Introduction

The subject of performance measurement is encountering increasing interest in both the

academic and managerial worlds. This, for the most part, is due to the broadening spectrum of

performances required by the present-day competitive environment and the new production

paradigm known as Lean Production or World Class Manufacturing (Hall et al., 1991). In

addition there is the need to support and verify the performance improvement programmes such

as Just-in-Time, Total Quality Management, Concurrent Engineering, etc. (Ghalayini and Noble,

1996).

These programmes are characterised by their ability to pursue several performances at the same

time, for example the increase in the product quality together with the lowering of the production

costs and the lead times, following the reduction in discards, waste, reworks, and controls.

Performance measurement is how organisations, both public and private, measure the quality of

their activities and services. An influential 1982 book, "In Search of Excellence," sparked

interest in measuring performance. Since then, business, government and other organisations

have sought to measure the extent to which they meet organisational goals. Performance

measurement may sound simple, but is often a complicated process that requires deep strategic

thinking and assessment.

Performance measurement systems (PMS), such as Kaplan and Norton’s (1992, 1996a) Balanced

Scorecard, focus on organisational performance and, although the impacts of these systems on

organisational performance is a much debated question, they may be considered as a means of

reaching performance objectives, thus the interest in these systems and their use. Considering

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their support role in both tactical and strategic decision making (Kueng et al., 2001), PMS are

designed for executives, although not exclusively, and thus have an executive information

system (EIS) component (Turban et al., 2002, 2007). PMS can be used collectively by the

managers of the organisation (Kaplan and Norton, 1996b). As internal systems, they have either

been acquired as packaged software or developed for the specific needs of the firm (Kueng,

2000; Sharif, 2002). As external systems, they are accessible in the form of external diagnostic

tools (Cagliano et al., 2001; Delisle and St-Pierre, 2006), with or without a benchmarking

function, and are used on an ad hoc and discretionary basis. Since the early 1990s, a number of

researchers have shown interest in PMS that support organisational and managerial development

in both large and small business enterprises (Bourne et al., 2000; Garengo et al., 2005), and in

public or government organisations (Ho and Chan, 2002).

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Literature Review

Definition, History and Formulation

There has been an evolution in the conceptualisation and definition of these systems since they

first appeared as objects of management research. In conjunction with the evolution of

information technologies, including web-based technologies, PMS can be enriched with new

system functionalities that allow them to move beyond simple measurement by providing more

extensive and customised support for decision making in the firm. Through this enrichment,

PMS now play a more important role in the organisation, extending beyond control toward

support for continuous improvement and managerial development (Sinclair and Zairi, 2000).

In light of this evolution, there is a need for a renewed conceptualisation and better definition of

PMS as a research object, in terms of their essential characterisation as information systems (IS),

if one wishes to study these systems, and understand in particular the individual and

organisational behaviours associated with PMS usage and management practices. In this regard,

the conceptualisations and definitions of PMS in the literature require more precision and

completeness. For instance, a definition wherein a PMS “is a balanced and dynamic system that

is able to support the decision-making process by gathering, elaborating and analysing

information” (Neely et al., 2002) does not sufficiently specify the unique characteristics of such

systems that distinguish them from other types of management decision-support systems.

Mainly, originating in management accounting and operations management studies (Neely et al.,

1995; de Toni and Tonchia, 2001), the PMS research domain to-date has developed outside the

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IS research field. A few researchers have attempted to establish links between IS and PMS

(Bititci et al., 1997a; Kueng et al., 2001), but these attempts have been isolated. References to IS

research are thus rare in PMS studies and, as defined presently in the PMS literature, these

systems are not present in the mainstream of IS knowledge. While seemingly related systems

such as EIS have been the object of past IS research (Bergeron et al., 1995), these systems have

evolved differently and thus cannot be assimilated to PMS.

In their recent literature review, Franco-Santos et al. (2007, p. 799) counted no less than 17

definitions of business PMS, underlining that a no-consensus situation on PMS definition can

“inhibit the development of the field”. Research is more problematic when the basic concepts

and definitions that underlie a research object lack clarity, precision, and uniformity.

Accumulating and integrating research results into a coherent body of knowledge is more

difficult, as the lack of a common language renders studies less comparable. Conceptual and

definitional imprecision also makes it more difficult to import knowledge from other disciplines

or fields, knowledge that could provide a deeper understanding of the phenomena under study.

Models of Performance Measurement Systems

In recognition of the need for more relevant, better structured and integrated performance

measurement systems, a number of frameworks and models for performance measurement have

been developed, the main models of PMSs found in the literature can be referred to five

typologies:

• models that are strictly hierarchical (or strictly vertical), characterised by cost and non-cost

performances on different levels of aggregation, till they ultimately become economic-

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financial (Berliner and Brimson, 1988; Lockamy and Cox, 1994; Partovi, 1994; Rangone,

1996), the first hierarchical model was that of Gold (1955), which connects productivity and

ROI.

• models that are balanced scorecard or tableaux de bord, where several separate performances

are considered independently, these performances correspond to diverse perspectives

(financial, internal business processes, customers, learning/growth) of analyses, that

substantially remain separate and whose links are defined only in a general way (Maskell,

1991; Kaplan and Norton, 1992), though recently their model has been integrated with some

vertical linkages, from the operational measures up to the financial ones (Brown, 1996);

• models that can be called “frustum”, where there is a synthesis of low-level measures into

more aggregated indicators, but without the scope of translating non-cost performance into

financial performance, typically the economic-financial measures are kept separate from the

aggregate ones of customer satisfaction (Lynch and Cross, 1991; Hronec, 1993).

The “frustum” approach permits the vertical architecture to be defined at the lowest levels,

involving the aggregation and synthesis of the performances, while at the higher levels the

“frustum” approach is nearer to a balanced architecture, thus with a tableaux of economic-

financial performances and customer satisfaction / market performances.

• models which distinguish between internal and external performances, these are the only ones

directly perceived by the customers (Bartezzaghi and Turco, 1989; Bolwijn and Kumpe,

1990; Johnson, 1990; Thor, 1993).

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• models which are related to the value chain, these models in respect to the preceding ones,

also consider the internal relationship of customer/supplier (Sink and Tuttle, 1989; Moseng

and Bredrup, 1993).

The above mentioned models are characterised by three different structural associations: vertical,

balanced (or a tableaux) and horizontal (or by process). These structural associations permit the

above PMS models to be classified as reported in Figure 1. As can be seen, the frustum models

as well as those that distinguish between internal/external performances (without reference to the

value chain) show both types of associations at the same time.

ARCHITECTURE VERTICAL

strictly hierarchical

models

"frustum" models

ARCHITECTURE BALANCED

"balanced scorecard"

models models with internal-external

performances

ARCHITECTURE HORIZONTAL (BY PROCESS)

models related to

value chain

Figure 1. Classification of the PMS models

Performance Measurement Systems Characteristics

The main characteristics of PMSs held in the literature can be grouped into three different

classes:

• PMS Formalisation – this class includes the formalisation of the measures, which involves the

identification of the object or phenomena to be measured and formalisation of the

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measurements which seeks to identify how exactly the object or phenomena will be

measured. In this two basic questions must be answered, what will be measured? And how

will it be measured? (White, 1996).

• PMS Integration – the performance measurement system is not, nor can it be, an isolated

system. This is mainly due to the fact that it shares inputs with other systems and also

because it produces outputs for other systems. As a consequence, the performance

measurement system has a precise position within the organisation, due to its tasks of

promoting the integration between the various areas of the business and deploying the

business objectives throughout the organisation (Bititci et al., 1997). A performance

measurement system must be integrated with at least three other types of systems, these

systems being the accounting system, the manufacturing, planning and control system and

finally the strategic planning system.

• PMS Utilisation – this class concerns the aim and use of the performance measurement

system. Wisner and Fawcett (1991) believe that organisation have PMS to compare one’s

own competitive position with that of its competitors and to check or evaluate the

accomplishments of one’s own objectives. Neely (1998) underlines three different roles for a

PMS: to comply, to check, and to challenge. Furthermore, a PMS serves different staff units

and functions of a firm, general management, quality management, production, new product

development, technology, distribution, customer service, etc. - Zairi, 1994).

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Notion and evolution of PMS

A number of parallel developments have led to the notion of an information system that

measures the performance of business enterprises in a multi-dimensional manner, that is, not

solely through financial statements. In the 1980s, among other developments, the activity-based

costing (ABC) and activity-based management (ABM) approaches extended the firm’s

performance logic beyond the purely financial by highlighting the cause-effect relationships that

could explain the performance of the firm’s operations and production function, thus using

financial and other types of measures. The phrase “performance measurement system”, although

already present in management literature (Ridgway, 1956, p. 240: “system of performance

measurement”), began to appear more frequently in the early 1990s, mainly in the fields of

management accounting and operations management, and was marked by Neely et al.’s (1995)

founding review of the PMS literature. In the same decade, this expression also started to appear

more often in professional publications, targeting the management accounting profession, among

others (e.g. CMA, 1999). The basic notions that underlie PMS have also evolved over time to

arrive at the present ways in which these systems are conceptualised, designed, and implemented

in organisations. These notions include:

• the focus of PMS, namely the notion of performance itself and its dimensions

• the performance logic that guides the design of PMS (architecture and performance

measurement framework), and

• the system characteristics of PMS (definition, organisational role and information output).

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These notions evolved notably in the early 1990s. PMS notions seem more evident as such in the

literature after 1990. The first observable activities were those that defined measurement

approaches, one then saw the development of measurement frameworks that were proposed to

practitioners. Before 1980, the situation was generally the following, essentially financial,

focusing on results or one where retrospective management existed. Measurement frameworks

were thus rather limited to the financial aspects of performance, as traditionally exemplified by

the use of financial ratios such as return-on-assets and earnings-per-share. In the 1980s,

performance was still envisioned as essentially financial in most organisations, but new measures

of operations/ production performance appeared, extending beyond costs . Information output

was essentially quantitative, operational in nature, internal, of short-term value, and focused on

results, but began to present cause-effect linkages that provided a prospective view of operations

and production management.

This led some to propose operational performance measurement models that took into account

the firm’s strategic objectives, such as Keegan et al.’s (1989) Performance Measurement Matrix

or models that focused on quality, customer satisfaction, time reduction, and cost reduction. With

the 1990s, systems became more integrated in functional (hierarchically) and inter-functional

(across business functions) terms (Neely et al., 2000). Also, in line with Skinner’s (1974) early

work, a pre-occupation with strategic alignment became more apparent, notably with Kaplan and

Norton’s (1992) Balanced Scorecard.

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Performance Measurement Frameworks

One of the most widely recognised performance measurement frameworks of today is the

balanced scorecard (Kaplan and Norton, 1996). Developed by Kaplan and Norton, and

popularised by the marketing efforts of major consulting companies, the phrase ``balanced

scorecard'' appears to have entered the management vernacular. It is, however, only one of

several performance measurement frameworks, which have been proposed. In 1989, for

example, Keegan et al. presented the performance measurement matrix. As with the balanced

scorecard, the strength of the performance measurement matrix lies in the way it seeks to

integrate different classes of business performance, financial and non-financial, internal and

external.

Figure 2. The Performance Measurement Matrix

The matrix, however, is not as well packaged as the balanced scorecard and does not make

explicit the links between the different dimensions of business performance, which is arguably

one of the greatest strengths of Kaplan and Norton's balanced scorecard. An alternative, which

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overcomes this criticism, is the results and determinants framework. This framework, which was

developed by Fitzgerald et al. (1991) following their study of performance measurement in the

service sector, is based on the assumption that there are two basic types of performance measures

in any organization, those that relate to results (competitiveness, financial performance), and

those that focus on the determinants of the results (quality, flexibility, resource utilisation and

innovation). The appeal of this distinction is that it highlights the fact that the results obtained

are a function of past business performance with regard to specific determinants, results are

lagging indicators, whereas determinants are leading indicators.

Figure 3. Results and Determinants Framework

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Conclusion

In concluding this literature review it is worth discussing one other relevant stream of

writing in the literature, namely that concerned with rules and guidelines for performance

measurement systems design, rather than the actual process. Authors, such as Globerson (1985)

and Maskell (1989), for example, made early contributions to this literature. Globerson (1985),

for example, states that:

• Performance criteria must be chosen from the company's objectives.

• Performance criteria must make possible the comparison of organizations that are in the

same business.

• The purpose of each performance criterion must be clear.

• Data collection and methods of calculating the performance criterion must be clearly

defined.

• Ratio based performance criteria are preferred to absolute numbers.

• Performance criteria should be under the control of the evaluated organizational unit.

• Performance criteria should be selected through discussions with the people involved

(customers, employees, managers).

• Objective performance criteria are preferable to subjective ones.

Similarly Maskell (1989) offers the following seven principles of performance measurement

system design:

• The measures should be directly related to the firm's manufacturing strategy.

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• Non-financial measures should be adopted.

• It should be recognised that measures vary between locations – one measure is not

suitable for all departments or sites.

• It should be acknowledged that measures change as circumstances do.

• The measures should be simple and easy to use.

• The measures should provide fast feedback.

• The measures should be designed so that they stimulate continuous improvement rather

than simply monitor.

The performance measurement system design principles proposed by Globerson (1985) and

Maskell (1989) can be categorised according to whether they relate to the process of designing a

performance measurement system, or whether they focus on the output of the process. Take, for

example, Globerson's (1985) assertion that ``the performance criteria should be chosen from the

company's objectives''. This is equivalent to Maskell's (1989) recommendation that ``the

measures should be directly related to the firm's manufacturing strategy'', and in both cases the

performance measurement system design principle being examined relates to the process of

designing a performance measurement system, look to strategy first, rather than the actual output

of the process. This provides a framework that can be used not only to appraise the performance

measurement system design processes proposed by various authors (Keegan et al., 1989; Wisner

and Fawcett, 1991; Azzone et al., 1991 and Kaplan and Norton, 1993), but also to inform the

design of such a process.

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