Dynamic Performance Management (DPM) is a framework that may enhance collaborative platforms, through a performance governance approach. DPM challenges conventional views of performance measurement, which are based on consolidated practices of accounting, and imply a bounded and static view of the system from which reported measures originate (Bianchi, 2016). It aims at enhancing “intelligent” performance management, which is particularly needed when dynamic and complex problems are faced. Ignoring the dynamic complexity of social “wicked” problems is a main cause of policy resistance and unpredictable behavior of the systems that a public agency may try to individually affect.

To face this risk, DPM supports decision-makers in assessing performance under a sustainability perspective and in fostering accountability. It also supports the design of performance management systems that enhance trade-off analysis in both time and space (Bianchi & Williams, 2015). Trade-offs in time concern the effects of policies in the short vs. long term; trade-offs in space relate to the effects of policies on a subsystem vs. another subsystem. For example, in order to fix financial problems in the short run, municipal governments may implement an indiscriminate cut in urban renewal investments. Though this may perhaps improve Municipal financial performance in the short run, it will reduce urban quality of life in the long run. In fact, such policy might gradually worsen both city infrastructures (e.g. transportation, sanitation, water distribution, energy, green areas, information) and supplied services. Perceiving the effects that indiscriminate investment cuts may generate on resident wellbeing is not easy, since such unintended outcomes are affected by inertial forces that performance management should promptly detect.

DPM may support policy makers in framing such trade-offs, and perceiving how delays affect performance. This allows them to place performance measures in the broader context of the relevant system (Bianchi & Rivenbark, 2014). 

DPM focuses not only on output measures, but also on the intermediate and final policy outcomes. It also tracks the critical success factors (i.e. “performance drivers”) impacting on these results. Such factors are measured as ratios between current strategic resource levels affecting them and the desired (or benchmark) levels. For instance, a capacity ratio (i.e. current capacity/desired capacity) can be a driver affecting the volume and quality of provided services. Tracking performance drivers may help decision makers in perceiving weak signals of future change in the end-results, so to promptly counteract the effects of discontinuity on performance.  Their continuous monitoring and improvement may enable decision-makers to pursue sustainable outputs and outcomes. As shown in fig. 1, the end-results feed back into the strategic resources for the cycle of performance to repeat (Bianchi, 2016; Bianchi et al. 2017).

In designing and implementing policies through DPM, strategic resources are modeled as stocks of tangible or intangible assets that decision-makers build up and deploy to affect performance drivers, and end-results. Some of the strategic assets (e.g. capacity) can be built or directly purchased in the market. Some others (e.g. skills, image, quality) can only be developed as an outcome of implemented policies.  As shown in Figure 22.1, strategic resource dynamics is symbolized by ‘faucets’, i.e. inflows and outflows affecting their acquisition and depletion in a given time. They accumulate into strategic resources, which are symbolized as reservoirs. Through the outcome flows, policymakers may affect the endowment of strategic resources that cannot be purchased in the market.

DPM borrows and applies the principle of feedback and the concepts of stocks and flows – used in system dynamics (SD) – into performance management, to foster a “shift of mind” from a static to a dynamic view. It basically adopts insight and qualitative SD modeling (Bianchi et al, 2019; Xavier & Bianchi, 2019).

Insight SD modeling is an established practice, which fosters a descriptive and causal perspective of policy design (Bianchi et al. 2017; Bianchi and Williams 2015). Such approach may provide the basis for quantitative modeling, as a further stage of analysis, which requires substantial data, time and SD simulation skills. This condition is missing in many contexts, where a cultural change towards outcome-based performance management and collaborative governance is starting or in a transient state.

Fig. 1: Basic structure of Dynamic Performance Management chart
(adapted from Bianchi, 2016, p. 73)


Implementing performance governance requires linking policy making at the organizational level with network policies that may affect local area outcomes. 

Outcome-based performance management is rooted in an organizational perspective. Therefore, it adopts an “inside-out” view, which implies that policy design is outlined through the “lenses” of each organization, rather than also the local area where the organization is located.

If we consider urban contexts, a Municipality would design policies aimed at primarily pursuing efficiency and effectiveness in the provision of public services to individuals, groups and organizations, so to impact on agency outputs and outcomes. For instance, such policies may produce better crime control which would contribute to enhance the society feeling of safety. Also, better services to households may influence resident satisfaction; improving public green areas or roads may increase local area attractiveness.

However, such results affect society outcomes only indirectly. In fact, an urban area is populated by many other stakeholders, whose policies also influence city attractiveness and quality of life.    

Therefore, consistency between the policies designed and implemented by different local area stakeholders is needed, particularly when “wicked” social problems are addressed.  For this goal, an “outside-in”view of sustainable performance assessment is required. Through this view, policy design is first about a local area, rather than individual organizations. This allows stakeholders to outline collaborative policies that generate shared strategic resources at society level, which also strengthen performance at agency level. Such change in mental models is possible if stakeholders are supported by learning facilitators to perceive that by pursuing their individual goals to the detriment of community outcomes may lead to a crisis or diminished performance for their organizations (Bianchi & Vignieri, 2020).

Through an “outside-in” view, each agency would focus – as a next step of policy making – on how to implement the community policies agreed with the other stakeholders, with the goal to affect the endowment of shared strategic resources in a local area. This approach also fosters the principle of accountability in cross-sector collaboration and supports consensus building.

The illustrated view ultimately aims at turning a society into a community, whose purpose is not only to comply with laws and prescriptions: a community is profiled by a widespread active citizenship. Such concept is much broader than juridical or legal citizenship. It underlies a pervasive sense of belonging to a same group, by community members, who share not only a geographical space, rules or legal obligations, but also goals, values and culture.

Based on such perspective, we may now outline the attributes of Dynamic Performance Governance (DPG), as an overarching framework to DPM for implementing an “outside-in” view of policy design when a local area is the object of policy assessment. This approach enhances collaborative governance regimes, i.e. “collaborative summits where partners periodically gather to review their joint performance”.

An “outside-in” view of DPG frames policy design as a process aimed at fostering sustainable outcomes in a local area. This supports stakeholders in sharing policies that will enable them to interact on a same system, by taking complementary roles in leveraging common goods and other strategic resources, at both community and organizational level. Although shared strategic resources are not individually owned by any of the stakeholder institutions – and therefore are not under their direct control – they are important levers to build and sustain local area performance (Bianchi et al, 2019). The aptitude of local area stakeholders’ policies to generate community outcomes, by leveraging shared strategic resources, is a fundamental condition for the performance sustainability of both the area and its individual organizations.

More specifically, by adopting a Dynamic Performance Management & Governance approach, through an “outside-in” view, the designed community development policies provide a basis for implementation at an organizational level.  This requires that the policies designed at corporate level by each stakeholder institution pursue organizational outcomes which are consistent with the targeted community outcomes. It also requires that corporate policies are consistently cascaded at departmental level, and that implementation results are constantly monitored, through performance drivers, and emerging outputs and outcomes. Such control process, if referred to the implementation of policies related to social “wicked” problems, should not be bounded to a feedback mechanism. It should also enable a proactive feedforward logic (Otley, 1999, p. 369), implying that the emerging problems or opportunities from implementation at departmental level may suggest possible changes in the designed policies at both institutional and community level. This is the core of a strategic dialogue (supported by learning facilitators) between stakeholders with different roles at both an organizational and interorganizational setting.

The illustrated “outside-in” view of performance governance enhances the aptitude of outcome-based performance management to deal with the high level of embeddedness of social “wicked” problems. For example, counteracting a rise in the abandoned houses in a historic city center, requires that municipal departments coordinate their policy implementation efforts, possibly in collaboration with other stakeholders. In fact, abandoned houses usually involve a loss of image and neighborhood attractiveness, with potential negative implications for tourism and other sectors. They may also concern crime control, sanitation, and healthcare. They may even require municipal policies aimed at supporting house owners to refurbish their properties. For example, funding and other financial incentives (e.g. reduced taxation), or improving the permit system transparency and promptness might be needed. Furthermore, in consideration of possible house owner resistance to invest in the historic center because of its neglect, such effort might entail the need of proper laws and regulations (e.g. on local trade) and of collaborative efforts with other institutions.


Bianchi, C. (2016). Dynamic performance management. Zurich: Springer International Publishing.

Bianchi, C. Bovaird, T., & Loeffler, E. (2017). Applying a dynamic performance management framework to wicked issues: How coproduction helps to transform young people’s services in Surrey County Council, UK. International Journal of Public Administration, 40 (10), 833–846.

Bianchi C.  Bereciartua P., Vignieri V, & Cohen A. (2019). Enhancing Urban Brownfield Regeneration to Pursue Sustainable Community Outcomes through Dynamic Performance Governance, International Journal of Public Administration, https://www.tandfonline.com/doi/abs/10.1080/01900692.2019.1669180  

Bianchi, C. & Rivenbark, W. (2014). Performance Management in Local Government: The Application of System Dynamics to Promote Data Use. International Journal of Public Administration, 37, 13, 945–954.

Bianchi C. & Vignieri V. (2020). Dealing with “abnormal” business growth by leveraging local area common goods: an outside-in stakeholder collaboration perspective, International Journal of Productivity and Performance Management, 0, 0, 1-22, DOI 10.1108/IJPPM-07-2019-0318

Bianchi, C. & Williams, D. (2015). Applying system dynamics modeling to foster a cause-and-effect perspective in dealing with behavioral distortions associated with a city’s performance measurement programs. Public Performance & Management Review, 38, 3, 395–425.

Xavier, J. & Bianchi, C. (2019). An Outcome-based Dynamic Performance Management Approach to Collaborative Governance in Crime Control: insights from Malaysia, Journal of Management and Governance, https://doi.org/10.1007/s10997-019-09486-w, 2019.