process that deteriorates performance indicators aptitude to detect good and bad performance (Meyer & O’Shaughnessy, 1993), causing discrepancies between actual and reported performance (Meyer & Gupta, 1994). By extending the span and the time frame of performance measurement (Bianchi & Rua, 2020; Xavier & Bianchi, 2019), policy-makers may influence an outcomebased organizational culture so that people’s behaviors may positively contribute to desired community outcomes. In this way, they may prevent performance paradoxes. Implementing outcome-based performance evaluation in public value-driven performance regimes requires addressing the challenges envisaged in this section. To this end, the following sections will illustrate how the DPM framework may provide the actors in the regime with the methodological support to assess policy outcomes.

3 Assessing Community Outcome Through DPM in Public Value-Driven Performance Regimes This section will illustrate how DPM may support the actors involved in a public value-driven performance regime to assess community outcomes. This will be done by applying the DPM methodological framework to assess community outcomes in three different policy contexts, i.e., (1) a policy network for urban brownfield regeneration, (2) a public-private partnership for destination marketing, and (3) a collaborative governance arrangement for the co-production of public service in the cultural field.

3.1

Applying DPM to Support a Policy Network in the Pursuit of Urban Brownfield Regeneration Policy Outcomes

Regeneration policies refer to a public program to pursue local areas’ social, financial, and environmental well-being (Davies, 2002). City growth and decay issues motivate the adoption of such policies (Couch et al., 2011; Needleman, 1965; Pacione, 1981) as they focus on the renovation of decayed urban areas (Tallon, 2013), such as brownfields, neighborhoods, waterfronts, and neglected zones (Williams & Dair, 2007). Urban growth and decay issues have primarily attracted the interest of urban planning scholars (Couch, 1990; Hall & Tewdwr-Jones, 2010). They have focused

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on the role of culture and arts (Bianchini & Parkinson, 1993; Bristow, 2019; Evans, 2009; Richards & Wilson, 2004; Tweed & Sutherland, 2007), tourism (González, 2011), and entrepreneurialism (Paddison, 1993) to foster urban “urban regeneration and renewal” (Tallon, 2010). In the scholarly debate, the pursuit of such policy goals has raised environmental sustainability concerns, such as pollution, water resource use, and soil consumption (Dixon et al., 2007a, 2007b; Pediaditi et al., 2007). Also, several studies have warned about potential shortcomings of urban regeneration, such as gentrification, segregation, and social exclusion (Bolton et al., 2019; Cameron, 2003; Geddes, 2000; Jamshidzadeh & Mafakherian, 2019; Martinez-Fernandez et al., 2012). To face the potential side effects of urban regeneration, scholars have suggested engaging stakeholders (Osborne et al., 2002, 2004; Shucksmith, 2000; Williamson et al., 2004) in the design and implementation of sustainable regeneration policy through collaborative governance (Cepiku et al., 2019; Davies, 2002) and policy network initiatives (Bianchi et al., 2021; McArdle, 2012). In such inter-institutional contexts, the role of public agencies—in leading multiactor collaboration among local area stakeholders—is considered a prerequisite to enhance public value generation. Lack of aptitude by stakeholders to design and implement urban regeneration policies often undermines the performance of the single organizations in a local area, which further reduces community quality of life. As discussed in Chap. 2, DPM may support policy-makers to implement an outsidein perspective of stakeholder collaboration. Such a view may help the actors involved in the regime to frame public value, its drivers, and the strategic resources needed to affect urban brownfield regeneration policies outcomes. To this end, the next section portrays a successful example of urban regeneration policies designed for “Puerto Madero,” a dismissed mercantile harbor in Buenos Aires (Argentina). The case of Puerto Madero (Buenos Aires, Argentina) will be used as a discussion basis to illustrate how the DPM framework may support the actors in public value-driven performance regimes to assess sustainable community outcomes in urban brownfield regeneration.

3.1.1

Pursuing Sustainable Urban Brownfield Regeneration Policy Outcomes Through Policy Network: The Case of “Puerto Madero”

The case of “Puerto Madero” describes how a collaborative policy network has converted a neglected area into a flourishing neighborhood through effective brownfield regeneration policies (Bianchi et al., 2021). As Box 4.1 describes, such an initiative is an example of urban renewal carried out through a significant involvement of the private sector.

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Fig. 4.1 Puerto Madero’s map (Source: CAPM)

Box 4.1 “Puerto Madero”: A Case of Urban Brownfield Regeneration Through Policy Network (Bianchi et al., 2021) Buenos Aires is the capital city of Argentina, with a population of three million inhabitants. Additionally, 14 million people live in the suburbs. “Puerto Madero” is the mercantile neighborhood located in the mouth of the “La Plata” river. The old harbor was dismissed in 1925 because of its limited commercial capacity compared to the growing ships traffic. In the early 1990s, the federal government promoted a megaproject to renew such an old neglected area into a new neighborhood for the city. To this end, the federal and the city governments settled an agreement to regenerate the site and improve the quality of life of its actual and prospective inhabitants. To this end, the property of the land and warehouses was transferred to a new stateowned public limited company named Corporación Antiguo Puerto Madero (CAPM). To outline a renovation plan for Puerto Madero, CAPM involved the Society of Architects (Sociedad Central de Arquitectos), which promoted a national contest of ideas. The plan went through several stages (Garay et al., 2013). From 1989 to 1992, CAPM sold the first lot of warehouses and historical buildings that were renewed to combine heritage restoration with modern development. From 1993 to 1995, three proposals submitted for the national contest of idea were allowed to renovate a 1.5 million m2 surface in 20 years (Fig. 4.1). Such proposals were combined into one big project to develop cultural, recreational, (continued)

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Box 4.1 (continued) and commercial spaces, including museums, coffee shops, restaurants, space for offices, public parks, and promenades. Then, from 1996 to 2000, to fund the project, a large portion of land and buildings were sold to a group of smallmedium firms and, lately, to a pool of much larger companies. In this period, the price of the land has ranged from US$150–300/m2. CAPM has reinvested the cash flows generated by such sales to complete the planned public works, such as urban infrastructure and capacity for public service provision. From 2001 to 2013, the financial and political difficulties of the country have caused a high governance uncertainty. CAPM played an active role in the renovation of Puerto Madero, particularly in developing collaborative policies which have actively engaged a wide network of stakeholders. The case of “Puerto Madero” portrays a “financially driven” (Bianchi, 2021, p. 350) governance mode in which the advantage of the cash flow originated by the asset selling rate is reinvested in public services and infrastructure capacity development, so as to balance potential shortcomings in terms of social inclusion. Such governance mode entails specific challenges for the project implementation. A described in Box 4.2, the local development agency (i.e., CAPM) has actively involved a wide range of stakeholders with the aim to outline and implement a master plan for the regeneration of “Puerto Madero.” Box 4.2 “Puerto Madero” (Continued): The Leadership Role of CAPM in Developing a Collaborative Network of Stakeholders CAPM has played a leading role in implementing the regeneration plans for Puerto Madero. The lack of public funding, the uncertainties associated with the national economic downturn, and the risk of power unbalance between a group of stakeholders (e.g., financial investors) over others (e.g., citizens or not-for-profit organizations) required strong leadership aptitude. In fact, for implementing the renovation project for Puerto Madero, CAPM involved a plurality of actors. As Fig. 4.2 shows, these actors can be positioned within three main circles around CAPM, depicting increasing stakeholders’ relevance, involvement, and power in the design and implementation of the project. At the core of the network, there are the CAPM shareholders, such as the federal government and the Government of Buenos Aires. In the inner circle, CAPM and the Society of Architects worked together with the Argentine Chamber of Construction, the Argentine Union of Construction, and the Center for Architecture and Urbanism Professionals to develop the network. Among the primary purposes of the network, there were (1) pursuing the financial sustainability of plan; (2) ensuring the quality of buildings and public (continued)

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Box 4.2 (continued) works; (3) delivering highly effective public services (e.g., education, sport, cultural, and recreational services) to prospect residents; and (4) generating public value through the preservation and exploitation of the cultural roots of the place. Achieving such collaborative purposes would improve Puerto Madero quality of life that in turn would enhance the place attractiveness. In fact, fostering collaboration among stakeholders may contribute to balancing financial and social outcomes and the need for public services. To this end, CAPM acted as the leader of such a policy network. Designing a new transportation system was the first challenge that required the engagement of the Port Authority of Buenos Aires, the railway authority, and the Argentine National Engineering Academy. In particular, the Academy dealt with the construction of the Autopista Ribereña—a project that reduced cargo trucks passing alongside Puerto Madero. Lately, the city and federal governments have built Paseo del Bajo—an underground highway. Also, improving pedestrian accessibility was another significant constraint to the development of the area since only five bridges connected the city to the harbor area. In fact, before the renovation plan, most Buenos Aires inhabitants perceived such an area as an isolated place. To this end, the construction of a large linear park crossing the city center along the north-south direction has reduced car traffic and improved the openness of the area. Specifically, such infrastructure has reduced the travel time by about 80%, with a remarkable impact on road safety and car emissions (Bereciartua, 2014). Public service companies and other major stakeholders provided essential services, such as drinkable water, fire services, natural gas, electric power, cable television, telephone, and data transmission. Also, the Society of Architects emphasized that renovating the area would have preserved the ecological reserve of the neighborhood. Such initiative allowed the network to meet the expectations of those environmentalists who in 1981 opposed one of the previous refurbishment projects for Puerto Madero. By 1992, the plan was adopted by the involved public stakeholders: the Urban Development Secretary of the City of Buenos Aires and the Deliberative Council (i.e., the legislative body). In the early 1990s, political instability led to a financial shortage and a drop in public funds. In this context, CAPM sold a quota of port warehouses to a few private developers, mainly local small-medium-sized private companies. Further, the Argentine Catholic University bought four warehouses to build a new campus. Similarly, the high school Colegio Nacional de Buenos Aires built a sports center in one of the most valuable lots in the area. Both initiatives have attracted upper-class young people to live in the area, which led shops, hotels, service companies, banks, and restaurants to set their business (continued)

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Box 4.2 (continued) operations in the area. The incoming of new residents and businesses increased the real estate market value. Lastly, CAPM endeavored to preserve and exploit Puerto Madero cultural heritage to foster place identity and the attractiveness of the place for tourists. In fact, the “Fortabat Museum of Art” and the ship museums “ARA Fragata Sarmiento” and “ARA Corbeta Uruguay” were opened. Moreover, the New Biennial of Young Art, in November 1991, and the international fair “America 92,” in 1992, contributed to renovate the cultural profile of the area. In the implementation of the regeneration policies for Puerto Madero, the leadership aptitude of CAPM mainly contributed to generating a common-shared view on the project’s goals. To this end, proper use of coordination mechanisms (e.g., public tender requirements and zoning) has conveyed stakeholders’ contribution toward achieving desired policy outcomes, as described in Box 4.3. Box 4.3 “Puerto Madero” (Continued): Results from Project Implementation Regeneration policies had a significant impact on Puerto Madero. CAPM and its stakeholders recovered about 170 ha, maintaining a high-quality spatial distribution of buildings and open and green spaces, including 28 ha of parks. The project created an exclusive residential and business hub just across the center of Buenos Aires. Puerto Madero hosts near 6000 residents, whose needs provide a job for about 45,000 people (Garay et al., 2013). Such policy results demonstrate how collaborative policies have turned a neglected area into a neighborhood with the lowest crime rate in the city of Buenos Aires (Bianchi et al., 2021), which is today recognized as a tourism icon and a center of progress, attracting both locals and visitors. According to official government data, for the renovation of the area, about US$2500 million of private investments were spent; US$158 million of income tax were paid by the private developers and US$19.9 million by CAPM. Surprisingly, each year the municipal administration collects around US$24.3 million as property tax revenues (Garay et al., 2013). Also, the renovation of the place has improved the quality of public spaces and fostered social identification since the old buildings, such as the warehouses on the waterfront, started to host university buildings, company headquarters, shops, and restaurants. Figure 4.3 compares the skyline of the harbor before and after the renovation. The largest sale of the lots, between 1996 and 1999, attracted commercial investors and large international real estate companies which have constructed (continued)

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Box 4.3 (continued) high-end residences, apartments, and offices, whose rent are affordable only for the upper class. Puerto Madero is a successful example of urban brownfield regeneration implemented by a collaborative policy network, particularly for the ability of CAPM, with substantial involvement of local stakeholders, to design and implement an effective project. The example of “Puerto Madero” teaches that the role of the leading organization is crucial to commit the involved stakeholders’ network on the pursuit of desired policy outcomes. Also, their active involvement in performance venues (Moynihan, 2008a; Rajala et al., 2020) was a valid governance strategy to accommodate differences in policy-makers’ perspectives on how solicited contributions may generate public value (Ansell & Gash, 2018). To this end, DPM may support the actors involved in a performance regime to assess public value generation process by providing them with a hierarchy of outcome measures and associated relevant performance drivers on which individual and collaborative policies would impact.

Fig. 4.2 Urban brownfield regeneration in Puerto Madero: CAPM and the stakeholders’ network

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Fig. 4.3 A comparative view of Puerto Madero area before the plan implementation (left) and after the renovation (right) (Source: Observatorio Metropolitano & Denseel—CC)

3.1.2

Assessing “Puerto Madero” Regeneration Policy Outcomes Through Dynamic Performance Management

As described by the case narrative, the policy implemented by CAPM and its stakeholders aimed to improve local area attractiveness by renovating buildings, warehouses, and brownfields. A primary concern for CAPM was the lack of public funding, which made it unavoidable to ensure the regeneration program’s financial sustainability. As depicted in Fig. 4.4, by reinvesting the cash flow (i.e., first-level intermediate outcome) generated by the asset selling rate (i.e., first-level intermediate outcome), CAPM improved the quality of infrastructure and public service capacity, which, in turn, affected quality of life (i.e., second-level intermediate outcome). Improved quality of life positively impacted Puerto Madero attractiveness (i.e., final

Fig. 4.4 The hierarchy of results: a causal chain that links intermediate to final outcomes associated with Puerto Madero regeneration policies

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outcome), i.e., building the preconditions for a further increase in the asset price per square meter (i.e., first-level intermediate outcome). The causal chain that links intermediate to final outcomes represents a hierarchy of results expected by CAPM and the stakeholders’ network. Such causal relationships set a discussion basis for framing Puerto Madero regeneration policy outcomes, public value drivers, and associated strategic resources through DPM. As shown in Fig. 4.5, for each policy outcome displayed in the “end results” section, the DPM model associates—as a co-flow—the change in the corresponding strategic resource by using a “chessboard” symbol. The change in the strategic resource endowments is crucial for building the preconditions to pursuing policy sustainability, i.e., balancing financial, social, and quality of public service policy performance. From a sustainability perspective, the financial outcomes generated by the asset selling rate could be regarded as an end to ensure profitability to invested capital and as a means to provide Puerto Madero policy-makers with enough financial resources to design and implement sustainable urban brownfield policies. This is not an easy task for CAPM and the stakeholders’ network due to the lack of public funding. In fact, CAPM and its stakeholders may only use the cash flow (i.e., first-level intermediate outcome) generated by the asset selling rate (i.e., first-level intermediate outcome) at the current asset price per square meter (i.e., first-level intermediate outcome) as potential source of funding. As Fig. 4.5 shows, local actors should consider leveraging on two performance drivers to affect cash flow. First, to gauge the financial requirement to complete the implementation of the regeneration program, they may relate available public funding to total financial needs through the public funding ratio. Based on such financial needs, CAPM may increase the number of assets on sale. Second, to increase cash flow, CAPM may improve place attractiveness as it will boost the asset price per square meter. To this end, CAPM should focus on the performance drivers (i.e., intermediate short-term results) affecting the change in Puerto Madero attractiveness, i.e., (1) quality-of-life ratio, (2) urban space saturation, and (3) the fraction of regenerated brownfields. Quality of life holds public services capacity and the quality of infrastructures as the core factors attracting residents in the area. As shown in Fig. 4.5, the intermediate outcome change in the quality of life is inversely affected by the service capacity saturation (i.e., performance driver), which compares the adequacy of available infrastructure and public services capacity and cultural and recreational spaces with the current request for such services. In turn, the stocks of residents, businesses, and tourists in the area affect such demand. To prevent or counteract service saturation, CAPM may invest the cash flow from the sale of assets to fund public service capacity acquisition. Urban space saturation gauges the negative impact of business ventures and tourism presences in the area on the change in Puerto Madero attractiveness. In this regard, though the presence of tourists and business makes the site more appealing for real estate investments, it may limit further development of Puerto Madero due to urban space saturation.

Fig. 4.5 A Dynamic Performance Management view of Puerto Madero brownfield regeneration policies (Bianchi et al., 2021)

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Lastly, the fraction of regenerated brownfields affects Puerto Madero attractiveness as it implies that more spaces have been renovated. The analysis of the Puerto Madero regeneration policies through DPM has revealed the causality linking policy outcomes, public value drivers, and the associated strategic resources. Such causal relationships may allow public policy-makers and local area stakeholders to assess community outcomes. By doing this, DPM may enable the actors in public value-driven performance regimes to frame potential trade-offs associated with the adopted policies that might limit outcome sustainability.

3.1.3

Framing Policy Trade-Offs Associated with Puerto Madero Brownfield Regeneration to Support Learning and Improvement in Public Value-Driven Performance Regime

In complex policy fields like urban regeneration and renewal, policy-making features conflicting situational decisions that may lead to irreconcilable effects. However, in front of such a situation, understanding how trade-offs might emerge over time may help the actor in the regime to design policies to mitigate adverse effects or prevent them in order to pursue policy sustainability. In the case of Puerto Madero, the use of DPM has revealed two potential tradeoffs associated with a “financially driven” governance mode of urban brownfield regeneration. This mode of governance entails “balancing the advantage of cash flow reinvestment for public services and infrastructure capacity development, with potential shortcomings in terms of social inclusion” (Bianchi et al., 2021). In particular, the DPM analysis reveals two main trade-offs associated with Puerto Madero regeneration policy design and implementation. A first trade-off concerns the decision to undertake a fast and intensive sale of assets in the short run against the possibility of earning future capital gains by selling holdings when the asset price per square meter has increased due to the rise in local area attractiveness. In this situation, a trade-off in time entangles CAPM and other local area policy-makers. Do they have to sell more assets in the initial stage of the project at a relatively low price? Or, do they have to postpone the sale of a more significant portion of assets to a future time when the asset price per square meter will be high? Such phenomena are described by the causal loop diagram2 portrayed in Fig. 4.6. The balancing loop B1 shows that the asset selling rate drains the stock of assets, which provides fewer assets to sell in the future. The reinforcing loop R1 portrays the 2 As illustrated in Chap. 2, a feedback loop exists when information that results from an action goes through the system structure and eventually returns to its point of origin, influencing future courses of action (Richardson, 1997). The multiplication of the signs characterizing the relationships among the involved variable determines whether the loop is positive or negative. A positive loop portrays a source of exponential growth or collapse over time, while a negative loop generates a goal-seeking behavior toward a point of equilibrium or an inertial decay toward zero.

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Fig. 4.6 A causal loop diagram portraying a policy trade-off in time associated with the implemented financial strategy to pursue Puerto Madero regeneration

primary strategy adopted by CAPM to regenerate Puerto Madero, which was based on a direct sale of assets on the market to trigger the renovation. The higher the asset selling rate is, the higher the stock of regenerated assets will be. Regenerated assets improve Puerto Madero attractiveness, which fosters further growth in the asset selling rate. In this way, CAPM may reinvest the liquidity generated through the initial sale of assets in the development of infrastructures and public services, which limits capacity saturation and improves quality of life and Puerto Madero attractiveness, leading to an increase in asset selling rate. Loop R2 in Fig. 4.6 describes such regeneration process. The reinforcing loop R3 shows that the cash flows generated by the asset selling rate—as previously described by the loops R1 and R2—can be further boosted by a potential increase in the asset price per square meter. Such capital gains will occur in the long term if CAPM and the stakeholders’ network may be able to cover the initial investments in infrastructure and public service capacity—perhaps through public funding. A second policy trade-off relates to potential unbalances among different policy domains. Even though an intensive business presence may contribute to increasing the attractiveness of the place, a too high growth rate in the number of companies located in the area may saturate urban space, which would reduce place attractiveness. Such a situation is usually regarded as a policy trade-off in space as it entails policy-makers to find a proper balance between business requests and resident needs. The loops B2, B3, and B4 in Fig. 4.7 portray the causal relationship that may lead to the envisaged policy trade-off. The investments by CAPM in the development of infrastructures and services improve the quality of life and attract residents in the

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Fig. 4.7 A casual loop diagram portraying potential trade-off in space associated with urban brownfield regeneration

area. Also, an increase in Puerto Madero attractiveness could lead businesses to exploit the site for commercial use, given its tourists’ appeal. However, the commercial exploitation of the area increases capacity saturation because businesses and tourists would gradually reduce the capability of infrastructure and service capacity to satisfy resident needs. This condition lowers residents’ perceived quality of life, which will, in turn, lead to a decline of residents (balancing loop B2) and to a deterioration in Puerto Madero attractiveness (balancing loop B4). Moreover, as the balancing loop B3 shows, intensive commercial exploitation of the area leads to a rising urban space saturation which may constrain the pursuit of brownfield regeneration policy outcomes. If the processes described by the loops B2, B3, and B4 take over the loops R1, R2, and R3, it will make it harder for CAPM and the stakeholder network to sell the assets and regenerate Puerto Madero. In fact, if, on the one hand, this policy might increase the attractiveness of the area for businesses, on the other hand, it would worsen social outcomes for the local community. Counteracting such potential adverse effects entails balancing commercial areas with cultural, recreational, and green spaces (e.g., sports centers, schools, public parks, and promenades). This policy may prevent the drop of quality-of-life outcomes in Puerto Madero due to less available space for residents to practice sports and enjoy recreation and culture. The analysis of Puerto Madero urban brownfield regeneration policy through DPM has shown the benefit of outcome-based performance assessment. In particular, a method focused on the causation underlying policy outcomes, public value drivers, and the associated strategic resources enabled the actors involved in the

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policy networks to assess policy sustainability. Also, the feedback analysis has allowed policy-makers to frame potential trade-offs in time and space that might undermine investigated policy outcomes. Implementing such robust performance management and governance routines (Bianchi, 2021) in cross-boundary performance dialogues (Rajala et al., 2020) may provide policy-makers with the method to support learning improvement in public value-driven performance regimes.

3.2

Improving Destination Image Through Outcome-Based DPM Insight Modeling

Places, like brands, have an image. Image “represents a promise of value and performance, incite beliefs, evoke emotion and inspire behaviors” (Kotler & Gertner, 2011, p. 35). Place image is a construct that conflates “the sum of beliefs and impressions people have about places” (Kotler et al., 1993, p. 14). Designing policies to improve place image is crucial to place competitiveness as “places compete in attracting visitors, residents, and businesses [. . .] a place with a positive reputation finds it easier to vie for attention, resources, people, jobs and money” (Morgan et al., 2012, p. 3). In fact, image is one of the most valuable assets of a place (Anholt, 2011), which does not reside in the municipality or in tourist agencies’ offices, but rather it is stored in people’s minds (i.e., a perception) in a remote location. Improving place image is not an easy task for local area policymakers since it requires addressing “a large number of associations and pieces of information connected with a place” (Morgan et al., 2012, p. 3). This implies that designing policies to improve place image is not primarily about marketing, as it has often been assumed. In this perspective, the image of a local area can be regarded as a shared strategic resource that is influenced by the capacity of both public and private sector organizations located in the context to build up and deploy a set of shared strategic resources. To this end, we suggest policy-makers and their stakeholders in a local context embrace an outcome-based perspective of performance management. To illustrate the benefits of such a perspective, the case of “Taormina-Etna district” will be analyzed through DPM in the next section.

3.2.1

A Public-Private Partnership to Manage a Tourism Destination: The Case of “Taormina-Etna District”

This section discusses the “Taormina-Etna district” (TED) case, i.e., a public-private partnership acting on behalf of a local stakeholder’s network to manage a tourism destination. As Box 4.4 illustrates, the primary aim of TED was positioning the destination among the most appealing destination in southern Europe and sustaining the long-term development of the area through tourism (Vignieri, 2019).

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Fig. 4.8 “Taormina-Etna district”: (a) the stakeholders’ network involved in the PPP and (b) the geographical position of the area

Box 4.4 “Taormina-Etna District”: Background of the Case “Taormina Etna district” is the brand for a local development agency partnered by several municipalities, 3 universities, 2 natural parks, the company managing the airport of Catania, the Chamber of Commerce of Catania, and about 270 private organizations, including local farms, wineries, and community associations (see Fig. 4.8a). The area of the district is located between Messina and Catania, along the Sicilian East Coast, in Southern Italy. The site has tourism potential as it is characterized by a coastline and includes natural preserves. As shown in Fig. 4.8, the area embodies the city of Taormina hosting the ancient theater of Tauromenion, built in the third century BC (Campagna, 2009; Rizzo, 1927), and the Etna volcano, i.e., the tallest active volcano in Europe currently 3329 m (10,922 ft.), which has been included in the UNESCO world heritage site list since 2013. The fertile volcanic soil supports extensive agriculture, with vineyards and orchards spread across the mountain’s lower slopes and the broad Plain of Catania to the south. To boost the socio-economic growth of the area, TED has invested local, national, and European funds to subsidize the hospitality industry (e.g., hotels and b&b capacity development), support destination marketing initiatives, and renovate some infrastructures (DTE, 2015). As described by the case narrative, the main goal of TED was marketing the destination. In fact, evidence from the fieldwork (DTE, 2009, 2015; Vignieri, 2017, 2019) reveals that TED has operated as a destination agency in order to boost tourism in the area. Box 4.5 reports the dynamics of main tourism-related variables from 2000 to 2014. Such data can be associated with the effects of the policies implemented by TED.

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Fig. 4.9 The dynamics of tourism presence, arrivals, and average holiday length from 2000 to 2014 (Vignieri, 2019)

Box 4.5 “Taormina-Etna District”: Main Results from Project Implementation From 2000 to 2014, as shown in Fig. 4.9, tourism presences (i.e., line 1), arrivals (i.e., line 2), and the average holiday length (i.e., line 3) have not increased. Over the same period, the number of hospitality structures has increased significantly (Fig. 4.10a) due to the project implemented by TED. Related to this, hotels hold a considerable fraction of the hospitality capacity, while non-hotels host the remainder, as shown in Fig. 4.10b. Dividing the data of Fig. 4.10b by that of Fig. 4.10a, one may determine the average hospitality capacity (i.e., Fig. 4.10c). As line 1 in Fig. 4.10c shows, the hotel’s average capacity has been constant. In contrast, the non-hotel average capacity has dramatically decayed, over the last 14 years, as line 2 in Fig. 4.10c shows. This phenomenon has led to a surge of competition within the hospitality industry, which has affected the average holiday length. In fact, to boost non-hotel profitability income, the plethora of unconventional accommodation suppliers has provided rooms at a remarkably lower price than average hotel price. This was made possible by the widespread internet booking platforms and thanks to a completely different cost configuration that profiles non-hotel structures. Such effects have reduced the destination appeal, as the average holiday length breakdown shows (i.e., Fig. 4.10d). (continued)

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Box 4.5 (continued) Besides accommodation, TED policies have targeted cultural infrastructures (DTE, 2015). From 2000 to 2014, the value of museum revenues (i.e., line 1 in Fig. 4.11) has increased though the volume of tickets sold per year has been stable over the same period (i.e., line 2 in Fig. 4.11). This means that there has been a price increase in museum tickets as visitors have been constant. Related to this, the guests of the ancient theater of Taormina represent 60% of total visitors, and their tickets influence 80% of museum revenues. From the evidence in Box 4.5, one may note that tourism presences (i.e., outputs) have been steady over the observed time horizon, regardless of the intensive marketing initiatives carried out by TED. In contrast, hospitality structures—particularly non-hotels—have thrived due to TED subsidies (e.g., output). Also, TED’s financial support has allowed non-hotel structures to deliver services that traditionally feature hotels offering (e.g., swimming pool, room service, gala dinner, and beauty centers). Such policy has generated counterintuitive effects for tourism in the area, which has led hotels to operate mainly during the high season (i.e., spring-summer) so as to face

Fig. 4.10 Tourism-related data: (a) hospitality industry, (b) hospitality industry capacity, (c) hospitality capacity per class of structure, and (d) average holiday length breakdown (Data source: Department of Tourism, Sicilia Region)

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Fig. 4.11 The dynamics of museum revenues and visitors in “Taormina-Etna district” area (Vignieri, 2019)

the financial implications generated by the diminishing profitability due to the rising competition in the hospitality industry. However, the decision to cut off most of the local area hospitality capacity (e.g., outcome) for several months led to a dramatic drop in the average holiday length (i.e., outcome). In fact, tourists—especially foreigners— could not benefit from discounts for extended holidays (e.g., a week) driven by the need of hotel owners to saturate installed capacity in the mid and low seasons. Also, a short holiday length has limited the range of tourist’s tours to the most accessible places (e.g., Taormina), with negative effects on the socio-economic development of those towns located out of a 3-day tourist’s tour reach. The result of “Taormina-Etna district” portrays the drawbacks of an output-oriented performance regime mainly focused on increasing presences through marketing initiatives and the availability of hospitality infrastructure. Such bounded perspective of policy-making and performance evaluation has undermined public and private organizations’ capacity to partner with TED to improve destination image over the long term (Vignieri, 2019). The following section will illustrate how an outcome-based DPM insight model may provide TED decision-makers with the methodological support to frame the key factors driving changes in local area image over time.

3.2.2

Framing the Source of Destination Image Through Outcome-Based DPM

To frame the source of destination image through outcome-based DPM, we assume that place image affects tourism presences and tourism presences may reinforce place image if people’s experience with the destination is positive. Literature in

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destination management and place image (Dinnie, 2004; Elliot et al., 2010; Gertner & Kotler, 2004; Matarazzo, 2012) underpins the proposed causation: place image ! tourism presence ! tourism experience ! place image. Based on such feedback, an improvement in destination image (i.e., outcome) can be regarded as the aftermath of different factors impacting people’s experience at the destination. We suggest embracing an outcome-based view of the policies affecting destination image through DPM to frame such factors. As Fig. 4.12 shows, the change in local area image (i.e., outcome) is affected by four performance drivers that depict key success factors associated with tourists’ experience at the destination. A first performance driver is tourism maturity, which captures the specific position of the area in the destination lifecycle.3 To this end, it holds the bed places growth rate, presence growth rate, and average holiday length to infer business and tourists’ interest in the area. Such information may help TED decision-makers to plan hospitality capacity expansion. The driver fit of contextual attributes gauges the influence of built environment characteristics on tourism experience. To this end, as Fig. 4.12 shows, such a driver considers the size of the hospitality structure, the infrastructure availability, and the density of tourism attractions. A third performance driver is the strength of local identity, which captures the extent to which local food offerings, cultural heritage, and local traditions are conveyed to tourists through integrated products and services. Lastly, the driver collaboration in PPP gauges TED partnership aptitude to attain shared goals. This driver focuses on cross-boundaries initiatives to implement innovative projects to shape a unique tourism experience. Though synthetic, tourism maturity, fit of contextual attributes, the strength of identity, and collaboration in PPP may be framed as crucial success factors4 on which to act to improve destination image. In turn, place image may affect tourism presences, leading to an increase in the TED budget, which can be invested to sustain such policy. However, each performance driver needs to be operationalized to provide the actors in the regime with intermediate performance information to support the design and implementation of specific policies to improve destination image. This implies designing performance drivers that causally link policy outcomes to the local area shared resource endowments. For each first-level performance driver previously illustrated, three second-level performance drivers can be identified, as Table 4.1 shows.

3

The concept of destination lifecycle includes several phases from introduction to redesign— through development and maturity stages—and has been widely applied in tourism research (Butler, 1980; McTaggart, 1980; Meyer-Arendt, 1985; Rushmore, 1984). 4 Main causal relationships that constitute the DPM model (Fig. 4.12) are grounded in fieldwork evidence gathered through interviews with key stakeholders, conference reports, and archival records. A detailed analysis of such work can be found in Vignieri (2017, 2019).

Fig. 4.12 The outcome-based DPM insight model portraying key factors affecting local area image [Adapted from Vignieri (2018, 2019)]

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Table 4.1 Operationalizing performance drivers: hierarchy, definition (italic), and unit of measures First-level performance drivers Tourism maturity (TM)

Fit of contextual attributes (FCA)

Strength of identity (SID)

Collaboration in PPP (COLL)

Second-level performance drivers Tourist fractional growth rate (TGR) ¼ change in tourism presences/tourism presences Hospitality capacity fractional growth rate (HGR) ¼ change in hospitality capacity/hospitality capacity Average holiday length (AHL) ¼ tourism presences/ change in tourism presences Average hospitality structure size (H-SIZE) ¼ hospitality capacity/hospitality structure Urban density (UD) ¼ urbanized land surface/total land surface Attraction accessibility (AA) ¼ total attraction/local area surface Local-oriented attraction ratio (LAR) ¼ n. of localoriented tourism attraction/total attraction Typical producers’ ratio (TYP) ¼ n. of firms producing labeled products/total firms Integration of supply chain (ISC) ¼ n. of restaurants using local products/total restaurants Cross-boundaries initiatives (CBN) ¼ n. of organizations involved in cross-boundaries collaborative project/total organizations Density and scope of touristic circuit (DSTC) ¼ n. of affiliated activities * category of activities * (surface of covered municipality/total land surface) Collaboration effectiveness (CE) ¼ attained TED goals/ goals to be accomplished

Unit of measure % per year % per year

Days Bed/structure Dimensionless Attractions/m2 Dimensionless Dimensionless Dimensionless Dimensionless

Dimensionless

Dimensionless

Equations from (4.1) to (4.4) operationalize first-level performance drivers by multiplying the normalized value5 of the second-level performance drivers6 comprised by each main measure.

5

Drivers are normalized through a benchmark value with the same unit of measure. In this way, the ratio value works as a non-dimensional input that converts the current value of such information into a min-max scalable output, which provides a multiplier effect. 6 The numerator of each second-level performance driver is the current state of performance, while the denominator is a benchmark which can be used by policy-makers as a reference to evaluate short-term performance. A benchmark may be internal (i.e., standard operating conditions or TED material thresholds) or external (i.e., tourists’ expectations, competitors’ performance, law-mandated or physical constrains).

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Tourism Maturity ðTMÞ ¼

TGR HGR  Benchmark TGR Benchmark HGR AHL  Benchmark AHL

Fit of contextual attributes ðFCAÞ ¼

H  SIZE Benchmark H  SIZE UD AA   Benchmark UD Benchmark AA

LAR TYP  Benchmark LAR Benchmark TYP ISC  Benchmark ISC

Strenght of Identity ðSIDÞ ¼

Collaboration in PPP ðCOLLÞ ¼

CBN DSTC  Benchmark CBN Benchmark DSTC CE  Benchmark CE

ð4:1Þ

ð4:2Þ

ð4:3Þ

ð4:4Þ

Such a methodological process implies identifying measurable facts or events that could turn a conceptual construct (i.e., the first-level performance driver) into a meaningful system of observations that can be collected and evaluated through performance measures (i.e., the second-level performance measures). On this concern, some caveats need to be underlined. Performance drivers are not binary indicators suitable for a positive/negative or good/bad evaluation, with each of them having autonomous significance. On the contrary, they are part of a system of ratio indicators, with each of them having a specific scale of reference where critical thresholds inform policy-making. As the equations below show, the four first-level performance drivers affect the change in local area image, i.e., a flow (Eq. 4.5) that updates the information stored in the intangible asset local area image (Eq. 4.6). Change in local area image ¼

½ðTM þ FCA þ SIDÞ  COLL  Local area image Time to change local area image ð4:5Þ Z

Local area image ¼

t

Change in local area image  dt

0

þ Local area imaget0

ð4:6Þ

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In Eq. (4.5), the driver tourism maturity, fit of contextual attributes, and strength of local identity are additive to each other, while the collaboration in PPP works as a multiplier of the first three variables. Differences exist among the two formulations (Sterman, 2000). The additive formulation assumes that each factor is unique and independent of the other two. The multiplicative formulation emphasizes that the value of collaboration dominates the other elements. We have also adopted a multiplicative formulation to embody in the model the idea that collaboration may help organizations to achieve purposes that could not be otherwise achieved (Emerson et al., 2012a; Huxham, 2003). An outcome-based DPM insight model may help local area policy-makers pursue policy goals as it reveals effective leverage points on which to act to affect the upstream strategic resources to influence performance drivers and improve policy outcomes. Based on such a causal perspective, a preliminary discussion to illustrate how DPM may support learning and improvement in public value-driven performance regimes could be developed. For instance, TED decision-makers may plan to expand hospitality capacity if the tourism maturity ratio indicates that tourism presences increase at a sustained pace per year. By sustaining capacity development, they may satisfy specific market segments, holding positive effects for place image. Likewise, TED decision-makers may improve cultural attraction accessibility by developing infrastructure, which is a component of the fit of contextual attributes. Also, this policy may positively impact the strength of local identity because infrastructures and other services enable tourists to travel within the area. The strength of local identity may positively affect destination image if public services allow tourists to visit many places to get in touch with people and discover traditions and local products. The level of collaboration among the organizations partnering with TED is crucial to propelling actions to build up and deploy local area shared strategic resource endowments. An example may clarify the potential of collaboration. In the case of a big event (e.g., a conference, an exhibition, or a music festival), collaborating with hotel owners is crucial as they may contribute with particular discounts and thematic parties or provide exclusive locations for collateral events. Similarly, cooperation with transportation companies is imperative to serve the event as its implementation may require adapting routes, frequency, and accessibility. If TED decision-makers disregard collaboration among local area stakeholders, it is unlikely that they will improve the destination’s image. An outcome-based DPM model may provide a sound basis for policy analysis through simulation.7 In this way, DPM may further support policy-makers’ understanding of how to pursue desired outcomes. 7

A detailed description of the model with full structure, equations, and validation has been published in Vignieri, V. (2017). Enhancing the Governance of Local Areas through Dynamic Performance Management. Department of Political Sciences and International Relations, University of Palermo. The PhD dissertation can be retrieved from https://iris.unipa.it/handle/10447/22043 5.

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Policy Analysis Through Outcome-Based DPM: The Role of Simulation Models for Learning and Improvement in Public Value-Driven Performance Regime

As illustrated in the previous section, an insight DPM model provides the actors in the regime with a key to governing local area performance. In this perspective, simulations may enable learning and improvement in public value-driven performance regimes if the adopted models—as illustrated in Chap. 3—deliver “qualitative statements about modes of behavior, appropriate performance indicators and effective leverage points” (Lane, 2012, p. 591). Such benefits are enabled by policy analysis through an outcome-based DPM simulation model. Figure 4.13 shows the full structure of the simulation model. It contains six sectors, i.e., tourism, hospitality, business, infrastructures and services, TED, and local area image. Figure 4.13 shows a comprehensive view of the model with a specific focus on the four first-level performance drivers impacting on local area image. Such causality is part of a broad feedback relationship involving place image ! tourism presences ! tourism experience ! place image, which underpins the study, as discussed at the beginning of the previous section. As part of the full mode, Fig. 4.14 provides a closer look at how such factors impact local area image to illustrate modeling and measurement logics. The model structure conveys four performance drivers into the variable labeled effects of the source of local area image. Through this variable, the model, first, computes a weighted average

Fig. 4.13 The full structure of the DPM-based simulation model to assess the effects of image on tourism presences [Adapted from Vignieri (2017)]

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Fig. 4.14 A detailed view of the model structure portraying the effects of performance drivers on the change of local area image

among three additive factors (i.e., tourism maturity, fit of contextual attributes, and strength of identity). Then, the result is multiplied by the driver collaboration in PPP. By doing this, the variable scales the effects of the four performance drivers from 0 to 1 to set the range of values that measures local area image. Such a model was built to compare current TED tourism policies against two alternative policies to assess their effectiveness in improving place image and tourism performance. The three policies could be synthetically described as follows8: 1. A conservative policy that reflects TED current marketing initiatives and subsidies for hospitality capacity development 2. An intensive marketing policy to invest TED budget primarily for promotional campaigns (e.g., web advertisement, participation in tourism fairs, and commercials) 3. A regenerative policy to protect the natural environment, renovate abandoned urban areas, and develop infrastructures

The decision-making logics behind such policies have emerged from the fieldwork. Related to this, Vignieri (2017, 2018) provides evidence.

8

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Fig. 4.15 Simulation outputs associated with TED conservative policy (years 2000–2040)

To evaluate the effectiveness of such policies, we will plot main variables associated with tourism development in the area, i.e.: • Tourism presences as a measure of the effects of local area image. • Hospitality capacity saturation indicates potential financial returns for the hospitality industry generated by bookings. • Indicated local area image, which conflates the effects of promotional campaigns on local area image. • Tourists perceived local area image, which is a construct that grasps tourism experiences at the destination. The simulation time horizon covers 40 years, from 2000 to 2040. We left TED conservative policy operating from 2000 to 2014 for each run so that the effects of the two alternative policies will start from the year 2015 onward. TED’s conservative policy is mainly driven by marketing initiatives and subsidies for hospitality capacity development. As Fig. 4.15 shows, tourism presences are unresponsive to such policies. Though marketing initiatives may improve the indicated image (i.e., line 2 on the right-hand side of Fig. 4.15) and tourists’ perceived local area image (i.e., line 3 on the righthand side of Fig. 4.15) in the short term, tourists’ perceived local area image will decay in the long run. Also, the adverse effects generated by the mismatch between indicated image and the perceived image will impact tourism presences (i.e., line 1 on the left-hand side of Fig. 4.15) and hospitality capacity saturation, with potential financial implications for the industry. The results of an intensive marketing policy9 are shown in Fig. 4.16. The graph on the left-hand side is comparative with line 1 that refers to TED conservative policy and line 2 to the intensive marketing policy. The chart on the right-hand side

9

From a policy modeling perspective, the intensive marketing policy doubles TED marketing budget for 3 years—from 2015 to 2018. Then, it sets such budget to the standard condition. Such policy emerged during an interview with the CEO of TED. In fact, during the meeting, the CEO said “[improving tourism in the area] is a problem of promotion. To be effective, we need a constant and—perhaps—increasing budget for this purpose. We would like to promote the area, but we don’t have enough financial resources.”

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Fig. 4.16 Simulation outputs associated with an intensive marketing investment policy (years 2000–2040)

plots the behavior of hospitality capacity, indicated image, and perceived local area image as simulation results of the intensive marketing policy. As Fig. 4.16 shows, an intensive marketing policy may improve tourism presences (i.e., line 2 on the left-hand side of Fig. 4.16) and hospitality capacity saturation (i.e., line 1 on the right-hand side of Fig. 4.16), but only in the short term. In fact, when the additional budget is over, both tourism presences and the indicated image (i.e., line 2 on the right-hand side of Fig. 4.16) slow down. In this situation, tourism performance is restrained by marketing investment (e.g., advertisements, tourism fairs, and commercials). This myopic policy stems from the idea based on which increasing the marketing budget may be a solution rather than a part of the problem. An explanation of such belief can be found in the policy-makers’ misperception of the fundamental characteristic of dynamic and complex systems, whose changes are affected by time delays and non-linearities (Forrester, 1971; Sterman, 1989). Social systems are likely to respond to a policy change in the desired way, though only for a short period of time, before their performance returns to the pre-policychange state. “Does this mean decision-makers are irrational or just plain stupid? Not at all” (Sterman, 2000, p. 603). They may be trapped by narrow mental models (Moxnes, 2004), or they may have an inclination to postpone or dilute decisions (Meadows, 1982), like it has happened for climate change interventions (Meadows et al., 1972; Moxnes & Saysel, 2009). Policy resistance (McPhee, 1989) means that “things bite back” (Tenner, 1996) because the system’s structure works to counteract the policy change, which has been designed to improve its behavior. In fact, in such a situation, a policy can make performance better before making it worse or worse before making it better. Misperceiving such fundamental features of social systems may lead policy-makers to ignore unintended consequences or policy failure. An alternative policy10 to improve tourism performance in the area may consider pursuing regeneration policy outcomes, such as preserving the natural environment, 10

Such policy cuts off the marketing budget provided by the regional administration from the beginning of the year 2105 to fund a regeneration plan which aims to recover the 10% of the land district (700 km2 out of 7100 km2) over the next 6 years.

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Fig. 4.17 Simulation outputs associated with a regeneration policy (years 2000–2040)

renovating dismissed urban areas (e.g., brownfield), and developing infrastructure. Figure 4.17 shows the simulation outputs associated with a regenerative policy. Similar to the previous figure, the left-hand side graph is comparative (i.e., it shows simulation outputs of the three policies), while the one on the right-hand side portrays the behavior of three different variables. As Fig. 4.17 shows, in the long term, a regeneration policy improves tourists’ perceived image (i.e., line 3 in the right-hand side graph of Fig. 4.17) and tourism presences (i.e., line 3 in the left-hand side graph of Fig. 4.17). In particular, tourists’ perceived image has positive effects on tourism presences as the regeneration projects improve tourists’ experience at the destination. Examples of regenerative projects might include renovating brownfields for cultural, sports, and residential purposes, recovering polluted areas, restoring cultural heritage, and maintaining natural parks, seafronts, or hiking paths. In this sense, implementing such a policy requires the collaboration of the local government and other key stakeholders in the area (e.g., real estate companies, community associations, businesses, and universities). Rather than focusing on short-term outputs, such as improving tourism through marketing initiatives, local area actors partnering with TED may embrace an outcome-oriented view of destination governance. This requires providing the actors in the regime with methodological support to bridge inter-institutional performance and organizational results. By doing so, outcome-based DPM simulation models may enhance learning and improvement in public value-driven performance regimes.

3.3

Framing Public Services Co-production Community Outcomes Through Dynamic Performance Management

The concept of co-production refers to a public service delivery mode by which “professionals and citizens making better use of each other’s assets, resources and contributions to achieve better outcomes or improved efficiency” (Bovaird & Loeffler, 2016, p. 254). Co-production has become somewhat popular in public

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administration literature as it frames varieties of participation in public services (Alford, 2014, 2016; Bianchi et al., 2017; Bovaird et al., 2015; Brandsen & Pestoff, 2006; Fugini & Bracci, 2016; Osborne & Strokosch, 2013; Ostrom, 1996; Pestoff, 2012). Also, it has captured inherent traits of service delivery (Lusch & Vargo, 2013; Osborne, 2020; Osborne et al., 2012). In fact, the practice of co-production has been found effective for the delivery of cleaning services (Brudney & England, 1983), elderly care (Pestoff, 2006), public housing programs (Alford, 2014), health-care services (Cepiku & Giordano, 2014), and outreach activities for youngsters (Bianchi et al., 2017). The core element of co-production is “the process through which inputs used to produce a good or service are contributed by individuals who are not ‘in’ the same organization” (Ostrom, 1996, p. 1073). As the statement has been that “public service organizations depended as much upon the community for policy implementation and service delivery as the community depended upon them” (Osborne et al., 2016, p. 640), policy-makers should find ways to involve the community in several areas of the public domains. To this end, a public organization may adopt a collaborative strategy (Ansell & Gash, 2007, 2018; Imperial, 2005) to deliver public services by integrating the voluntary contributions of citizens, service users, families, neighbors, and community organizations. This relationship implies governing “the provision of services through regular, long-term relationships between professionalized service providers (in any sector) and service users or other members of the community, where all parties make substantial resource contributions” (Bovaird, 2007, p. 847). Such collaborative patterns for public service delivery make “porous” (Alford, 2016, p. 159) the distinction between the governance and the production of the service due to the relational nature of the interactions among involved parties. This means that co-producers may contribute in several stages of the public service delivery process, i.e., from “co-planning to co-assessment” (Bovaird & Loeffler, 2012, p. 1125). Governing co-production initiatives to improve the quality of service outcomes entails dealing with civic engagement, citizen participation, and self-organization to integrate voluntary user contributions into a process for service planning and delivery. Co-production differs from contract-based service provision because it is grounded on the trust among networked partners, builds on users’ engagement in the collaborative process, and relies on users’ commitment to the shared undertaking. In fact, it is not uncommon that public organizations collaborate with community associations to co-produce—for instance—cultural and touristic services in small towns (Buonincontri et al., 2017; Durose et al., 2013). Such undertaking for service delivery configures a “hybrid model” (Cepiku, 2016, p. 142) of collaboration through which public policy-makers may leverage stakeholder’s contributions to improve public service effectiveness (Loeffler & Bovaird, 2016). The use of appropriate performance management routines may strengthen inter-institutional collaboration to help policy-makers pursue community quality-of-life outcomes (Bianchi et al., 2017; Jakobsen & Andersen, 2013). Lack of concern for dynamic complexity factors may jeopardize their aptitude to frame the

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effects of co-production. By modeling the interactions among “official producers” and “co-producers” (Nabatchi et al., 2017, p. 766) through DPM, policy-makers may frame how professional groups, non-profit organizations, citizens, and community associations affect the value generated by such collaborative initiatives. To this end, the following section illustrates an example of public service co-production designed to deliver the guided tour of the museum’s venue in a small town (Vignieri, 2020b). Such a case will be used as a discussion basis to show how DPM may support the actors in public value-driven performance regimes to assess community outcomes associated with the co-production of cultural services.

3.3.1

A Collaborative Governance Strategy for Co-producing Public Service Outcomes: The Case of “Museo Civico di Castelbuono”

This section discusses the “Museo Civico di Castelbuono” (MCC) case,11 i.e., a civic museum located in a small town. As Box 4.6 illustrates, MCC sets out a collaborative platform to deliver the guided tour of its venue with the significant involvement of community organizations and volunteers (Vignieri, 2020b). Box 4.6 Co-Producing the Guided Tour of the MCC Venue The “Museo Civico di Castelbuono” (MCC) is the city museum to which the municipality entrusts the preservation and promotion of the local heritage. The mission of MCC is to show “the Castle” (i.e., the institutional seat), which is the symbol of the small town and the heart of its history, culture, and religious practice, as shown in Fig. 4.18. The cultural offering of the MCC, such as exhibitions of contemporary arts, concerts, and conferences, and the beauty of the venue (see Fig. 4.18) attract many visitors from the broad region. Table 4.2 reports visitors of the MCC from the years 2014 to 2018. Since 2017, the MCC has offered a guided tour service involving the museum employees, the Pro Loco (i.e., a community association promoting culture and tourism in the small town), and the tourists. Pro Loco employs volunteers whose task is to assist tourists in visiting the museum according to the service logic presented in Fig. 4.19. The process unfolds as follows. Beforehand, tourists can customize the guided tour of the MCC by selecting routes, language (i.e., English, German, Japanese, and Italian), and length of the visit through the Pro Loco website. A (continued)

11

The empirical work was set in Castelbuono, a small town in Sicily (Italy), from 2016 to 2018. To frame the collaborative setting co-producing the guided tour of the MCC venue, we used document analysis, semi-structured interviews, and questionnaires (Vignieri, 2020a, 2020b).

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Box 4.6 (continued) volunteer forwards the booking to the museum administration, which books an appointment between a volunteer guide and the visitors on the selected date. Then, the museum personnel handle the ticketing procedure, while the Pro Loco volunteers provide the guided tour. In doing this task, each volunteer employs additional resources such as time and knowledge of a foreign language, art, and history, including communication and pedagogical skills.

Fig. 4.18 Pictures of the MCC venue: (1) external view (Courtesy of Museo Civico Castelbuono, photo credit Minutella, V.); (2) the courtyard (Courtesy of Museo Civico Castelbuono, photo credit Minutella, V.); (3) a detail of a door (Courtesy of Museo Civico Castelbuono, photo credit Minutella, V.); (4) the modern and contemporary art pinacoteque (Courtesy of Museo Civico Castelbuono, photo credit Puccia, M.); and (5) temporary exhibition space (Courtesy of Museo Civico Castelbuono, photo credit Minutella, V.)

Table 4.2 Visitors of the Museo Civico di Castelbuono: years 2014–2018 Year Visitors

2014 34,833

2015 30,816

Source: Museo Civico di Castelbuono

2016 36,243

2017 38,040

2018 39,212

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Fig. 4.19 Outlining the co-production process for the guided tour of the MCC venue

The case of “Museo Civico di Castelbuono” portrays an example of public service co-production in which the strategic resources that mainly influence the capacity of the network to deliver the service are provided by external volunteers. As described by the case narrative, the co-production of the guided tour of the MCC venue has involved a group of volunteers as members of a community organization committed to promoting the heritage of the town. Such a process can be framed as an “individual” form of co-production (Bovaird et al., 2015, p. 4) because volunteers’ contribution is mainly driven by a sound motivation to be engaged in the cultural life of the community. In this perspective, co-production is a trigger for active citizenship since it helps to build “a shared ideal in which the individual is fully part of the whole community” (Nabatchi et al., 2017, p. 768). By engaging volunteers, MCC may lever in additional resources that further organizational and community performance once integrated into the service delivery process. As a result of the collaborative undertaking, co-producing the guided tour of the museum venue generates intermediate and final outcomes. Figure 4.20 portrays the main causal relationships among such outcomes. A primary concern for the MCC was offering a high-quality guided tour service in different languages to museum visitors (second-level intermediate outcome). The change in the number of volunteers allows the museum to increase the volume of guided tours delivered per month (i.e., first-level intermediate outcome). Also, the number of volunteers affects the change in the available competencies (i.e., firstlevel intermediate outcomes) because volunteers speak different languages or have diverse educational backgrounds (e.g., humanities or social sciences). The capacity of the network to deliver guided tours at a promised standard affects service quality

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Fig. 4.20 The hierarchy of results: a causal chain linking intermediate to final outcomes associated with the co-production of cultural services in a small town

(i.e., second-level intermediate outcome). Such intermediate outcome influences motivation as it drives rewards for volunteers (Meier & Stutzer, 2008; Tham et al., 2021). Examples of rewards include personal well-being, satisfaction, public recognition, favorable evaluations, social media mentions, participation in networking events, and involvement in cultural projects (Phillips & Phillips, 2010). As Fig. 4.20 shows, the effects of volunteer motivation are twofold. On the one side, volunteer motivation drives co-production service capacity development (i.e., final outcome). Such outcome makes the service delivery mode sustainable and enables “knowledge transferring” (Koliba et al., 2011b, p. 120) process on other service areas. On the other side, personal and social incentives foster active citizenship in the local community (i.e., final outcomes) as the active engagement of citizens in the collaborative undertakings may strengthen their role in the small town beyond voters and service users (Levine & Fisher, 1984; Nabatchi et al., 2017; Vignieri, 2020b). The causal chain that links intermediate to final outcomes provides a piece of fabric for developing a DPM insight model to assess community outcomes associated with the co-production of cultural services. This is the goal of the next section.

3.3.2

Implementing Outcome-Based Performance Assessment in Public Value-Driven Performance Regimes to Enhance Collaboration, Stakeholders’ Learning, and Institutional Adaptation Through Dynamic Performance Management

As shown in Fig. 4.21, for each policy outcome displayed in the “end results” section, the DPM model associates—as a co-flow—the change in the corresponding strategic resource by using a “chessboard” symbol. The first relevant community outcome is the change in volunteers, i.e., an intermediate outcome that affects the number of Pro Loco volunteers involved by

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Fig. 4.21 A Dynamic Performance Management view of the community outcomes associated with the co-production of the guided tour of the MCC

the co-production initiative. The number of volunteers influences the availability of competencies (e.g., knowledge, professional, and personal skills) that the network can deploy to deliver the service. In fact, the volunteers’ skills adequacy ratio impacts the change in service quality because such performance driver compares the quality and quantity of available competencies with the characteristics of service demands. This means that service quality could be improved if the service delivered by volunteers matches visitors’ expectations (e.g., available languages, timeliness, the relevance of provided information, and politeness). Service quality is a strategic resource for the network because it influences the change in volunteer motivation through the service quality ratio. This implies that if the service quality is adequate to the current museum cultural offering, volunteers’ motivation will rise, thanks to the rewards provided by the museum administration. As illustrated in the previous section, the change in volunteer motivation is a key intermediate outcome for ensuring public service co-production sustainability as it enables capacity development and contributes to other societal ends. In this perspective, as Fig. 4.21 shows, volunteer motivation affects the change in co-production service capacity through the performance driver volunteers’ motivation ratio, which gauges co-producers’ volunteering attitude based on current rewards obtained. Also, volunteer motivation affects volunteers’ churn rate, which is the volunteers’ quitting rate. The capacity of the MCC to serve guided tours (i.e., intermediate outcome) is influenced by the service capacity ratio, i.e., a performance driver comparing co-production service capacity to current service demand. A shortage of service capacity may lead the network to adopt a corrective policy, such as searching for volunteers amidst community members interested in volunteering. However, such

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strategic resource is affected by the level of active citizenship in the local community, which in turn depends on the performance driver “community activism,” i.e., a measure of the average length of voluntary work. In this sense, an enduring community engagement positively impacts the change in active citizenship leading to an increase in the community members interested in volunteering. In this way, Pro Loco and the MCC may search for new volunteers among community members. Moreover, active citizenship may be a vehicle to pursue other community development. Such institutional development can be regarded as a form of adaptation, meaning that the local governance may either take advantage of the trustful relationship among partners by harnessing the “potential transformative change of collaboration” or “develop the capacity to continue to act while adapting to changing conditions” (Emerson & Nabatchi, 2015a, p. 86). As illustrated by the example, a DPM insight model may help local decisionmakers (i.e., museum managers and community organization leaders) assess the community outcomes generated by the co-production of the cultural service. The methodological support provided by DPM may provide local area actors with a causation framework to ground policy analysis on the effects of stakeholders’ contribution to the community outcomes (Douglas & Ansell, 2021). By doing this, policy-makers may position public value at the forefront of the performance regime to mature a policy orientation toward improving quality of life, active citizenship, and other relevant community outcomes. Implementing this requires that performance management routines configure learning forums where stakeholders may share their cultural perspectives on the issue at hand through a sensemaking dialogue (Moynihan, 2008b; Moynihan et al., 2011; Rajala et al., 2020; Rajala & Laihonen, 2019). In this way, the DPM framework may enhance public value governance (Bryson et al., 2014), foster stakeholders’ learning (Jakobsen et al., 2018; Moynihan, 2005; Moynihan et al., 2020), and promote institutional adaptation (Emerson & Nabatchi, 2015a, 2015b).

4 Conclusion The contemporary reality of public administration portrays a complex and plural institutional environment in which several organizations—from both the public and private sector—interact together (Edelenbos & van Meerkerk, 2016; Klijn, 2016) to address policy problems (Noordegraaf et al., 2019; Peters, 2017) that cannot be solved by an individual organization acting alone (Emerson et al., 2012b, p. 3). Not infrequently, at the local level, leading organizations initiate governance initiatives (e.g., public-private partnerships, policy networks, and collaborative governance) (Bovaird & Löffler, 2009) to pursue community outcomes that cannot be otherwise attained (Agranoff & McGuire, 2003). As illustrated in Chap. 1, public value-driven performance regimes could capture the increasing concerns of public administration on how to enhance inter-

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institutional governance initiatives to deliver public value to the administered community (Ansell & Gash, 2007; Crosby & Bryson, 2010; OECD, 2016, 2017; Osborne, 2010). Examples of such settings could include policy networks (Klijn & Koppenjan, 2000), network governance (Klijn, 2008; Provan & Kenis, 2007), collaborative governance arrangements (Edelenbos & van Meerkerk, 2016; Emerson et al., 2012b), public-private partnerships (Osborne, 2002; Skelcher, 2005), and other hybrid forms of collaboration (Cepiku, 2016; Lægreid & Rykkja, 2015; Skelcher, 2005). Within such inter-institutional settings, several public and private organizations conflate their aims and values with the intent to outline collaborative policies to address shared community problems. Given the tension of such arrangements to “social betterment” (Henry et al., 2000), the assessment of whether “particular activities lead to certain expected outcomes” (Dahler-Larsen, 2005, p. 615) is a matter of enquiring for the actors in the regime (Kristiansen et al., 2019; Rajala et al., 2020). This requires understanding the value (i.e., the worth) of what has been delivered to the community. As illustrated in Chap. 2, a systems understanding of policy results entails framing local area performance as a multidimensional construct (Bianchi et al., 2021; Bianchi & Vignieri, 2020). This implies supporting the actors in a public value-driven performance regime with outputs and outcomes measures at organizational and inter-institutional levels to gauge the short- and long-term effects of adopted policies under the financial, quality of public service, and social performance domains. In this way, relevant actors in the local context may “review their joint performance” (Douglas & Ansell, 2021, p. 951) by assessing policy sustainability under multiple interrelated perspectives. Implementing such performance management routines in cross-boundary settings requires a robust methodological framework that may help policy-makers and their stakeholders understand how individual and collaborative policies affect public value generation processes over time (Bianchi et al., 2021). The need for robust methodological support stems from both the institutional complexity shaping contemporary public administration—as discussed in Chap. 1— and the dynamic complexity characterizing community problems (i.e., wicked issues), as illustrated in Chap. 2. Advocating DPM as a method to implement inter-institutional performance management routines in cross-boundary settings has required dealing with several methodological challenges ranging from policy analysis, performance measurement, to evaluation. Such challenges have set the methodological context for illustrating the benefit of using DPM to enhance public value-driven performance regimes. By applying the DPM framework to different policy contexts—in Chaps. 3 and 4—this book has illustrated how the DPM framework may provide the actors in a public value-driven performance regime with the required methodological support to learn from outcome-based policy analysis. In particular, Chap. 3 has illustrated how learning through DPM may foster a dialogic form of policy-making and performance evaluation (i.e., policy learning) by

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discussing the role of communication and reflection within learning forums through fieldwork. In Chap. 4, DPM has been applied to three cases pertaining to three different policy contexts (i.e., public-private partnerships, policy networks, and collaborative governance) with the intent to show how such method may support policy-makers and their stakeholders to develop a robust causation analysis that may help them to pursue community outcomes, foster stakeholders’ learning, and promote institutional adaptation. By showing the causal links underlying community outcomes, public value drivers (i.e., performance drivers), and the associated strategic resources, the DPM framework may foster stakeholders’ accountability and learning and their tension for sustainable performance improvement (Bianchi, 2016, 2021; Bianchi et al., 2021; Bivona & Cosenz, 2018). While a robust method has the power to support a causal performance analysis in cross-boundary settings, it may also be subject to limitations. DPM does not pretend to portray detailed cause-and-effect relationships that can be sic et simpliciter turned into a simulation model, without any further analytical search of data or extension of the model boundaries. The system modeling perspective here presented borrows qualitative modeling to enrich performance management so as to broaden decisionmakers’ heuristics in policy analysis (Ghaffarzadegan et al., 2011; Kim et al., 2013; Sterman & Sweeney, 2007). This endeavor, as discussed in this book, implies several caveats. In the interinstitutional policy context, a central authority “is weak or non-existent” (Levin et al., 2012, p. 124) since the governance is multi-level, is multi-actor, and often involves several interrelated policy domains (Head & Alford, 2015; Lægreid & Rykkja, 2014). Such a condition may hamper the effectiveness of policy-making, especially because taming the surge of adverse effects of wicked issues requires timely interventions from all the actors involved in the network (Pollitt, 2015). However, this is not an easy task: a timely response may not occur as expected by local leaders since “the prehistory of antagonism or cooperation between stakeholders will hinder or facilitate” (Ansell & Gash, 2007, p. 553) shared policy responses. These aspects of institutional complexity shape the attributes performance regimes since they affect the characteristics of the routines adopted therein (Douglas & Ansell, 2021; Moynihan et al., 2011; Rajala & Laihonen, 2022). Rather than barriers to advancing our knowledge, the relationship between institutional complexity and performance management routines should be regarded as a matter for new robust investigations. Further research in performance management that commences walking on this road certainly will pose new theoretical and methodological issues to our current understanding of performance governance (Moynihan, 2009), but it will also bring us new interdisciplinary perspectives that will enrich our methods for enhancing performance regimes in cross-boundary settings.

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