Nonfinancial Information and Accounting: AReconsideration of Benefits and Challenges

Joan Luft

SYNOPSIS: Recent years have seen widespread interest in supplementing or replac-ing accounting information with nonfinancial information �NFI� in a variety of uses suchas incentive compensation, prediction of costs and profits, and firm valuation. The jointuse of NFI and accounting has had mixed results, however. Research has documentedbenefits to such use but has also documented significant challenges. This commentarysummarizes research that addresses two particularly important challenges in usingcombinations of accounting and NFI: measuring nonfinancial performance accuratelyand weighting measures appropriately when multiple accounting and nonfinancial mea-sures are used together. These challenges are related, in that the nature and magni-tude of measurement error helps to determine appropriate weights on multiple mea-sures. Two common themes appear in strategies for dealing successfully with thesechallenges. The first is that matching information properties to decision types can limitthe need for costly or infeasible improvements in measurement. Measurement errorsthat have significant negative impact on some decisions can be innocuous when theinformation is used for other decisions. The second theme is a portfolio approach tomeasurement error: the negative decision effects of error in individual measures can besignificantly mitigated by well-chosen combinations of NFI and accounting measures.

INTRODUCTION

Proposals to supplement conventional accounting with the use of nonfinancial information�NFI� have exerted a powerful appeal in recent years. Balanced scorecards and similarperformance measurement systems have been advocated intensively and are widely used by

organizations �e.g., Eccles et al. 2001; Kaplan and Norton 2001a, 2001b, 2001c, 2008�. Business-risk or strategic-systems audits, which rely on NFI to understand the client’s business, have beenput forward as a way to conduct efficient high-quality audits in a challenging economic andregulatory environment �Bell et al. 2002; Peecher et al. 2007�. Financial analysts use NFI toforecast earnings and stock prices �Dempsey et al. 1997; Chandra et al. 1999; Rajgopal, Ven-katachalam, and Kotha 2003; Peecher et al. 2007�, and the Financial Accounting Standards Board

Joan Luft is a Professor at Michigan State University.

The author is grateful to Karen Sedatole, Tyler Thomas, two anonymous reviewers, and Ella Mae Matsumura �editor� forhelpful comments.

Accounting HorizonsVol. 23, No. 3 American Accounting Association2009 DOI: 10.2308/acch.2009.23.3.307pp. 307–325

COMMENTARY

Submitted: May 2007Accepted: May 2009

Published Online: August 2009Corresponding author: Joan Luft

Email: luftj@bus.msu.edu307

�FASB� has considered mandating the reporting of nonfinancial measures along with traditionalfinancial statements �FASB 2001; Maines et al. 2002; Upton 2001�.1

Recent evidence, however, suggests that high initial expectations about the value of NFI werenot fulfilled in many instances. NFI appeared particularly value relevant �that is, associated withstock prices� for Internet firms in the later 1990s, but this value relevance fell significantly �not tozero, however� after the end of the Internet bubble �Demers and Lev 2001; Rajgopal, Venkatacha-lam, and Kotha 2003�. Many firms that adopted NFI-based incentive systems subsequently dis-carded them �e.g., 42 percent in the sample analyzed by HassabElnaby et al. �2005��. Recentresearch on business risk audits has reported considerable unwillingness by auditors to rely onNFI-based approaches �Knechel 2007; Curtis and Turley 2007�. After relatively intensive consid-eration at the beginning of the decade, the FASB has not acted to mandate NFI reporting.

Given the “retreat to the financial” that appears in this recent research, a number of questionsarise for accountants. First, why, if at all, should accountants be involved with the developmentand use of NFI—rather than, for example, leaving customer-satisfaction measurement to market-ers and employee-morale measurement to human resource specialists? Second, what has beenlearned from the experience of recent years about the actual benefits and challenges of using NFIin conjunction with accounting information? Third, if and when accountants are involved with NFIdevelopment and use, what assistance does accounting research provide to deal with the observedchallenges in the development and use of NFI? The remainder of this commentary addresses thesethree issues in turn.

NFI AND ACCOUNTINGMost organizations use a wide range of data that is important to the organizational mission but

falls outside the purview of the organization’s financial function. Accountants typically have littleto do with NFI documenting �for example� procedures for engineering experiments or biometricindicators for high-security employee IDs. What makes selected NFI the business of accountantsor users of accounting information?

Accounting research summarized below provides evidence that selected NFI can be used bothto substitute for and to complement accounting information in tasks for which accounting istypically important, such as forecasts of future financial performance or evaluation of currentperformance. Accounting and NFI work together as a portfolio of measures, in which the value ofusing and refining accounting measures depends on the information properties of NF measuresincluded in the portfolio, and the information value of any specific NF measure depends on theproperties of accounting.

In consequence, the accountant’s tasks depend on the properties of NFI as well as of account-ing information. Whether accountants should, for example, devote significant effort to developingfinancial measures of intellectual capital as an input to the valuation of knowledge-intensive firmsdepends on how cost-effectively NF measures such as patents and publications can provide thesame information. In this case, accounting and NF measures are substitutes, and more informativeNFI means less need for accountants to develop �or users to seek out� financial measures.

In contrast, when NFI complements accounting, more use of NFI means more use of account-ing, because accounting is more valuable when used together with NFI than when used alone. For

1 The definition of “financial” and “nonfinancial” varies across users. Some regard all measures denominated in dollars orother currency �e.g., cost of quality measures� as financial, while measures like defect counts or satisfaction ratings arenonfinancial �e.g., Nagar and Rajan 2001�. Others regard financial measures as consisting primarily of GAAP earningsand its components and stock prices or returns, while measures like customer profitability or cost of quality arenonfinancial even though dollar-denominated �Kaplan and Norton 2001a; Upton 2001�. In general, the observations inthis paper apply to NFI identified by either definition.

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example, in Amir and Lev �1996�, accounting earnings alone appear irrelevant to stock prices forwireless communication firms; but when NF measures of growth potential are included in themodel, earnings become significantly value relevant.2 Similarly, in performance evaluation andreward systems, accounting earnings that are imperfect measures of employees’ actions can bemore heavily weighted �i.e., more dollars of reward can be provided for a given increase inearnings� when appropriate NFI is also included in the reward base �Feltham and Xie 1994; Dataret al. 2001�. In such cases, more informative NFI means that accountants can more confidentlyadvocate the use of earnings �or other accounting� information in decision making, even thoughearnings is not a perfect measure of firm, business-unit, or individual performance.

Thus accounting and nonfinancial measurement can usefully inform each other and do not, inprinciple, benefit from being performed in isolation. But as described in more detail below, resultsof recent experience with joint use of accounting and NFI have been mixed, and users of multiple-measure systems have expressed a number of specific frustrations with them.

BENEFITS AND CHALLENGES OF NFI USEBenefits

Benefits of combining selected NFI with accounting measures have been documented innumerous studies in recent years. The incremental explanatory power for earnings and stockreturns provided by NFI has been well established �e.g., Amir and Lev 1996; Ittner and Larcker1998; Hughes 2000; Trueman et al. 2000; Nagar and Rajan 2001; Rajgopal, Venkatachalam, andKotha 2002, 2003; Rajgopal, Shevlin, and Venkatachalam 2003; Smith and Wright 2004�, al-though prior earnings is often a stronger predictor �e.g., for stock returns in Francis et al. 2003�.Brazel et al. �2007� find that NFI—specifically, inconsistency between patterns in financial and NFinformation—is a significant indicator of financial fraud.

The use of diverse financial �F� and NF measures to manage organizations �e.g., to allocateresources and reward employees� appears to be positively associated with organizational perfor-mance on average. The association is often weak, however; there is considerable variation in theexperience of individual organizations, and attempts to predict which types of organizations willbenefit more from NFI use than others have had mixed results �Hoque and James 2000; Banker etal. 2000; Ittner, Larcker, and Randall 2003; Said et al. 2003; Chenhall 2005; Van der Stede et al.2006�.

Another stream of studies focuses on associations between NFI and learning, which may notbe captured clearly in tests of the direct �especially short-term� association between NFI use andorganizational performance. Chenhall �2005�, using survey data, finds that organizational learningmediates the effect of customer-measure use on strategically important customer outcomes. Thatis, how strongly the use of customer measures is associated with successful customer outcomesdepends on how strongly the use of customer measures is associated with organizational learning.

Campbell �2008�, using archival data from a restaurant chain, finds that NF-based incentivesincrease performance, and a portion of the performance improvement remains after the incentiveis reduced or eliminated, apparently because of nonreversible learning gains. Similarly, experi-mental results in Farrell et al. �2008� indicate that even in settings where leading NF indicatorshave no incentive value because employees have long horizons, employee performance is higher

2 A discussion of this study by Shevlin �1996� expresses some reservations about the analyses employed, but the generalprinciple that accounting can be more informative when complemented by NFI remains valid. For example, Brazel et al.�2007� find that a measure developed by combining revenue growth and growth in NF measures �e.g., number ofemployees or facilities� is a significant predictor of fraud; revenue growth without the comparison to NF growth seemsunlikely to be an equally valuable fraud indicator.

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when compensation contracts include forward-looking NF measures than when they do not. Theinclusion of forward-looking measures appears to induce more focused testing of task strategies,which increases performance over time.3

Sometimes incentives for current performance conflict with learning, but well-chosen NFI cancontribute to resolving this conflict. Dye �2004� observes that the actions by managers that do themost to increase profit in the current period do not always provide the most valuable informationabout which actions will improve profits in the future. Thus the evaluation and reward systemmust carefully balance incentives for performance against incentives for experimentation andlearning.4 Analytic results in Dye �2004� indicate that the better an organization’s informationsystem tracks the intermediate outcomes �product quality, customer satisfaction, etc.� betweenmanagers’ actions and financial performance, the more worthwhile it is for managers to experi-ment, because they can learn more from the outcomes of their experimentation.

Challenges to Effective Use of NFIIn spite of the benefits of NFI use documented above, attempts to implement systems of F and

NF measures have had mixed success. Some of the challenges to effective NFI use are primarilymanagement issues: for example, leadership failures in implementation �Kaplan 2006� and failureto link the NFI to strategy �Ittner and Larcker 2003� or even agree on a strategy to which NFIcould be linked �Kasurinen 2002�. Other challenges relate to information design and use, however,and thus fall more clearly in the domain of accounting.

Two key information-related challenges to effective NFI use are measuring important nonfi-nancial factors and weighting multiple F and NF indicators appropriately in decisions like resourceallocation, planning, and performance evaluation and reward. A Deloitte �2007� survey of seniorexecutives and board members identifies “understanding of how to measure non-financial driversof performance” as the primary requirement for more successful use of NFI. According to Ittnerand Larcker �2003�, one of the most common mistakes organizations make in using NFI isincorrect measurement. A number of studies have documented the importance of measurementproblems in blocking the development and use of multiple-performance-measure systems �e.g.,Malina and Selto 2001, 2004; Cavalluzzo and Ittner 2004; Andon et al. 2007�.

Incorrect weighting of multiple measures can also lead to disappointments with NFI, anddetermining appropriate weights is likely to be a difficult and conflict-ridden process �see Malinaand Selto �2001, 2004�, for examples�.5 Users of NFI are sometimes simply unsure about appro-priate weights; for example, a manager quoted in one field study says: “I don’t have a sense ofwhich of these �measures� … have the most leverage compared to others” �Malina and Selto 2004,459�.

Even when users of NFI have more confidence about weights on multiple measures, theweights can be systematically mistaken, thus supporting decisions with disappointing outcomes.Daniel and Titman �2006� and Rajgopal, Shevlin, and Venkatachalam �2003� argue that the equitymarkets systematically overweight selected NFI. In single-firm studies, Ittner, Larcker, and Meyer

3 Results of an experiment by Webb �2004� indicate that incentive effects of contracting on forward-looking measuresdepend on the prima facie plausibility of the measures’ effects on future performance, and learning might also beaffected by this factor.

4 Note that the tradeoff Dye �2004� describes is not the usual tradeoff between actions that improve current performanceand actions that improve future performance, but between actions that improve current performance and actions thatreduce managers’ uncertainty about what will improve future performance.

5 Weights can be explicit in formal prediction models or incentive-compensation formulas, or implicit in subjectiveevaluations or predictions that are more influenced by some measures than others. They can also be implicit in otherelements of the control system; for example, the intensity with which managers respond to variances on differentmeasures �Lillis 2002�.

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�2003� and Malina and Selto �2001� find that managers initially put significant weights on NFIwhen a new performance measurement system is introduced but soon redistribute the weight tomore “traditional” financial or market-share measures, possibly because managers regard theinitial weights on NFI as mistakes. Moers �2005�, using proprietary information from a largeEuropean industrial firm, finds that the use of more diverse performance measures is associatedwith more lenient and more compressed evaluations. That is, it appears that supervisors weightmultiple measures differently for different subordinates to avoid the unpleasant task of giving lowevaluations or evaluations that differ much across individuals.

Thus both measurement and weighting pose significant problems for organizations. Popularunderstanding of measurement and weighting problems sometimes seems limited to a belief that“accurate” or “reliable” measures should be chosen, and that “more important” measures shouldbe weighted more heavily. In this view, when performance is multidimensional �e.g., both inno-vation and cost management are important�, an appropriate approach would be to choose for eachdimension �innovation and cost management� the most accurate measure that can be acquired orconstructed at a reasonable cost and then weighting the measures based on the relative strategicimportance of the performance dimensions.

Both analytical and empirical research indicate, however, that sometimes inaccurate measuresare quite serviceable in decision making, and at other times important measures need to beweighted lightly �see below for examples�. Hence a more refined approach to measurement andweighting can be helpful, and research suggests two important principles for making such refine-ments.

The first principle is to match information properties appropriately to decisions. The type andmagnitude of error that make a measure virtually useless for one decision can be relativelyharmless in another. Because error reduction is often costly, identifying the decision settings inwhich it is more valuable can make NFI use more cost effective.

The second principle is to take a portfolio approach in performing this matching: that is, toconsider the information properties of the set of F and NF measures used, rather than evaluatingthe measures one by one. Important dimensions of performance are sometimes difficult to mea-sure, but even a poor measure can be useful if other measures in the set can reduce the firstmeasure’s negative effects on decision quality.

The remainder of this commentary first provides brief definitions of decision and measure-ment error types as a basis for successful matching. The following sections then summarizerecommendations for measurement of NFI that can be drawn from recent research, followed byrecommendations for weighting of F and NF measures in multiple-measure portfolios.

DEFINITIONSDecisions

Based on Demski and Feltham’s �1976� distinction between decision-influencing anddecision-facilitating uses of information, decisions are categorized as follows:

�1� Performance evaluation for purposes of reward is a decision-influencing use of informa-tion. Here NFI, in combination with accounting, is used as a basis for determiningperformance-based rewards such as bonuses, equity compensation, and promotions. Ameasure �or portfolio of measures� is better for this purpose, the better it captures em-ployee efforts and talents that increase firm value �or other relevant organizational objec-tives�, and the less it responds to “window-dressing” or the occurrence of random exter-nal events that influence organizational outcomes.

�2� Provision of predictor variables is a decision-facilitating use of information. Here NFI isused, for example, as a leading indicator to forecast future financial performance, and to

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estimate the expected return of alternative investment projects as a basis for choosingbetween them. A measure �or portfolio of measures� is better for this purpose when itsupports more accurate predictions.

Measurement ErrorsMeasurement errors can be usefully divided into two categories, bias and random error. When

NFI is upwardly or downwardly biased, on average it overstates or understates actual values. NFIcan also, or instead, contain random error �noise�. If the measure is noisy but unbiased it isaccurate on average but overstates or understates in particular instances.

MEASUREMENTMeasurement issues in the use of NFI take a number of forms. When multiple measures of a

particular performance dimension �e.g., quality, customer satisfaction� are available, criteria areneeded for choosing among the available measures. When accountants and managers are con-structing portfolios of F and NF measures, they need to respond to users’ concerns about thepossible inaccuracy of the measures included. �See Malina and Selto �2001, 2004� for examples ofthese problems in a field study.� Methods of addressing these problems include not only reducingthe error in a given measure, but also mitigating the negative decision consequences of irreducibleerror and identifying decision settings in which the particular errors are relatively innocuous andthus NFI can be useful in spite of measurement error.

The recommendations below begin with identifying relevant errors and matching them todecisions, and then continue with means of mitigating errors and/or their effects.

Identify Error in Measuring the Construct, Not the IndicatorA measure such as patent counts is an indicator of an underlying construct such as firm-value-

increasing innovation. In economic models of management accounting �see, e.g., Lambert �2007��,both bias and random error are defined with respect to the underlying construct that the measureintends to capture, not with respect to the indicator. Thus a patent count that correctly reports thenumber of patents the organization actually received is not error-free in the sense that is relevanthere.

A focus on accuracy in the indicator—for example, choosing NF indicators that are preciselycountable and discarding those that are not—can lead to disappointing experiences with NFmeasures that are “accurate” but are neither good predictors nor good motivators. For predictionsa patent count would be error-free in this sense if it measured exactly the innovativeness thatgenerates future expected revenues. For reward decisions a patent count would be error-free if itmeasured exactly the employees’ efforts that contributed to this innovativeness. Neither F nor NFmeasures are likely to be error-free in this sense. Sometimes “softer” or “fuzzier” measures suchas knowledgeable subjective judgments of innovation quality can be more accurate in measuringthe underlying construct and can provide better support for predictions and performance evalua-tions.

Care needs to be taken about possible biases in subjective measures—for example, favoritismin subjective performance evaluations—but such measures should certainly not be discarded out ofhand in favor of “harder” �more objectively countable� measures. Analytic research finds thatincluding subjective measures of performance can improve overall performance evaluation andmotivation �Prendergast 1999�, and Gibbs et al. �2004� find empirical evidence consistent with thisprediction, particularly when employees have long tenure—perhaps because long tenure is anindicator of their trust in the subjective evaluation system, and/or subjective evaluation is moreaccurate for employees with longer track records.

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Match Measures to Decisions: Different Errors in Performance Evaluation and PredictionA measure that is inaccurate and unsatisfactory for one decision type can sometimes serve

very well for another. For example, a division might generate a large number of high-value patentsbased largely on work that was done before the present divisional manager’s arrival. �This can betrue for some years after the manager’s arrival in settings where R&D is a slow and cumulativeprocess.� The patent count can thus be quite inaccurate as a measure of the current divisionalmanager’s contributions to firm value but quite accurate as a predictor of future revenues.

Careful matching of measures to decision types based on construct-measurement error avoidstwo mistakes in judgment about NFI measures. One mistake in this setting would be to regard thepatent count as a “good” measure because it is an excellent predictor, and therefore to insist onusing it as a basis for rewarding the manager. The other mistake would be to regard it as a “bad”measure because it works poorly as a basis for reward, and therefore to fail to use it in predictions.

Match Measures to Decisions: Random Error and Prediction CharacteristicsThe predictive power of NFI for future financial performance or for other NFI is often low,

and measurement error in both predictor and predicted variables is one of the sources of this lowpredictive power.6 In consequence, the errors of NFI-based predictions will be large; but both themagnitude of the prediction error and the seriousness of its consequences depend on specifics ofthe decision setting. A NF measure with considerable random error in it can still be valuable inmaking some predictions and need not be discarded; but conversely, the fact that the measureprovides valuable predictions in some decision settings does not mean it will provide valuablepredictions in other settings.

Key elements of the decision setting are the number of observations to be predicted and theuse to which the predictions are to be put. Holding the prediction model constant, the expectederror in the prediction of mean future financial performance will be smaller for the mean of a largenumber of observations than for the mean of a small number or for the prediction of a singleobservation. Thus a model with an R2 that provides tolerable error levels in predicting the futureprofits of a large portfolio of firms can be problematic for predicting the future profits of a smallnumber of business units �cf., use of NFI in contemporary budgeting techniques �Hansen et al.2003��, or forming expectations as a basis for judging the plausibility of an audit client’s unauditednumbers �cf., Bell et al. 2002�.

The consequence of large prediction errors in small-sample predictions depends on whetherthe decision effects of the errors offset. Consider, for example, an auditor who is responsible forfour clients. In this setting, error effects do not offset. Forming too low an expectation of earnings,resulting in unnecessary audit work and conflict with one client, does not make up for forming toohigh an expectation of earnings and failing to find a significant error or irregularity at anotherclient.

In contrast, consider a manager who forecasts sales of four different product lines and adds upthe forecasts to get a total sales revenue forecast, and assume that accuracy of the total salesrevenue forecast is the primary objective in this case. In this setting the errors tend to offset. Anoverstatement in the revenue prediction for one product line is partly offset by an understatementin the revenue prediction for another product line. Holding constant the predictive ability of the

6 Although NFI is a significant predictor of future financial performance, the R2s of models relating NFI to financialperformance are often low: 1 percent to 5 percent for customer satisfaction in Ittner and Larcker �1998�, and single-digitor low double-digit incremental R2s for various NFI in Nagar and Rajan �2001�, Francis et al. �2003�, Amir and Lev�1996�, Trueman et al. �2000�, and Rajgopal, Venkatachalam, and Kotha �2002, 2003�. Lambert �1998� makes the pointthat measurement error in the NF predictors could account for the low R2s of some models.

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model and the sample size �four clients or products�, the overall error effect is less damaging in thecase of the sales forecast. In consequence, different levels of measurement error can be toleratedin the two different settings.

Match Measures to Decisions: Innocuous Measurement BiasesBias can seem like a more serious problem than mean-zero random error, because a biased

measure does not represent the true value of the underlying construct even on average, while anoisy but unbiased measure does. For some decisions, however, pure bias is a relatively innocuoustype of measurement error. The presence of bias in measures need not always be an obstacle toimplementing NFI-based performance measurement and prediction, and accepting some bias inreturn for a reduction in random error can be worthwhile when such tradeoffs are possible.7

Situations in which bias is relatively harmless are identified separately below for performanceevaluation and prediction.

Performance EvaluationPerformance evaluations and rewards are often based not on the observed NFI measure itself

but on the change in the measure or a comparison between the measure and a target �Murphy2000�. These practices significantly mitigate the effect of bias. For example, suppose that a cus-tomer satisfaction measure has an upward bias of two points on a 10-point scale. �Perhaps thequestions are designed to make customers reflect on positive more than negative experiences.�When the true value changes from four to six, the reported value changes from six to eight. In thiscase, the change of two units is an unbiased measure although the absolute levels are biased. Usersdo not need to know the amount of the bias; they only need assurance that it is stable over time.8

Similarly, when performance evaluation is based on comparing a measure to a target, thecomparison can do much to eliminate the effects of bias. If the target is based on past performance,then the comparison to target is very similar to a change measure. If the target is based on externalbenchmarks instead, and decision makers have some awareness of the existence and magnitude ofthe bias, they can set the target accordingly. The more upward bias there is likely to be in themeasure, the more the target performance should exceed an unbiased external benchmark.

Providing Predictor VariablesWhen NFI is used to provide predictor variables, bias can be harmless as long as the predic-

tive models �or individuals’ subjective prediction strategies� were developed using previous datawith the same bias. The bias will be captured in the intercept of the model, and both the coefficientrepresenting the effect of NFI and the prediction itself will be unbiased. In such settings, the profitincrease associated with �for example� an increase in reported customer satisfaction from six toeight in the past provides a reasonable basis for estimating the profit increase associated with anincrease in reported customer satisfaction from six to eight in the future, even though the measureitself is overstated.

Reduce Error by Aggregating Multiple MeasuresIt is not always intuitively obvious that total measurement error can be reduced by using more

measures that contain error, but this is often a cost-effective way of decreasing random error. Such

7 For example, in sample-based measures like customer satisfaction surveys or defect counts, sample composition andsample size choices can determine the magnitudes of noise and bias and the terms of tradeoff between the two.

8 When �as is often the case�, a NF measure captures the underlying construct with random error as well as bias, then achange in the reported measure or a comparison to target will still contain error. But it is important to realize in suchcases that the random error, not the bias, is the problem that must be addressed.

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reductions can be worthwhile for organizations, because random error has significantly negativeeffects on decision making. Random error in predictor variables reduces the accuracy of predic-tions, and random error in employees’ evaluations reduces the motivation that a given level ofmonetary incentive provides to risk-averse employees. Random error in performance measures canalso reduce the ability of an organization to attract talented but risk-averse individuals, because itreduces their certainty that they will be rewarded for the exercise of their talents.

The basic principle of reducing random error by averaging multiple observations is intuitivein some instances. For example, average divisional earnings over several periods are likely to beregarded as being a more reliable measure of a divisional manager’s talents and efforts than asingle period’s earnings.

Research also provides examples of more sophisticated combinations of measures via statis-tical methods in prediction settings. Rajgopal et al. �2002� use factor analysis to reduce a largenumber of specific NF information items such as new product introductions and managerialteam-building actions. Taken singly, the specific actions are too numerous, diverse, and ambiguousin their implications to be easily used as predictors. But combined into two factors, they explain asubstantial portion of the cross-sectional variation in e-commerce firms’ stock market returnspost-IPO, even after controlling for reported earnings and analysts’ forecasts of future earningsand revenues. Similarly, Demers and Lev �2001� and Dikolli and Sedatole �2007� use factoranalysis to combine multiple measures of website performance into two factors that have signifi-cant explanatory power for stock returns and future profitability of e-commerce firms. Data-reduction techniques of this kind offer considerable promise for reducing random error in NFImeasurement.

Mitigate Error Effects: Offsetting Deliberate Bias

Bias is particularly troubling when it is the result of deliberate actions—“window-dressing” orgaming—on the part of individuals whose performance is being measured. In such cases it maynot be stable across individuals or time, as motivations to introduce bias will vary across indi-viduals and across time. A well-designed portfolio of measures for performance evaluation andreward can reduce intentional bias, however, by including measures on which window-dressinghas countervailing effects. That is, actions taken to window-dress one measure and increase theemployee’s reward will tend to make another measure look worse and thereby decrease theemployee’s reward, thus reducing the overall incentive to window-dress. �See Feltham and Xie�1994� and Datar et al. �2001� for analyses of the construction of multiple-measure evaluation andreward systems.�

For example, consider rewarding managers for a particular NF measure, high inventory turn-over, as a measure of effective inventory management. Managers may game the measure, resultingin high values of reported turnover but not effective inventory management, as too-low inventorieslead to stockouts, poor customer service, and even reduction in product innovation, because ofmanagers’ uncertainty about whether radically innovative products will move quickly enough tokeep the inventory turnover measure high �see examples in Melnyk et al. �2005� and Melnyk et al.�forthcoming��. Thus gaming of the inventory management measure has negative effects if themeasure is used alone. But when it is used as a component in a portfolio of measures that includesGAAP income, managers’ incentives to game the measure are mitigated. Understocking willimprove inventory turnover but reduce income by reducing sales. Conversely, the use of inventoryturnover as a measure mitigates the tendency of managers to bias reported income upward throughoverproduction �see Roychowdhury �2006� for evidence of overproduction as an earnings man-

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agement technique�.9 Overproduction will increase current GAAP �absorption costing� income,but will make inventory turnover worse. The combination of measures limits gaming and moti-vates decisions more congruent with organizational goals.10

WEIGHTING MULTIPLE MEASURESJust as the problem of measurement does not reduce to a problem of finding the single most

accurate measure for a given construct, so the problem of weighting does not reduce to a questionof which measures are “more important” in general. In prediction models using NFI, error in themeasures as well as the predictive importance of the underlying constructs can influence weightestimation. In performance evaluation and reward systems, optimal weights on F and NF measuresdepend not only on the underlying constructs’ strategic importance or contribution to firm value,but also on a complex array of factors such as contract length and individuals’ time horizon�Dikolli 2001; Dutta and Reichelstein 2003�, product architecture in supply chains �Baiman et al.2001�, whether the incentive contract is implicit or explicit �Budde 2007�, how tasks are bundledtogether—for example, whether one employee is responsible for sales only and another for serviceonly, or each is responsible for a mix of sales and service �Hughes et al. 2005�—and whether theincentive compensation is simply paid out based on measured performance or a bonus pool isdetermined first and then divided among employees �Rajan and Reichelstein 2006�.

This section focuses on how measurement properties—a key concern of accountants—affectthe weighting of NFI and related financial measures. Measurement-property effects are not alwaysintuitively obvious. For example, Hemmer �1996� shows analytically that adding a measure ofcustomer satisfaction to an incentive system based on accounting earnings can either increase ordecrease the optimal weight on earnings, depending on whether the customer satisfaction measureis the mean level of satisfaction or the number of customers that exceed a certain satisfactionthreshold.

Because appropriate weighting is not always intuitive and weighting decisions are often madesubjectively, an important element of effective use of NFI is avoiding common biases in subjectivedecision making. Hence the recommendations below include notice of potential biases in subjec-tive weighting and techniques for mitigating these biases, insofar as recent research has addressedthese issues. Weighting problems and solutions differ considerably between performance evalua-tion and prediction settings, and thus the two settings are presented separately.

Weighting in Performance Evaluation

Weight High-Random-Error Measures Cautiously, Even When ImportantThe effect of measurement properties on incentive weighting that has received the most

attention in accounting research is the negative effect of random measurement error11 on optimalweights when a measure is used as a basis for rewarding risk-averse employees �Lambert 2007�.The larger the error typically is in a measure of employees’ efforts and talents, the more uncertain

9 An alternative solution for the overproduction problem under absorption costing is of course to use variable costinginstead, but this is not the best solution in all settings. For example, the public nature of GAAP income can mean thatimportant rewards are attached to absorption-costing income �e.g., reputation, career concerns� even if variable costingis used internally. Or, if incentive compensation and its basis must be made public, the organization may prefer to useGAAP earnings as the basis because it is already public information. In such settings, adding NFI to the evaluationsystem may be preferred.

10 Datar et al. �2001� point out that reducing gaming by creating combinations of measures is not always an equallyfeasible solution: it is more feasible when the number of different activities performed by the individual being evaluatedis not large compared to the number of performance measures.

11 In this context, random measurement error means error in the measure as a measure of employee actions, not a measureof quality, innovation, etc. as such.

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and less motivating is the compensation based on the measure, and the less valuable it is for anorganization to weight the measure heavily; that is, to pay large amounts for changes in the levelof the �unreliable� measure.

Low incentive weights on strategically important NFI measures such as innovation or cus-tomer satisfaction of course reduce the motivational value of the NFI. If there is no way ofmitigating the risk created by using unreliable �high-error� measures, then low weighting is oftenthe lesser of two evils. However, as the following recommendations indicate, there are often waysof mitigating this risk by taking advantage of the portfolio properties of sets of F and NF mea-sures. To the extent that irreducible random error remains, there are potential gains from avoidingcommon decision errors in dealing with this error, as described in the last set of recommendationsfor performance-evaluation uses.

Use Measures with Negatively Correlated Errors to Allow Higher WeightsRandom error in both accounting and NFI as measures of employee efforts and talents is often

caused by external shocks such as macroeconomic changes. In a well-constructed portfolio ofmeasures, the effects of these shocks on some measures will be negatively correlated with theireffects on other measures, resulting in a lower error in the performance evaluation based oncombining all the measures.

For example, a plant manager might be responsible for both unit cost and quality of theproduct but might be unable to predict or significantly influence production volume. �cf., manag-ers’ responsibility for unit costs, quality, and customer service, all of which are influenced byuncontrollable volume fluctuations, in the disk-drive manufacturer studied by Davila and Wouters�2005�.� In this case, an unexpected upward spike in volume adds positive error to the costmeasure �unit costs go down, but not because of the manager’s efforts or talents� and addsnegative error to the quality measure �unexpected volume stresses the production system andincreases defects, but not because of the manager’s lack of effort or talent�. The reverse happenswhen production volume spikes downward. In this case, if only cost or only quality was includedas a basis for the manager’s performance evaluation, the volume shocks could add considerably toerrors in evaluation. But if the manager is evaluated on a weighted sum of the cost and qualitymeasures, the positive and negative errors tend to offset, and the error in the overall evaluation isrelatively small. In consequence, substantial errors in individual measures do not result in equallysubstantial errors in the overall evaluation on which compensation is based. Both measures cantherefore be weighted relatively heavily—that is, significant monetary incentives can be offeredfor performance on both dimensions—without imposing excessively costly risk on the manager�example from Krishnan et al. �2005�, based on Feltham and Xie �1994��.

Leverage the Effects of Reducing Error in One Measure to Allow Higher Weights on OtherMeasures

Reducing the random error in an important NF measure to allow it to be weighted heavily isoften costly—for example, customer satisfaction measures can be improved through more sophis-ticated survey design and the collection of larger samples. Cost-benefit analyses of such actionsshould not neglect the fact that improving one measure can improve overall performance evalua-tion and motivation by allowing heavier weights on other measures as well. Further developmentof the example from the previous recommendation illustrates this potential benefit.

Suppose that a measure of product quality contributes significant random error to the overallperformance evaluation of the plant manager in the example, and the error is not fully offset bynegatively correlated error in other measures. In this case, the manager’s compensation cannotdepend too heavily on quality because the measure is too unreliable. In consequence, compensa-tion also cannot depend too heavily on cost �or other measures�—that is, the manager’s pay cannot

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be very performance-based—because a high weight on cost and a low weight on quality will skewthe manager’s efforts suboptimally toward cost. In such a case, lowering the measurement error inquality will allow not only quality but also cost to be weighted more heavily, and pay can be morestrongly performance-based without skewing employees’ allocation of attention and effort�Feltham and Xie 1994�. Similarly, improvements in accounting that reduce the measurement errorin cost �e.g., a well-designed activity-based costing system� allow compensation to depend moreheavily not only on cost but also on quality, thus increasing employee motivation for both objec-tives.

Avoid Common Subjective Weighting Errors

Because weights on performance measures are often determined subjectively in incentive-compensation systems, avoiding common subjective decision errors can increase gains from usingNFI. For example, Krishnan et al. �2005� provide experimental evidence that nonexpert compen-sation system designers tend to incorporate negative error correlation effects into their weightingchoices �Recommendation 2�, but are less likely to realize that decreasing the independent randomerror in one measure means that the weights not only on that measure but also on other measuresshould be increased �Recommendation 3�.

The basic principle that compensation for risk-averse employees should not depend heavilyon high-error performance evaluations is often intuitively clear. But in some instances it is not,resulting in significant obstacles to successful implementation of portfolios of F and NF measures.One recurring problem appears to be weighting a NF measure heavily based on its strategicimportance without discounting for its error. The overweighting can lead to unsatisfactory results�large compensation changes unconnected with changes in employee efforts� and potentially anoverreaction against the measure. Malina and Selto �2001�, in a field study of a balanced scorecardadoption, find that initially heavy weights on “learning and growth” and “corporate citizenship”measures were sharply reduced later because of the unreliability of the measures. Ittner, Larcker,and Meyer �2003� describe another large firm in which significant initial weights on NF measureswere rapidly reduced, perhaps in part because of reliability concerns—and arguably representingtoo extreme a reaction, as the NF measures were given zero or near-zero weights in bonusdetermination, even though they were significantly associated with future financial performanceand could be influenced by managers’ actions.

An opposite problem is that evaluators can be sensitive to random error in performancemeasures but respond by deliberately increasing the incentive weight in response to higher randomerror. They believe that a larger amount of risky pay �instead of a fixed risk premium� is a goodway to compensate employees for risk. Krishnan et al. �2005� document this belief experimentally,noting that the practitioner literature sometimes expresses similar beliefs �e.g., Bloom 1999�.Arguments for a larger amount of risky pay as an appropriate way of compensating for risk cansometimes be found in the justifications offered for high levels of risky executive compensation�e.g., justifications mentioned by Bettis et al. 2008�. But in general, making employees’ compen-sation more dependent on a measure when it is more unreliable as an indicator of their efforts andtalents seems to be an unpromising basis for incentive compensation.

Another frequently observed problem in subjective weighting arises from comparative evalu-ation of multiple managers. Lipe and Salterio �2000�, in a much-replicated experiment, find thatwhen managers of two divisions are being evaluated subjectively, based on balanced scorecardstailored to the strategy of each division, evaluators tend to put more weight on the measures sharedby both divisions than on those unique to each division, although the unique measures are meant

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to be equally important to divisional strategy.12 Banker et al. �2004�, Libby et al. �2004�, and Dillaand Steinbart �2005� replicate this finding and identify ways of reducing �though not usuallyeliminating� the apparent overweighting of common measures. Providing additional training onthe balanced scorecard, emphasizing the strategic relevance and reliability of the unique measures,or requiring that the evaluator explicitly justify the evaluation all increase relative weights on themeasures unique to individual divisions.13

Weighting in PredictionsThe accuracy of predictions based on portfolios of F and NF measures depends in part on the

accuracy of the weights placed on individual predictors. These weights �coefficients in predictivemodels� can also play an important role in resource allocation. For example, a higher weight onNF measure 1 than on NF measure 2 in a model predicting profits suggests that, if the cost ofimproving performance on either measure is the same, more resources should be devoted toimproving 1 than to improving 2.

As noted in the previous section, stable bias is relatively innocuous when estimating and usingweights on multiple predictors: weights in predictive models are unaffected by stable bias. Ran-dom error can be more problematic, but the nature and magnitude of the problems depend on thepredictive decisions being made and on characteristics of the random error.

Consider a balanced scorecard, in which learning and growth is expected to lead to improve-ments in internal business processes, which lead in turn to customer-measure improvements andhigher financial performance. Internal business process measures can be used both to test theeffects of learning and growth initiatives �e.g., is a higher level of measured employee skillsassociated with higher product quality?� and to predict customer and/or financial measures. Rec-ommendations for matching NFI characteristics with decisions are made in the context of thisexample.

Match Error Reduction Efforts to Prediction-Model Characteristics and UsesThe quality measure plays a dual role in the example given above. It is predicted by learning

and growth measures in one model, and it is a predictor of customer and/or financial measures inanother model. In some cases, a good estimate of one of these two predictive models may havehigher priority than a good estimate of the other predictive model. There may, for example, bemore ex ante uncertainty about the strategy component represented by one of these models thanthe other, or there may be more important managerial decisions dependent on the weights in onemodel than in the other.

Random error in the quality measure has different effects on determining the weights in thesetwo models, and so reduction in random error may matter more or less depending on the relativeimportance of the two models. When quality is the dependent variable, predicted by learning andgrowth, random error in the quality measure is not an obstacle to estimating unbiased weights�coefficients� on these indicators. But when quality is the independent variable, predicting cus-tomer or financial measures, random error in the quality measure can be more problematic.

Random error in predictors creates misweighting if the error is correlated with the reported

12 Arya et al. �2005� point out that common measures can be more informative because they allow evaluators to removecommon measurement error via relative performance evaluation. Thus “underweighting” unique measures can be ap-propriate because unique measures in effect contain more error. However, it is also possible that when divisions haveradically different strategies, the error in their common measures may not be common: factors other than managers’actions may affect the same measure differently in the different divisions.

13 Whether the increased weights on unique measures in these studies are better weights is unclear; but it is not unrea-sonable to suppose that modest positive weights on the unique measures are better than the zero weights that appear insome experimental settings.

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predictor—for example, if instances of particularly high reported quality are likely to be overstatedand particularly low instances are likely to be understated. This kind of error will result in biasedweights on NFI in predictive models, not only with OLS regression but also with subjectivejudgments based on “high-low” comparisons: these analyses will tend to underestimate the actualeffects of NF performance on financial performance.

Suppose, for example, that managers compare the product-quality levels of business unitswith highest and lowest profits, and find that a difference of five points on a 10-point quality scaleis associated with a $20 million profit difference in business units of similar size. It appears that,at least as a rough estimate, a one-point gain in quality is associated with a $4 million differencein profit. But suppose that measurement error in quality means that the real difference between therelevant observations of quality is only four points, perhaps because the extreme observations arethe result of outright clerical errors or faults in the quality-measure construction. If this is the case,then a one-point gain in actual quality is associated with a $5 million difference in earnings ratherthan a $4 million difference. The measurement error in quality has downwardly biased the esti-mate of the effect of actual quality on earnings, possibly leading to mistaken judgments about thevalue of initiatives to promote quality.14

Moreover, random error in one of the NF predictors included in a model with multiplepredictors �e.g., other NFI and past earnings� not only biases the estimate of the noisy predictor’scoefficient; it also can bias the coefficient estimates of other measures in the model, in unknowndirections and amounts, unless the other predictors in the model are uncorrelated with the truevalue of the noisy measure �Greene 2000�. Because it is quite likely that NF predictors arecorrelated—consider innovation, quality, and customer satisfaction, for example, as predictors offinancial performance—this can be a significant problem in identifying weights for predictivemodels.

Whether the weights in such a setting are too unreliable for use in important decisions, andwhether resources should be devoted to random-error reduction, depends in part on the propertiesof the F and NF measures employed, and second, the intended uses of the predictive model. If thevariation in the true value of the measure is large relative to the variance of the measurementerror—for example, if the product quality in different observations used in the estimate is actuallyradically different—then coefficient bias will not be large �Wooldridge 2006�. But if the variationin the true value of the measure is not large—for example, if real differences in product qualityacross observations are modest and random measurement error is large—then the coefficient biasin regression analyses can be substantial.

Mitigate Scale-Compatibility Biases on Subjective Predictions When NeededWhen predictions are made subjectively instead of based on regression models, a variety of

common judgment biases can affect weighting. Jackson �2008� calls attention to scale-compatibility bias in a study of the use of NFI by nonprofessional investors in screening invest-ments. Consistent with prior psychological research, these investors tend to weight informationmore heavily when it is scaled in the same way as the screening criterion than when it is scaleddifferently.

The scaling differences in Jackson �2008� are relatively slight �ratings versus rankings�. Thewide variety of scales used in NFI �counts, ratios, seven-point scales, etc.� could exacerbate thisproblem: it could, for example, lead to underweighting of NF relative to F information in predict-

14 The effect is intuitively clear for a pair of “high-low” observations, but it also occurs with OLS regression when themeasurement errors and the reported values are correlated �i.e., high reported observations are likely to contain largepositive errors�. Random error in a NF predictor does not bias the coefficient, however, if the magnitude of the error iscorrelated with the true level of the underlying construct rather than with the reported measure �Wooldridge 2006�.

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ing financial performance. However, in Jackson’s �2008� experiment, the scale-compatibility biasis eliminated when investors compare several firms at once rather than completing the screeningevaluation of one firm before examining the next firm: the cognitive processes involved in simul-taneous rather than sequential analysis counteract the judgment bias.

Mitigate Self-Serving Biases in Weighting Multiple NF and F MeasuresAnother potential problem with subjective weighting is self-serving biases. Because NFI can

be interpreted and weighted in a variety of ways, individuals can easily use NFI to supportself-serving judgments—for example, judging that their favored management initiative has astronger effect on profit than a nonfavored initiative. Often at least some part of the bias is notevident to the individual suffering from it; the judgment is sincere and thus not easily altered byincentives for greater truthfulness.

For example, Tayler �2008� provides experimental evidence that managers using balancedscorecard information judge customer-value-creation initiatives with no financial value as moresuccessful when they have chosen the initiatives, even though the evidence available �customerand financial measures� does not support this judgment, and the biased judgment generates nofinancial reward for them. This self-serving bias is reduced, however, when individuals are re-sponsible for selecting the scorecard measures and the scorecard is explicitly framed as a causalmodel of performance �consistent with Kaplan and Norton �2001a��, rather than simply a balancedset of four perspectives on performance. It appears that the causal-model representation drawsattention to the failure of the expected positive association between customer measures and finan-cial measures, and responsibility for choosing the measures induces individuals to take moreseriously the conclusions the measures suggest.

Especially When Complex Predictive Relations are Likely, Supplement Subjective Weightingwith Statistical Analysis

The relations of NF measures to each other and to financial performance often take complexforms. For example, Ittner and Larcker �1998� document strongly nonlinear effects of customersatisfaction on future revenues. Nagar and Rajan �2005�, Dikolli and Sedatole �2007�, and Chen�2007� find significant interactions among F and NF measures in predictive models of individual-firm performance: that is, the magnitude, or even the sign, of a NF measure’s effect on futurefinancial performance depends on the level of another measure. Nagar and Rajan �2005� find thata path model including both direct and indirect effects of NFI provides a different �and stronger�explanation of financial performance in retail banks than a standard multiple regression.

Subjective weighting of predictors tends to be less accurate for nonlinear, interaction, andindirect relations than for linear additive relations �Karelaia and Hogarth 2008; Diehl and Sterman1995�. When it seems likely, based on managers’ knowledge of causal processes in the firm, thata good predictive model will include nonlinearities, interactions, and mediated �indirect relations�,it may be time to call in the statisticians rather than rely on subjective estimation if accurateweighting in predicting models is important to the organization.

CONCLUSIONMeasurement and weighting of NFI are challenging problems, and the experience summa-

rized in recent research does not provide complete solutions to these problems. It does, however,identify important features of potential solutions. First, aiming at the highest possible accuracy ineach measure is often not the most cost-effective approach to measurement. When multiple mea-sures �F and NF� are used together, the portfolio characteristics of the measures—the way theyoffset random error and bias in each other—can offer important opportunities for effective use ofimperfect measures.

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Second, NF �and F� measures are not “accurate” or “inaccurate” as such: they are accuratewith respect to particular decision requirements. The type, magnitude, and effect of measurementerrors vary, depending on the decisions for which the measures are used. The fact that a particularNF measure is useful in predicting stock returns does not necessarily make it equally useful inmanaging the firm or auditing its financial statements, and vice versa.

Finally, weights on NF �and F� measures, both for performance evaluation and for prediction,depend on the error properties of the whole portfolio of measures as well as on the relevance orimportance of the measure to organizational objectives. Optimal weighting is a particularly com-plex task, vulnerable both to statistical estimation problems and subjective judgment biases. Re-search has engaged frequently with these questions in recent years, but more remains to be done.

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