Productivity and performance measurement in the construction sector

Full text

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Productivity and performance measurement in the
construction sector
Bjørn Andersen, NTNU – Norwegian University of Science and Technology
Jan Alexander Langlo, SINTEF Industrial Management
Abstract
Performance measurement has been applied systematically in many different sectors to drive
improvements in productivity. Attempts have been made to establish measurements and
benchmarking in the construction sector, but we still have not reached a situation where
performance is measured systematically and consistently along the construction process. This
paper reviews performance measurement theory pertaining to the construction sector to
understand how performance is currently measured. Using participative workshops and
interviews, we have mapped requirements of different stakeholders toward a performance
measurement system. Based on literature and collected data, we outline issues to consider when
attempting to develop further mechanisms for measurement. We also report briefly from a study
to analyze different measurement and benchmarking systems to understand strengths and
weaknesses of these initiatives. Based on this analysis, we have in Norway established a project
to systematically test the CII 10-10 system to see whether it can meet measurement
requirements at several levels in the construction sector.
Keywords: Performance measurement, benchmarking, productivity, construction sector
1. Introduction
The word of mouth says that the construction industry around the world has great potential for
improvement in productivity and performance, and this impression is supported by current
research (Abdel-Wahab and Vogl, 2011, Ingvaldsen et.al., 2004). Multiple efforts have been
launched to address this issue in recent years, but the main challenges for the industry are to
document the real performance of the industry, understand where improvements are needed, and
document whether improvement efforts have had the effect they were supposed to have. The
fact is that the construction industry does not have a common measure, nor a common tool to
measure, how productivity and performance improves or falls over time. This paper will argue
that the construction industry should drop productivity as a measure and instead adopt
performance measurement in order to support performance improvement efforts. The paper will
also illustrate how a comprehensive performance measurement system should be constructed to
address five different performance levels, and it will outline an ongoing effort to test the
suitability of one performance measurement tool on the project level.

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Today, productivity is the dominating indicator for performance in the construction industry.
Productivity is the only measure available on an overall level in most countries, which is also
the case in Norway, the country we have studies the most closely. The Norwegian Central
Bureau of Statistics, SSB, provides annual productivity statistics. These statistics are based on
the annual economic reports provided by all organizations registered doing business in Norway.
Figure 1 illustrates how productivity for the construction industry has declined, using the
productivity in 1995 as a benchmark. By comparison, the productivity for non-construction has
nearly doubled in the same time span. The statistics are, however, flawed and certainly not
100% reliable. First of all, not all organizations in the construction industry are represented in
these statistics. Some organizations are included in other industries, such as oil and gas, since
they have projects within both industries. Second, these figures are very high-level and fail to
capture activity-level improvements. It is also a major problem that improvements that entail
moving toward industrial production, i.e., off-site prefabrication of building modules, often has
a double negative effect on construction productivity; this production is often transferred from
construction to manufacturing in industry statistics. This means that the most productive
activities are removed from the construction statistics, leaving the industry worse off than before
the improvements were implemented.
Figure 1 - Comparison of productivity improvement over time
Why, then, is it so hard to measure performance and get reliable measurements? Some of the
answers are found in the characteristics of the industry itself. The construction industry
comprises a large number of small construction firms, and a few larger contractors. A typical
construction project consists of several small or medium-sized organizations temporarily
working together, and they normally do not have any long-term relationship or partnership
lasting longer than the duration of the project. They usually apply a contract model that does not
give incentives for carrying out improvement efforts that will result in improved performance or

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increased value creation for other organizations. In other words, most improvement efforts are
focused on increasing value for one single organization, and they do not necessarily find it
interesting to help other organizations in the same project to increase their profits from that
particular project. These characteristics results in sub-optimization and makes it harder to
establish common measures and tools for performance measurement.
The long-term objective for the research project presented in this paper is to establish a set of
tools that will support structured performance improvement efforts in the construction industry.
The first step in this research effort is to select and test one tool for performance measurement
in a range of Norwegian construction companies representing different actors in the project
value chain. In addition, the first step will make sure that the experiences gained by each
company is documented and shared between the companies.
2. State-of-the-art
According to Vogl and Abdel-Wahab (2015), in their review paper on the topic, there are not
many papers that attempt to synthesize the existing literature in productivity research. Some
attempts have been made (Yi and Chan, 2014; Dolage and Chan, 2014; Panas and Pantouvakis,
2010). Thus, it is difficult to find authoritative overviews of approaches and methods used to
measure productivity/performance in construction. The fact that the approaches employed
originate from quite different academic fields further complicates efforts to provide such an
overview.
Notice that we deliberately wrote “productivity/performance”, since these terms are often used
either interchangeably or simultaneously (and then having somewhat different meaning) in
publications. Many authors have discussed these terms, e.g., Page and Norman (2014), stating
that “productivity measures how efficiently inputs are used to produce outputs” whereas
“performance measures how well something achieves its intended purpose.” Takim et al (2003)
defined performance measurement as “the regular collecting and reporting of information about
the inputs, efficiency and effectiveness of construction projects… used to judge project
performances, both in terms of the financial and non-financial aspects and to compare and
contrast the performance with others, in order to improve program efficiency and effectiveness
in their organizations”. These and many other definitions emphasize that performance is more
detailed, with productivity often being seen as one aspect of performance.
Roughly, two main fields have contributed to the body of literature:
• Economics, discussing different ways to measure (and compare) productivity at a national
and sector level.
• Operations management, looking at mechanisms to measure (and improve) performance at
a business process and project level (both fields have also touched upon enterprise level
measurements).
Under the economics approach, there are again two primary approaches; single-factor and
multiple-factor models (Crawford and Vogl, 2006). The most widely applied single-factor

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model is the measurement of (average) labor productivity, which is typically applied by national
statistics bodies in many countries. The calculation is done by dividing the aggregate
output/value created by the labor input spent to produce the output. Multi-factor models attempt
to explain productivity by considering factors beyond labor productivity, such as capital,
technological progress, management, skills, etc. There are advantages and disadvantages of both
approaches (ibid). Single-factor models require less data and allow cross-national benchmarking
due to their widespread use, but at the expense of accuracy. Multi-factor models provide better
insight through more fine-grained measurements, but require more data that can be difficult to
collect. Extensive criticism has also been raised regarding the accuracy of high-level economics
models, for example lack of suitable data (Allmon et al., 2000), that available statistics are
unable to determine whether productivity in reality has decreased or improved (Rojas and
Aramvareekul, 2003), and that a trend from on-site building to offsite manufacturing shifts this
activity from the construction to the manufacturing sector (Haas et al, 2000). Irrespective of
which economics model is applied, there is little relevance in terms of application at an
operational level, in projects and companies and actors in the sector have therefore pursued
alternatives (Harrison, 2007).
The operations management field offers bottom-up approaches, often termed “activity-level”
measurement, based on measuring individual construction activities/processes. Equal to
economics-derived approaches, also here different mechanisms have been developed. Back in
2000, Allmon et al described how economics-based measurements could be replaced by
activity-level measurements, but still relying on publically available statistics (Means’ Building
construction cost data, published by the R.S. Means Co. Inc. from 1960-1997 in the US and
deflating costs using the Consumer Price Index). A different approach to operational
performance measurement builds on so-called performance measurement frameworks, defined
by Brown et al (1997) as “a complete set of performance measures and indicators derived in a
consistent manner according to a forward set of rules or guidelines.” According to Yang et al
(2010), the primary frameworks applied in the construction industry are excellence models (e.g.,
the European Foundation for Quality Management excellence model), the Balanced Scorecard
framework (Kaplan and Norton, 1996), and key performance indicators models (e.g., the KPI
framework developed through the Construction Best Practice Program in the late 1990s (Lin
and Shen, 2007)). As we show later, systems based on key performance indicators seems to
have become more prevalent lately, as seen in the operational performance measurement
systems currently available.
Goodrum et al. (2002) outlined some advantages of activity-level measurement over traditional
aggregate measurement; by measuring output in real quantities (e.g., square area of building
spaces or volume of materials) the issue of price indexes is eliminated, by measuring labor
effort in terms of labor hours there is no need for using cost-index-based deflators, and it is
easier to compare input and output changes over time. More importantly, while high-level
productivity measurements at best capture a very few factors that affect the performance of the
sector and its projects, there are a number of issues that affect the long-term viability of the
sector. One set of such factors was proposed by Page and Norman (2014), factors such as
building to quality the market needs, maintaining health and safety standards, developing and

5
maintaining skills, adopting technology, innovating, etc. According to Harrison (2007), the key
disadvantage of more detailed activity-level measurement is that assembling a complete sector
view would require summing up all tasks in some manner, while it would be easy to omit tasks.
And for all tasks, large amounts of high-quality data are required.
A last topic of relevance when it comes to activity-level measurements is at which level such
measurements can target. Yang et al (2010) reviewed performance measurement studies
undertaken in the construction sector and found three levels being discussed; the project level,
the organizational level, and the stakeholder level. Of these, project level measurements came
first (Lin and Shen, 2007), and encompassed a large numbers of different dimensions of
performance, e.g., environmental performance, human resource performance, procurement
performance, safety performance, technology innovation, etc. (many of these coincide with
Page and Norman’s factors (2014)). For enterprises in the construction sector, project-level
measurements are valuable but the need for more aggregate company-level assessments induced
efforts to measure performance at the organizational level (as reported by for example Bassioni
et al, 2005). Such measurements cover both financial, as would be expected, and non-financial
aspects (Bassioni et al, 2004). The third level, stakeholder-focused measurements, is arguably
also important, as project success is ultimately judged by different stakeholders. Wang and
Huang (2006) found that the owner’s, supervisor’s, and contractor’s performances were
significantly related to the different criteria of overall project success. According to Yang et al
(2010), there has been less work at this level of measurement.
We have so far briefly mentioned some more specific models/systems for performance
measurement. To conclude this chapter, we outline in some more detail a selection of such
models/systems, based partly on previous studies and partly on mapping undertaken ourselves.
Going back to 2003, Takim et al reported from work to synthesize systems of measuring project
performance in the United Kingdom, the USA, France, India, Hong Kong, Saudi Arabia and
Malaysia. They found that these systems had different aims and focus of measurement;
measuring both productivity and performance at a project level, assessing project viability, as
well as targeting project quality. As far as we have been able to ascertain, few of these systems
are still in use today, and we therefore conducted a renewed search for existing performance
measurement systems. Table 1 lists the systems found that we judged to be most relevant for
further investigation (we make no claim that our search was exhaustive).
Table 1: Overview of selected existing performance measurement systems
Performance
measurement system
Country of
origin
Measurement purpose
Data
providers
Construction Industry
Institute: 10-10 program
USA
Assess project performance at
the end of each project phase
through performance
measurement and
benchmarking
Project
manager,
project
participants
Construction Industry
Institute: General
Program
USA
Detailed performance analysis
after project completion using
benchmarking
Project owner

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Constructing Excellence:
KPIzone
UK
Detailed performance analysis
after project completion using
benchmarking
Project owner
Benchmark Centre for
the Danish Construction
Sector: KPI System
Denmark
Evaluation of contractors in the
form of a scorecard as a basis
for more informed future choice
of contractors and contractor
improvement
Project owner
Performance Based
Studies Research Group:
Performance Information
Procurement System
USA
Evaluation of contractors to
allow selecting future
contractors based on other
criteria than price
Project owner
Project Norway: Health
Check/Project
Evaluation
Norway
Assessing performance
throughout the life cycle of a
project
Project
participants
BRE: Key Performance
Indicators for
Construction Industry
UK
Performance measurement for a
set of performance indicators
that can be compared across
projects or companies
Project
participants
Customer satisfaction
measurements
In use in
different
countries
Measurement of client/end-user
satisfaction with the completed
building
Client/end-
user
We will refer to Table 1 and these systems in the discussion section of the paper.
3. Methods
The research presented in this paper has developed slowly, from an idea presented and
discussed at a formal search conference in the Norwegian industry to identify ways to improve
the performance of the sector. This turned into a research project, started in 2013, and currently
has funding until 2017. The mentioned search conference prioritized performance measurement
as one of six initiatives to be undertaken, as a means to truly knowing the current status,
verifying whether improvement efforts have an impact, and undertaking industry analyses. The
Norwegian Directorate for Building Quality (DiBK) funded an initial study to document the
challenges facing the industry when trying to implement performance measurement and use the
data to improve their performance. In turn, the first study laid the foundation for a secondary
study, where the aim was to select and test one specific performance measurement tool in order
to gain experience in both how that tool worked, what proper handling of the tool demanded of
the organization using it, and how structured performance measurement should be carried out in
practice. The main project started in august 2015, and will be carrying on until summer of 2017.
The long-term objective for the whole portfolio of research effort is to develop a set of tools for
performance measurement in the construction industry in order to support future efforts for
continuous improvements in the industry.
A combination of different methods for data collection and data analysis formed the methodical
framework for the research presented in this paper. Data collection was carried out using

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interviews, focus groups and expert panels. In addition, document studies were used as well as
existing descriptions and documented experiences in using the different performance tools
under evaluation. The representatives from the construction industry also participated in
workshops where the findings were discussed and scrutinized. To a large extent, the same
methods were used to analyze the data, especially focus groups, expert panels, and workshops.
The integrity and validity of the data were ensured through triangulation, both researcher
triangulation (using more than one researcher to analyze the same data) and the application of
different types of data.
We have made some choices regarding methods, research approach and sample selection. First,
we have not been able to identify and review a complete sample of tools available for
performance measurement in the construction industry. Hopefully, our sample is satisfactory
and encompasses the most frequently used systems that are publicly available. Second, the
organizations involved come from the Norwegian construction industry. Still, our research has
shown that there are many similarities internationally in the construction industry. Our findings
could therefore be valid for other countries, even though this has not been the aim for this study,
Finally, the research is still ongoing so our findings are not finalized yet, and the results should
be reviewed in this perspective.
4. Results and Discussion
As chapter 2 showed, measurement of productivity and performance has been extensively
discussed in relation to the construction sector. However, although many countries measure
high-level productivity and several performance measurement framework and systems have
been developed, we still have not reached a situation where performance is measured
systematically and consistently along the construction process. As briefly discussed in chapter 3,
as part of the research project aimed at investigating a possible new, operations-focused
measurement system, we mapped the needs of different stakeholder groups in terms of
measurement and measurement data. This led to the identification of five characteristics to
consider when designing a performance measurement system (and based on these five
characteristics, the subsequent development of a framework for evaluating a performance
measurement system in terms of fulfillment of measurement requirements). The five
characteristics are:
• Level of measurement; at which level of construction sector activity the measurements are
undertaken, as discussed by Yang et al (2010) and treated in more detail below
• Who undertakes the measurements; who is responsible for collecting the measurement data,
e.g., the project owner, engineering consultants, contractors, authorities, external actors,
etc.
• Dimension of performance measured; the performance measurement literature suggests a
wide range of aspects of performance that can be measured, spanning dimensions like time,
cost, quality, flexibility, SHE, communication, innovation, learning, environmental impact,
and ethics
• Project phase targeted; construction projects can be divided into a number of different
phases and the measurement needs will typically vary across phases (this is for example

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evident in how CII has chosen to link the 10-10 performance measurement and
benchmarking system to five project phases)
• Type of project/building/infrastructure; not surprisingly, we found that the usefulness of
the selection of performance indicators applied vary from project type to project type, e.g.,
office building, laboratory facility, rail line, etc.
Regarding the first of these dimensions, the sector/activity level addressed by the
measurements, for activity-level measurements, Yang et al (2010) identified three levels; the
project level, the organizational level, and the stakeholder level. In our mapping of
measurement stakeholder groups, we found five distinct groups that also logically correspond
with measurement levels:
• The country level; by this, we think of the economics-type measurements undertaken at a
high level of aggregation, typically for the whole construction sector of a country. This
level of measurement has many stakeholders with highly differing needs for measurement,
but some key measurement purposes can be to demonstrate the importance of the
construction sector in society, promote the reputation of the sector, improve
competitiveness, feed national and supranational statistics to allow tracking of trends and
benchmarking.
• The project/value chain level; we have combined the terms project and value chain to
clearly communicate that this level addresses whole projects and their (often multiple)
value chains that contribute to delivering the project (this corresponds to the project level
identified by Yang et al (ibid). To measure and promote better performance of projects, this
level of measurement must be able to assess the performance across individual actors to
determine how well they collaborate to perform the project. Such measurements should
provide detailed insight into the performance of the projects, especially which performance
drivers affect overall performance, as well as stimulate behavior that create win-win
situations.
• The company/organizational level; meaning the individual (private) company or (public or
non-profit) organization participating in the construction project (corresponding to the
organizational level discussed by Yang et al (ibid). Many measurements at this level are
required by authorities or for accounting purposes, but other measurements that aggregate
results from the organizations’ projects are also important.
• The construction process level; academic sources and practitioners apply different terms to
the lowest activity level of a construction project, e.g., construction processes, business
processes, work processes, and we refer to these processes, for example land acquisition,
production of drawings, electrical installation, etc. These measurements serve several
different purposes; provide a platform for fact-based improvement efforts, promote an end-
to-end view on processes to counter sub-optimization, assess effects of changes in
processes, etc.
• The user level; this last one is not as clear a “level” as the other four and could even be
argued is a phase of the project rather than a level of measurement. What we mean here is
the users’ perceived performance of the project and its deliverables, an important aspect of
performance often overlooked in existing literature, and one that can only be measured
after completion of the project, ideally even only after some period of use, this indicating
that this is a matter of project phase. However, we could also argue that this is a singular
instantiation of the stakeholder level outlined by Yang et al (ibid), and we have therefore
chosen to include it as the fifth level. The purpose of these measurements are to assess
whether the delivered building or infrastructure fulfills it purpose (usability), to understand
the long-term life cycle performance of it, evaluate user satisfaction, etc.

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Put together, the five characteristics of a performance measurement system can be construed as
spanning a five-dimensional matrix. Every single cell in such a matrix would represent a unique
measurement context be populated, and in an absolutely complete performance measurement
system, each cell would be populated with at least one performance indicator serving a unique
purpose. In reality, the different combinations carry highly differing relevance, and a
“complete” measurement system would be extremely complicated and contain many
measurements of little interest. To identify cells of the matrix that seem to have the highest
potential for delivering relevant measurements, we defined a set of criteria to use when mapping
measurement requirements among sector stakeholders:
• Is there a measurement need and purpose?
• Is there sufficient availability and quality of the data required?
• How much effort and cost will be involved in establishing the measurement?
• Do other established performance measurement systems undertake this measurement, thus
allowing benchmarking?
Regarding the issue of data availability and quality, it is obvious that any performance
measurement system and regardless of which exact performance indicators are defined, will
require a fair amount of performance data. An effective construction sector system should strive
to exploit lessons learned from other sectors when it comes to data collection, where we see that
there a number of “archetypes” of measurement approaches that pose different opportunities:
• Manual or automatic harvesting of data from public records, e.g., industry statistics,
accounting systems, license data, etc.
• Automatic data collection from different sensors or sources. This is an area with a large
potential for effective data collection. Approaches can include collecting measurements
from sources like moisture sensors, drone-mounted cameras, scanners, step counters on
smart phones, etc. See Akinci (2015) for a presentation of some opportunities different
technologies provide for data collection.
• Exploiting so-called crowdsourcing where large numbers of users cooperate to populate
databases with relevant data. An example of this approach is the Danish construction
scorecard system where public sector project owners score suppliers according to a number
of performance dimensions, where this data is later made available to other project owners.
• Establishing a structured database for benchmarking, where users are encouraged to input
performance data by allowing them to compare their own data against others’. Several of
the performance measurement systems listed in Table 1 build on this principle.
Irrespective of the chosen performance measurements and which approach is used to collect the
performance data required, the purpose of any measurement effort is obviously to give users of
the system new insight. A fundamental analysis is to identify relationships between so-called
outcome measures/result indicators and performance drivers (Kaplan and Norton, 1996). Result
indicators say something about the end performance level of an activity or process, for example
quality expressed by user satisfaction or warranty costs, productivity expressed as the portion of
craft hours spent on value-added activities, or safety expressed as recordable incidents or time
away due to injuries. These are the results of different performance drivers, characteristics about
how the processes are designed or other contextual factors, for example the level of involvement
of building contractors in the engineering phase, degree of prefabrication, distance between

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storage and work area on site, etc. Important insights can be gleaned from investigating whether
changes in results indicators can be explained by differences in performance drivers, as this can
help identify good practices that will improve performance.
The identified characteristics of performance measurement systems and assessment criteria
presented here can be combined to either evaluate existing systems or as guidelines when
designing a new system. In the end, the criteria we developed and that were used to evaluate the
systems mentioned in Table 1 are those shown in Table 2 below. The table also shows our
assessments of each system, and we underline that these were subjective assessments made
considering our requirements for a measurement system to be pilot implemented in Norway.
The conclusion was that there is not one single system that meets all requirements. However,
we found that the CII 10-10 system seems best suited for adaptation into a national construction
industry system for Norway as it had the best score of all the systems presented in Table 2. This
system will be tested in a number of companies and at the aggregate level during the next two
years.
The 10-10 system provides benchmarking of project performance based on anonymously
surveying project management team members and collected facts on project progression. 10-10
surveys by phase instead of at completion, and uses five distinct but overlapping phases (front-
end planning/programming, engineering/design, procurement, construction, commissioning/
start-up) using simple statement-based questions. In addition, the system uses ten leading
indicators (input measures) and ten lagging indicators (outcome measures) to collect facts,
hence the name 10-10. A project benchmarks its performance with other projects in the same
phase, and it is possible to see how the performance of the project develops during the project
life cycle. A more thorough description can be found on the CII web site1.
5. Conclusions
This paper reports from a study undertaken on the use of performance measurement in the
construction sector, a study that addressed several issues that need to be resolved for such
measurements to be meaningful and have an effect. Previous studies have investigated different
levels measurements can be undertaken at, typically distinguishing between country/industry
and project/activity level measurements. We have found that in order to provide a
comprehensive set of measurements that serve a number of different purposes, an extensive
measurement system is needed that addresses five levels; the industry/sector level, the company
level, the project level, the process level, and the user “level”. Furthermore, the range of issues
targeted for measurement must also be expanded, from pure labor productivity assessments and
1 https://www.construction-institute.org/benchmarking/10-10.cfm?section=pa

11
a narrow set of project level factors to a wide set of performance indicators that can be used to
facilitate understanding, improvement, and aggregation to higher levels.
Having looked into existing measurement systems and evaluated these against a set of
requirements, it seems clear that there is no extant system that meets all requirements. It is
probably not likely that one unified system ever will exist, but we see that it is crucial that if
several systems must be used, these must be linked and allow aggregation of data across the
different measurement levels.
In the Norwegian construction sector, we have found the CII 10-10 system to be the one
existing system that promises to meet the most of our requirements. Together with about twenty
organizations in the industry, we will initiate extensive testing of this system to see how well it
manages to serve both process and project level improvement efforts, analyses into performance
drivers and their effects, as well as industry-level statistical needs. We will report from this
work in future papers, and in the mean time hope other countries experiment with other
measurement models and systems so that we jointly can move the industry forward.

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Table 2: Evaluation of existing performance measurement systems (* = criterion met, (-) = criterion partly met, _ = criterion not met, N/A = not
relevant)
Criteria!
Systems+
!
CII!10-10!
CII!General!
program!
Constructing!
Excellence!KPIzone!!
Benchmark!Centre!
for!the!Danish!
Construction!
Sector!KPI!System!
Performance!
Based!Studies!
Research!Group!
PIPS!
Project!Norway:!
Health!
Check/Project!
Evaluation!
!CCI!KPI!Engine!!
!
Customer!
satisfaction!
measurements!
System!availability,!can!be!put!to!use!without!extensive!adaptations!
*!
(-)!
(-)!
*!
(-)!
*!
*!
*!
User-friendliness!
*!
_!
_!
*!
(-)!
*!
_!
*!
Data!presentation,!how!easy!is!it!for!users!to!comprehend!presented!data!
*!
(-)!
(-)!
*!
*!
*!
(-)!
*!
Ease!of!extracting!data!and!analyzing!the!data!to!create!insight!into!project/company/industry!
(-)!
(-)!
(-)!
(-)!
(-)!
(-)!
_!
!
Ability!of!the!system!to!handle!a!large!number!of!users!
*!
*!
*!
*!
(-)!
*!
*!
*!
Potential!to!be!used!by!most!actors!within!the!sector!
*!
*!
(-)!
(-)!
(-)!
*!
*!
_!
Likelihood!that!the!system!will!be!available!in!the!long!run!
*!
(-)!
_!
(-)!
(-)!
*!
(-)!
*!
Usage!of!existing!data!to!minimize!data!collection!needs!
*!
(-)!
(-)!
(-)!
(-)!
(-)!
*!
_!
Quality!check/verification!of!data!before!used!in!analyses!
*!
*!
_!
(-)!
(-)!
_!
_!
_!
Ability!to!deliver!new!insight!through!data!analysis!
(-)!
(-)!
(-)!
(-)!
_!
_!
(-)!
_!
Usefulness!at!different!levels!of!measurement!
*!
(-)!
(-)!
(-)!
*!
*!
*!
(-)!
Possibility!for!aggregating!data!
(-)!
(-)!
(-)!
_!
_!
_!
!
(-)!
Overall!assessment!of!ability!to!satisfy!requirements!for!a!measurement!system!
*!
(-)!
(-)!
(-)!
(-)!
_!
(-)!
_!

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