1M A N A G E M E N T A C C O U N T I N G Q U A R T E R L Y S P R I N G 2 0 0 6 , V O L . 7 , N O . 3
I
t is well accepted that performance measurement
plays many important roles in running an organiza-
tion. These include translating strategy into
desired behaviors and results, communicating
these expectations, monitoring progress, providing
feedback, and motivating employees through
performance-based rewards and sanctions. For a long
time, managers had primarily used accounting-based
measures for these purposes. But with the advent of
new competitive realities such as increased customiza-
tion, flexibility, and rapid response to customer expecta-
tions, as well as new manufacturing practices such as
Just in Time and total quality management, many have
argued that accounting-based performance measure-
ment systems are no longer adequate. In the past
decade especially, a wide variety of measures and sys-
tems have been proposed and implemented to over-
come the purported limitations of accounting-based
measures in these environments. A prominent example
of these new approaches is integrated performance
measurement systems, such as the balanced scorecard.1
While proponents have made a persuasive case for
the new measures and measurement systems, the sup-
port they have provided for these new systems mostly
The Use and Usefulness
of Nonfinancial
Performance Measures
A SURVEY OF 128 FIRMS LOOKS AT HOW COMPANIES USE FINANCIAL, NONFINANCIAL,
AND SUBJECTIVE PERFORMANCE MEASURES IN ORDER TO BETTER
UNDERSTAND HOW DIFFERENT PERFORMANCE MEASURE TYPES CONTRIBUTE TO AND
AFFECT MANAGEMENT STRATEGIES.
B Y C H E E W . C H O W , P H . D . , A N D W I M A . V A N D E R S T E D E , P H . D .
EXECUTIVE SUMMARY Using survey data from manufacturing managers of 128 firms, this study empirically examines
the extent to which firms combine financial, quantitative nonfinancial, and subjective performance measures. Both
the relative use of measure types and specific measures within each type are found to vary with the companies’ manu-
facturing strategies. This supports claims that the three types of measures play different roles in supporting a firm’s
operations. A related finding is that the measure types have different impacts on important employee actions, such as
risk taking, efforts at innovation, relative emphases on the short vs. long term, and the propensity to game the perfor-
mance evaluation system.
2M A N A G E M E N T A C C O U N T I N G Q U A R T E R L Y S P R I N G 2 0 0 6 , V O L . 7 , N O . 3
has been in the form of anecdotal evidence with limited
scope, claims based on proprietary studies, or even sim-
ple, though intuitively appealing, illustrations. Further-
more, there is a tendency to downplay, if not outright
ignore, the potential shortcomings and limitations of the
alternatives being proposed. While the limited scope of
such support does not necessarily negate the potential
usefulness of the proposed changes in performance
measurement, it is insufficient for guiding informed
adoption decisions.
Managers need a more systematic understanding of
the advantages/benefits and the disadvantages/costs of
the new approaches compared to those of traditional
accounting-based systems. The aim of this article is to
contribute toward building such an understanding.
Specifically, we investigate the relative use of financial,
quantitative nonfinancial (“nonfinancial” for short), and
subjective performance measures by a sample of 128
firms. The term “subjective measures” is used to repre-
sent nonfinancial measures that are derived from sub-
jective judgment. We further explore whether financial,
nonfinancial, and subjective performance measures dif-
fer on such characteristics as controllability and vulnera-
bility to measurement errors and, more importantly,
impact on such behaviors as risk taking, efforts at inno-
vation, and gameplaying.
The results indicate that companies with different
manufacturing strategies use different mixes of the
three types of measures. This is consistent with each
type of measure performing a different role in support-
ing operations. Further supporting this inference is that
the three types of measures do have some different
effects and properties which, interestingly, are not
always in the directions suggested by prior literature.
To help readers follow our study and to interpret our
findings, we start by reviewing the key points that have
been made about the characteristics and impacts of
financial, nonfinancial, and subjective performance
measures. Then we explain our data collection
approach and present the results. We conclude with a
discussion of the implications for management account-
ing practice.
COMMON CLAIMS
Recent coverage of performance measures has criticized
periodic financial measures as being too aggregated, too
late, and too backward-looking to help managers under-
stand the root causes of performance problems, initiate
timely corrective actions, encourage cross-functional
decision making, and focus on strategic issues. A typical
example used to illustrate these shortcomings is “dollar-
ized” variance information.2 Most unfavorable variances
have multiple causes that stem from problems in multi-
ple departments. Yet traditional accounting-based
reporting systems tend to be structured along depart-
mental lines. This mismatch between the root causes
and report structure, along with a focus on the aggre-
gate financial impact rather than operations, may induce
managers to avoid taking responsibility, attempt to opti-
mize locally, and/or engage in dysfunctional behaviors
to maximize short-term performance at the expense of
long-term effectiveness and competitiveness.
These and many other criticisms of financial mea-
sures are intuitively appealing and likely have consid-
erable validity. In deciding whether to increase the use
of nonfinancial measures—and, if so, which ones—it is
important to recognize that nonfinancial measures are
not free of limitations. For example, if a firm tracks
the percentage of shipments delivered on time, there
may be an incentive to sacrifice one late but important
shipment to ensure the on-time delivery of many
smaller shipments.3 Moreover, at least some nonfinan-
cial performance measures may be difficult to measure
accurately, efficiently, or in a timely fashion. In a study
of business executives by Wm. Schiemann & Associ-
ates, the executives widely acknowledged the limita-
tions of traditional financial measures. Nevertheless,
they still favored them over nonfinancial measures
because they saw them as generally being less
ambiguous. As a group, the executives were less will-
ing to bet their jobs on the quality of a variety of non-
financial information than on the quality of financial
information.4 This is particularly true when nonfinan-
cial performances are subjectively assessed, due to
potential evaluation biases.5
It is possible, of course, that examples and studies
like these merely reflect how things are instead of how
they can be. In other words, financial measures may be
inadequate because managers have not fully reaped
their benefits—and not because of inherent limitations.
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Table 1: Frequencies (Percent of Usage) of Specific Performance Measures†
EMPHASIS ON QUALITY
IN MANUFACTURING
LOW HIGH HIGH–LOW DIFFERENCE
1. FINANCIAL MEASURES
Asset deployment (e.g., ROI) 33.3 41.0 7.7
Total gross or contribution margin 63.9 74.4 10.5
Unit gross or contribution margin 41.7 48.7 7.0
Total manufacturing cost 66.7 84.6 17.9
Unit manufacturing cost 47.2 66.7 19.5
Manufacturing cost budget line-items:
Labor cost variances 58.3 76.9 18.6
Material cost variances 55.6 71.8 16.2
Indirect cost (overhead) variances 58.3 71.8 13.5
Maintenance expenditures 32.4 51.3 18.9
Dollar amount spent on manufacturing process improvements 30.6 35.9 5.3
2. OBJECTIVE NONFINANCIAL MEASURES
2.1. Internal operating measures
Production volume 62.2 76.9 14.7
Labor productivity 75.7 82.1 6.4
Machine productivity 45.9 41.0 -4.9
Material usage 59.5 79.4 19.9
Setup efficiency 32.4 35.9 3.5
Manufacturing cycle time 40.5 64.1 23.6
Inventory levels 73.0 76.9 3.9
Product defects 83.8 94.9 11.1
New product introductions 16.2 43.6 27.4
New product design efficiency 5.4 35.9 30.5
2.2. Employee-oriented measures
Employee satisfaction 13.5 61.5 48.0
Employee skills 27.0 61.5 34.5
Employee empowerment 16.2 38.5 22.3
Safety measures 54.1 82.1 28.0
Employee training 32.4 69.2 36.8
Employee turnover 21.6 56.4 34.8
Absenteeism 56.8 64.1 7.3
2.3. Customer-oriented measures
Market share 19.7 18.2 -1.5
Time to fill customer orders 37.8 59.0 21.2
Delivery performance 83.8 97.4 13.6
Time to respond to customer problems 27.8 56.2 28.4
Product flexibility 27.0 35.9 8.9
Customer satisfaction 37.8 76.9 39.1
Customer acquisition 16.2 25.6 9.4
Customer retention 26.2 43.3 17.1
3. SUBJECTIVE MEASURES
My long-term perspective on the business 50.0 71.8 21.8
My ability to effectively acquire new skills/knowledge 47.2 59.0 11.8
My willingness to share knowledge within the organization 52.8 69.2 16.4
My cooperation with other departments within the organization 75.0 82.1 7.1
Employee spirit/morale in my department 47.2 79.5 32.3
My management style/leadership skills 83.3 92.3 9.0
My loyalty toward the firm 31.4 59.0 27.6
† Several respondents also provided additional write-in measures (typically one or two). Some were highly idiosyncratic to their specific organizations (e.g., my ability to work cooperatively
with the Japanese top management). Less idiosyncratic measures provided include capital expenditure measures and cost reduction measures for financial; order backlog, product return turn-
around cycle time, and quoted lead time accuracy for nonfinancial; and my ability to manage people and processes under my direct control and influence people and processes under my peers’
control for subjective measures.
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Similarly, it is possible that the purported shortcomings
of nonfinancial measures are the outcomes of ineffec-
tive implementation and use. The important point is
that the effective design and use of performance mea-
surement systems requires a systematic and balanced
investigation of the characteristics, strengths, and weak-
nesses of financial vs. nonfinancial performance
measures.
As academics without a vested interest in particular
outcomes, we undertook such an investigation by solic-
iting the experiences of manufacturing managers from a
large sample of firms. While ours is not the first study to
survey managers on the topic, a distinguishing feature
of our study is that it includes numerous specific perfor-
mance measures of each type. Another important
advance over previous studies is that we distinguish
between nonfinancial measures that are quantitative
and objectively measured and ones that are subjectively
determined. Such a distinction helps to increase under-
standing of the use and characteristics of subjective per-
formance measures, which is important because
subjective measures are increasingly being promoted
due to many aspects of work and performance not
being readily quantifiable.
We also explored if the relative uses of the three
types of performance measures, as well as specific mea-
sures within each type, vary with firms’ manufacturing
strategies. Because a major criticism of financial mea-
sures is their inadequate ability to support modern man-
ufacturing systems and initiatives, we focused
specifically on firms’ relative emphasis on quality. In
the specific case of quality-focused manufacturing,
proponents of quality initiatives have argued that such
initiatives tend to change the focus of work (e.g., pre-
vention vs. inspection) within subunits of the firm and
intensify the degree of interdependence among organi-
zational subunits.6 Quality strategies also are seen as
involving lead-lag relationships (e.g., prevention vs.
warranty costs) and aspects of work that are difficult to
quantify (e.g., cooperativeness). Because of these attrib-
utes, it has been argued that quality initiative imple-
mentation is better supported by nonfinancial than
financial measures, as the former can more effectively
secure commitment to quality initiatives, communicate
their significance throughout the organization, and cap-
ture the multiple relevant aspects of complex, diverse,
and team-based tasks.7
DATA COLLECTION
Consistent with our focus on manufacturing strategy,
we limited our sample to the manufacturing sector.
Within each firm, we directed our survey to the manag-
er or director of manufacturing. See “Sample Selection”
on p. 7 for an explanation on how we obtained the sam-
ple of 128 firms.
The first section of the survey asked participants to
indicate the specific measures currently used for evalu-
ating manufacturing performance. There was one sub-
section on financial measures, three subsections on
objective nonfinancial measures (internal operating
measures, employee-oriented measures, and customer-
oriented measures), and one subsection on subjective
performance measures. Each subsection listed a large
number of performance measures that we had identi-
fied based on a wide reading of both the academic and
practitioner literatures. Table 1 lists the specific mea-
sures in each category. Respondents could check off
measures from the list as well as write in additional
measures. To curb a potential upward bias in the num-
ber of measures reported—in other words, situations
where financial, nonfinancial, or subjective perfor-
mances are tracked but not used—we explicitly directed
respondents “to only check (or write in) those measures that
are reported, analyzed, and discussed on a regular basis for
the purpose of performance measurement and evaluation.”8
Since we wanted to compare the use of performance
measures by firms with different emphases on manufac-
turing quality, we also asked the manufacturing man-
agers to indicate the extent to which the following
activities occurred in their firms, where 1=“not at all”
and 5=“very high extent”:
1. Nonmanagement employees are evaluated for quali-
ty performance;
2. Nonmanagement employees participate in quality
improvement decisions;
3. Building awareness about quality among nonmanage-
ment employees is ongoing;
4. Quality performance data are displayed at employee
work stations/areas;
5. Suggestion programs for quality improvement among
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nonmanagement employees are used;
6. There are programs in place to improve cycle times
(e.g., by reducing time delays or nonvalue-added
activities in manufacturing); and
7. There are programs in place to coordinate quality
improvements with other departments within the
organization.
These seven practices encompass items consistently
identified by the academic and practitioner literature as
being critical aspects of quality initiatives: employee
involvement, process improvements, and cross-
departmental coordination.9
SURVEY RESULTS
Across all firms in the sample, the average performance
measurement system contains a wide variety of mea-
sures, with internal operating measures (26%) and
financial measures (25%) used the most. There were
lower but still nonnegligible proportions of subjective
performance assessments (19%), employee-oriented
measures (15%), and customer-oriented measures
(15%). Categorizing the measures differently, the aver-
age ratio in the sample is about 25 financial perfor-
mance measures to 75 nonfinancial measures, whereas
the average ratio of objective to subjective measures is
about 82 objective to 18 subjective.
Table 1 lists the frequency that each specific measure
is used by firms that put relatively low vs. high emphasis
on quality in manufacturing. Table 2 summarizes these
data by measurement category. To make the patterns
easier to see, both Tables 1 and 2 compare the one-
third of respondents with the highest emphasis on quali-
ty manufacturing to the one-third of respondents with
the lowest emphasis on quality. The significant differ-
ences between the two types of firms in their use of
each measure type are shown in Table 2 in bold. (Signif-
icant differences are based on t-tests at a 10% two-tailed
probability level.) The pattern that emerges from the
summary presentation in Table 2 is that firms that place
relatively greater emphasis on quality in manufacturing
use more nonfinancial measures (especially ones relating
to internal operations and employees) and subjective
measures. What is interesting is that the nonfinancial
measures are not used as substitutes for financial mea-
sures, as these firms also use more of the latter.
Does this mean that firms with a greater emphasis on
manufacturing quality simply use more of all kinds of
measures? The findings in Table 1 suggest that this is
not the case. Rather, the firms in the sample use differ-
ent mixes of specific measures to support their strategy.
While firms with greater emphasis on quality in manu-
facturing report using more of most measures, they use
two measures less frequently: machine productivity and
market share. In both cases, though, the difference
between firms with high vs. low quality emphases is
small (4.9% and 1.5%, respectively). At the other end of
the spectrum, seven measures have over 30% higher
usage rates by firms that emphasize quality: employee
satisfaction, customer satisfaction, employee training,
employee turnover, employee skills, employee
spirit/morale, and new product design efficiency. A fur-
ther eight measures have higher usage rates by quality-
focused firms in the 20%-30% range. Remarkably, none
of these 15 measures are from the financial category.
The picture that emerges is that firms that emphasize
quality also pay more attention to manufacturing and
customer-order-filling cycle times, new product
introductions and design efficiency, employee skills,
safety, training, turnover, empowerment, and employee
and customer satisfaction. They also expect managers
in charge of manufacturing to have a long-term perspec-
tive on the business, engender strong employee spirit or
morale in their units, and exhibit loyalty towards the
firm. These patterns provide support for claims that
nonfinancial performance measures (both objective and
subjective) are better than financial measures at helping
firms implement and manage new manufacturing.
Table 2: Relative Mix and Uses of
Different Measure Types by Firms
EMPHASIS ON QUALITY
IN MANUFACTURING
LOW HIGH
1. Financial measures 6 7
2. Objective nonfinancial measures 13 16
2.1. Internal operating measures 6 7
2.2. Employee-oriented measures 3 5
2.3. Customer-oriented measures 4 4
3. Subjective measures 4 5
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To assess how different measurement types con-
tribute to firm performance more directly, we also asked
the manufacturing managers to indicate the degree to
which they had found each type to possess the follow-
ing attributes, where 1=“not at all” and 5=“very high
extent”:
1. Encourage risk-taking;
2. Encourage innovation;
3. Encourage a short-term focus on the business;
4. Encourage gamesmanship or manipulation;
5. Contribute to the quality of short-term operational
decision making;
6. Contribute to the quality of long-term strategic
decision making;
7. Provide focus on the goals of the department;
8. Encourage the alignment of objectives across
departments;
9. Be influenced by factors outside the manager’s
control; and
10. Be free from measurement problems.
Table 3 reports each measurement type’s average rat-
ings on each attribute. This table also indicates signifi-
cant differences across measurement types’ average
ratings, based on t-tests at a 10% two-tailed probability
level. There is a significant difference for at least one
comparison per measurement attribute. Three features
of these results are particularly worthy of note.
First, as compared to financial measures, nonfinancial
measures are seen by the manufacturing managers as
providing the greatest encouragement for risk taking
and innovation and also are more effective at curtailing
short-termism and gamesmanship. These differences
are in line with popular belief. Compared to both finan-
cial and nonfinancial measures, subjective measures are
seen as being the most effective at curtailing short-
termism and gamesmanship.
Second, in contrast to popular claims, nonfinancial
measures are not seen as significantly different from
financial measures in their contribution to operational
and strategic decision making and their capacity to align
intra- and interdepartmental objectives. Surprisingly,
subjective measures are seen as being the least effec-
tive among the three measurement types along these
dimensions (except for “strategic decisions”). A plausi-
ble explanation for this is that the strongest weight for
performance evaluation is still being placed on financial
Table 3: Average Ratings and Significant Differences in Selected
Attributes and Consequences of Financial, Nonfinancial, and
Subjective Performance Measures
Indicate the extent to which each of the following performance indicators: FINANCIAL NONFINANCIAL SUBJECTIVE
Encourages risk-taking? *† 2.74 3.06 2.90
Encourages innovation? *† 3.14 3.47 3.14
Encourages a short-term focus on the business? *§† 3.29 2.94 2.70
Encourages gamesmanship or manipulation? *§† 2.25 2.08 1.94
Contributes to the quality of the short-term operational decisions you make? §† 3.21 3.27 2.79
Contributes to the quality of the long-term strategic decisions you make? † 3.21 3.27 3.13
Provides focus on the goals of your department? §† 3.68 3.72 3.14
Encourages alignment of objectives across departments? §† 3.31 3.40 3.11
Is influenced by factors outside your control? *§† 3.20 2.79 2.47
Is free from measurement problems? *§† 3.47 3.26 2.83
N = 128.
* = significant difference between financial vs. nonfinancial;
§ = significant difference between financial vs. subjective;
† = significant difference between nonfinancial vs. subjective (p < 0.10, two-tailed).
7M A N A G E M E N T A C C O U N T I N G Q U A R T E R L Y S P R I N G 2 0 0 6 , V O L . 7 , N O . 3
measures. In our sample, the performance evaluation
weights are, on average, 49% on financial, 30% on non-
financial, and 21% on subjective measures (not tabulat-
ed). When financial performance dominates the
performance evaluation, it is perhaps no surprise that
departmental financial measures provide the primary
focus for managers’ short-term decision making.
Finally, and perhaps not unexpected, subjective mea-
sures are seen as being most susceptible to measure-
ment problems, financial measures are considered the
least vulnerable, and nonfinancial measures fall some-
where in between. In contrast, financial measures are
considered by the manufacturing managers to be most
sensitive to factors outside their control, subjective
measures the least, and nonfinancial measures again fall
in between. These results suggest that different mea-
sures have different limitations. Although financial per-
formance may be measured more accurately, it typically
reflects the aggregate impacts of multiple factors and,
thus, may be relatively uncontrollable (e.g., aberrations
in financial performance caused by market shocks). In
contrast, while nonfinancial and subjective performance
evaluations may have lower measurement precision,
they are focused more easily on components of opera-
tions that the manager can control.
THE IMPLICATIONS OF PERFORMANCE
MEASURES
This study has sought to advance a balanced and sys-
tematic understanding of how different performance
measure types—financial, nonfinancial, and subjec-
tive—may contribute to effective management. Taken
as a whole, a rather clear implication of the findings is
the need to be cautious about popular claims that nonfi-
nancial measures are “superior” to traditional financial
measures across the board. Rather than being an
either/or choice, the challenge is to select the optimal
combination of measures across the different types.
This inference is supported by our finding that the dif-
ferent measure types are seen as having different
strengths and weaknesses (e.g., encouraging risk taking
vs. supporting decision making). While some types can
be used occasionally as substitutes for others, it may be
best to look at the different types of measures as com-
plements to each other. Further support is provided by
the pattern of performance measurement usage across
firms with different emphases on quality in
manufacturing.
While our study sheds some light on the use and
selected characteristics of each measure type, much
more can be learned about how the attributes and
effects of the different measures may vary across con-
Sample Selection
To obtain a sufficiently large sample for statistical test-
ing purposes, we collected data from both the United
States and Europe. We limited our U.S. sample to firms
in Southern California, where the authors’ schools are
likely to have some goodwill. Using the Explore data-
base from CorpTech for this region, we selected firms
with at least $2.5 million in annual sales and at least 50
people to ensure that firms in the sample would have
sufficient scale to use, or need, formal performance
measurement systems. We obtained 87 useable
responses (representing an effective response rate of
13%). In Europe, we partnered with the Vlerick Leuven
Ghent Management School (VLGMS). Here too, we
restricted our target sample to Belgium, where VLGMS
enjoys considerable goodwill. Using VLGMS’s database
on Belgian firms, we selected a sample of manufactur-
ing firms that matched the U.S. target sample on sales
and employment. We obtained 41 useable responses
(representing an 11% response rate).
Thus, our total sample size is 128 manufacturing
firms (87 + 41). Across these firms, the average number
of employees in the manufacturing department is 227,
while that for the firm as a whole is 3,216. The annual
average production value (sales) in the manufacturing
department is about $86 million, while that for the firm
as a whole is about $1.2 billion. Thus, the firms in our
sample are of at least middle-sized, rather than small,
operations. Moreover, the respondents have been work-
ing for about 11 years at their current company and six
years in their current position as the manager or direc-
tor of manufacturing, indicating that they are sufficiently
experienced for providing knowledgeable answers to
our survey. Finally, we observe no key statistically sig-
nificant differences among firm and respondent charac-
teristics between the U.S. and European samples.
8M A N A G E M E N T A C C O U N T I N G Q U A R T E R L Y S P R I N G 2 0 0 6 , V O L . 7 , N O . 3
texts and for specific purposes (such as supporting deci-
sion making vs. conducting performance evaluations
and providing incentives). We hope that, in addition to
reporting findings of value to managers and manage-
ment accountants, our study also stimulates future stud-
ies in these areas. n
Chee W. Chow, Ph.D., is professor of accountancy emeritus
at San Diego State University. He can be contacted at
(619) 594-5331 or chow@mail.sdsu.edu.
Wim A. Van der Stede, Ph.D., is an assistant professor at the
University of Southern California in Los Angeles, Calif. He
can be contacted at (213) 740-3583 or wim@marshall.usc.edu.
1 Robert S. Kaplan and David P. Norton, The Balanced Scorecard:
Translating Strategy into Action, Harvard Business School Press,
Boston, Mass., 1996.
2 Joseph Fisher, “Use of Nonfinancial Performance Measures,”
Journal of Cost Management, Spring 1992, pp. 31-38.
3 Ibid.
4 John H. Lingle and William A. Schiemann, “From Balanced
Scorecard to Strategic Gauges: Is Measurement Worth It?” Man-
agement Review, March 1996, pp. 56-61.
5 Scott Hawkins and Reid Hastie, “Hindsight-Biased Judgments
of Past Events after the Outcomes Are Known,” Psychological
Bulletin, May 1990, pp. 311-327.
6 For example, see Anne M. Lillis, “Managing Multiple Dimen-
sions of Manufacturing Performance: An Exploratory Study,”
Accounting, Organizations and Society, August 2002, pp. 497-529.
7 Shirley J. Daniel and Wolf D. Reitsperger, “Linking Quality
Strategy with Management Control Systems: Empirical Evi-
dence from Japanese Industry,” Accounting, Organizations and
Society, October 1991, pp. 601-618.
8 For situations where financial, nonfinancial, or subjective per-
formances are tracked but not used, see: Bonnie P. Stivers,
Teresa Joyce Covin, Nancy Green Hall, and Steven W. Smalt,
“How Nonfinancial Measures Are Used,” Management
Accounting, February 1998, pp. 44-49.
9 For example, see Lillis, 2002; and Christopher D. Ittner and
David F. Larcker, “Total Quality Management and the Choice
of Information and Reward Systems,” Journal of Accounting
Research, vol. 33 Supplement, 1995, pp. 1-34.
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