A dynamic approach to applying performance …



Designing Performance Management Systems in

Academic Institutions:

a Dynamic Performance Management View

Abstract

This paper illustrates how to design and implement performance management systems in universities by identifying and modeling those factors impacting on academic performance through a dynamic performance management view. Particularly, combining performance management with System Dynamics modeling may allow academic decision-makers to better identify key-performance drivers for pursuing a sustainable performance improvement in universities. In the second section of the paper, a number of examples based on empirical findings from a field project aimed at designing a dynamic performance management model for the University of Palermo are discussed.

Key-words: Academic institutions, performance measurement and management systems, accountability, dynamic performance management view, field project.

1. Introduction

In the past two decades the Italian academic system has been affected by a number of law reforms, aiming at fostering an improvement of University performance, through the introduction of a set of parameters, based on which public funds are allocated.

Such policy has only partially achieved successful results. In fact, several problems have arisen because of the introduction of performance standards by the Italian Ministry of Research. Among such problems, there are: a “means-ends inversion” and a bounded attention of policy-makers in both time and space.

Improving performance management and accountability in academic institutions implies the understanding of a more complex system than a simplistic set of parameters used for the allocation of Ministerial funds. Such system should, rather, embody the organizational structure and processes, and encompass the interactions of the University actors with several stakeholders in the external environment. Namely, key-performance indicators and corresponding drivers, as well as strategic resources affecting them must be properly tracked and managed by decision-makers. Furthermore, delays and non-linearities often significantly affect the accumulation and depletion of strategic resources, and the associated performance drivers and end-results.

Such dynamic complex context requires that proper “lenses” are adopted to manage performance and foster accountability from inside the institution, first of all. The emphasis on a performance management approach, focused on the characteristics of the organizational system, requires that controllers and organization designers in Academic institutions produce an effort to understand problems/issues and opportunities that mostly characterize their own organization, rather than only applying external schemas designed by a Ministerial institution.

Based on the described conceptual framework, the aim of this paper is to illustrate how to design and implement performance measurement/management systems in universities by identifying and modeling those factors impacting on academic performance through a dynamic performance management view. Namely, combining performance management with System Dynamics modeling allows academic decision-makers to better identify key-performance drivers for pursuing a sustainable performance improvement in universities.

The second section of the paper is devoted to discuss a number of examples based on empirical findings from a field project aimed at designing a dynamic performance management model for the University of Palermo.

2. Research background

Academic institutions have recently been affected by significant reforms aimed to improve their own performance levels. The reason for these reforms has been inspired by various factors, such as budgetary restrictions imposed by national Governments and the “marketization” of the Higher Education sector (Clark, 1998; Deem, 1998).

Regarding this, the ordinary funding allocation carried out by the National Governments is strictly dependent on the performance that each academic institution achieves. Particularly, academic performance is assessed by the Ministry of Education on the basis of specific criteria and parameters which, above all, tend to measure intangible outputs and outcomes, such as quality in education and research activities, efficiency, effectiveness, internationalization and impact on the community.

This has led universities to increase their autonomy and accountability to successfully perform and compete in a worldwide competitive system. Both autonomy and accountability have involved greater emphasis on performance measurement and management (Lapsley & Miller, 2004).

On this concern, an important factor to sustain performance improvement and accountability processes in universities can be recognized in their own Planning and Control (P&C) systems. In order to support an academic performance improvement according to a sustainable P&C perspective, it appears necessary to design strategic P&C systems capable in enabling decision-makers to successfully steer universities towards their goals achievement (Salter & Tapper, 2002; Broadbent, 2007). As suggested by Otley (1999), P&C systems provide information that is intended to be useful to managers in performing their jobs and to assist organizations in developing and maintaining viable patterns of behavior.

In this perspective, a pilot project aimed to outline factors impacting on organizational performance and to model them through a dynamic performance management view, has been started in collaboration to the Rectorate and the CEO office board of the University of Palermo (Italy).

The aim of this paper is to show the conceptual framework behind this project and to discuss its first empirical findings. Particularly, the paper aims to demonstrate how tracking the feedback relationships between end-results, performance drivers and strategic assets in an academic institution, can significantly improve the ability of its decision-makers to manage and measure organizational performance.

In addition, identifying administrative products, mapping the underlying processes and matching them to key-responsibility areas is a major component for developing a System Dynamics (SD) model-based performance management approach. SD models may support decision-makers in identifying those policy levers on which to act to undertake sustainable performance improvement programs in universities.

Developing SD models also supports decision-makers in Academic Institutions to better recognizing and measuring key-performance indicators and the factors impacting on them. Simulation also supports one in distinguishing possible trade-offs between short- and long-term expected outcomes from adopted policies and underlies a feedback structure to monitor the causes of actual results.

3. On the causes and implications of the latest academic reforms in Italy

Over the last ten years, the Italian academic system has undergone a series of reforms which have deeply changed the way of running public universities.

The causes underlying such reforms are essentially due to two macro phenomena which have highlighted the unsustainability of an outdated system:

– the economic crunch that Governments have faced for some time;

– the competitiveness – at both national and international level – of the Higher Education sector, which found Italian universities unprepared.

As for the first phenomenon, the economic crunch has pushed Governments to improve investment allocation towards all public sectors (e.g., education, healthcare, infrastructures). This has involved a significant cut in financial resource transfers from central bodies to local authorities and has also delayed the enforcement of national development plans. The critical state of public finance has speeded up the implementation of reform processes. On this concern, given the increasing tightening of public funds, the reform has aimed at allocating public funds according to a performance-based ranking among institutions operating in the same sector. Such a mechanism – which aims to increase public organizations’ performance – has thus implied a rise of competitiveness among universities at national level: universities have now to focus on performance management in order to improve both quality of products/services supplied to customers and expenditures rationalization (Saravanamuthu & Tinker, 2002; Adler and Harzing, 2008; Marginson & van der Wende, 2009).

As for the second phenomenon, the growing competition among universities has determined a “marketization” of the Higher Education and, as a result, universities are now seen as “business-focused organizations”. To Amaral & Magalhães (2002: p. 6) “education is no longer seen as a social right; it has become a service”. Students started to be seen as customers or clients and universities viewed as service providers, that want to meet their client’s needs and expectations (Meek, 2003). As far as the subject of university “marketization” is concerned, the metaphor of the “Ivory Tower” by Powell & Owen-Smith (1998) appears meaningful: according to such metaphor, as universities are gradually identified with commercial richness, they also lose their uniqueness in the society. Universities are any longer seen as the “ivory towers” of intellectual activities and truth thoughts, but rather as enterprises run by arrogant people aiming at capturing as more money and social influence as possible. In such a context, the poor competitiveness of most Italian universities has clearly come out, particularly in comparison to other Countries’ best practices (e.g., USA). Actually, the rapid development of a Higher Education “market” has pointed out several critical issues related to managing academic institutions which, in most cases, were unprepared for the challenges introduced by a competitive environment (Neely, 1999; De Boer & Goedegebuure, 2001). Such phenomenon tackles an improvement in reputation which may bring new investments for both research and educational activities: concerning this, the past substantial investments of public resources towards the Higher Education sector have not resulted in an equivalent quality of research and teaching (Bleiklie, 2001).

Furthermore, the competitiveness of the academic system reflects the competitiveness of its country. In fact, by focusing efforts on the interaction between research, education and professional training, a national economic system may refine those assets and strengths allowing different “production systems” to compete with their rival economies. In this respect, innovation, technology and professional competences are unanimously considered as the only driving forces capable to face global challenges in the long-term, specifically in those well-developed economies where competition is no more based on the cost of inputs or on economies of scale (Czarniawska & Genell, 2002).

Therefore, innovative changes within the Italian academic system are leading university management bodies to discuss their current and outdated managing systems in order to ensure a successful survival throughout time.

4. Italian university public financing: the performance-based funding system

In Italy, the law concerning Italian university public financing has changed over the last twenty years and, as a result, has strongly modified university management[1]. Actually, following Great Britain pilot scheme, a number of changes have taken place all over Europe trying to harmonize the different academic systems in order to face education globalization challenges based on both higher competitiveness and customer satisfaction orientation (Jongbloed & Vossensteyn, 2001; Dill & Soo, 2005).

In particular, the measures adopted in Italy have been oriented to a decentralization of power from the Ministry of Education to universities, by (1) enlarging their financial and management/organization autonomy, (2) promoting accountability in internal and external communication processes, and (3) making decision-makers aware of their responsibilities along the hierarchical scale. Both autonomy and accountability have involved greater emphasis on the design of performance measurement and management systems in universities. Therefore, autonomy and performance measurement have been introduced as complementary aspects on which a total changing process is based.

Namely, the increase in autonomy has involved a major overhaul of public university funding system. Public funding (i.e., transfers from the Ministry of Education) represents the most important source of financing for Italian universities. On this regard, reforms have aimed to link the financial resource allocation system to the performance measurement of each university in order to reward those institutions resulting “virtuous”, by allocating a higher amount of public funds.

In the past, the public financing system provided resources to universities to accomplish a widespread “Welfare State” task oriented to ensure a satisfying and homogeneous performance level in educational activities. Such financing system was independent from efficiency, effectiveness and quality levels reached by academic institutions in providing educational services and research outputs towards end-users.

Nowadays, Italian universities operate in a new context characterized by a strong competitiveness as a result of the new public financing system that allocates resources on the basis of a performance-based ranking: in other words, the performance of each university is yearly assessed by the Ministry of Education which, subsequently, distributes the largest part of public funds to top ranked universities. Such mechanism is based on a meritocratic principle of resource allocation and, at the same time, its application encourages a performance alignment among all national academic institutions in terms of education quality, research output and management efficiency (Agasisti & Catalano, 2007; Bolognani & Catalano, 2007).

Therefore, the academic competitiveness is based on the performance level that each university is able to reach and on the resulting capability to obtain more funds (the so-called performance-based funding system). This means that the adoption of a rewarding system aims at putting in competition public universities to achieve not only financial resources, but above all increasing performance levels which may improve educational services towards citizens (Keenoy & Reed, 2008).

Particularly, the academic performance is measured by the Ministry of Education through a set of indicators which takes into account not only research and education activities, but also other critical issues, e.g., the level of internationalization, the ability to manage strategic resources, the capability to be funded by external financing bodies and sponsors.

4.1 Critical issues on the ministerial performance-based funding system: the need for implementing sustainable development-oriented indicators

In Italy, university performance indicators adopted by an external evaluator and funder, like the Ministry of Education, are based on “macro” measures. They provide limited information which make highly ambiguous and partial any effort aimed to understand and diagnose academic performance. In fact, the Ministry of Education essentially focuses individual financial measures and other isolated statistical data, as surrogates of “good performance”, with a aim to generate incentives and competition for funding among universities. For instance, one of the indicators to measure teaching quality refers to the credits gained by students because of the exams sustained within their own curriculum of study. However, an unintended result related to the use of only this measure, as an indicator of “good performance” in teaching, is that – in order to get more funds - universities may adopt loose students’ evaluation schemes. Though in the short-term this policy might work, in terms of higher cash flows, in the long run it might compromise both educational quality and university reputation[2].

Ministerial parameters are mainly focused on output, rather than outcome measures (Ammons, 2001: p. 12-14) and related processes. Such myopic and bounded view may result into a simplistic performance assessment, that may lead to distorted or wrong short-term evaluations, if observed under a perspective of university sustainable development [3].

Potential risks of inconsistency in ministerial assessment may regard the following issues (Cosenz, 2011):

– allocating more funds to universities that have shown a better performance is likely to weaken the competitiveness of other universities. As a consequence, it may enlarge the imbalance in the quality of the academic activities carried out by the latter in comparison to the former. Though this rule can be acceptable as a principle to encourage good performance, it might be questioned if one considers that “knowledge” is a public good;

– the outcome indicators, used by the Ministry of Education to measure the ratio between the quality of training and the employment rate of graduates from each university, do not take into account the features of the geographical areas where universities are located and this may involve a socio-economic imbalance in the development of regions;

– the ministerial effort to increase competitiveness in the academic sector and to lead Italian universities towards higher performance levels in education and research, should be accompanied by a parallel action aimed to promote the streamlining of both bureaucratic procedures and supporting activities carried out by back-office units;

– the ministerial performance measurement system mainly focuses on the short-term and, therefore, it may not be consistent with broader goals of university sustainable development.

Even though the above issues reveal a limited and incomplete assessment framework of academic performance, the design of performance measurement systems cannot overlook ministerial guidelines and criteria. In fact, excluding ministerial parameters from the set of performance measures adopted by universities runs the risk of diverting academic decision-makers’ attention on those measures leading to stable or increasing funding from the State.

However, a “sustainable development”-oriented performance measurement system should also include a wider range of indicators – in respect to the restricted range of ministerial parameters – based on which decision-makers may evaluate the progress resulting from the adoption of a given strategy or emerging problems that require proper analysis/diagnosis and reaction. This means that universities need a systemic and selective approach in identifying a balanced mix of indicators to support strategy design/implementation and performance management (Boland & Fowler, 2000).

For instance, indicators that universities should set in order to affect their decision-makers’ behavior towards competitiveness can refer to:

– quality of education, research, management and supporting activities (Dearlove, 1998). On the one hand quality has to be measured by comparing the delivered “product/service” to end-users’ expectations (e.g., availability and professionalism of front-office workers, exhaustiveness of teaching contents, relevance of publications, size of classrooms). On the other hand, quality has to be assessed by considering the efficiency level reached in administrative processes (e.g., mistakes in handling workload, waste of consumption materials, equipment breakdowns);

– time, referred to both end-users’ expectations on academic service provision (e.g., average waiting time in university administrative offices, delays in class scheduling, delays in updating curricula) and to production processes related to the efficiency level (e.g., time to complete administrative procedures, waiting time for payments and reimbursement, wages payment delays);

– productivity, considered as the ratio between achieved outputs or outcomes and resource consumption (e.g., the average number of publications per single researcher or department);

– flexibility, which represents the organization ability to timely adapt to external changes with a minimum waste of resources (e.g., average time to implement new administrative procedures, study programs and syllabus, assessment systems).

Designing a performance measurement system entails not only a nominal definition of expected results, but also their measurement, through proper indicators. A performance measurement system may represent a fundamental tool to support decision-makers in university management (Neely et al., 2004). It also acts as a coordinating mechanism, supporting organizational units to better interact with other units located on both lower hierarchical levels and on a same level.

Therefore, performance measurement is an integral part of a wider strategic management activity aimed at reaching a sustainable development in the academic service delivery (what) and its underlying processes (how). High-quality academic performance and sustainable development cannot be only conceived as the outcome of legislative reforms. Rather, their achievement depends on the use on a regular basis of strategic performance management tools tailored to the needs of academic institutions and to their organizational critical factors. This means that performance assessment must be oriented to support an enhancement of those critical success factors creating value in academic activities (Van de Walle & Van Dooren, 2010).

5. Designing academic performance management systems: from a financial equilibrium to a value creation perspective

As a consequence of the described changes, new performance management models are needed (Miller, 2007; Parker, 2002, 2011).

A prevailing view of managerial performance has traditionally been focused on the financial balance between expenditures and collections with the goal to pursue a financial equilibrium (Fitzgerald, 2007; Sporn, 2003; Modell, 2001; Pendlbury & Algaber, 1997). However, such a perspective today seems to be too bounded. In fact, though financial equilibrium is a fundamental principle to observe in any organization, evaluating performance requires a focus on also other perspectives related to the quality of programs and the outcomes from undertaken policies (Chenhall & Langfield-Smith, 2007). Therefore, not only financial balance, but also value creation (Moore, 1995) for a wide range of stakeholders should be the building block for a sustainable university organizational model (Guthrie & Neumann, 2007; Parmenter, 2007; Cave et al., 1997).

In academic institutions, value creation processes encompass several organizational units interacting to deliver “products/services” to external clients (e.g., students, enterprises, scientific community). Such units cannot be identified only in relation to the front-office and peripheral levels. They are rather related to back-office and central levels too. A lack of coordination between different units involved in the delivery of “products/services” may substantially limit the capability of an organization to generate value. This is particularly crucial for universities (Weick, 1976; Reponen, 1999). Here, a potential risk of structural dualism can affect the physiological interaction between Rectorate/Central offices and Schools/Research departments.

In order to overcome such risk, proper efforts should be made on different organizational levers, to foster coordination. Relevant means to pursue such goal can be related to organizational design (e.g., in term of organizational structure and coordinating mechanisms) and performance management systems. Regarding this, setting performance measures to drive the behavior of central and back-office units towards the desired outcomes, plays a key role. This is not an easy task, since it implies an understanding of the critical processes generating value in delivering such services. Therefore, a focus on only front-office units to measure performance in satisfying “customer” needs, often based on only surveys, is a too bounded approach (Bianchi, 2010; Broadbent, 2007; Propper & Wilson, 2003).

In order to set performance measures fostering the generation of value, according to an outcome and sustainable development perspective in academic institutions, critical factors to focus are (Bianchi, 2009a; Bianchi, 2010):

– “products/services”, “clients” (i.e. users) and the underling administrative processes leading to such services;

– the end-results measuring final targets, and corresponding performance drivers, to promptly detect and affect the symptoms of change in performance. Such indicators should provide a basis to settle proper incentive mechanisms, driving managers’ efforts towards desired outcomes;

– both responsibility areas and policy levers to affect results.

In this respect, a sustainable academic service delivery system should primarily take into account:

1) how a given set of “products/services” is delivered;

2) who is accountable for the achievement of results directly and indirectly associated to the provision of “products/services”;

3) where and when to intervene through proper corrective actions to bring back universities towards pre-set goals achievement.

In the next sections of this paper, an analysis of the above critical factors will be developed.

5.1 Mapping the value chain of academic service supply

Regarding university management, the identification of “products/services” and “clients” provides an important key to outline an approach aimed to affect academic performance according to a value creation perspective[4]. An administrative “product” may take a different connotation as a function of the “client” to whom it is delivered (Pitman, 2000). In fact, if we focus on an external client (i.e., on those people/institutions/stakeholders operating outside the university), then it is possible to identify a final “product” or, more frequently, a package of final products that is demanded. For instance, a bachelor degree is a final product, whose logical premise for a student (seen as a “client”) is linked to the provision of a package of final products which are logically and sequentially related each other[5].

In order to supply a final product to an external client, back-office units are expected to deliver a set of “instrumental” products to their internal clients. Internal clients are those back-office units which receive from the units operating backwards in the value chain leading to the final product, those products/services that will be further processed by them to make progress in the supply of services to an external client.

Therefore, performance in delivering an “instrumental” product affects the performance of the internal “client” receiving such product. This, in turn, influences the performance of the other internal “clients” who are sequentially located along the academic value chain leading to the delivery of the “final” product.

Competitive advantage, as well as end-users’ satisfaction, mostly depend on both quality and efficiency resulting from the development of “instrumental” products.

Figure 1 displays the logical relation between “administrative” products, as described so far.

[pic]

Fig. 1 – Logical hierarchies among different administrative “products” referred to “clients”.

Based on the discussed framework, figure 2 shows that, if one refers to a given “final” product, it is possible to identify a system of products resulting from the fulfillment of administrative processes by each organizational unit whose only “clients” are internal in a given university. Such a top-down approach (which gradually moves from synthesis to analysis)[6], implies a more selective search of relevant data to track academic performance. Moving backwards in such analysis, i.e. from final to instrumental “products”, allows academic decision-makers to: (1) frame the performance management cycle leading to “final” products, (2) make performance drivers explicit and (3) promptly change policies to drive a university towards sustainable development.

[pic]

Fig. 2 – The identification of “clients” and “products” within the academic value chain.

For instance, the ‘Undergraduate Students Prospectus’ is a final product that a university delivers to its potential enrolling students (i.e., external clients) as a result of administrative steps underlying critical factors impacting on university outcomes. Such critical factors can be identified in relation to internal clients and corresponding “instrumental” products, as well as to the processes carried out by back-office units located along the value chain. If we consider instrumental products in a chronological sequence, we may identify the following: course syllabus outline delivered by Departments to Faculty Boards; study programs delivered by Faculty Boards to the Academic Senate; curriculum proposal approved by the Academic Senate and sent to the “Education” central unit; ‘Undergraduate Students Prospectus’ duly recorded and delivered by the “Education” central unit to potential enrolling students.

For each administrative “product” delivered to external and internal “clients”, the identification of factors impacting on related academic performance requires a mapping effort concerning:

1) underlying processes and activities;

2) involved responsibility areas;

3) related available policy levers, and allocated resources;

4) performance indicators (fig. 3).

[pic]

Fig. 3 – A framework to link university back-office and central units to front-office and peripheral units in the delivery of “products/services”, according to a value generation perspective.

5.2 A three-dimensional perspective to frame performance in academic institutions

A three dimensional framework can be adopted in order to operationalize the previous analysis. Three inter-connected views are relevant to manage academic performance (Bianchi, 2009b; 2012); they are:

1) an “objective” view;

2) an “instrumental” view;

3) a “subjective” view.

The “objective” view implies that products generated by the fulfillment of administrative processes are made explicit. As previously remarked, such an approach requires that a backwards analysis, aimed at identifying final/instrumental products and related external/internal clients, is adopted.

The “instrumental” view allows decision-makers to identify end-results and performance drivers. Related to them, it also supports decision makers to understand how strategic resources allocation may affect performance. It also explores how strategic resources are in turn influenced (i.e. increased or depleted) by end-results. Particularly, this perspective aims at defining a set of measures with regard to both performance drivers and end-results. Possible examples of performance drivers related to the management of academic institutions can be those which measure the promptness in updating curricula, the effectiveness of academic equipment (e.g. number of breakdowns) or the employees’ satisfaction. In order to affect such drivers, each responsibility unit is expected to build up, preserve and deploy a proper endowment of strategic resources (Ewell, 1999).

The “subjective” view provides a synthesis of the previous two perspectives, since it makes explicit – as a function of pursued results – processes and activities to be undertaken, together with related objectives and performance targets to be included in the budgets of each organizational unit. This view requires that performance measures associated to academic services delivery are made explicit, and then linked to the goals and objectives set by decision-makers operating in different organizational units.

The following figure provides a synthesis of the three dimensions of performance management as above described.

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Fig. 4 – Three views for designing a performance management system in academic institutions.

6. Designing a system for academic performance measurement: empirical results emerging from a pilot project in the University of Palermo

The University of Palermo (UNIPA), Italy – established in 1805 – is made up of twelve faculties, operating in Western Sicily also through the branches located in Trapani, Caltanissetta and Agrigento. Since 2008, UNIPA has started a renewal in organizational processes, to increase the quality of teaching and research activities, and also to foster efficiency.

To this end, a change in the organizational structure was made. Today, UNIPA is organized around the following organizational units:

1) Education;

2) Research & Development;

3) Economy and Finance;

4) Human Resource;

5) Technical Services;

6) Property and Patrimonial Estate;

7) Legal Affaires;

8) Network Services.

On April 2010 a pilot project aimed at designing a performance measurement system has started. Both Rectorate and peripheral Administrative staff are involved in the project. Based on empirical findings emerging from such endeavor, the next section of this paper will outline the findings that have emerged so far in the design of performance indicators related to the ‘Education’ and ‘Research & Development’ units of UNIPA.

Fig. 5 describes an example of dynamic performance management framework focusing the university image and financial resources. Particularly, such a framework illustrates a set of measures in relation to both performance drivers and end-results regarding four administrative products, i.e. “publications”, “enrollments”, “graduation” and “graduated students employment”. Both responsible academic structures and management areas are identified with reference to each product.

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Fig. 5 – A three dimensional approach to frame performance at UNIPA: an example focused on image and liquidity.

In designing such a framework, a systemic perspective of the University has been adopted to underline how end-results of a given product provision contribute in enhancing (or depleting) strategic resources of another one located downwards the value chain. The main focus has been oriented to both UNIPA image and liquidity.

In addition, performance drivers are identified in correspondence to end-results, whereas related strategic resources are made explicit for each selected product. Particularly, performance drivers are ratios between a current state (resource) and a benchmark, which affect performance, usually through a normalized graph function (Bianchi & Rivenbark, 2012)[7]. Benchmark values may come from competitor standards or expected targets as set up in internal strategic planning processes [8].

The design of the above framework started from identifying end-results related to each selected product. Respectively, we identified:

- change in submitted articles, change in published articles, change in UNIPA authors’ citations, change in UNIPA image and cash flows, as results of the publication process;

- change in enrolled students, change in curricula of study, change in UNIPA image and cash flows, as results of enrollments;

- change in both graduated students and in UNIPA image, as results of graduations;

- change in employed graduated students, change in agreements with enterprises and in UNIPA image, as results of graduated students’ employment.

Subsequently, the design of performance drivers has been based on those factors affecting the above end-results. On this concern, referring to ‘publications’, the ratio between submitted and planned articles per researcher per year and the time to develop research projects influence the change in submitted articles. Such indicator, that measures researchers’ productivity, also affects cash flow since it basically impacts on the ministerial index used to allocate public funding towards universities. The UNIPA research quality compared to its competitors’ quality[9] influences the change in both articles published in scientific journals and UNIPA authors’ citations. The ratio between published and submitted articles together with the ratio between UNIPA and competitors authors’ citations are drivers of the change in university image. In addition, we linked the described performance drivers to their related strategic resources. Namely, these latter are: liquidity, researchers and their skills, published and submitted articles, citations, libraries and scientific database.

Concerning ‘enrollments’, the ratio between UNIPA and its competitors image, the ratio between innovative and existing curricula, and the time to promote curricula, influence the change in enrolled students. The ratio between innovative and existing curricula also affects the change in UNIPA image. The ratio between new enrolled students at UNIPA and at competitor universities may generate more cash flows through enrollment fee payments. Again, performance drivers depend on a set of strategic resources, which include: UNIPA image, curricula of study, human resources, liquidity and UNIPA enrolled students.

As for ‘graduations’, we identified four performance drivers. The relative time to complete curricula and the relative teaching and tutoring time affect the change in graduates. On the other hand, the ratio between UNIPA students graduated in time and total graduates and the ratio between UNIPA and competitor universities students graduated in time, influence the change in UNIPA image. In this case, strategic resources to be used to improve performance are: UNIPA enrolled students, lecturers and their skills, laboratories and other education equipment.

Regarding the ‘employment of graduated students’, the ratio between actual and planned placement agreements with enterprises affects the change in employed UNIPA graduates. The UNIPA image is influenced by both the ratio between graduates at work (calculated also after three years from graduation) and the total graduates, and the ratio between UNIPA and competitor universities graduates at work. The ratio between UNIPA and competitor universities image impacts on the change in agreements with enterprises. These performance drivers may be affected through the following strategic resources: UNIPA image, UNIPA graduates, placement agreements with enterprises, human resources.

7. Framing the performance of fund raising processes at UNIPA: a dynamic performance management view

An academic performance management system can be useful to steer universities according to a sustainable development perspective. Actually, several factors – such as management complexity, resistance to changes, uncertainty and turbulence from the external environment – strongly limit academic decision-makers in understanding management control results and, consequently, make strategy design and implementation quite problematic.

Namely, the dynamic complexity underlying academic institution management represents one of the main causes for the unsatisfying performance levels achieved so far by Italian universities (Cepiku & Meneguzzo, 2009). On this regard, a major implication of dynamic complexity refers to a difficult identification of the drivers related to the processes impacting on academic institutions. This problem is also due to the adoption of a static and bounded approach to performance management systems, as described in sections 3 and 4.

Empirical results emerging from translating current performance management approaches into practice reveal that a static analysis of value creation processes does not take into account time delays existing between the adoption of a given policy and its related effects, and provides a limited contribution to improve strategic learning processes of academic decision-makers.

To overcome such undesired effects, management practice can be supported by combining performance management systems with SD models[10]. As a wide range of research and studies prove, the SD methodology is applied to different disciplinary contexts as it can analyze dynamic complexity of social systems through sketching and using conceptual and simulation models aimed at interpreting phenomena. Particularly, in the described project to model performance at UNIPA, SD modeling has been used to provide decision-makers with proper lenses understanding the feedback-loop structure underlying organizational performance, and to identify alternative strategies to adopt so as to change the structure for performance improvement (Morecroft, 2007; Richmond, 2001; Ritchie-Dunham, 2001; Warren, 2008). As strategic learning tools, SD models can be properly linked to financial models to support the performance management cycle (Bianchi, 2002; Bianchi, 2012) according to a dynamic perspective (Bianchi & Montemaggiore, 2008). As Fitzgerald (2007) remarks, “measurement is not an end in itself. To be effective it must be part of a feedback control system where corrective action is taken within the process – and results are fed back into consideration of future strategy”.

In this section, a dynamic performance management perspective related to the UNIPA “Research & Development” area will be described. In order to frame critical issues related to short- and long-term performance attainment of this area, our analysis has been focused on the following final products:

1) scientific publications;

2) patents and academic spin-off;

3) fund raising through research projects funded by external calls for application;

4) fund raising through research partnerships with external financing bodies;

5) PhD programs set-up and implementation.

To design dynamic performance management models related to such ‘products’, we have adopted the approach described in previous sections.

In the “Research & Development” area, one of the most important goals is improving the capability of the University to attract external funding, e.g., by submitting research proposals to the European Commission calls. Here we will focus our analysis on an insight model framing the delivery of a specific ‘product’ related to the “R&D” area, i.e. the agreements with external financing bodies to raise funds for research activities. Actually, the capability of a public University to attract research funds from external institutions plays an important role in the annual performance evaluation conducted by the Italian Ministry of Education and Research (MIUR).

As discussed in the previous section, image is a strategic resource affecting the capability of a university to invest in specific research projects. Image is likely to affect the behavior of a number of stakeholders, which can influence university cash flows (e.g., enterprises, banks, and public sector organizations).

The main actors playing a crucial role in promoting, searching and developing funded research projects to be run jointly with third party organizations at UNIPA are: (1) the academic departments and (2) the administrative fund raising office. This latter directly reports to a first level unit called “Institutional Research”. These bodies are used to undertake a number of initiatives to develop new contacts and start agreements with third-party organizations (e.g., organizing events and meetings to advertise research activities, legal and technical assistance in drawing up agreements).

The key-processes related to an agreement with external financing bodies can be described as follows:

1) promotion of University research activities;

2) preliminary negotiations and research project proposals between academic departments and external institutions;

3) research project set-up and definition;

4) final agreement stipulation;

5) research project funding management and operation;

6) research project scientific management and development.

The strategic resources mostly affecting performance in managing such processes are:

– financial resources the University invests in fostering research skills development (i.e., training), hiring new academics (i.e., lecturers, researchers, assistant-associate-full professors, PhDs), and purchasing new equipment (e.g., scientific database);

– University and its key-players’ image;

– academics using ‘relational capital’ to attract external investors in new research project partnerships (Stewart, 1997);

– research skills;

– research equipment, i.e. laboratories, scientific database, libraries, etc;

– both submitted and published articles, together with related citations.

A proper use and coordination of such strategic resources can allow UNIPA to affect end-results by targeting a number of performance drivers. Here, the following drivers have been identified: (a) the ratio between research funds acquired by the external bodies and total funds allocated by UNIPA for research activities; (b) the ratio between published and submitted articles, (c) the ratio between UNIPA authors’ citations and competitor universities citations, (d) the researchers’ productivity defined the ratio between papers and researchers per year, (e) the perceived research quality resulting from the ratio between actual and planned research findings. Among these parameters, only the first one is adopted by the Italian Ministry of Education to measure University performance, while the others are introduced to improve performance measurement effectiveness and, as a result, to support strategic learning processes of academic decision-makers.

[pic]

Fig. 6 – A dynamic performance management view model of fund raising processes.

Fig. 6 depicts a dynamic performance management insight model that highlights the main feedback loops in relation to UNIPA fund raising processes.

Particularly, the reinforcing loop R1 shows how an improvement of University image positively influences – other conditions being equal – the acquisition of new research contracts with external funders, which may foster again an improvement of image.

Loop R2 shows how an increase in liquidity directly affects investments in research skills development. This may improve the research quality indicator (i.e., the ratio between actual and expected research findings) and, consequently, increases new publications on high-ranked journals. Such increase in publications directly affects the number of citations reached by UNIPA authors. This, in turn, positively affects the ratio between UNIPA and competitor universities citations. Such ratio positively influences University image. Again, this may imply new research agreements with external investors providing funds to carry out joint research activities. Also, external funds invested in research activities positively affect the ministerial parameters (namely, the ratio between research funds acquired by external bodies and total funds allocated by UNIPA for research activities). This leads to higher public funding and, therefore, liquidity.

As described regarding loop R2, financial investments may also be focused on both hiring academics and purchasing new equipment: as showed in loop R3 and R4, this increases academic staff (i.e., researchers, scholars, professors) and research equipment which affect the research productivity indicator. As a result, fostering productivity may generate more new articles to be submitted to high-ranked journals. In this case, submitting more articles to high-ranked journals positively contributes in collecting new publications. On the one hand, a higher stock of publications positively affects citations and its related driver (i.e., the ratio between UNIPA and competitor universities citations). On the other, it may improve the ratio between published and submitted articles (see loop R5). Both ratios directly influence the University image, whose improvement may provide new research agreements with external funders. More external funding to research activities positively influence ministerial indicators and allow UNIPA to obtain more public funds, which generate liquidity.

Similarly, the loop R6 remarks that, by using ‘relational capital’, academics may directly contribute in collecting new contracts with external investors and, therefore, raising more public funds generating more liquidity.

On the other hand, an increase in liquidity allows academic decision-makers to invest in hiring more academics; this involves an increase in total salaries paid by the University and, consequently, a decrease in liquidity (B1).

As illustrated by loop B2, UNIPA invests its liquidity also to finance research activities. This may reduce the ratio between external and internal funds invested to research. A decrease in the ministerial parameter directly affects public funding allocation towards UNIPA, which again feeds into liquidity.

Loop B3 addresses the connection between UNIPA and its competitors. Particularly, an increase in research contracts between the University of Palermo and external funders weakens the performance-based ranking of the other Italian competitor universities. According to the ministerial performance-based funding system, such a circumstance may lead to decreasing public resource allocation towards such universities. Therefore, a lower ranking of other Italian universities may involve a stronger reaction aimed at counteracting such a phenomenon. In other words, Italian competitor universities will be more focused on adopting counteracting policies to be more competitive. As a result, competitor universities counteracting policies may imply a reduction in the public funding allocation towards the University of Palermo. This determines ceteris paribus a decrease in liquidity and, eventually, a reduction of investments in research skills development. The impoverishment of research skills directly influences the quality of research activities; this may obstacle an improvement of UNIPA image, which feeds back into research contracts between the UNIPA and its external funders.

Loop B4 illustrates that investments in both acquiring new research equipment and hiring academics impact on researchers’ productivity that increases the number of articles to be submitted for publishing. More submitted articles negatively affect the ratio between published and submitted articles which, in turn, directly influences the University’s image. An improvement of image causes an increase in new research agreements with external funders whose contribution directly impacts on the ministerial parameter. This may lead to collect more public funding that, eventually, enables the University of Palermo to accumulate more liquidity to be invested again.

Loop B5 implies that an increase in University image may involve new research contracts with external funders and, as a result, more external funds towards research activities that improve ministerial indicators. This causes a negative impact on the performance-based ranking of competitor universities which, in turn, put a major effort in adopting counteracting policies aimed at increasing their authors’ citations, in order to negatively impact on the ratio between UNIPA and its competitors citations. A decrease of such indicator directly influences UNIPA image.

8. Conclusions

As a result of the recent reforms which have implied radical changes in running Italian public universities, this paper has outlined a dynamic performance management view to design and implement performance management systems aimed at pursuing sustainable development in academic institutions. Such an approach is necessary to tackle possible undesired effects which may stem from a bounded view in designing university management systems based only on ministerial performance measures. By using a value creation perspective, this paper has addressed the need to design performance measurement/management systems which may balance short- and long-term, and support a better coordination between front-office and back-office units, as well as central and peripheral structures.

This paper has also emphasized how modeling feedback relationships between end-results, performance drivers and strategic resources, may support decision-makers in managing and measuring the performance of academic institutions. In addition, the intent to link back-office units to front-office in performance evaluation, has led us to remark how crucial is identifying administrative products, mapping the underlying processes and matching them to key-responsibility areas. Actually, the identification of processes, internal clients and related products, available resources, policy levers, and responsibility areas, provide the backbone for an effective implementation of performance improvement programs in academic institutions.

Combining SD models with performance management enables decision-makers to better identify and measure key-performance indicators and to effectively influence policy levers to pursue a sustainable development in universities.

The application of such approach to UNIPA has been discussed in the second section of the paper.

Further research will be necessary to develop more applied knowledge on academic performance management systems. However, on the basis of the analysis hereby presented and related empirical results, it seems realistic to expect further improvements in our research according to the logical framework here described.

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[1] Law 537/1993; Law 244/2007; Law 133/2008; Law 240/2010.

[2] Other examples of ministerial indicators are: the number of graduates with regular curricula length, the amount of funding from external financing bodies, the average number of fellowships per doctorate programme, the fraction of fellowships externally-funded for doctorate programmes, the ratio between the working graduates after a year from their degree achievement and the total number of graduates in the same year, the number of credits earned during non-academic activities, the percentage of foreign students enrolled in degree courses, the percentage of foreign students enrolled in doctorate programmes.

[3] For instance, focusing efforts on the search of high-volume and value funding of research projects from external institutions in the short-term – regardless the strategic relevance for future research of the findings emerging from such projects – could jeopardize the allocation of resources to more long-term innovative projects.

[4] It is worth remarking that, if one refers to academic services, by “product” it is possible to mean a result generated by the fulfilment of a process or a combination of processes, in favour of a given “client”. By “client” we mean, instead, an entity (either an individual, or group of people, or a front/back-office organizational unit, or other institutions) who benefits from a given “product” delivered by administrative processes.

[5] For instance, the issue of an identification code and its related certificate; the issue of a certificate of enrolment to a new academic year or of the approved syllabus; the transcript of records; the provision of internships.

[6] A top-down approach contrasts with a bottom-up approach which, starting from analytical elements, moves towards synthesis. This latter emphasizes the use of statistical methods designed to collect data and information which, starting from the analysis of different organizational units performance, are meant to reach an overall measurement system able to express the university global performance. Nevertheless, such data acquisition would lead to a random collection of information characterized by a lack of both selectivity and systemic perspective on performance achievement processes. Consequently, such approach may limit academic decision-makers in steering universities according to a sustainable development perspective.

[7] Performance drivers are different from performance indexes. Performance indexes are synthetic measures of the quality or state of the system. They do not affect performance. Implying that an improvement in such indexes generates an improvement in other variables underlies inverting between causes with effects.

[8] Neely et al. (1995) argue that benchmarking is used as a means of identifying improvement opportunities as well as monitoring the performance of competitors. They also cite Camp (1989) as having the most comprehensive description of benchmarking: benchmarking as the search for organization best practices that lead to superior performance. In terms of performance management, however, Neely et al. cite Oge & Dickinson (1992) who suggest that organizations should adopt closed loop performance management systems which combine periodic benchmarking with ongoing monitoring. As a result, closed-loop performance management systems are able not only to provide the measure related to the performance of each organizational unit, but also to explain how their distinctive performance contributes to the overall academic result.

[9] The research quality may be measured by comparing actual and planned research findings or by the ratio between UNIPA and competitor universities high-ranked publications.

[10] An in-depth overview of System Dynamics methodology can be found in Forrester (1961) and Sterman (2000).

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