Int. Journal of Business Science and Applied Management, Volume 7, Issue 1, 2012

Developing a performance measurement system for public

research centres

Deborah Agostino

Dipartimento di Ingegneria Gestionale, Politecnico di Milano

Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy

Telephone: +39 02.2399.4073

Email: deborah.agostino@polimi.it

Marika Arena

Dipartimento di Ingegneria Gestionale, Politecnico di Milano

Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy

Telephone: +39 02.2399.4070

Email: marika.arena@polimi.it

Giovanni Azzone

Dipartimento di Ingegneria Gestionale, Politecnico di Milano

Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy

Telephone: +39 02.2399.3981

Email: giovanni.azzone@polimi.it

Martina Dal Molin

Dipartimento di Ingegneria Gestionale, Politecnico di Milano

Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy

Telephone: +39 02.2399.4044

Email: dalmolin86@gmail.com

Cristina Masella

Dipartimento di Ingegneria Gestionale, Politecnico di Milano

Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy

Telephone: +39 02.2399.4081

Email: cristina.masella@polimi.it

Abstract

This study aims at developing a performance measurement system (PMS) for research and development (R&D)

activities carried out by public research centres. Public research institutions are characterized by multiple

stakeholders with different needs, and the management of R&D activities requires balancing the multiple goals

of different stakeholders. This characteristic is a key issue in the process of construction of the PMS. Empirical

evidence is provided by an Italian public research centre, where the researchers carried out a project aimed to

develop a PMS following action research principles. This project gave the possibility to researchers to interact

with different stakeholders and integrate their different information needs in a comprehensive set of key

performance indicators (KPIs). As a result, multidimensional framework for measuring R&D performance in a

public research centre is proposed and a set of Key Performance Indicators is developed, suggesting

implications for academics and practitioners.

Keywords: performance measurement, public research centres, stakeholders, accountability, decision-making

Int. Journal of Business Science and Applied Management / Business-and-Management.org

1 INTRODUCTION

In recent years public research systems in several industrialized countries, underwent an intense

transformation process due to the increasing organizational complexity of research & development (R&D)

activities and the hit of the worldwide financial crisis. Leading research institutions claim that many complex

problems of the society demand innovative solutions which combine knowledge from different scientific areas

that can be achieved through interdisciplinary research (National Academies, 2005). To achieve this, very often,

research centres carry out interdisciplinary R&D projects, which require collaborative and sometimes informal

behaviours (Cross, Borgatti and Parker, 2002; Allen, James, & Gamlen, 2007) and new forms of organization

and management (Welter, Jooß, Richert, Jeschke, & Brecher, 2012). At the same time, the financial crisis has

reduced the overall government spending and specifically the budget for research centres (e.g. Arena

&Arnaboldi, 2013). This reduction of funding has increased the competition between research institutions, while

at the same time they are required to demonstrate their value for money. To face these challenges, research

institutions need to consciously manage their core processes, the creation and development of their knowledge

assets (Rowley, 2000), and consistently redesign their support processes and managerial instruments (e.g.

Arena, Arnaboldi, Azzone & Carlucci, 2009; Arena, Arnaboldi & Azzone, 2010a;Welter et al. 2012).

In this context, our paper focuses on one specific managerial instrument - i.e. performance measurement

system (PMS). In the last decades, measuring R&D performance has become a fundamental concern for R&D

managers and executives. Scholars and practitioners have already recognized the relevance of performance

measurement in R&D in relationship to different purposes (e.g. Chiesa, Frattini, Lazzarotti&Manzini, 2009;

Kulatunga, Amaratunga&Haigh, 2011). A PMS can be useful for motivating researchers, evaluating R&D

projects’ profitability, supporting decision-making, communicating the centre’s results to external

constituencies, and stimulating organizational learning.

However, most of the current research about PMS in R&D focuses on private sector organizations (e.g.

Bremser&Barsky, 2004; Chiesa et al., 2009) and these results are not easily applicable to Government-funded

research centres, since they overlook some key characteristics of these organizations. In industrial firms, R&D

activities are primarily financed by the company itself and they represent one of the activities in their value

chain. In this context, the company does not have the need of searching for research funding, given that it is self

financed and research outputs represent the input for further processes of the firm’s value chain. The research

results are incorporated into products and, in the end, brought to the market and sold by the company,

‘hopefully’ resulting in an increase in the revenue and profits of the firm, and contributing amortize the R&D

investment. On the contrary, in Government-funded research centres, such as public research institutions, the

research activity represents the core mission of the organization and the R&D activity needs to be financed by

external parties. This structure has two main implications. On the one hand, the role played by the research

activity is central, because the output of the research represents the end itself. Research institutes contribute to

the early stages of the innovation process of various customers within the national innovation system and serve

thus as an important research infrastructure, (Shenker, 2001; Leitner & Warden, 2004). On the other hand, the

need for searching funding is associated with the pressure of demonstrating the ability of generating research

outputs that provide value for the society. The output of public research centres is expected to have a positive

impact on the wider society; their principal aim is to disseminate the research results and to have a return in

terms of scientific-technological progress, driving the national strength (Senker, 2001; Coccia, 2004). However,

once the funding are received, mainly by the public government (Senker, 2001; Coccia, 2004), the research

centre has to be accountable on how it spend public money. In this sense, Coccia (2005) argued that research

institutions are dependent on the government for carrying out their activities. As consequence of these

specificities, public research centres are subject to different and contrasting stakeholders pressures that are wider

compared to private sector organizations (Dixit, 2002; Coccia, 2004; Arnaboldi, Azzone & Savoldelli, 2004).

Public government, the wider society, the researcher, the administrative manager but also private partners, have

different objectives and their alignment can be extremely complex, especially in the current R&D landscape

characterized by reduced finance and higher competition between institutions.

To our best knowledge, relatively a few contributions have dealt with the issue of R&D performance

measurement in the public sector (e.g. Coccia, 2001a; 2004; Leitner & Warden, 2004; Secundo, Margherita, Elia

& Passiante, 2010) and a comprehensive framework for measuring performance of public research institutes is

still missing, setting the motivation for this work. This paper has the objective to develop a performance

measurement system (PMS) for a public research centre. The centre has to deal with the informative needs of a

diversified range of stakeholders that require accountability in relationship to the centre’s activities and, in

particular, the use of funding, but also aim to encourage collaboration between different research units and

support decision making processes. The PMS has been developed through action research, whereby the

researchers interacted with different stakeholders and integrated their different information needs to a

comprehensive set of key performance indicators (KPIs). Interviews and meetings with scientific and

Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella

administrative directors over a time horizon of ten months were useful for designing a dashboard for public

research institutions that integrates the information needs of a plurality of stakeholders.

The paper is organized as follows. The next section outlines the state of the art on the issue of performance

measurement for R&D activities. The third section details the methodology used for data collection and

analysis, describing the phases of the research project. The fourth section presents the PMS framework and the

set of indicators that have been developed. Finally, the last section clarifies contributions and limitations of this

research.

R&D performance measurement in research centres

Various studies on public research centres show a growing interest in R&D performance measurement

(Luwel, Noyons & Moed, 1999; Senker, 2001;Coccia, 2004; Coccia & Rolfo, 2007). Originally, particular

attention has been given to the use of bibliometric and technometric indicators to evaluate public research centre

performances (e.g. Narin & Hamilton, 1996; Luwel et al., 1999; Noyons, Moed & Luwel, 1999). Bibliometric

involves the quantitative analysis of the scientific and technological literature. It relies on the assumption that

the frequency with which a paper or a patent is cited is a measure of the impact of the paper or patent. The most

common bibliometric indicators are literature indicators – i.e. indicators that measure the scientific performance

based on the number of publications and the count of citations in the scientific literature, and, obviously, the

higher the better. Similar considerations are applicable to patents too. Those patents which are most highly cited

and most science linked are also the patents that tend to be most heavily licensed, and are therefore making the

largest contribution to the economy (Narin & Hamilton, 1996).However, bibliometric indicators provide a

partial and non-systemic picture of the performance of a research centre, because they overlook some relevant

elements such as the impact of the research activity on the society and they ignore the role played by

government funding (Coccia, 2004).

Moving from these considerations, Coccia proposed a weighted index to provide a synthetic measurement

of public research’s activities - Relev Model I and Relev Model II - (Coccia2001b; 2004, 2005). The Relev

Model is based on an input-output model, where inputs consist in public funds, personnel payrolls and cost of

labour, and outputs are represented by self-financing deriving from activities of technology transfer, training

(e.g. number of degree students, PhD students), teaching (measured as the number of courses held by

researchers), international and domestic publications, international and domestic conference proceedings. Based

on a weighted input – output ratio, the Relev model attempts to synthesize the performance of public research

centres in a unique score. This score has the final objective to support external accountability, providing

evidence of the value for money generated by these institutions. Indeed, these studies generally adopt one

specific perspective. They reflect the aim of governments to define metrics that could be used to assess the

research centres performances in order to facilitate the identification of the most and the least productive

laboratories, and to support policy decisions on the level and the direction of the public funding of research

(Coccia, 2004). These kinds of metrics, instead, are less useful to support internal decision making and report a

centre’s performances to a broader range of stakeholders.

To respond to this need, a few studies started to focus on the use of key performance indicators for

assessing R&D results in public sector research centres (e.g. Leitner & Warden, 2004; Chu, Lin, Hsiung & Liu,

2006; Secundo et al., 2010). This stream of research is based on Intellectual Capital reporting models (e.g.

Stewart, 1997; Edvinsson & Malone, 1997). At a general extent, the Intellectual Capital encompasses three

components: human capital, structural capital, and relational capital. The human capital refers to the

characteristics of the entire organization’s staff and management. The structural capital refers to organizational

infrastructure. The relational capital refers to the establishment, development and maintenance of relationships

with external stakeholders. The combination and integration of these forms of capital determines the entity’s

results that include both economic performances and other intangible results, such as research- and society-

oriented results (Leitner & Warden, 2004). These approaches to performance measurement provide a more

complete picture of the resources available to research centres, however they seem to overlook the performances

of the centres in the transformation process of inputs in outputs (e.g. they overlook issues such as efficiency and

productivity).

Finally, further evidence in relationship to R&D performance measurement is provided by the private

sector literature, where the topic has been investigated from two different perspectives. In particular, one stream

of research focuses on the choice of the performance dimensions and the performance indicators that are best

suited to the characteristics of R&D. Some of these works consist in the application of well-known PMS

frameworks, such as Balanced scorecard, Skandia navigator, Intangible asset monitor, to R&D activities (e.g.

Kerssens-Van Drongelen & Bilderbeek, 1999; Bremser & Barsky, 2004). Other works consist in the

development of ad-hoc frameworks and set of indicators to assess the performance of R&D (e.g. Pawar &

Driva, 1999; Kim& Oh, 2002; Marr, Schiuma & Neely, 2004; Mettänen, 2005). These papers can provide a

reference point for the identification of performance indicators for R&D, though they are not tailored to the

specific characteristics of public sector organizations. The second stream of research, instead, focuses on the

Int. Journal of Business Science and Applied Management / Business-and-Management.org

strategic design of the PMS in R&D firms and addresses the relationship between the characteristics of the

PMS, the objective of the PMS and contextual variables, such as type of R&D activity, industry and company’s

size (e.g. Kerssens-van Drongelen & Cook, 1997; Chiesa & Frattini, 2007; Chiesa et al. 2009). These papers

highlight how the PMS can be configured in a different way on the basis of the objectives that it is supposed to

pursue (e.g. diagnostic, motivation and coordination), hence suggesting to pose particular attention to the

different objectives of the stakeholders of a research centre.

The above review has highlighted that there has been widespread dispute among researchers and

practitioners about which measures are more suitable in assessing the performance of research organizations,

and, at present, this debate is still open (Elmquist & Le Masson, 2009; Whelan, Teigland, Donnellan & Golden,

2010; Dumay & Rooney, 2011).

2 RESEARCH METHOD

Choice of the research method

This paper reports the results of a project aimed at developing a PMS for an Italian technological research

centre. To carry out this goal we adopted action research (AR) as research methodology. Action research has

been selected given its aim “to contribute both to the practical concerns of people in an immediate problematic

situation and to the goals of social science by joint collaboration within a mutually acceptable ethical

framework” (Rapoport, 1970: 499). On the one hand, this methodology gives the possibility to focus on a

problematic practical situation, the need to introduce a PMS to deal with the current complex research

environment that represents at the same time a relevant academic concern. On the other hand, the collaborative

nature of the interaction between the researchers and the employees of the research centre allowed capturing the

specific requirements from each stakeholder supporting the definition of the most appropriate set of KPIs.

Specifically, we conducted AR drawing on the seminal work of Susman & Evered (1978). According to the

authors, AR is seen as a cyclical process articulated in five phases: diagnosing, action planning, action taking,

evaluating, and specifying learning (see Figure 1). At the centre of the cycle there is a client system that is the

social system in which the members face problems to be solved by action research. Different techniques were

used for data collection and analysis, including in-depth interviewing, direct observation and documental

analysis (data were drown from the records, memos, and reports that the client system routinely produces). The

interviews were recorded and transcribed, though the empirical material was not codified, but instead analyzed

textually, with each author highlighting emergent themes. Overall 64 interviews were performed and three

official plenary meetings took place; the process was highly participative (Reason & Bradbury, 2001) and

different stakeholders involved continued to provide comments about the researchers’ proposals.

Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella

Figure 1: The action research cycle (Susman and Evered, 1978)

ACTION PLANNING

Considering alternative

courses of action for

solving a problem

ACTION TAKING

Select a course of action

EVALUATING

Studying the

consequences of action

SPECIFIYING LEARNING

Identifying general findings

DIAGNOSING

Identifying or defining a

problem

Development

of a client

system

infrastructure

The client system infrastructure

The client system is a public research centre in Italy, active in different fields of technological

development. The centre currently employs more than 1,000 people, with more than 900 researchers. The centre

is governed by an executive committee that defines the centre’s long term strategies and is in charge of its

management. It is characterized by a dual structure: the scientific director is responsible for all the research

activities of the centre; the administrative director, on the other hand, is responsible for the administrative

structure. The distinctive characteristic of the centre is represented by interdisciplinary R&D activities that cover

several fields, ranging from robotics, neuroscience, energy, to smart materials and drug discovery. The

interdisciplinary nature of the research activity is then reflected in the organizational structure that consists of 12

central research units, 11 research centres located at the premises of, and in collaboration with, other research

institutions both in Italy and abroad, and 3 facilities that provide support services to the research units (e.g.

animal facility).

At the beginning of the project, a working group was constituted within the client system, including the

Head of the Management Control Office and a member of its staff, the Head of the ICT Office, and two internal

consultants. The working group interacted with the researchers in all the phases of the AR cycles. In addition,

different employees of the centre were involved as informants and prospect users of the PMS (see Table1).

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Table1: The client system

Participants and informants

Organizational unit

Role in the project

Scientific Director

Central administration

Initiator and user

Administrative Director

Central administration

Initiator and user

Head of the Management Control Office

Central administration

Head of the client working group

Management and Control Office Staff

Central administration

Member of the client working group

Head of ICT Office

Administrative office

Member of the client working group

Consultant 1

External

Member of the client working group

Consultant 2

External

Member of the client working group

Head of RU 1

Research unit

Informant and user

Head of RU 2

Research unit

Informant and user

Head of RU 3

Research unit

Informant and user

Head of RU 4

Research unit

Informant and user

Head of RU 5

Research unit

Informant and user

Head of RU 6

Research unit

Informant and user

Head of RU 7

Research unit

Informant and user

Head of RU 8

Research unit

Informant and user

Head of RU 9

Research unit

Informant and user

Head of RU 10

Research unit

Informant and user

Head of RC 1

Research centre

Informant and user

Head of RC 2

Research centre

Informant and user

Head of RC 3

Research centre

Informant and user

Senior Scientist 1

Research unit

Informant and user

Senior Scientist 2

Research unit

Informant

Senior Scientist 3

Research unit

Informant

Senior Scientist 4

Research facility

Informant

Technician 1

Research facility

Informant

Technician 2

Research unit

Informant

Technician 3

Research unit

Informant

Team Leader 1

Research unit

Informant

Team Leader 2

Research unit

Informant

Head of RF 1

Research facility

Informant and user

Head of RF 2

Research facility

Informant and user

Head of RF 3

Research facility

Informant and user

Head of Research Office

Support office

Informant and user

Head of Project Office

Support office

Informant and user

Head of Technology Transfer Office

Support office

Informant and user

Head of Human Resource Office

Administrative office

Informant

Head of Engineering office

Administrative office

Informant

Head of Health and Safety

Administrative office

Informant

Head of Purchasing Office

Administrative office

Informant

Head of Legal Office

Administrative office

Informant

Head of Internal Audit Office

Administrative office

Informant

Head of Accounting Department

Administrative office

Informant

Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella

The AR Cycles

In this section we outline the phases of the two AR cycles, posing particular attention to specifying the

sources used for data collection in each step of the research.

AR Cycle I

The diagnosing phase started with a literature review concerning performance measurement in research

organizations, a series of project meetings and a brainstorming session on the role of the performance

measurement system in the specific setting. The outcome of this phase was the identification of the process-

oriented model (Pollanen, 2005) as a starting point for the development of the PMS and the acknowledgement

of the existence of different information needs in relationship to the PMS from a plurality of stakeholders.

Action planning consisted in mapping data and information needed to support the development of the PMS. A

list of informants and relevant documentation was prepared by action researchers in collaboration with a

working group of the research centre. In addition a list of critical issues (interview protocol) to be discussed with

the informants was prepared. In the action taking phase, 38 interviews were carried out with the heads of

scientific units and researchers of the centre, and also the administrative personnel. The analysis was

complemented with public documents and confidential reports, entering in detail the scientific activity carried

out by each department. Based on these data, a prototype of the PMS model and a preliminary set of KPIs were

developed. In the evaluating phase the proposed model and the preliminary set of KPIs were presented in two

plenary meetings, one involving the scientific director, the administrative director and a selected number of

other internal officers and one involving the heads of the research units. Feedback, comments and suggestions

for improvements were collected. Finally, specifying learning consisted in summing up the learning outcomes of

the AR cycle. All the comments and feedbacks deriving from the evaluation phase were integrated. The

outcome of this phase was twofold: the validation of the overall PMS model and an ‘explosion’ in the number of

KPIs due the receipt of many proposals from the participants. Overall 42 indicators and several variations to

each of them were suggested, posing the basis for the second AR cycle, aimed to the refinement of the set of

indicators. The following table outlines the techniques used for data collection and analysis in different steps of

the AR cycle, the role of the researchers and the client working group and the output produced (Table 2).

Table 2: AR Cycle I

Techniques

Role of the researchers

Role of the client

working group

Output

Diagnosing

phase

Literature review,

Project meetings,

Brainstorming meetings

Challenge the

informants to identify

emerging issues

Contribute to the

discussion

Definition of the role of

the PMS in the research

centre

Action

planning

Literature review

Project meetings

Define the agenda and

design data collection

tools

Support the researchers

in identifying potential

informants and provide

feed-backs and

suggestions

Design of the data

collection tools to

support the preparation

of the PMS prototype

Action

taking

Literature review

Interviews

Project meetings

Perform interview and

data analysis

Contribute to the

discussion and

participate in data

analysis

Development of the

PMS overall model and

preliminary set of

indicators

Evaluating

Plenary meetings

Present the PMS

prototype

Contribute to the

discussion

Presentation of the PMS

prototype in two plenary

meetings

Specifying

learning

Internal meetings

Collect and analyse

feed-backs and

suggestions

Support researchers in

interpreting feed-backs

and suggestions

Integration of feed-backs

and suggestions

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AR Cycle II

The diagnosing phase started with a structured analysis of the comments received concerning the set of

indicators and the acknowledgement that the proposals received needed to be organized and selected. Action

planning consisted in the definition of shared selection criteria to reduce the number of the indicators, still

ensuring that the information needs of different stakeholders were met. To this aim, the following selection

criteria were adopted: relevance, measurability, cost and timeliness (Lynch and Cross, 1991; Neely et al., 2003).

In addition, to support the selection of the KPIs, a second round of 26 interviews was planned with selected

informants. Action taking consisted in carrying out the interviews with an application of the selection criteria to

define the new set of KPIs. For each KPI, an information protocol was prepared (see Table 4). It defines in

operational terms how the indicator should be computed, the unit of measure, the level of detail, the relevance of

the indicator, the frequency of data collection and the owner of these data (see also Arena & Azzone, 2010).

Table 3: The information protocol

KPI

Number of Patents

Definition

Number of patents distinguished by category

Computation procedure

Number of patents of the year distinguished by the following categories:

First filing

Extension abroad

Abandoned

Granted

Data source: Patents database

Data calculated over the following time horizon: year

Unit of measure

Units

Level of detail

Scientific structure

Research unit

Research team

Relevance of the indicator

This KPI evaluated the output of the research centre and its related research units in

terms of patents, with respect to the different phases of a licensing process (first filing,

extension abroad, abandoned and granted)

Frequency of data collection

Year

Owner of the measure

Technology Transfer Office

In the evaluating phase the results were presented in a new plenary meeting involving the scientific and

administrative directors. Feed-backs we received mainly dealt with some specifications in the information

protocols in the indicators, leading to new revisions. Hence, after this second round of evaluation the final set of

KPIs was proposed suggesting practical directions for the research institute, but also general guidelines at the

academic level (see Table 4).

Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella

Table 4: AR Cycle II

Techniques

Role of the researchers

Role of the client

working group

Output

Diagnosing

phase

Project meetings

Identify key issues based

on the output of the first

AR cycle

Contribute to the

discussion

Identification of the key

issues

Action

planning

Literature review,

Project meetings

Define the selection

criteria and design data

collection tools

Support the researchers

in identifying potential

informants and provide

feed-backs and

suggestions

Design of the selection

criteria and data

collection tools to

support the PMS

revision

Action

taking

Project meetings,

Interviews

Perform interview and

data analysis

Participate in data

analysis and support the

researchers in the

definition of the

information protocols.

Development of the final

set of indicators based

on the selection criteria

Evaluating

Plenary meetings

Present the final set of

KPIs

Contribute to the

discussion

Final presentation

Specifying

learning

Project meetings

Collect and analyse

feed-backs and

suggestions

Practical directions for

the research institute, but

also general guidelines

at the academic level

3 RESULTS

Results are organized in three sections. First, we introduce the overall PMS model; then, we outline

different performance dimensions; finally, we present the complete list of KPIs.

The PMS model

The starting point for the development of the PMS model is the production system process of a R&D

organization. It can be represented through a process-oriented model(Brown & Svenson, 1998; Pollanen, 2005),

in which research inputs are processed and transformed into output, such as publications or patents. Accordingly

to this general model, performance measures can be defined on the basis of three interrelated elements: input,

output and outcome. Input refers to the amount of resources used in performing a certain activity; output refers

to the result of a transformation process; outcome refers to the long-term impact of the output on the external

environment(Lettieri & Masella, 2009). Based on the three above elements, three performance dimensions can

be identified, namely effectiveness, efficiency and impact (Azzone, 2008). Effectiveness refers to the output

characteristics, both quantitatively and qualitatively; efficiency refers to the ratio between output and input;

impact is a measure of the outcome and it refers to the long-term effects of the output on the external context.

Interaction with research and administrative personnel was useful in recognizing the importance of the

aforementioned dimensions. The quality of the output, the impact of the output on the society and the amount of

resources required for generating the output emerged as crucial aspects for successfully manage the research

centre. However, two further aspects were highlighted as central and they were consequently added to the

traditional model: risk and network. The risk is associated with the uncertainty that characterizes the research

activity. It is widely acknowledged that the context in which research institutes operate is characterized by

continuous changes and high variability (Leitner & Warden, 2004). When discussing with the administrative

and the research director, a question continuously emerged was the following: do we have enough resources, in

terms of quantity and quality, in order to deal with the unpredictability of the events, such as a change in

technology or a budget reduction? The importance of posing the attention to the quantity and quality of

resources to deal with uncertainty, led us to complement the traditional model with the risk dimension. The

network dimension is specifically related to the research activity. The heads of research units underlined several

times the importance of working collaboratively in order to achieve results. Publications as well as projects or

patens are rarely the result of a single researcher; rather, they derive from the joint working of more teams,

belonging to the same department, but also to different departments or different research institutions. This

reason justifies the choice of adding a further dimension to the model, the network, in order to capture the ability

of working collaboratively and output of this collaboration.

The final model we obtained (Figure 2) is characterized by five dimensions, suggesting that the holistic

management of a public research centre requires to consider the quantity and the quality of the output

(effectiveness), the impact of the output on the society (impact), the ability in transforming input into output

(efficiency), the quantity and the quality of the available resources (risk) and the ability and effects of working

collaboratively (network). Following, each dimension will be discussed separately, posing the attention on the

different relative importance given to stakeholders.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Figure 2: The proposed PMS model

RESEARCH

CENTER

INPUTS

OUTPUTS

Effectiveness

 Publications

 Projects

 Patents & licences

 Technology transfer

 Human resources

 Financial resources

 Equipments

COMMUNITY

OUTCOME

Impact

Efficiency

Risk

Network

The performance dimensions

Effectiveness

The effectiveness dimension allows to evaluate the output of the R&D activities defining the level of

achievement of research objectives (Garcia-Valderrama and Mulero-Valdigorri, 2005). This aspect is related to

a key issue that is what is the output of a research centre and how to measure it (Steiner and Nixon, 1997). In the

case at hand, the output is represented by publications, funded projects, patents and technology transfer

activities. Publications and projects were the ‘immediate answer’ when the informants were asked about the

output of the research centre. This answer is in line with extant studies on research institute that recognized that

publication, books and reports represent the explicit transfer of knowledge of research bodies (e.g. Coccia,

2001b). On the other hand, patents and technology transfer activities emerged in a more ‘fragmented’ way.

Some scientific directors and researchers were fully aware of the relevance of these activities, as emerges from

the following quotation:

“Technology transfer is a key pillar in the mission of our institution, otherwise,

what are we here for?” (Head of RU 8).

Whilst in the other case, patenting was seen somehow as a secondary activity that can be explained

considering that the institute was at that time measuring outputs in terms of number of scientific publications

and funding obtained through competitive projects. The Head of the Technology Transfer Office pointed out:

“Technology Transfer activity is a green field. It needs to be organized and then

evaluated because in the early years of the research centre, the attention has been

attracted by scientific publications only. Yet we are growing and we have now a

portfolio of 68 patents and several commercial projects. They need to be monitored

and managed” (Head of Technology Transfer Office)

Based on these considerations, seven KPIs were defined to measure the research centre effectiveness: four

of them aim to evaluate the quantity of the output and the other three the quality (see Table 5 for the details of

the KPIs).

It is interesting to highlight that there was a polarization in the perceived usefulness of different aspects of

effectiveness depending on the personnel qualification. The administrative personnel and the executive

committee were mainly interested in the quantity of the output generated in terms of ‘how much money’, ‘how

much patents’, ‘how much publications’ (several informants). They considered these KPIs particularly useful to

support external accountability; in fact they have been included in the annual report and have been published on

the website of the research institute. Instead, the scientific personnel considered these measures too ‘generic’

Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella

and posed particular attention to the quality of the output of research activities, highlighting the importance to

account for ‘good publications’, ‘impact on the scientific community’ and ‘participation in breakthrough

innovation projects’ (several informants). From this perspective, the Impact Factor (IF) and Citation Index (CI)

distribution were evaluated particularly useful to support motivation and decision making processes of research

units. Finally, the scientific director underlined the importance of the integration of the two types of indicators,

for a decision making purpose, to have an overall idea of the productivity of each RU. At the same time, he also

recognized the limitations of these figures: the impossibility to compare them between RUs because their

average value varies widely from one research area to another one.

Efficiency

The efficiency dimension evaluates the amount of resources used to generate the output, (i.e. publications,

funded projects, patents, and technology transfer activities). It represents a relevant aspect in publicly funded

services as governments are interested in receiving feedback on how public resources are used (Coccia 2001a;

Moreno & Tadepalli, 2002; Lettieri, Masella & Nocco, 2009).

The diversity of the output of research centres requires to define different measures in order to assess the

value of the resources to generate a unit of the output, either a publication, a patent or a financed project, leading

to the identification of four KPIs (see Table 2).

The relative importance of these measures was different according to the stakeholder and its organizational

position. The administrative director and the scientific director were the stakeholders most interested in

efficiency indicators. They strongly underlined the need to keep under control how many resources the centre

employs for carrying out research activities and the time spent doing this.

“Monitoring costs is a key issue for us. We have to keep out costs under control and

we need to be able to show how much efficient we are. […] From time to time,

someone stands up and claim they we spend too much. We need to be able to prove

that it is not true, we spend money, but we produce more” (Administrative director)

In order to satisfy his requirements, a synthetic efficiency measure to evaluate the overall ability of the

administrative staff in supporting research activities was included: the ratio between the external budget and the

number of FTE (Full Time Equivalent). The fundamental resource of the institute is represented by people, and

this measure gives the possibility to evaluate the cost for managing a unit of personnel. The executive

committee considered efficiency relevant too, though from a different perspective. The committee poses

particular attention on two specific performance indicators, cost of a scientific publication and incidence of the

external budget on the internal budget, both considered useful in supporting the external accountability. These

numbers are already included in the annual report of the institute and they are explicitly depicted with the

purpose to increase the awareness of external stakeholders about the research activity. The heads of the research

units and the researchers, on the other hand, downplayed the importance of efficiency measures, following the

idea that ‘research is what counts’ no matter how much it costs. Some of them even argued that measuring the

‘cost per output’ is not relevant for the management of a department, ‘what they need to know is the budget’.

Impact

The impact dimension measures the outcome, i.e. the effects of the output of the research activity on the

external environment. This aspect has been highlighted several times as crucial for the research centre. It

represents the mission of the institute:

“The Foundation has the scope to promote the technological development of the

country and the technological education, in line with the scientific and technological

national policies with the final aim to support the development of the productive

Italian system” (Foundation Statute, 2012: 3).

The importance of measuring the outcome is closely connected with the need of being externally

accountable, providing information to external stakeholders on how public money is used. The administrative

director was clear on this point since the kick-off meeting of the project:

“The philanthropic mission of our Institute is to generate know how and

employment for our country. We are receiving public money and we have, not only

the duty to account about how we are using public funding, but also we want to

demonstrate that we are using this money to support the technological development

of our country”(Administrative Director).

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However, the selection of the most appropriate impact indicator required to balance the trade-off between

the relevance of measuring the outcome and the cost for collecting these data. In fact, the measurement of this

aspect is far from being straightforward given the intangibility of the output and the existence of external factors

that makes it difficult to isolate the impact of research activities on the territorial environment. The KPIs finally

selected were related to cash flows from patents, licenses and technology transfer (see Table 2). They are a

proxy of the outcome because they give the possibility to account for the first order effect of a research activity,

which are leading indicators of second orders effect about impacts on the environment.

The scientific and the administrative directors of the centre were particularly keen on starting measuring

these aspects, whilst part of the administrative and research staff were somehow worried. The motivation at the

basis of this situation was that at present that the cash flows generated by these activities are still modest and

they feared that numbers could lead to wrong interpretations. Directors of research units did not perceived the

importance of measuring impact, being more attracted by KPIs about the quantity and the quality of the

publications.

“Patents, for my department, represent a cost. They do not generate income; this

information can be useful at the overall institute level. It takes some years for a

patent before gene rating money; hence, it is not relevant to manage my activity.

I’ve already seen the result of my research!” (Head of SF3)

Risk

The risk dimension is associated with the need to deal with the uncertainty and the variability of the

research context, as stated by the scientific director:

“Research trends change every year. On the basis of what happens outside, I need to

reconfigure the research activity inside here. The organizational structure should be

flexible and follow these changes accordingly. I need to be able to modify timely

the structure of scientific units; acquire new competencies, understand what

happens if I close a department and open a new one” (Scientific Director).

In particular, the emphasis given to flexibility and adaptability drove the choice of posing particular

attention to how the centre can monitor its resources in order to ensure its ability to respond to unexpected

events. In highly uncertain contexts, resources and the capability to reconfigure them are conceived as the basic

mean through which an organization can face risks (Arena, Arnaboldi & Azzone, 2010b; Arena, Arnaboldi &

Azzone, 2011; Lettieri, 2009; Lettieri, Masella & Radaelli, 2009). Hence, monitoring the ‘state of resources’ can

provide relevant information about how the organization could face a transformation. Obviously, this also poses

the problem of identifying which are the key resources, i.e. which are the resources critical for the success of

research institute. In the case at hand, four main elements were identified: human resources, financial resources,

scientific equipment and reputation; and seven indicators were defined to monitor these elements (see Table 5).

Once again, a different level of awareness and sensitivity of the stakeholders towards different issues

became visible. In particular, the scientific director was very attentive to read the implications of any variation

in different types of resources, going beyond numbers.

[…]“I want to monitor the age of scientific personnel because it provides very

useful information to me. I want a young research centre with a high turnover and

many PhD: by monitoring the age of the staff I can account for this aspect avoiding

the risk of increasing the average age” […]“The trend of PhD students for me is a

signal. If their number falls down, it is an alarm bell to me. First, they are a

resource, they are our primary row material. Second, if they stop coming here, they

going somewhere else. This means that other institutions are more attractive than

us. Why? I mean, it’s something to keep under control” (Scientific Director).

On the other hand, the administrative director and the executive committee appeared more focused on

financial resources and reputation (in particular in relationship to the public opinion) that was sees as a

considerable source of risk.

“We are on the newspaper almost every day. Public opinion poses particular

attention on the way in which we are employing public money. If we do not justify

how the public funding is used, we are immediately accused of wasting financial

resources that could be used for doing something else” (Administrative Director).

Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella

Finally, directors of RUs showed more interest for the scientific reputation as it gives the possibility to

understand how individual researchers, not only the centre as a whole, are known and considered in a specific

field of research.

Network

The network dimension refers to the ability of the research personnel working together with researchers

from internal teams or from other external institutes. This aspect finds a rationale in the organizational changes

that are affecting the research activities. Collaboration is nowadays considered an essential requirement (Saez et

al., 2002) to survive in a research landscape characterized by inter-disciplinary activities (National Academies,

2005). The network dimension accounts for this collaborative behaviour considering the structure of

relationships between actors, the quantity and the quality of the joint output and the cost of collaboration. This

aspect is emerging in the public sector literature as a relevant performance dimension to evaluate collaboration

(e.g. Provan & Milward, 2001), while it has received less attention in the R&D field (e.g. Allen at al., 2007).

Three KPIs were included in the set of indicators, focusing on the quantity and the quality of joint publications

(see Table 2). Even though collaborative activities relate also to patents and research projects, no network KPIs

were associated with these typology of output. With respect to patents, the decision was to not include indicators

because of the limited amount of patents compared with publications. Considering projects, measurement

difficulties were identified, mainly related to the complexity of accounting joint internal projects. The main

concern associated with this aspect was the following:

“The majority of financed projects is carried out in a collaborative way; it is

therefore not useful to know the level of collaborations as it is by definition that we

are collaborating. This is not true for internal research projects, but they are usually

carried out by the single research team”. (Head of RC 3).

Again, the importance of evaluating the network dimension was different depending on the type of

stakeholder. Collaborative activities emerged several times during interviews and meeting with the scientific

personnel and the scientific director.

“We are working several times with people from other research centres. This is

associated with a greater effort in terms of coordination but better results because of

different competences (Senior scientist 1).”

On the contrary, they were never mentioned by the administrative staff and the executive board.

Int. Journal of Business Science and Applied Management / Business-and-Management.org

The final set of indicators

Table 5 outlines the final set of indicators related to the five performance dimensions previously introduced

and highlights the priorities assigned to each indicator by different stakeholders.

Table 5: The set of indicators and stakeholders priorities

Dimension

KPI

Stakeholders

Admin.

Director

Scientific

Director

Executive

committee

Head of

Effectiveness

Number of scientific publications

Number of patents

Number of technology transfer activities

Stock of competitive projects

IF distribution

CI distribution

Success rate of competitive projects

Outcome

Revenue generated by patents, licenses and spin-off

Revenue generated by technology transfer activities

Efficiency

Cost of a scientific publication

Cost to manage and maintain a patent

External budget/Internal budget

Internal budget/FTE

Risk

Scientific reputation

Press coverage

Capex and Opex

Employees (category, nationality, age, degree, genre)

Turnover

Organizational climate

Saturation of equipment

Network

Number of joint publications

IF of joint publications

CI of joint publications

During the second phase of the action research cycle, the cost and validity of the proposed set of KPIs was

verified, providing practical guidelines to the research centre for the implementation of these measures. Results

of this phase vary according with the performance dimension.

KPIs related to effectiveness are diversified in terms of cost of measures. Most of them can be easily

calculated because they base on data that are already collected for different purposes. The most relevant

exceptions are the Impact factor and Citation index distribution. These indicators, though considered very

important for the assessment of the quality of the research by the scientific director, require ad hoc data

collection and further elaboration in order to ‘clean’ data. Effectiveness indicators are particularly relevant to

have an overall idea of how the centre is performing and to assess the performances of each RU with respect to

its research areas; however, they require some cautions in relationship to data interpretation, since different RUs

cannot be easily compared with each other based on these numbers (e.g. bibliometric indicators change

significantly in different research fields).

KPIs in the outcome dimension aim to provide a proxy of the impact of the research activity on the society.

Data about cash flows are already monitored for accounting purposes. However, they provide only a partial

Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella

information about the impact of the research activity on the society. Hence, it could be interesting to

complement them through qualitative information to have a more comprehensive idea of research outcomes.

KPIs related to the efficiency can be quite easily calculated because the institute already monitors cost

information very precisely. However, the interpretation of these data can be controversial. In particular, this is

the case of the cost per scientific publication that answer to the need of external accountability. Similar to

effectiveness indicators, it varies significantly across different research units depending on the specific research

field (equipment, materials, and other research instruments significantly vary in different research areas,

resulting in different costs per publication).

KPIs concerning the risk dimension are characterized by some differences in term of measurement costs.

Capex, opex, turnover and data about employees can be determined easily, instead this is not the case for

indicators about the reputation, saturation of key equipment and the organizational climate that entail ad hoc

data collection. Reputation and saturation of key equipment capture two aspects that are particularly relevant for

the research centre, but, at present, they cannot be calculated automatically, and the computation ask a great

effort to collect data and to elaborate them; in the next future, these indicators could benefit from the

exploitation of some supporting tools to collect data routinely. The organizational climate, on the other hand, is

assessed through a survey yearly and it suffers from the limitations that are typical of similar approaches (e.g.

risk of bias, limited timeliness).

Finally, KPIs in the network dimension exploit the same data base of those related to effectiveness, and

share their strengths and weaknesses. In addition, they provide an overall picture of the extent to which

researchers of different RUs collaborate, without entering into details of the contribution of different parts to the

research outputs.

4 CONCLUSION

This paper aimed at constructing a performance measurement system (PMS) for a public research centre

characterized by the presence of a diverse set of stakeholders with multiple objectives, ranging from making

decisions, motivating researchers to demonstrating external accountability. The PMS has been developed

through action research with respect to a specific context, an Italian technological research centre, where the

researchers recursively interacted with different stakeholders in the construction of the PMS. Based on the state

of the art literature and data collected from the interviews, a multidimensional framework for measuring R&D

performance was proposed and a set of KPIs was developed. The traditional process-oriented model (Pollanen,

2005) that encompasses the performance dimensions of effectiveness, efficiency and impact, was complemented

with two further dimensions of risk and network. The former aims to monitor the uncertainty that characterizes

the research activity, and the latter allows to capture the ability of working collaboratively. The introduction of

these two dimensions moves from the idea that the PMS should be aligned with the characteristics of the

organizations in which it is used and the trends of development they should deal with (e.g. Chiesa et al.,

2009).The set of KPIs puts together the information needs of different stakeholders that showed a definite

interest in relationship to a certain subset of measures, often disregarding the others. The integration of different

information needs in a comprehensive set of indicators aimed at providing a holistic picture of the centre

performances to all the relevant actors, raising their attention on the existence of potential trade-offs between

different measures.

This study contributes to the extant literature in two different ways. First, models, frameworks and

methodologies for measuring R&D performances have mostly focused at the firm level, with an economic or

strategic focus (Secundo et al., 2010). We deployed them in the context of a public research institute, where

attention of researchers, so far, concentrated on the one specific stakeholder – the government – that is mainly

interested in an overall assessment of research centres’ performances in relationship to funding allocation.

Second, we tried and integrated the concepts for performance measurement and performance management of

interdisciplinary collaborative research, which depicts new challenges for promoters and science managers in

the research environment of the twenty-first century (Agostino & Arnaboldi, 2012; Arena & Azzone, 2005;

Arnaboldi & Azzone, 2010).

Since the research was conducted in a specific organization, the possibility of generalizing the results to

similar institutions is a key issue. Hence, it is important to distinguish between what is general in scope and

what is case-specific. First, the multi-dimensional framework proposed aims to be general in scope. The use of

the process oriented model as a starting point and the integration of the two dimensions of risk and network aim

to make the PMS model more coherent with the pressures that public research organizations are currently

experiencing in different countries. Focus on efficiency and effectiveness, call for interdisciplinary research and

growing uncertainty are key challenges for these institutions. Moreover, risk and network represent two trends

of development in relationship to performance measurement in different types of organizations (e.g. Provan and

Milward, 2001), though, in most of cases these performances are not formally integrated in the PMS (e.g. Arena

et al., 2011). This paper represents a possible way to explicitly integrate these aspects in the PMS, opening the

path to future research to further adapt the proposed model to different contexts. The second result of general

Int. Journal of Business Science and Applied Management / Business-and-Management.org

validity is the approach followed to develop the set of KPIs. The choice of the KPIs was guided by the priorities

of different stakeholders. Their requirements and the purpose of use in relationship to different information were

mapped and cross-checked in order to build a set of indicators that would be comprehensive and transversal to

the whole organization. The set of indicators, on the other hand, is case specific and reflects the characteristics

of the research centre under investigation, even though it could provide source of inspiration for similar

organizations.

Finally we address the limitations of this work. The data were collected through a qualitative and

collaborative methodology, action research; hence the results suffer from the limitations that are typical of this

research methodology. In particular, action research is situational, hence, many of the relationships between

people, events, and things are a function of the situation as relevant actors currently define it and they change as

the definition of the situation changes (Susman & Evered, 1978). Accordingly, while the collaboration between

the researcher and the institute staff led to the identification of the KPIs, it may have also resulted in a degree of

bias. However, this methodology gave us the possibility of having lively, detailed and involved discussions with

members of the organizations. Our ideas about performance dimensions and KPIs were critically challenged in

order to satisfy their specific requirements. Moreover, the active participation of users and the integration of

their different informational requirement contributed to reducing resistance to the project and promoting the

development of the PMS. An important field of future research remains the applicability of the proposed model

and methodology to other public research organizations in order to verify its generalizability in different

institutions operating in different countries.

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