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THE ACCOUNTING REVIEW American Accounting AssociationVol. 88, No. 4 DOI: 10.2308/accr-504362013pp. 1433–1457

Productivity-Target Difficulty, Target-BasedPay, and Outside-the-Box Thinking

R. Alan Webb

University of Waterloo

Michael G. Williamson

The University of Texas at Austin

Yue (May) Zhang

Northeastern University

ABSTRACT: In an environment where individual productivity can be increased through

efforts directed at a conventional task approach and more efficient task approaches that

can be identified by thinking outside-the-box, we examine the effects of productivity-

target difficulty and pay contingent on meeting and beating this target (i.e., target-based

pay). We argue that while challenging targets and target-based pay can hinder the

discovery of production efficiencies, they can motivate high productive effort whereby

individuals work harder and more productively using either the conventional task

approach or more efficient task approaches when discovered. Results of a laboratoryexperiment support our predictions. Individuals assigned an easy productivity target and

paid a fixed wage identify a greater number of production efficiencies than those with

either challenging targets or target-based pay. However, individuals with challenging

targets and/or target-based pay have higher productivity per production efficiency

discovered, suggesting these control tools better motivate productive effort. Collectively,

our results suggest that the ultimate effectiveness of these control tools will likely hinge

on the importance of promoting the discovery of production efficiencies relative to

motivating productive effort. In doing so, our results provide a better understanding of

conflicting prescriptions from the practitioner literature and business press.

Keywords: incentives; outside-the-box thinking ; productivity ; targets.

We thank Jacob Birnberg, Jasmijn Bol, Larry Brown, Clara Chen, Harry Evans, Lynn Hannan, Steve Kachelmeier, KhimKelly, Justin Leiby, Theresa Libby, Jeremy Lill, Tim Mitchell, Andrew Newman, Derek Oler, Adam Presslee, SteveSmith, Hun-Tong Tan, two anonymous reviewers, participants at the 2011 Management Accounting Section ResearchConference and the 2011 AAA Annual Meeting, and workshop participants at Georgia State University, NanyangTechnological University, Texas Tech University, the University of Illinois, The University of Texas at Austin, andUniversity of Waterloo for providing helpful comments. We gratefully acknowledge funding from a McCombs School of Business Research Excellence Grant and the Social Sciences and Humanities Research Council of Canada.

Editor’s note: Accepted by John Harry Evans III.Submitted: May 2011

Accepted: February 2013 Published Online: February 2013

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I. INTRODUCTION

I

n a rapidly changing and highly competitive business environment, many organizations look to

their employees to improve productivity. Employees can boost productivity through increased

efforts using conventional task approaches or by directing their efforts toward identifying and

using more efficient task approaches. Identifying production efficiencies often requires individualsto think ‘‘outside-the-box,’’ which we define as trying to discover original and better ways to

accomplish a task (Shalley 1995). Indeed, many argue that achieving significant productivity

breakthroughs is critically dependent on the extent to which employees can effectively move

beyond traditional approaches to performing work activities in order to identify more efficient

approaches (Magretta 2002; Chen and Jones 2005).1 We examine the effects of productivity-target

difficulty and pay contingent on meeting and beating this target (hereafter, target-based pay) in an

environment where individual productivity can be increased through both efforts directed at a

conventional task approach and more efficient task approaches that can be identified through

outside-the-box thinking.

Minimal research has examined the influence of management control tools such as target setting and target-based pay in these environments. More attention comes from the practitioner

literature and business press in the form of competing prescriptions. Some advocate the use of

challenging productivity targets that can only be achieved by discovering production efficiencies

(e.g., Kaplan and Norton 1996; Thompson et al. 1997; Chen and Jones 2005). Conversely, others

recommend using easily attainable targets that give individuals the flexibility or slack to search for

production efficiencies or improved task-related strategies (e.g., Wood et al. 1990). Moreover, these

articles provide different perspectives as to whether financial incentives tied to attaining targets

further motivate or inhibit productivity (Thompson et al. 1997; Chen and Jones 2005). Systematic

empirical research is needed to provide a better understanding of the basis for the conflicting views

that have emerged in the practitioner literature.

To contribute to a better understanding for why these conflicting views have emerged, we

develop and test theory suggesting that challenging targets and target-based pay can actually have

competing effects on two important underlying determinants of individual productivity relative to

easy targets and fixed pay. On the one hand, challenging targets and target-based pay may

negatively affect productivity by hindering the discovery of production efficiencies. Theory

suggests that performance-contingent pay can focus individuals’ efforts excessively on

conventional approaches to task performance rather than engaging in the relatively riskier activity

of searching for production efficiencies, particularly when performance targets are easy to attain

(Amabile 1996). While challenging targets may motivate effort to search for production

efficiencies, distraction theory from cognitive psychology suggests that individuals faced with

the pressure of attaining tough targets may not be successful, because the resulting stress reducesworking memory (Eysenck 1982; Beilock et al. 2004; Beilock and Carr 2005; Markman et al.

2006). Discovering production efficiencies often requires strategies that require intensive usage of

working memory such as monitoring the environment for potential efficiencies, generating and

testing hypotheses about them, and processing feedback about the tests’ success (Dienes and Berry

1997; Maddox et al. 2004; Shalley 1995; Ziori and Dienes 2008). Thus, by inducing anxiety that

consumes working memory, challenging targets can hinder individuals’ ability to perform these

1An illustration of outside-the-box thinking leading to production efficiencies is the advent of manufacturingcells, which involves ‘‘taking the production tools to the product ’’ rather than the conventional ‘‘taking theproduct to the production tools’’ ( Chaneski 2005; Sofianopoulou 2006 ). Such production efficiencies minimizethe often costly and inefficient movement of raw materials and work-in-process. While we provide a manufacturing example and use the term ‘‘production efficiencies,’’ our research question is relevant to any

setting in which the identification of task improvements is possible through outside-the-box thinking.

1434 Webb, Williamson, and Zhang

The Accounting Review

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activities and ultimately inhibit their ability to discover production efficiencies (Beilock et al.

2004).

On the other hand, challenging targets and target-based pay can each independently enhance

productivity by motivating productive effort as individuals work harder and more productively

using either conventional task approaches or more efficient approaches once discovered. Inenvironments with known, effort-sensitive task approaches, prior research demonstrates that

challenging targets can induce higher effort and productivity than easier targets (Bonner and

Sprinkle 2002; Locke and Latham 1990). Moreover, in these environments, prior research also

shows that target-based pay can lead to higher effort and productivity than fixed pay ( Bonner et al.

2000). As such, we expect that challenging targets and target-based pay will each motivate

individuals to work harder and more productively using either conventional task approaches or

more efficient task approaches when discovered relative to those with easy targets and fixed pay.2

Ultimately, because (1) theory suggests that these control tools can have competing effects on the

identification of production efficiencies and productive effort, and (2) we have no theoretical basis

for predicting which performance dimension will have the strongest impact on productivity because

the relative impact is likely context specific, we can make no directional prediction for overall

productivity.

We test our predictions using a laboratory experiment where participants receive boxes of

letters (18 columns 3 7 rows) and, for each box, record the number of times a ‘‘search letter ’’

appears. Productivity is defined as the quantity of correct responses given. Participants can search

for the correct answer either by the conventional way of simply counting the number of times the

search letter appears in the box as they were initially trained or by identifying embedded more

efficient ways of determining the appropriate counts. These production efficiencies include patterns

within either the box itself or answers across boxes that, if identified, can significantly increase

productivity. We manipulate two factors between-subjects at two levels each. First, we manipulate

target difficulty by assigning either (1) an easy productivity target that can be achieved through the

conventional way of performing the task, or (2) a challenging productivity target that can only be

achieved by identifying new production efficiencies. Second, we manipulate contract type by

paying participants either (1) compensation contingent on meeting and beating their assigned

productivity target, or (2) a fixed wage irrespective of performance. We hold expected pay across

these four conditions constant.

We find that participants assigned both an easy productivity target and paid a fixed wage

discovered a greater number of production efficiencies than those with either challenging targets or

target-based pay. However, individuals with challenging targets and/or target-based pay exhibit

higher productivity per production efficiency discovered, suggesting that these control tools

motivate greater productive effort. Finally, we find that the competing effects of our control tools onthe discovery of production efficiencies and productive effort result in similar levels of overall

productivity across differing combinations of target difficulty and pay schemes.

Our results have important implications. First, they suggest that in settings where employees

boost productivity through both the discovery of production efficiencies and productive effort, no

simple prescription exists regarding performance target difficulty and whether to base pay on

meeting and beating these targets. Rather, our results suggest that the ultimate effectiveness of these

control tools hinges on the importance of promoting the discovery of production efficiencies

relative to motivating productive effort. Holding expected pay constant, if the objective is to

encourage employees to think outside-the-box, then the use of easy targets combined with fixed pay

2 Because theory and the related evidence are less clear about the combined effects of target difficulty and target-

based pay on productive effort (Bonner and Sprinkle 2002), we do not posit an interaction prediction.

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may be more effective. Alternatively, organizations seeking to motivate more productive effort may

be better served by using challenging targets or target-based pay. Moreover, by illustrating the

competing effects of target difficulty and target-based pay on these two fundamental determinants

of performance, our results provide a better understanding of why competing prescriptions have

arisen in the practitioner literature and business press.We also contribute to field-based research that explores ways to motivate and facilitate the

discovery of production efficiencies. For example, prior research explores the efficacy of employee

suggestion programs that directly reward individuals or groups for providing innovative ideas

(Welbourne et al. 1995). However, rewarding ideas can be challenging because the process

involves a high degree of subjectivity, and emphasizing the discovery of ideas rather than their

development and implementation may not necessarily lead to productivity gains (Jain et al. 2010).

Thus, similar to our setting, many organizations reward outside-the-box thinking based on whether

the innovations can be translated into measurable outcomes (Zingheim and Schuster 2007). By

tying rewards to measurable outcomes, prior research suggests that incentive schemes such as

gain-sharing plans can lead to more efficient production such as using less labor hours per unit

produced (Welbourne et al. 1995). However, in the natural environment, it is difficult to isolate

whether these gains are effort-based or caused by more efficient production techniques.3 Our

research highlights the importance of breaking productivity down into these important determinants

when examining the efficacy of incentive mechanisms such as productivity targets and target-based

pay.

The next section provides background and develops the hypotheses, and Section III describes

the method used to test these hypotheses. Section IV presents the results, and Section V provides a

summary discussion of the results and conclusions.

II. BACKGROUND, THEORY, AND HYPOTHESES

Background and Research Setting

A common key to the success of today’s organizations is the ability and willingness of their

employees to engage in outside-the-box thinking, which involves embracing ‘‘new ways of

conceptualizing old problems’’ (Magretta 2002, 90) and can result in productivity-enhancing

approaches to performing a task (Shalley 1991, 1995). Importantly, research often describes

outside-the-box thinking as occurring in settings in which well-defined constraints exist with

respect to the availability of resources or the amount of time available to identify solutions

(Magretta 2002; Matthews 2004). When allocating these scarce resources, an ongoing challenge is

motivating employees to move away from their relatively safe, conventional task approaches andengage in a riskier search for new, better ways to perform the task. That is, outside-the-box thinking

often involves a number of cognitive processes that include monitoring the environment for

potential production efficiencies, generating hypotheses about potential efficiencies, testing these

hypotheses, and processing feedback about the tests’ results that may or may not lead to the

successful discovery of a more efficient task approach (Shalley 1995).

For the purposes of our study, a conventional approach to task performance is based on prior

experience or instruction (e.g., ‘‘the way we’ve always done things’’), which, while sensitive to

productive effort, imposes an effective upper limit on performance potential. Conversely,

3 Moreover, in the natural environment, it is unclear whether such incentive mechanisms facilitate the discovery of new production efficiencies or simply motivate individuals to reveal already obvious production efficiencies that they otherwise have an incentive to withhold, perhaps because management could use the information to set

higher targets or make reductions to the labor force (Sprinkle and Williamson 2004).



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production efficiencies, if discovered and successfully implemented, allow for greater productivity.

Importantly, once discovered, production efficiencies are also sensitive to effort. That is, simply

discovering the efficiencies will not necessarily lead to higher productivity; individuals must still

apply effort in using them.4

In sum, to enhance the study’s external validity, we develop our hypotheses in the context of a

setting that our literature review suggests should possess the following features: (1) individuals can

choose the extent to which they will perform a task using a conventional approach versus

attempting to identify production efficiencies; (2) exerting more effort using conventional

techniques will lead to productivity gains; (3) identifying and using production efficiencies will

result in greater productivity gains than the conventional approach, assuming effort is held constant;

and (4) constraints exist affecting the extent to which the identification of production efficiencies

can successfully occur (e.g., limited time, task complexity, limited feedback) ( Thompson et al.

1997; Bailey and Bristow 2004; Matthews 2004). In all, we create an environment where, subject to

resource constraints, both identifying production efficiencies and working harder using either

conventional or more efficient production approaches (i.e., productive effort) can increase

productivity. Within this environment, we discuss how varying productivity-target difficulty and

the presence and absence of target-based pay can combine to influence the identification of

production efficiencies and productive effort.

Identifying Production Efficiencies

Productivity-Target Difficulty and the Identification of Production Efficiencies

When determining the level of productivity-target difficulty most likely to encourage

discoveries of production efficiencies, theory and the practitioner literature offer two potential

strategies. First, set challenging productivity targets that can only be achieved through the

identification of production efficiencies.5

Here, the utility that many individuals derive from target attainment would likely motivate them to expend effort searching for production efficiencies as the

only means of attaining challenging productivity targets (Hollenbeck and Klein 1987; Locke and

Latham 1990; 2002; Klein 1991; Lee et al. 1997; Bonner and Sprinkle 2002). Second, set

productivity targets that can be easily achieved using conventional approaches such that individuals

have the flexibility or slack to engage in the risky search for production efficiencies (Sprinkle et al.

2008). Because this literature suggests that either challenging or easy targets may be more likely to

encourage individuals to spend their scarce time thinking outside-the-box, we focus our theoretical

development on targets of these two types.6

While both challenging and easy targets could potentially encourage outside-the-box thinking,

the effectiveness of these efforts may differ across these target types. Specifically, psychology-basedresearch suggests that the pressure experienced while attempting to attain challenging targets can have

dysfunctional consequences on the effectiveness of outside-the-box thinking (Eysenck 1982; Huber

1985; Earley et al. 1989). Distraction theory in cognitive psychology proposes that stress induced by

factors such as challenging targets or target-based pay can cause people to ‘‘choke under pressure,’’

4As an example, the QWERTY keyboards now available on most cell phones can be thought of as a productionefficiency that permits faster and more accurate typing of text messages compared to using alphanumerickeypads. However, for the productivity gains to be realized, users of phones with QWERTY keyboards still must exert effort in using the more efficient technology when typing their messages.

5Importantly, in our setting, a challenging target is similar to traditional definitions of a stretch target that can onlybe attained by identifying production efficiencies ( Thompson et al. 1997).

6Moreover, theory suggests that an intermediate productivity target that could be achieved by working very hardusing conventional approaches would encourage individuals to direct disproportionate efforts to the less risky

conventional task approach in order to ensure a high probability of target attainment ( Sprinkle et al. 2008).



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particularly on tasks that place high demands on working memory (Beilock et al. 2004; Beilock and

Carr 2005; Markman et al. 2006, 944). Working-memory-intensive tasks involve cognitive operations

that impose inter-related demands on information storage and processing (Beilock et al. 2004, 598).

Distraction theory posits that pressure or anxiety reduces the attentional resources available in

working memory, which in turn, hinders cognitive performance (Markman et al. 2006).

Moreover, prior research suggests that the anxiety of trying to reach a challenging target impairs

many of the same processes that can facilitate the discovery of production efficiencies. Specifically, to

discover production efficiencies, individuals often must utilize strategies that require significant

working memory resources as they consciously use information in the task environment to develop

hypotheses about specific efficiencies, test them, and receive feedback regarding the accuracy of their

reasoning (DeShon and Alexander 1996; Maddox et al. 2004; Stadler 1997; Shalley 1995; Ziori and

Dienes 2008; Dienes and Berry 1997). To the extent that the pressure induced by challenging targets

impairs these processes, challenging targets could hinder individuals’ ability to identify production

efficiencies (Beilock et al. 2004; Markman et al. 2006).7 If so, then the lower pressure resulting from

easy targets relative to challenging targets may actually help facilitate more effective outside-the-box

thinking, leading to the discovery of more production efficiencies.

Target-Based Pay and the Identification of Production Efficiencies

Target-based pay relative to fixed pay may also lead to dysfunctional effects with respect to the

identification of production efficiencies (e.g., Humphreys and Revelle 1984; Wood et al. 1987). In

particular, target-based pay may focus attention on conventional approaches at the expense of outside-

the-box thinking. For example, Shapira (1976) and Pittman et al. (1982) report that participants

receiving target-based pay focus narrowly on the attainment of the target in order to receive their

reward. As a result, they choose simpler versions of a game or puzzle that increase their expectancy of

success, while those receiving fixed pay prefer more challenging versions, which they likely find

more intrinsically interesting. Similarly, Amabile (1996) offers that performance-based pay in general

motivates people to focus excessively on doing what they need to do to earn rewards; as a result, they

direct their efforts toward less risky and more predictable task approaches.

The extent to which target-based pay affects effort directed to the conventional task approach

would likely depend on the difficulty of the assigned productivity target. In our setting, an easy

productivity target is attainable by using the conventional approach to the task. Thus, individuals

who are assigned an easy target and paid to meet or exceed it may disproportionately use the less

risky conventional approach.8 That is, they would spend less time attempting to identify production

efficiencies than if paid a fixed wage.

Since identifying production efficiencies provides the only means of reaching challenging

targets in our setting, pay tied to attaining challenging targets would be unlikely to reduce the time

7 Research shows that tasks involving implicit learning processes, where individuals learn complex systems suchas grammar rules without necessarily intending to do so, do not place high demands on working memory and areless prone to the negative effects of pressure ( Dienes and Berry 1997; Markman et al. 2006). For example,employees could have a stroke of brilliance in developing an outside-the-box solution but not necessarily be ableto articulate the steps that led to this solution. As discussed in the next section, because prior research argues that more working-memory-intensive processes are often antecedents to successful attempts at outside-the-boxthinking including monitoring, hypothesizing about, and testing potential production efficiencies, we created anexperimental setting designed to encourage participants to engage in these processes. That said, to the extent individuals utilize implicit learning processes in our setting, it biases against our ability to find a detrimentaleffect of challenging targets on the discovery of production efficiencies.

8 Our target-based pay contract pays individuals for both meeting and exceeding their assigned target (e.g., Fisher et al. 2003). We believe this provides a stronger test of our theory since all participants, even those with easytargets, can benefit more from identifying production efficiencies than from using the conventional approach to

task performance.



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and effort spent searching for them. However, the effectiveness of linking pay to meeting and

beating challenging targets on promoting outside-the-box thinking may be limited. That is, pay tied

to target achievement, similar to challenging targets, could put further pressure on or induce anxiety

in individuals, hindering the identification of production efficiencies. However, it may also be the

case that challenging targets create a ceiling effect with respect to dysfunctional cognitive

consequences predicted by distraction theory, leaving little room to observe any incremental

negative effects of target-based pay.

Hypothesis 1

The preceding discussion of the dysfunctional consequences of challenging targets and target-

based pay in an environment where risky, working-memory-intensive processes facilitate the

discovery of production efficiencies results in the following expectations. First, individuals assigned

an easy performance target and paid fixed wages will discover more production efficiencies than

those pressured with a challenging target regardless of how they are paid.9 Second, the effect of

target-based pay will depend on productivity-target difficulty, such that target-based pay will lead to fewer discoveries of production efficiencies when targets are easy. When targets are challenging,

target-based pay may have limited room to further affect the discovery of production efficiencies.

Finally, there is no clear theoretical basis for predicting whether the dysfunctional effects of

combining an easy target with target-based pay will be more or less severe than those that arise by

using challenging targets. As a result, we make no formal predictions comparing these particular

combinations of target difficulty and pay. Accordingly, our first hypothesis predicts an ordinal

interaction between target difficulty and target-based pay. For clarity, we use two hypotheses to

describe the form of the expected interaction:

H1a: Individuals assigned easy targets and fixed wages will discover more production

efficiencies than those assigned challenging targets.

H1b: For individuals assigned easy targets, those paid a fixed wage will discover more

production efficiencies than those with target-based pay.

Productivity-Target Difficulty, Target-Based Pay, and Productive Efforts

While challenging targets and target-based pay may harm productivity by hindering the discovery

of production efficiencies, these control tools may also have a competing productivity-enhancing effect

by motivating productive efforts. For the purpose of our study, productive effort is distinct from the

effort directed toward identifying production efficiencies. Instead, productive effort refers to the

intensity of effort deployed using either the conventional task approaches or more efficient approaches

once discovered (Bonner and Sprinkle 2002). As noted earlier, our predictions assume that even when

using more efficient task approaches, greater productive effort leads to higher productivity.

Our notion of productive effort represents a standardized measure of productive output,

controlling for the approach used to perform a task.10 As such, it permits a meaningful comparison

of the effort-inducing effects of targets and target-based pay across heterogeneous groups of

9 This prediction assumes that individuals will find the task of identifying production efficiencies intrinsically interesting/ motivating. Thus, individuals will exert effort to discover efficiencies, even absent extrinsic rewards for doing so. Thisassumption is consistent with prior management accounting research (Bonner and Sprinkle 2002).

10 Our earlier example of QWERTY versus alphanumeric keyboards on cell phones applies here. Productive effort isintended to capture the effort exerted by individuals in using either the QWERTY keyboard or the alphanumerickeyboard. While total productivity (e.g., the number of text messages) will naturally differ across the two taskapproaches (keyboard types), productive output captures the effort individuals exert in using their chosen approach.



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individuals who differ with respect to their willingness or ability to identify production efficiencies.

Given management’s general interest in motivating employees to be as productive as possible,

regardless of their ability to identify and employ production efficiencies, we believe productive

effort is an important construct to examine (Simons 2000; Bonner and Sprinkle 2002; Locke and

Latham 2002). We base this section’s hypotheses on theory and evidence from studies that examinesettings where all individuals use similar and relatively straightforward ‘‘conventional’’ task

approaches and performance is sensitive to effort. Here, where no opportunities exist to identify

significant production efficiencies, more effort generally leads to higher productivity (Locke and

Latham 1990; Bonner and Sprinkle 2002).

In settings where a single straightforward conventional approach exists for performing a task,

theory and empirical evidence suggests that both challenging targets and target-based pay can

lead to higher productivity than easy targets and fixed wages (Locke and Latham 1990; Bonner

and Sprinkle 2002). First, considerable evidence supports a key prediction of goal theory that

there will be a positive association between target difficulty and effort, which in turn positively

impacts performance until individuals reach the limits of their ability (Hollenbeck and Klein1987; Locke and Latham 1990, 2002; Bonner and Sprinkle 2002). In fact, prior research

demonstrates that individuals continue to maintain effort even in situations where it is unlikely

that the target will be attained ( Bonner and Sprinkle 2002). Second, relative to a fixed wage, prior

research shows that target-based pay also increases productivity by enhancing productive effort

and task persistence. Based on an analysis of 131 experiments, Bonner et al. (2000) report that

target-based incentive schemes are significantly more likely to lead to performance gains than

fixed pay.

We believe that theory related to the productive effort-inducing effects of target difficulty and

target-based pay from single-task-approach settings will generalize to a setting where both

conventional and more efficient task approaches can be employed. Prior research suggests that

assigning individuals challenging productivity targets or using target-based pay will lead to

higher productive effort regardless of their selected task approach(es). That is, these individuals

will not only be more productive when using conventional task approaches to solve problems, but

they will also be more productive in using more efficient approaches if discovered. Thus, while

challenging targets and target-based pay may hinder the discovery of production efficiencies,

these control tools could still lead individuals to more productively utilize them once discovered.

While prior research illustrates that challenging targets and target-based pay can independently

boost productivity in these environments, the joint effects of these control tools are unclear

(Bonner and Sprinkle 2002). Indeed, there is some evidence indicating negative interactive

effects of target difficulty and target-based pay such that compared to easier targets, very

challenging targets lead to worse performance when coupled with a bonus for target attainment

(e.g., Lee et al. 1997 ). Accordingly, we make no predictions about the joint effects of these

control tools.

The above arguments suggest that, controlling for the increase in productivity resulting from

discoveries of production efficiencies, individuals with either a challenging target or target-based

pay will have higher productive effort. In other words, challenging targets and target-based pay will

lead to higher productivity per production efficiency discovered than easy targets and fixed pay.

These predictions, stated in the alternative form, are as follows:

H2a: Individuals with challenging productivity targets will have higher productivity per

production efficiency identified than individuals with easy targets.

H2b: Individuals with target-based pay will have higher productivity per production efficiency

identified than individuals with fixed pay.



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Productivity-Target Difficulty, Target-Based Pay, and Productivity

Collectively, the preceding hypotheses indicate that we expect challenging targets and target-

based pay to have competing effects on two important productivity determinants. Our theory

development above leads us to expect that these control tools may hinder the discovery of

production efficiencies, but simultaneously lead to greater productivity per production efficiencyidentified. Because theory does not provide a clear basis for predicting which effect will dominate

in affecting overall productivity or whether the effects will be offsetting, rather than a directional

prediction we pose the following research question:

RQ: Will the difficulty of the assigned productivity target and target-based pay affect overall

productivity in a setting where conventional approaches and more efficient approaches, if

discovered, can be used to perform a task?

III. METHOD

Participants

We recruited 98 undergraduate student volunteers from business classes at The University of

Texas at Austin. These students participated in one of three 60-minute experimental sessions, with

27 to 39 participants per session. As participants arrived, they were randomly assigned to a separate

computer terminal, read a set of computerized instructions, and worked individually on a task as

discussed in greater detail below.

Preliminary Period

To familiarize participants with aspects of our task, they worked through a five-minute

preliminary period. From a letter-sized envelope, participants removed a packet with 20 pages stapled

together. As shown in Figure 1, each page contained six boxes with seven rows and 18 columns of

letters. Each box had a single search letter identified at the top right-hand side of the box. Figure 1

contains a screen shot of one page from our task in which the search letter in the top box is ‘‘E.’’

The participants’ task was to count the number of times the search letter appeared in each box.

Participants recorded answers in the appropriate cell in the six columns (numbered box 1 through 6)

by 20 rows (numbered page 1 through 20) spreadsheet at their computer. The program immediately

checked the response. If incorrect, a message box appeared informing the participant of the wrong

response, the wrong answer was removed from the spreadsheet, and the next answer could not be

entered into the spreadsheet for five seconds.11 Otherwise, participants could record an answer for

any box at any time, i.e., they did not have to go in order.Participants received $0.10 for each correct response provided during the preliminary period.

The computer spreadsheet informed participants of the time remaining, the number of correct

answers they had recorded, and the compensation they had earned. The program also provided a

summary of their performance and compensation at the end of the period.

Production Periods

After the preliminary period, participants read additional instructions about the three ten-

minute production periods they would complete next. The task was similar to that of the

11 After the five-second delay, participants could either record another answer for the same box following anincorrect response, or go on to another box. We locked the spreadsheet following an incorrect response so that it would not be advantageous for participants to randomly guess answers.



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FIGURE 1

Task Example



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preliminary period. However, the instructions now informed participants that there were two ways

to identify answers for each box. First, they could simply count the number of times each search

letter appeared in its corresponding box of letters, the ‘‘conventional approach.’’ Second, they could

identify shortcuts, which in our task represent the ‘‘production efficiencies.’’12 As stated in the

instructions, ‘‘

Shortcuts include patterns in a particular box across the pages, patterns across boxeswithin a single page, and/or patterns within a single box which will help identify the answer.’’13As

further described in the instructions, the same shortcuts were used on each new page of boxes, and

they were placed in the same location. So, for example, if participants identified the shortcut for the

first box on the first page of materials, then that same shortcut applied to the first box on each

subsequent page. Moreover, the same shortcuts were used in each production period; once

discovered, a shortcut could be used repeatedly throughout the production periods. Figure 2

describes each shortcut we used for this study.14

We believe our shortcuts represent a valid operationalization of outside-the-box thinking for a

number of reasons. First, they involve a unique approach to performing the task that is very distinct

from the conventional approach of simply counting letters that, if discovered, can result in

substantive improvements to productivity. Second, prior research associates pattern recognition,

which is inherent to shortcut discovery in our setting, with successful attempts at outside-the-box

thinking (Baron 2006). Third, attempting to identify patterns often requires similar cognitive

processes required to identify production efficiencies more generally. Specifically, both pattern

recognition and outside-the-box thinking often involve monitoring the environment for potential

production efficiencies or patterns, generating hypotheses about potential efficiencies or patterns,

testing these hypotheses, and processing feedback about the tests’ success (Shalley 1995; Dienes

and Berry 1997; Maddox et al. 2004; Ziori and Dienes 2008).15

We instructed participants that they were free to choose either strategy for determining correct

answers for each period. Furthermore, we informed them that while counting is a reliable way to

complete the task, it takes more time than using the shortcuts once they have been discovered. On

the other hand, shortcuts will initially take time to discover but will allow the determination of

correct answers much faster. Thus, participants faced the choice of allocating their effort between

the low-risk strategy of simply counting letters and the riskier strategy of searching for shortcuts,

which would potentially significantly improve productivity.

Before starting the production periods, participants correctly answered several quiz questions

to ensure they understood their instructions and completed a short questionnaire. After completing

12Given participants’ lack of experience with the task and the relatively short time they spent in the experiment,

they likely would not have searched for production efficiencies had we not informed them of their existence. That said, in the natural task setting, employees would have more experience with their work activities and wouldlikely be sensitive to the possibility that more efficient task approaches exist and could be identified if they wereto devote time and resources to discovering them.

13While we did not inform participants until the production periods, these shortcuts were also present during thepreliminary period.

14We conducted several pilot tests to develop shortcuts that were detectable, but not so obvious that the task wouldbe easy to perform. Participants discovered all six shortcuts at least once and, as will be reported in the next section, on average participants across all conditions found about 50 percent of the available shortcuts.

15To provide evidence that our task requires working-memory-intensive hypotheses generation and testingprocesses, we examine participants’ responses to box one, where per Figure 2, the answers repeated 1, 2, 3 acrosspages. After observing the answers, 1 for page one, 2 for page two, and 3 for page three, we anticipated that our participants would generate and test that 4 was the answer to page 4, rather than the correct answer of 1. Whileour software program only records whether participants provide a correct or incorrect answer for each cell andnot their specific answers, 87 percent of participants providing an answer for page four, box one initially entereda wrong answer consistent with them testing whether the answer followed the sequence. For all other cells,participants provided wrong answers 20 percent of the time, which is significantly lower (v2¼247.83, p , 0.01).

These relative error rates suggest that participants engaged in hypotheses generation and testing.



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the three ten-minute production periods, participants completed a short post-experimental

questionnaire. Finally, participants received cash compensation for the preliminary period and

the three production periods. Average compensation totaled around $18 for the 60-minute session.

Independent and Measured Variables

We manipulated two aspects of participants’ performance evaluation and reward system design

at two levels, each between-subjects. First, we manipulated the level of productivity-target

difficulty. We assigned half of our participants to an easy productivity target of 10 correct responses

FIGURE 2

Task Shortcuts



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per production period. We assigned the other half of our participants to a challenging productivity

target of 90 correct responses per production period.16

Second, we manipulated participants’ pay contract type. Half of our participants received a

fixed-wage contract that paid them $7 per production period irrespective of their performance. The

other half of our participants received a target-based contract ( Fisher et al. 2003). Specifically, theseparticipants received a fixed wage of $2, a bonus of $0.10 3 the assigned productivity target as a

bonus for reaching this target, and a piece rate of $0.10 for every correct response for exceeding

their assigned productivity target. In essence, participants received a $0.10 piece-rate if their

performance exceeded their assigned productivity target.17

Dependent Variables

To determine the number of shortcuts discovered by participants, we asked them to describe

the shortcuts found and the period in which they were discovered. To validate the self-reports, we

had an independent coder, blind to the study’s hypotheses, review the pattern of responses

electronically collected for each participant to determine if it corresponded with the self-reportedshortcuts discovered. For example, the coder determined that a participant who indicated that she

had discovered the shortcut for box one—and consecutively entered the correct response for that

box several times before providing a response for another box on any page—had discovered that

shortcut. Where the pattern of responses did not clearly indicate whether a self-reported shortcut

had been discovered, the coder only gave credit when the description provided by the participant

unambiguously corresponded to the actual shortcuts embedded in the instrument. This approach

resulted in a high correlation between the coded and self-reported number of shortcuts (r ¼ 0.94, p

, 0.001), and the coded number of shortcuts discovered is highly correlated with the total number

of correct responses for the three production periods (r ¼0.77, p, 0.001).18 As a result, we believe

that the construct validity of our shortcuts discovered measure is acceptable.We used the number of correct responses each period as our measure of overall productivity

and divided that amount by the total number of shortcuts discovered to determine productivity per

production efficiency. As discussed below, by controlling for productivity caused by discoveries of

shortcuts, this measure captures productivity resulting from productive efforts.

IV. RESULTS

Test of Hypothesis 1

Our first hypothesis predicts an ordinal interaction such that participants in the fixed wage, easy

productivity target condition will identify more production efficiencies than those in the challenging

target conditions (H1a). In addition, participants in the fixed wage, easy target condition will

16 Consistent with definitions in the literature, we consider an easy target as one achievable almost 100 percent of the time and a challenging productivity target as one achievable approximately 25 percent of the time (e.g., seeLocke and Latham 1990; Merchant and Van der Stede 2007). In our study, 100 (22.4) percent of our participantsultimately reached their assigned target of 10 (90).

17 To ensure equivalent compensation magnitudes across our fixed wage and target-based contract conditions, weran a pilot study and set our pay parameters such that $7, the fixed wage compensation,¼ $2þ ($0.103 averagepilot performance per production period).

18 Prior research indicates that the ability to verbalize rules discovered and followed is associated more withworking-memory-intensive cognitive processes rather than less cognitively taxing implicit learning (DeShon andAlexander 1996; Ziori and Dienes 2008). Accordingly, we interpret the high correspondence between the self-reported and coded measures of shortcuts as supporting our expectation that identifying patterns that lead toproduction efficiencies involves more working-memory-intensive processes than implicit learning.



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identify a greater number of production efficiencies than those paid to meet and beat an easy target

(H1b). Panel A of Table 1 provides descriptive statistics for the number of times each production

efficiency (shortcut) was discovered and shows that all shortcuts were discovered at least once, with

shortcuts 1, 2, and 6 discovered more frequently than the others. Panel B shows the average number

of Shortcuts discovered, by experimental condition.Panel C of Table 1 provides the results of an ANCOVA with the total number of Shortcuts as

the dependent variable, Productivity Target difficulty and Contract type as the independent

variables, and Gender as the covariate.19 As reported in Panel C, we observe a significant main

effect for Contract (F ¼5.17, p ¼0.01) qualified by a significant Contract 3 Productivity Target (F

¼5.91, p ¼0.01) interaction.20 In Panel D of Table 1, we report the results of the contrast tests used

to evaluate H1a and H1b. As indicated by these results, participants in the easy target/fixed wage

condition identified significantly more production efficiencies than participants in the challenging

target conditions (t ¼ 2.07, p ¼ 0.02).21 On average, participants with an easy target and a fixed

wage discovered about 3.7 shortcuts compared to 3.0 in the two challenging target conditions. The

contrast results in Panel D also show that participants assigned an easy target and fixed wage

discovered significantly more shortcuts than their counterparts with an easy target and target-based

pay, who on average found only 2.4 shortcuts (t ¼ 3.15, p , 0.01). Moreover, for participants

assigned a challenging target, those paid under a target-based contract discovered the same number

of shortcuts as those paid under a fixed wage contract. Thus, relative to the combination of an easy

target and fixed wage, both challenging targets and target-based pay created dysfunctional

consequences with respect to participants’ discovery of shortcuts. Overall, these results support H1a

and H1b.22,23

To evaluate whether participants’ behavior is consistent with the reasoning underlying our first

hypothesis, we first examine the amount of time spent looking for shortcuts in each condition. We

asked participants to self-report the percentage of time spent each period searching for shortcuts.

We converted this to ‘‘time’’ by multiplying their self-reported percentages by the ten minutes

available each production period. Time spent searching for shortcuts is significantly correlated with

the number of shortcuts actually found (r ¼ 0.36, p , 0.01), which we believe supports the

construct validity of the self-reported measure of time allocation.

Table 2, Panel A reports descriptive statistics for the average amount of Time searching for

shortcuts for the three production periods for each condition, and Panel B reports the results of an

19Prior research suggests that the success of solving puzzles such as those embedded within our experimental taskdiffers across gender (Amabile 1996). Thus, we use a gender indicator (male ¼ 0, female ¼ 1) as a covariate to

control for this possibility and include it in the reported results.20

Reported results are based on one-tailed tests unless otherwise noted.21 As discussed earlier, we expect participants in the easy target/fixed wage condition to discover shortcuts to the

extent that they are intrinsically motivated to do so. In a pre-experiment questionnaire, we measured participants’intrinsic interest in the task using a task attractiveness measure similar to Fessler (2003). In the easy target/fixedwage condition, participants scoring above the median on this measure identified more shortcuts than thosescoring below the median (4.5 versus 3.2 shortcuts; t ¼ 2.40, p ¼ 0.01). Across the other three conditions,participants scoring above the median on this measure did not identify more shortcuts than those scoring belowthe median (2.7 versus 2.9 shortcuts; t ¼ 0.41, p . 0.50). These results suggest that the detrimental effects of target-based pay and challenging targets offset the potential benefits of intrinsic motivation for outside-the-boxthinking.

22 Consistent with our predictions, untabulated results also show that participants with an easy target and fixedwage discovered more shortcuts than the other three conditions combined (t ¼ 2.74, p , 0.01).

23Results from a repeated measures ANCOVA (not tabulated) show that none of the two-way or three-wayinteractions involving Period and either Productivity Target or Contract are significant (all two-tailed p-values. 0.17). The number of shortcuts identified in periods one, two, and three by experimental condition (based onself-reported measures) are as follows: 1.3, 3.1, and 3.7 in easy/fixed; 0.7, 2.5, and 3.0 in challenging/fixed; 0.9,

2.0, and 2.4 easy/target-based; 0.9, 2.4, and 3.0 challenging/target-based.



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TABLE 1

The Effect of Productivity Target and Contract on Number of Shortcuts Discovered

Panel A: Number (Percentage) of Participants Discovering Each Shortcut

Shortcut

Participants Who Discovered

Number %

1 85 86.7

2 75 76.5

3 52 53.1

4 1 1.0

5 25 25.5

6 60 61.2

Panel B: Means (Standard Deviations) for Shortcuts Discovereda

(n ¼ 98)Easy Target Challenging Target Average

Fixed Wage Contract 3.7 3.0 3.4

(1.4) (1.5) (1.5)

n ¼ 24 n ¼ 25

Target-Based Contract 2.4 3.0 2.7

(1.6) (1.2) (1.5)

n ¼ 25 n ¼ 24

Average 3.0 3.0

(1.7) (1.3)

Panel C: Analysis of Variance

Factor df

Sum of

Squares F p-valuee

CONTRACT b 1 10.89 5.17 0.01

PRODUCTIVITY TARGET c 1 0.02 0.01 0.93

CONTRACT 3 PRODUCTIVITY TARGET 1 12.45 5.91 0.01

GENDERd

1 2.30 1.09 0.30

Error 93

Panel D: Planned Contrasts

t-statistic p-value

H1a Easy Target/Fixed Wage versus Challenging Target Conditions 2.07 0.02

H1b Easy Target/Fixed Wage versus Easy Target/Target-Based Contract 3.15 0.002

a Total number of shortcuts discovered across the three production periods.

bContract: 0 ¼ fixed wage, 1 ¼ target-based.

cProductivity Target: 0 ¼ easy target (10), 1 ¼ challenging target (90).

dGender: 0 ¼ male, 1 ¼ female.

eReported p-values are two-tailed unless testing a one-tailed prediction, as signified by bold.



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ANCOVA using the same factors as above for this measure. We observe a significant Productivity

Target main effect (F ¼ 2.81, p ¼ 0.05) qualified by a significant Productivity Target 3 Contract

interaction (F ¼2.73, p¼0.05). The effects of target difficulty on searching Time are significant for

participants working under a target-based contract (not tabulated, F ¼ 6.48, p , 0.05) but not a

fixed-wage contract (not tabulated, F ¼ 0.01, p . 0.90). As expected, participants with an easy

target and a fixed wage contract spent marginally significantly more time (5 minutes) searching for

shortcuts than those with an easy target and a target-based contract (3.9 minutes) (not tabulated, F¼

2.47, p ¼ 0.06, one-tailed). For those with challenging targets, the Time spent searching for

shortcuts did not differ across contracts (not tabulated, F ¼ 0.61, p . 0.40).24

We also expect that the pressure associated with meeting a challenging target would make the

time spent searching for shortcuts less efficient. We examine this possibility by dividing the Time

TABLE 2

The Effect of Productivity Target and Contract on Time Spent Looking for Shortcuts

Panel A: Means (Standard Deviations) for Minutes Spent Looking for Shortcutsa (n ¼ 98)

Easy Target Challenging Target Average


(2.5) (2.8) (2.6)

n ¼ 24 n ¼ 25


(2.5) (2.3) (2.5)

n ¼ 25 n ¼ 24

Average 4.4 5.2

(2.5) (2.6)

Panel B: Analysis of Variance

Factor df

Sum of

Squares F p-valueb

CONTRACT 1 1.76 0.28 0.60

PRODUCTIVITY TARGET 1 17.73 2.81 0.05


GENDER 1 23.09 3.66 0.06

Error 93

a Average time spent searching for shortcuts per period for the three production periods based on participants’ self-reported time allocation.

bReported p-values are two-tailed unless testing a one-tailed prediction, as signified by bold.

24We utilized a three-period experiment because we believed that this design choice gave the challenging target thebest chance of encouraging individuals to search for and potentially identify shortcuts, which biases against H1.Specifically, participants assigned to challenging targets would quickly learn that they could not reach the target without identifying shortcuts. Thus, they would have an opportunity to search for shortcuts in early periods toincrease their chance of attaining the challenging targets in later periods. Participants responded as anticipated inthat those not reaching their performance target in period one increased the percentage of time searching for shortcuts in the next period from 36.8 percent to 57.6 percent (t ¼ 3.03, p , 0.01). As a result, while noparticipant reached an assigned challenging target in period one, 22.4 percent of these participants ultimately

reached a challenging target by period three.



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participants spent searching for shortcuts by the number of shortcuts they discovered, i.e., we

examine Time per Shortcut discovered.25 Descriptive statistics for Time per Shortcut are reported in

Table 3, Panel A, and the ANCOVA results using the same factors as above are reported in Panel B.

Consistent with our theory development, we observe a significant main effect of Productivity

Target (F ¼ 2.94, p ¼ 0.04), indicating that participants assigned challenging targets were less

efficient at finding shortcuts than those assigned an easy target. As shown in Panel A of Table 3,

participants with an easy target spent an average of 4.7 minutes per shortcut discovered compared to

5.9 minutes per shortcut for participants with a challenging target.26 We also observe a marginally

significant main effect (F ¼ 2.13, p ¼ 0.07) of Contract type, with the means in Panel A showing

that target-based pay leads participants to be less efficient at finding production efficiencies than

those paid a fixed wage. As reported in Panel A of Table 3, participants paid a fixed wage spent

about 4.7 minutes finding each shortcut, while those under the target-based contract spent 5.9

minutes per shortcut discovered.

TABLE 3

The Effect of Productivity Target and Contract on Time Spent per Shortcut Found

Panel A: Means (Standard Deviations) for Minutes Spent per Shortcut Founda (n ¼ 98)



(2.7) (3.2) (2.9)

n ¼ 24 n ¼ 25


(2.5) (5.5) (4.3)

n ¼ 25 n ¼ 24

Average 4.7 5.9

(2.6) (4.5)


Factor df

Sum of

Squares F p-valueb

CONTRACT 1 27.41 2.13 0.07



GENDER 1 68.52 5.34 0.02

Error 93

a Total time spent searching for shortcuts across the three production periods divided by the total number of shortcutsdiscovered.

bReported p-values are two-tailed unless testing a one-tailed prediction, as signified by bold.

25To simplify the reporting for this measure, we divide the total number of shortcuts discovered by the total time

spent looking for shortcuts across all three production periods.26

We also observe that participants who were assigned challenging targets make marginally significantly moremistakes than those in the easy target condition, which is suggestive of higher levels of stress or anxiety (F ¼1.74, one-tailed p ¼ 0.09).



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While these results are consistent with the pressure induced by challenging targets and target-

based pay leading to inefficiencies in discovering shortcuts, an alternative explanation is that

shortcuts become increasingly difficult to find and that these control tools motivate individuals to

persist in trying to do so. For example, participants working in the target-based pay condition who

were assigned a challenging (easy) target found 3.0 (2.4) shortcuts and took an average of 6.7 (5.1)

minutes each to find them. Thus, it may be that the first two shortcuts were relatively easy to

discover, but the third one was more difficult, which could account for the higher Time per

Shortcut . However, given that the Time per Shortcut in the easy target/fixed wage condition is

significantly lower than the other three conditions combined (t ¼ 1.54, p , 0.10) and these

participants found the most shortcuts overall (Table 1, Panel B, 3.7), this alternative explanation

seems less descriptive of the actual behavior observed in our experiment. Accordingly, we believe

the results in Table 3 are consistent with our expectation that the pressure induced both by

challenging productivity targets and target-based pay results in a less efficient use of the time spent

trying to discover shortcuts.

Test of Hypotheses 2a, 2b, and Our Research Question

Our second set of hypotheses predict that either a challenging target (H2a) or target-based pay

(H2b) will lead to higher productivity per production efficiency identified relative to an easy target

or fixed pay. The measure of productive effort we use to test these hypotheses is productivity per

shortcut discovered in period three calculated as: period three productivity 4 total shortcuts found.

During period three, the final production period, participants would have been utilizing all

discovered shortcuts. Since implementing discovered shortcuts involves recording simple patterns

that do not vary much in their complexity, we expect that the average incremental productivity

benefit of each discovered shortcut would be similar, i.e., productivity per shortcut would not reflect

differences in the efficiencies generated by specific shortcuts discovered.

To assess the validity of this expectation, we plot the distribution of participants’ period three

productivity by the number of shortcuts they ultimately discovered. The plot, shown in Figure 3,

suggests a reasonably linear relation between the number of shortcuts discovered and productivity in

FIGURE 3

The Distribution of Period 3 Productivity by Number of Shortcuts Discovered



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period three. We also conduct a regression analysis with period three productivity as the dependent

measure and the number of shortcuts discovered as the independent variable. Untabulated results

show that each shortcut discovered increased productivity by an average of 16.4 (t ¼18.63, p , 0.01)

and that the number of shortcuts discovered explains a large proportion (78 percent) of the variance in

period three productivity. As such, we believe productivity in period three per production efficiency

identified represents a reasonable measure of productive effort, i.e., how hard participants worked

using either conventional task approaches or more efficient task approaches once discovered.27

Panel A of Table 4 reports the descriptive statistics by condition for Productivity per Shortcut

in period three, and Panel B summarizes the results of an ANCOVA using the same factors as in the

previous analysis. Consistent with H2a, Productivity Target has a significant effect on Productivity

per Shortcut (F ¼ 2.72, p ¼ 0.05), with participants who were assigned a challenging target being

TABLE 4

The Effect of Productivity Target and Contract on Productivity per Shortcut Discovered

Panel A: Means (Standard Deviations) for Productivity per Shortcut Discovereda (n ¼ 87)



(4.0) (3.5) (4.03)

n ¼ 22 n ¼ 22


(6.0) (5.0) (5.4)

n ¼ 20 n ¼ 23

Average 21.7 23.5

(5.4) (4.3)


Factor df

Sum of

Squares F p-valueb

CONTRACT 1 145.71 6.51 0.007



GENDER 1 9.45 0.42 0.520

Error 82

a Productivity in period three divided by total number of shortcuts discovered for all three production periods.

b Reported p-values are two-tailed unless testing a one-tailed prediction, as signified by bold.

27As a second measure of productive effort, we also use OLS regression to estimate the effect on productivity of our experimental conditions controlling for the number of shortcuts found. Here, we estimate the followingequation Productivity period 3¼ a þ b1(Total Shortcuts Discovered ) þ b2(Challenging Target Dummy Variable) þb3(Target-Based Pay Dummy Variable) þ b4(Challenging Target 3 Target-Based Pay) þ b5(Gender DummyVariable) þ b6( Preliminary Period Productivity) þ b7(Years in College) þ e. Further supporting H2a and H2b,regression results suggest that, controlling for the total number of discovered shortcuts (b1¼16.08, t ¼17.10, p ,0.01), those with challenging targets ( b2¼ 8.00, t ¼ 2.24, p¼ 0.01) and target-based pay (b3¼ 5.11, t ¼ 1.37, p ¼0.09) exhibited higher levels of productivity. The person-specific controls ( b5 through b7) are all significant (allp-values , 0.10).



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more productive than those assigned an easy target (respectively, means 23.5 and 21.7). Consistent

with H2b, there is also a significant Contract type effect (F ¼ 6.51, p , 0.01), with participants in

the target-based pay conditions (mean ¼ 24.0) exhibiting greater Productivity per Shortcut than

those in the fixed wage conditions (mean ¼ 21.3).28 Finally, untabulated results indicate that

Productivity per Shortcut in the easy target/fixed wage target condition is significantly lower than

the other three conditions combined (t ¼ 3.26, p , 0.01).

Figure 4 summarizes our experimental results. As hypothesized and summarized in this figure,

target difficulty and target-based pay had competing effects on the discovery of production

efficiencies and productive effort. As such, our research question examines the effects of these control

tools on total productivity. Table 5, Panel A presents descriptive statistics of Productivity for period

three, since over 80 percent of the total number of shortcuts that would eventually be discovered had

FIGURE 4

Summary of Results

a For all variables, we observe no statistically significant differences across our challenging target conditions so

we combine these conditions for this summary.

28Table 4 includes only those results for the 87 participants who discovered at least one production efficiency.Including the productivity scores of the 11 participants who did not discover a shortcut also yields significant

main effects of both contract and productivity target (both p , 0.05, one-tailed).



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been found by the beginning of the final period. Thus, the competing effects of targets and

performance pay should be observable by period three. Table 5, Panel B summarizes the results of an

ANCOVA using the same factors as above, to evaluate the research question.29 Only the Contract 3

Productivity Target interaction is marginally significant (F ¼ 3.66, p ¼ 0.06, two-tailed) in Panel B.

The descriptive results in Panel A of Table 5 show that the interaction is driven by the decrease

in productivity for participants who were assigned an easy target under the target-based contract

(mean¼55.8) compared to fixed wage (mean¼75.1). Untabulated results show that this decrease is

significant (F ¼ 5.76, p , 0.05, two-tailed). Thus, the negative effect of easy targets and target-based pay on the discovery of shortcuts (Table 1, H1b) was not offset by the positive effect of this

combination on productive effort found in support of H2b (Table 4).30 Conversely, untabulated

TABLE 5

The Effect of Productivity Target and Contract on Productivity

Panel A: Means (Standard Deviations) for Productivitya



(27.9) (29.2) (28.4)

n ¼ 24 n ¼ 25


(27.8) (24.4) (27.2)

n ¼ 25 n ¼ 24

Average 65.2 71.0

(29.2) (26.7)


Factor df

Sum of

Squares F p-valueb

CONTRACT 1 1802.68 2.38 0.13



GENDER 1 139.62 0.18 0.67

Error 93

a Total productivity for the third production period.b Reported p-values are two-tailed.

29 Results from an untabulated repeated measures ANCOVA show that none of the two-way or three-wayinteractions involving Period and either Productivity Target or Contract are significant (all p-values . 0.15).Productivity means across periods one, two, and three by experimental condition are: 31.2, 54.9, and 75.1 ineasy/fixed; 25.8, 55.5, and 70.2 in challenging/fixed; 24.8, 40.7, and 55.8 easy/target-based; 27.5, 48.4, and 71.9challenging/target-based.

30 While easy targets combined with target-based pay resulted in relatively low productivity in our setting, someorganizations claim to use this combination because of factors not incorporated in our design. For example, tyingpay to easy targets protects employees against exogenous uncertainties in their environment and reducesincentives to engage in productivity management practices ( Merchant and Manzoni 1989). While our resultsindicate that this combination comes at the cost of outside-the-box thinking, this resultant cost may be exceeded

in some environments by benefits not captured in our setting.



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analysis also shows that productivity does not differ significantly (F ¼ 0.20, p . 0.80) among

participants who were assigned an easy target and paid a fixed wage, and those who were assigned

challenging targets under either fixed or target-based pay. Importantly, this comparison indicates

that the beneficial effect of challenging targets on productive effort (H2a) offsets their negative

impact on the discovery of production efficiencies (H1a).

V. CONCLUSIONS

In an environment where individual productivity can be increased through efforts directed at

either a conventional task approach or more efficient task approaches that can be identified through

outside-the-box thinking, we use an experiment to examine the effects of productivity-target

difficulty and target-based pay. Our results suggest that challenging targets and target-based pay

have competing effects on two important productivity determinants. On the one hand, both

challenging targets and target-based pay hurt productivity by hindering the discovery of production

efficiencies. Participants assigned to an easy target and paid a fixed wage identified the most

production efficiencies. On the other hand, challenging targets and target-based pay both enhanceproductive effort by motivating participants to be more productive per production efficiency

identified.

These results suggest that in settings where conventional approaches and more efficient

approaches that can be identified through outside-the-box thinking exist, the choice of reward

system depends on the value of promoting the discovery of production efficiencies relative to

motivating productive effort. Holding expected pay constant, our results indicate that if the

discovery of production efficiencies is a key objective for management, then the use of easy targets

with fixed pay may yield better outcomes.31 More generally, by illustrating the differing effects of

target difficulty and target-based pay on these two fundamental determinants of productivity, our

results provide a better understanding of why there are conflicting prescriptions in the practitioner

literature and the business press. That is, because our results suggest that different combinations of

target difficulty and pay schemes can lead to similar levels of productivity, it is not surprising that

there is not an unequivocal view in practice regarding the most effective approach to reward system

design.

Our results also have interesting implications for the burgeoning management accounting

literature examining the impact of target-based contract design on risk-taking. While prior research

demonstrates that these contracts can be designed to promote greater risk-taking, these studies

typically operationalize risk-taking as a choice among distributions with varying means and

variances, and evaluate whether individuals choose the option that provides the greatest expected

value to an assumed employer (e.g., Ruchala 1999; Sprinkle et al. 2008). While representing

successful risk-taking in this fashion may be descriptive of some environments, our results suggest that this representation would be less descriptive of a setting where the outcome of risky decisions

is not a simple function of individuals’ willingness to take risks. In particular, challenging

productivity targets encouraged our participants to engage in the risky endeavor of searching for

production efficiencies, but the pressure induced by these targets hindered their success.

Limitations of our study provide opportunities for future research. First, we examine an

environment in which participants identify production efficiencies under short-term time pressure.

31While we held expected pay constant across our various reward conditions, the cost of motivating individuals tosearch for production efficiencies relative to exerting more conventional efforts may vary in practice.Additionally, the organization should consider other costs such as the relative sacrifice of employee productiveeffort and firm resources needed to generate production efficiencies. That is, while shortcut discovery had a largeeffect on productivity relative to more conventional efforts in our setting, the recommended target level and

contract type would ultimately depend on the trade-off between these two aspects of performance in practice.



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While time pressure is descriptive of many environments, other settings provide opportunities to

ruminate about potential efficiencies over a longer period of time. Future research can examine

whether and when our results extend beyond environments with short-term time pressure. Second,

not all of our participants may have perceived pay tied to target achievement as directly rewarding

them for discovering production efficiencies. Instead, some may have interpreted the assignedtargets as the productivity level they could attain simply by counting letters. This factor may have

led some participants who were assigned the challenging target to underestimate the importance of

looking for shortcuts. Thus, the effects of more closely linking compensation to discovering

production efficiencies would be useful to explore in future research. Third, since we informed

participants that production efficiencies existed, the key uncertainty faced by our participants was

whether they would be able to find the shortcuts, as opposed to whether they existed or their

production-enhancing benefits. While we believe that employees in the natural environment would

be sensitive to the possibility that more efficient ways exist to carry out their tasks, future research

can examine whether our findings generalize to an environment with greater uncertainty

surrounding the existence of production efficiencies.

REFERENCES

Amabile, T. M. 1996. Creativity in Context: Update to the Social Psychology of Creativity. Boulder, CO:

Westview Press.

Bailey, C., and M. Bristow. 2004. Thinking outside the box. Training Magazine (February): 25–27.

Baron, R. A. 2006. Opportunity recognition as pattern recognition. Academy of Management Perspectives

20 (1): 104–119.

Beilock, S. L., and T. H. Carr. 2005. When high-powered people fail: Working memory and ‘‘choking

under pressure’’ in math. Psychological Science 16 (2): 101–105.

Beilock, S. L., C. A. Kulp, L. E. Holt, and T. H. Carr. 2004. More on the fragility of performance: Choking

under pressure in mathematical problem solving. Journal of Experimental Psychology: General 133

(4): 584–600.

Bonner, S. E., and G. B. Sprinkle. 2002. The effects of monetary incentives on effort and task performance:

Theories, evidence, and a framework for research. Accounting, Organizations and Society 27 (4/5):

303–345.

Bonner, S. E., R. Hastie, G. B. Sprinkle, and S. M. Young. 2000. A review of the effects of financial

incentives on performance in laboratory tasks: Implications for management accounting. Journal of

Management Accounting Research 12 (1): 19–64.

Chaneski, W. S. 2005. Manufacturing cells can eliminate wastes. Modern Machine Shop 77 (11): 44–46.

Chen, C. C., and K. T. Jones. 2005. Are companies really ready for stretch targets? Management

Accounting Quarterly 6 (Summer): 10–18.

DeShon, R. P., and R. A. Alexander. 1996. Goal setting effects on implicit and explicit learning of complex

tasks. Organizational Behavior and Human Decision Processes 65 (1): 18–36.

Dienes, Z., and D. Berry. 1997. Implicit learning: Below the subjective threshold. Psychonomic Bulletin and

Review 4 (1): 3–23.

Earley, P. C., T. Connolly, and G. Ekegren. 1989. Goals, strategy development, and task performance:

Some limits on the efficacy of goal setting. Journal of Applied Psychology 74 (1): 24–33.

Eysenck, M. 1982. Attention and Arousal: Cognition and Performance. Berlin, Germany: Springer.

Fessler, N. J. 2003. Experimental evidence on the links among monetary incentives, task attractiveness, and

task performance. Journal of Management Accounting Research 15 (1): 161–176.

Fisher, J., S. Peffer, and G. Sprinkle. 2003. Budget-based contracts, budget levels, and group performance. Journal of Management Accounting Research 15 (1): 51–74.

Hollenbeck, J. R., and H. J. Klein. 1987. Goal commitment and the goal-setting process: Problems,

prospects, and proposals for future research. Journal of Applied Psychology 72 (2): 212–220.



July 2013

http://dx.doi.org/10.5465/AMP.2006.19873412http://dx.doi.org/10.1111/j.0956-7976.2005.00789.xhttp://dx.doi.org/10.1037/0096-3445.133.4.584http://dx.doi.org/10.1016/S0361-3682(01)00052-6http://dx.doi.org/10.2308/jmar.2000.12.1.19http://dx.doi.org/10.2308/jmar.2000.12.1.19http://dx.doi.org/10.1006/obhd.1996.0002http://dx.doi.org/10.3758/BF03210769http://dx.doi.org/10.3758/BF03210769http://dx.doi.org/10.1037/0021-9010.74.1.24http://dx.doi.org/10.2308/jmar.2003.15.1.161http://dx.doi.org/10.2308/jmar.2003.15.1.51http://dx.doi.org/10.1037/0021-9010.72.2.212http://dx.doi.org/10.1037/0021-9010.72.2.212http://dx.doi.org/10.2308/jmar.2003.15.1.51http://dx.doi.org/10.2308/jmar.2003.15.1.161http://dx.doi.org/10.1037/0021-9010.74.1.24http://dx.doi.org/10.3758/BF03210769http://dx.doi.org/10.3758/BF03210769http://dx.doi.org/10.1006/obhd.1996.0002http://dx.doi.org/10.2308/jmar.2000.12.1.19http://dx.doi.org/10.2308/jmar.2000.12.1.19http://dx.doi.org/10.1016/S0361-3682(01)00052-6http://dx.doi.org/10.1037/0096-3445.133.4.584http://dx.doi.org/10.1111/j.0956-7976.2005.00789.xhttp://dx.doi.org/10.5465/AMP.2006.19873412


24/27

Huber, V. L. 1985. Effects of task difficulty, goal setting, and strategy on performance of a heuristic task.

Journal of Applied Psychology 70 (3): 492–504.

Humphreys, M. S., and W. Revelle. 1984. Personality, motivation and performance: A theory of the

relationship between individual differences and information processing. Psychological Review 91 (2):

153–184.

Jain, R. K., H. C. Triandis, and C. W. Weick. 2010. Managing Research, Development and Innovation—

Managing the Unmanageable. 3rd edition. Hoboken, NJ: John Wiley & Sons.

Kaplan, R., and D. Norton. 1996. The Balanced Scorecard: Translating Strategy into Action. Boston, MA:

Harvard Business Review Press.

Klein, H. J. 1991. Further evidence for the integration of goal setting and expectancy theories.

Organizational Behavior and Human Decision Processes 49 (2): 230–257.

Lee, T. W., E. A. Locke, and S. H. Phan. 1997. Explaining the assigned goal-incentive interaction: The role

of self-efficacy and personal goals. Journal of Management 23 (4): 541–559.

Locke, E. A., and G. Latham. 2002. Building a practically useful theory of goal setting and task motivation.

American Psychologist 57 (9): 705–717.

Locke, E. A., and G. P. Latham. 1990. A Theory of Goal Setting and Task Performance. Englewood Cliffs,NJ: Prentice Hall.

Maddox, W. T., F. G. Ashby, A.D. Ing, and A.D. Pickering. 2004. Disru

webwilliamsonzhang2013 productivity-target difficulty and outside-the-box thinking

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