paper 1.pdf

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Journal of Operations Management 24 (2006) 779–790

Six Sigma: The role of goals in improvement teams

Kevin Linderman a,*, Roger G. Schroeder a,1, Adrian S. Choo b,2

a Operations and Management Science Department, Curtis L. Carlson School of Management,

University of Minnesota, 3-150 CarlSMgmt Building, 321-19th Avenue South, Minneapolis, MN 55455, USAb Rensselaer Polytechnic Institute, Troy, NY, USA

Received 1 May 2005; received in revised form 1 August 2005; accepted 3 August 2005

Available online 27 December 2005

Abstract

The tenets of goal theory have been well established as a motivation mechanism in the management literature. However, some

quality-management advocates, such as W. Edwards Deming, often criticize the use of goals. This research investigates the tension

between goals and quality management in the Six Sigma context. We find empirical support that goals can be effective in Six Sigma

improvement teams when teams adhere to the Six Sigma tools and method. However, challenging goals are counterproductive when

Six Sigma teams do not use the tools and methods rigorously. This research reconciles the differences between quality management

and goal theory by showing that the Six Sigma tools and method interact with goals.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Quality management; Six Sigma; Goal theory; Teams; Process improvement

1. Introduction

Much has been written about quality management

over the last few decades (Ahire et al., 1995; Sousa and

Voss, 2002). Most of the research focuses on studying

quality-management practices and associated success

factors (Kaynak, 2003; Sousa and Voss, 2002). However,

research on how motivational factors influence quality-

management outcomes is scant. Motivation is the process

that accounts for an individual’s intensity, direction, and

persistence toward a goal (Robbins, 2003, p. 155). While

goal theory research suggests that specific, challenging

goals lead to higher performance (Locke and Latham,

1990), at least one quality-management authority has

expressed a conflicting view. For example, Deming

* Corresponding author. Tel.: +1 612 626 8632.

E-mail addresses: [email protected] (K. Linderman),

[email protected] (R.G. Schroeder), [email protected]

(A.S. Choo).1 Tel.: +1 612 624 9544.2 Tel.: +1 612 626 9723.

0272-6963/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.jom.2005.08.005

(1986) viewed arbitrary numerical goals as counter-

productive. Research in quality management from a goal-

theoretic perspective can help resolve these conflicting

viewpoints.

Reviews of the goal-setting literature have consis-

tently demonstrated the effectiveness of individual goal

setting on performance (Locke and Latham, 1990; Locke

et al., 1981). In fact, Miner (1980) rated goal theory

‘‘high’’ in both criterion validity and usefulness in

application. (Criterion validation indicates ‘‘how well

scores on a measure correlate with the criterion on

interest’’ (Singleton and Straits, 1993, p. 122). This gives

an indication of practicality or usefulness of a measure.)

Also Pinder (1984) said, ‘‘goal theory has demonstrated

more scientific validity to date than any other approach

on motivation . . .. Moreover, the evidence indicates that

it probably holds more promise as a motivational tool for

managers than any other approach.’’

Research on the effects of goals on group

performance, however, is still emerging. O’Leary-Kelly

et al. (1994) performed a quantitative meta-analysis of

K. Linderman et al. / Journal of Operations Management 24 (2006) 779–790780

goals in the group setting and found a significant

relationship between group goals and group perfor-

mance. None of the studies in their meta-analysis

considered the effect of goals on quality-improvement

teams. Quality-improvement teams are central to

quality improvement (Scholtes et al., 1996) because

effective teams can perform significantly better than a

collection of individuals (Mohrman et al., 1995;

Katzenbach and Smith, 1993). Setting challenging

group goals can promote team effectiveness (Locke and

Latham, 1990) because goals tell teams what needs to

be done and how much effort to expend.

Little research in quality management uses goal

theory. Some quality-management movements, such as

the Zero Defects, have argued for setting very high

goals. However, some quality thinkers have been

critical of the goal-theoretic perspective. For example,

Deming (1994, p. 41) said ‘‘. . . a goal that lies beyond

the means of its accomplishment will lead to

discouragement, frustration, and demoralization . . ..A numerical goal accomplishes nothing.’’ Deming

systematically rejected the use of goals as a source of

motivation (Carson and Carson, 1993). Deming’s

‘‘negative views on quantitative goal setting are at

odds with both the historical management prescriptions

and contemporary research on goal setting and

motivation’’ (Duncan and Van Matre, 1990, p. 5).

Carson and Carson (1993) compare and contrast

Deming’s views with goal theory and reconcile

differences when possible. Hackman and Wageman

(1995) considered research issues related to quality

management and noted, ‘‘It is not surprising that there is

disagreement among the TQM authorities about goal

setting, because the nature of the work done by quality

teams raises some complex issues about how goals and

objectives are properly framed.’’ The conflicting views

of goals in quality management (e.g. Deming versus

Zero Defects) suggest the need for more scholarly

investigation. Empirical testing of goal theory in the

quality context can help reduce the confusion between

goal theory and quality management.

Recently, Linderman et al., (2003) developed a set of

propositions about Six Sigma from a goal-theoretic

perspective. Schroeder et al., (2003) provide a definition

of Six Sigma and compare it with other quality-

management approaches. These researchers argue that

nothing is radically new in Six Sigma but Six Sigma

does place a strong emphasis on challenging specific

goals (see also Pande et al., 2000). In fact, the name Six

Sigma suggests a goal of 3.4 parts per million defective.

The Six Sigma approach to process improvement also

employs numerous goals; for example, setting project

improvement goals to increase performance by a factor

of 10 (10�), hiring Black Belts on the basis of cost-

saving goals, and selecting improvement projects based

on financial and strategic goals. As a result, Six Sigma

provides an ideal context to study the relationship

between goal theory and quality management. Our

research begins to test the tenets of goal theory in Six

Sigma projects. Because Six Sigma is consistent with

quality-management principles, these results should

also be generalizable to other quality practices.

2. Theoretical development

Goal-setting theory suggests that challenging goals

lead to enhanced performance because they mobilize

effort, direct attention, and encourage persistence and

strategy development (Locke and Latham, 1990).

According to goal-setting theory, goals are effective

because they indicate the level of performance that is

acceptable (Locke and Latham, 1990). Specific goals

are critical to individuals because they establish a

minimum acceptable performance level, but ambiguous

goals either do not make clear the appropriate

performance level or indicate to individuals that a range

of performance levels is acceptable (Locke and Latham,

1990). Difficult goals, if accepted, lead to greater

individual effort and persistence (Locke and Latham,

1990; Locke et al., 1981; Tubbs, 1986). However,

assuming that the results of goal theory for individuals

also apply to groups may be subject to the ecological

fallacy (Singleton and Straits, 1993, p. 69). The eco-

logical fallacy can occur when erroneous information

from one level of aggregation (e.g. individuals) is used

to draw inferences about another level of aggregation

(e.g. groups). In this setting, the effect of goals motivating

individuals may differ from that of groups. As a result,

further research needs to be conducted at the group

level in goal theory (Weingart and Weldon, 1991).

In response to this phenomenon, scholars have been

studying goal theory in group settings (Weingart and

Weldon, 1991; Weldon et al., 1991; Weingart, 1992;

O’Leary-Kelly et al., 1994; Durham et al., 2000; Knight

et al., 2001). In general, scholars have found a

significant relationship between group goals and group

performance (O’Leary-Kelly et al., 1994; Locke et al.,

1997). Goal theory also suggests that specific goals

result in higher levels of performance than vague non-

quantitative goals such as ‘‘Do best’’ goals (Locke and

Latham, 1990). ‘‘Do best’’ goals are goals that are

implied by the task or occur when the subject is told to

do the best he or she can. O’Leary-Kelly et al. (1994)

found that goal specificity also applies to group goals.

K. Linderman et al. / Journal of Operations Management 24 (2006) 779–790 781

Fig. 2. Goal level and performance.

Six Sigma is known for employing specific challen-

ging process improvement goals (Pande et al., 2000). In

fact, Pande et al. (2000, p. 44) indicates, ‘‘A clear goal is

the center piece of Six Sigma. It is extremely

challenging, but still believable.’’ Quantitative improve-

ment goals used in Six Sigma included setting target

levels for defects per million opportunities (DPMO)

and/or Process Sigma. The 10� rule is one common

approach used in Six Sigma to establish improvement

goals. For example, if the current DPMO is 30,000 for a

process, the improvement goal is to reduce the DPMO

by a factor of 10–3000.

Fig. 1 illustrates the proposed relationship between

Six Sigma and goals under investigation in this research.

The figure indicates a direct effect of challenging and

explicit goals and a direct effect of the Six Sigma Tools/

Method on team performance along with a moderating

effect or interaction between goals and Six Sigma. These

proposed effects are explained below.

Although explicit challenging goals can lead to

higher performance, a drop in performance can occur

if goals become too challenging (Erez and Zidon,

1984). If goals are viewed as unattainable, individuals

may exert less effort, which would decrease

performance (Erez and Zidon, 1984; Locke and

Latham, 1984). Erez and Zidon (1984) found a

bipolar relationship between goals and performance.

When reviewing the literature goal difficulty and

performance relationship Locke and Latham noted,

‘‘in all cases goals are linear except when subjects

reach the limits of their ability at high goal difficulty

levels’’ (1990, p. 27). OM scholars have also argued

for considering bipolar (or non-linear) relationships

when applying behavioral theory to operations

management phenomena (Bendoly and Hur, 2005).

In Six Sigma, excessively challenging goals could

risk being viewed as unattainable and thus exhibit a

non-linear relationship between goal difficulty and

performance. Fig. 2 illustrates that challenging Six

Sigma goals lead to higher performance; however,

Fig. 1. Six Sigma and goals.

when goals become excessive, performance actually

declines.

Hypothesis 1. The degree of challenging goals used in

a Six Sigma project improvement team is concavely

related to project performance.

Research in goal theory suggests that the ability to

achieve goals moderates the relationship between goals

and performance (Locke, 1982). Goal theorists have

argued that difficult goals on complex tasks may

actually prove to be more detrimental to performance

than assigning no goals at all because they may create a

level of anxiety that interferes with goal accomplish-

ment (Earley et al., 1989). This perspective is consistent

with the views of some quality-management authorities.

According to Deming, ‘‘A numerical goal accomplishes

nothing. What counts is the method—by what

method?’’ (Deming, 1994, p. 41). Quality management

has advocated the use of quality tools and methods in

problem solving (Breyfogle, 1999; Gitlow et al., 1995;

Hoerl, 1998; Ishikawa, 1985; Kume, 1985, 1995;

Mizuno, 1988). Using quality improvement tools and

methods should reduce the task’s complexity by guiding

the search for solutions to complicated problems,

which, in turn, facilitates goal achievement.

Six Sigma advocates rigorous application of the

quality tools in each step of the problem-solving

methodology (Schroeder et al., 2003; Sanders and Hild,

2000). In Six Sigma, prescriptive tools are suggested for

each step in the improvement methodology (Rath and

Strong, 2000). Using Six Sigma tools and method

provides a mechanism for improvement teams to

achieve their goals, especially for challenging projects.

As a result, the degree to which Six Sigma teams adhere

to the tools and method should alter their ability to

achieve improvement goals, more so when goals are

challenging then when they are easy to achieve.


Hypothesis 2. The degree that Six Sigma teams adhere

to the use of tools and method positively moderates the

effect of challenging goals on project performance.

The application of quality tools and methods is

grounded in rational decision-making (March, 1999;

Harrison, 1999). Rational decision-making emphasizes

a systematic step-by-step approach to problem solving.

Handfield et al. (1999) found that appropriate use of

quality tools can lead to improved performance. To the

degree that improvement teams follow the Six Sigma

tools and method they can make better decisions, which

improves project performance. Furthermore, this is

consistent with prior research that suggests the degree

of implementation of quality practices is positively

related to organizational performance (Douglas and

Judge, 2001).

Hypothesis 3. The degree of adherence to Six Sigma

tools and method is positively related to project per-

formance.

3. Methods

3.1. Research environment

The primary source of data for this study comes from

a high-tech manufacturing firm using Six Sigma that we

refer to as MFG. MFG is a Fortune 500 company with

more than 60,000 employees worldwide. This leading

manufacturer of electronic components has annual

revenue of more than US$ 6 billion. It has been using

Six Sigma for 3 years and is very advanced in its

application. At the time of this research, MFG had about

200 full-time Black Belt specialists and had completed

more than 1500 Six Sigma projects, resulting in savings

of more than US$ 400 million from its Six Sigma

efforts. Black Belts are highly trained full-time

specialists in process improvement who receive more

than 4 weeks of training and hands-on project

improvement experience. MFG is also training most

of its employees in Six Sigma basics. These individuals

receive 2 weeks of training and are called Green Belts.

In MFG, each Six Sigma project is assigned a team of

employees who have substantial knowledge of the

process or product to be improved. They serve on a part-

time basis and often have Green Belt training. A full-

time Black Belt specialist leads each of these teams.

The Black Belt usually reports to the team sponsor,

called a Champion, who is from senior management and

is trained in Six Sigma basics. The Champion provides a

holistic view of the organization, which helps establish

project buy-in and ensures the availability of resources

to the team. Most projects have a target savings of at

least US$ 175,000, but this savings target does not

necessarily apply to all projects. Some projects may

have zero financial savings but generate other important

strategic benefits that are difficult to quantify in

financial terms.

Six Sigma project improvement teams at MFG

employ a structured improvement method and use

numerous quality tools. In MFG, the structured

improvement approach is called DMAIC (Define,

Measure, Analyze, Improve, Control). This approach

is patterned after PDCA (Plan, Do, Check, Act). In each

improvement step, standard quality tools like failure

modes effects analysis (FMEA), cause–effect charts,

and Statistical Process Control are used. These tools

include many of the seven classic tools of quality

control and the seven new tools for problem formulation

and diagnosis (Gitlow et al., 1995).

3.2. Data collection

A questionnaire was developed to collect data from a

sample of MFG’s 1500 completed Six Sigma projects.

Most recently completed projects by the Black Belts

were selected to minimize the measurement error due to

recency effect. That is, the sampling time frame

considered only projects completed with in the last 6

months to avoid problems associated with memory loss.

Because Black Belts came from different business units

in MFG, the projects selected were fairly random and

representative of the population of all completed

projects at MFG at the time of survey. Black Belt

and team members from each selected project were

invited to participate in this survey. Using multiple

informants reduced the common method bias and

increased validity compared with a single-informant

survey design.

The questionnaire was divided into parts: Black

Belts answered one part of the survey that contained

questions about performance. Team members answered

the other part of the survey about goals, tools, and

method. To a certain extent, the separation of theoretical

constructs into different parts of the survey further

reduced the common respondent bias while at the same

time reduced the number of questions per survey, which

tended to increase the response rate.

The survey was web-based: personalized email

invitations to participate in the survey were sent out by

the Vice President and Executive Director for Six Sigma

to 1233 team members and Black Belts from 324

projects. Each personalized email had a customized link


that brought the respondent directly to the survey for a

specific project. Extra efforts were made to ensure

confidentiality and avoid the negative effects of Social

Desirability Bias (Nunnally and Bernstein, 1994).

Responses collected via the Internet were stored in a

protected Microsoft Access database. Follow-up remin-

ders and thank-you emails were sent every week for 3

weeks before we concluded the data collection with a

satisfactory number of responses. Some 951 persons

completed the survey for about a 77% response rate. At

the project-level, we collected data for 206 projects,

which represents a 63.5% response rate. The data for

each project are based on responses from the Black Belt

and at least two team members.

3.3. Variables and measures

Although we had multiple informants for all

projects, not all theoretical constructs had multiple

responses. The Performance construct had multiple

responses from Black Belts and at least one team

member for all projects, while other constructs had

multiple responses from only a third of the projects. We

assessed the inter rater reliability (IRR) (James et al.,

1984) and deleted 18 projects with inconsistent

responses (i.e. IRR � 0, >1) and low agreement (i.e.

IRR < .10). The remaining 188 projects had an average

IRR greater than 0.87 for all constructs with multiple

responses. We also compared, using a one-way

ANOVA, within-project variance and between-project

variance for the various constructs. F-statistics indi-

cated significant differences for between-project var-

iances. Although we did not have multiple responses for

all constructs, the results of the two tests showed

sufficient evidence that the data were consistent at the

project-level and that it was appropriate to aggregate the

item scores for theoretical constructs with multiple

responses. We aggregated multiple responses by taking

averages for the respective item scores in each project to

arrive at a sample of 188 projects. Then we removed all

projects designated as a Design For Six Sigma (DFSS)

because these projects require a methodology different

from the DMAIC that traditional Six Sigma projects

use. Finally, we removed all projects that involved

‘‘soft’’ dollar-savings and considered only ‘‘hard’’

dollar-saving projects. Soft dollar-savings projects are

often tied to the firm’s long-term strategy, which often

makes assessing performance difficult. This process

resulted in a total of 128 projects.

While quality-management measurement scales

existed in prior research (e.g. Saraph et al., 1989),

none was useful for this research, which required scales

for project improvement goals, project performance,

and adherence to Six Sigma tools and method. The

researchers developed the scales based on a literature

review and revised the scales through a pilot study at

MFG. All multi-item measures in this study use 7-point

Likert scales that ranges from 1-strongly disagree to 7-

strongly agree. The scales were pre-tested through a

pilot study at MFG by six Black Belts and eight team

members. Five university researchers also knowledge-

able in Six Sigma pre-tested the instrument. The pilot

study assessed three main characteristics of the

survey—clarity (i.e. is the question clear and easy to

answer?), content (i.e. does the question make sense and

is it appropriate?), and the average time taken to answer

the questionnaire. Appendix A provides a summary of

the scales.

3.3.1. Team project goals

Team project goals consisted of three items. This

scale had an alpha of 0.59 (Cronbach, 1951). New scales

often result in lower alphas making this scale minimally

acceptable for further use (Carmines and Zeller, 1978).

Using principle component analysis (PCA), the

resultant eigenvalue associated with this scale is 1.6,

above the minimum acceptable level of 1.0, and

explains 54.9% of the variation. All items loaded into

a single factor and the factor loadings for each item

ranged from 0.633 to 0.791, all above the acceptable

lower bound of 0.4 (Carmines and Zeller, 1978).

3.3.2. Six Sigma Tools/Method

Six Sigma Tools/Method consisted of four items.

This scale had an alpha of 0.68 (Cronbach, 1951), which

is considered acceptable. Using principle component

analysis, the resultant eigenvalue associated with this

scale is 2.056 and explains 51.4% of the variation. All

items loaded into a single factor and the factor loadings

for each item ranged from 0.631 to 0.814.

3.3.3. Team performance

Team performance consisted of five items. This scale

had an alpha of 0.90 (Cronbach, 1951). Using principle

component analysis, the resultant eigenvalue associated

with this scale was 3.659 and explained 73.2% of the

variation. All items loaded into a single factor and the

factor loadings for the items ranged from 0.784 to

0.919.

All of the scales were evaluated using principle

components analysis. For each scale factor scores were

created using the regression method. ‘‘Conceptually the

factor score represents the degree to which each

individual scores high on the group of items that have


Table 1

Correlations

Variable Mean S.D. 1 2 3 4

1 Project

Performance

4.09 0.56 1.00

2 Domestic 0.16 0.37 �0.33* 1.00

3 Goals 2.01 0.41 �0.11 �0.08 1.00

4 Six Sigma 3.07 0.41 0.38* �0.13 0.13 1.00

* Correlations significant at level P < 0.01.

high loadings on a factor. Thus, higher values on the

variables with high loadings on a factor will result in a

higher factor score’’ (Hair et al., 1998, p. 119). The

factor scores provide the basis for subsequent analysis.

3.3.4. Control variable

Discussion with MFG executives indicated that there

might be a difference in performance of Six Sigma

projects between domestic and international sites.

Domestic sites were more involved in R&D efforts

and production was often initiated domestically, then

transferred to international sites. MFG also observed a

lot of enthusiasm for Six Sigma from the international

sites; people at the domestic sites tended to be more

skeptical. Scholars have noted the importance of

considering the international context when studying

operations management (Prasad and Babber, 2000),

which suggests the need to control for domestic versus

international location.

4. Results

Mean, standard deviation, and correlations of the

variables under consideration are displayed in Table 1.

The collinearity diagnostics (including Variation Infla-

tion Factors) indicate that multicollinearity is not a

problem. A significant correlation exists between Team

Performance and Six Sigma and between Team

Performance and Domestic Projects.

Table 2 provides the results of the regression

analysis. Model 1 gives the regression of the control

variable on the Project Performance variable, which is

significant (F = 15.541, P < 0.001). Model 2 adds in

the main effects of Six Sigma Tools/Method and Goals,

Table 2

Regression analysis for Six Sigma goals and performance

Variable Model 1 Model 2

b t b

Domestic �0.891 �3.942*** �0.796

Goals �0.180

Goals2 �0.007

Six Sigma Tools/Method 0.355

Six Sigma Tools/Method � Goals

Six Sigma Tools/Method � Goals2

R2 0.11 0.251

F 15.54*** 10.298***

DR2 0.141***

* P < 0.1.** P < 0.05.

*** P < 0.001.

this also gives a significant model (F = 10.298,

P < 0.001) and suggests the main effects influence

Project Performance. Also, the R2 improves by 0.11 in

Model 2, but the Goals2 term is not significant. Model 3

adds in the interaction terms between Goals and Six

Sigma Tools/Method. The model is significant

(F = 7.798, P < 0.001) and the R2 improves by

0.028. Model 4 in Table 2 drops the non-significant

predictor variables (Goals2, Six Sigma Tools/Meth-

od � Goals2) from the regression equation. This model

is also significant (F = 11.669, P < 0.001) along with

all the predictor variables. We removed the non-

significant terms because they can alter the interpreta-

tion of the model (Neter et al., 1996, p. 308). Regression

diagnostics indicate no problems with outliers, normal-

ity, or homoscedasticity. We find that the R2 of 0.275 is

acceptable given the limited number of variables used to

predict performance. Subsequent analyses are con-

ducted on Model 4.

Model 4 suggest that performance of domestic

projects is significant and results in lower performance

than international projects (t = �3.736, P < 0.001), All

other models (Models 1–3) give similar conclusions.

These results are consistent with our expectations that

Model 3 Model 4

t b t b t

�3.672*** �0.797 �3.711*** �0.830 �3.736***

�2.278** �0.164 �2.007** �0.160 �2.042**

�0.149 �0.034 �0.602

4.470*** 0.347 3.683*** 0.355 4.546***

0.155 2.166** 0.138 2.032**

0.004 0.081

0.279 0.275

7.798*** 11.669***

0.028*


international Six Sigma projects had a higher level of

performance than domestic projects at MFG.

Interpreting the other coefficients of the regression

model is more difficult. Without interactions, the

coefficients of the regression equation indicate the

change in mean response with a unit increase in

the predictor variable. However, the meaning of the

coefficients is not the same in the presence of

interaction terms (Neter et al., 1996, p. 308). Model

4 indicates a significant interaction between Goals and

Six Sigma Tools/Method (t = 2.032, P < 0.001). In this

setting, the mean response, E [Project Performance],

with a unit increase in Goals when Six Sigma Tools/

Method is held constant and given by the following:

E ½Pro ject Per formance�

¼ �0:16þ 0:138 ðSix Sigma Tools=MethodÞ

Similarly, with a unit increase in Six Sigma Tools/

Method and Goals held constant is given by the

following:

E½Pro ject Per formance� ¼ 0:355þ 0:138ðGoalsÞ

The conditional effects plot (Neter et al., 1996, p.

310) provides a way to interpret the regression equation

in the presence of an interaction effect. Fig. 3 gives the

conditional effects plot for E [Project Performance] as a

function of Goals conditioned on Six Sigma Tools/

Method. The plot considers values between the 1st and

99th percentile ranking for goals, conditioned on the

1st, 50th, and 99th percentile ranking of Six Sigma

Tools/Method. In general, the conditional effects plot

indicates that when Six Sigma Tools/Method is low,

challenging goals result in lower performance. How-

ever, when Six Sigma Tools/Method is high, challen-

ging goals result in higher performance. As a result, Six

Fig. 3. Goal difficulty cond

Sigma Tools/Method has a reinforcement or synergistic

(Neter et al., 1996, p. 311) effect on goals.

Similarly, Fig. 4 provides the conditional effects plot

for E [Project Performance] as a function of Six Sigma

Tools/Method conditioned on Goals. The plot considers

values between the 1st and 99th percentile ranking for

Six Sigma Tools/Method, conditioned on the 1st, 50th,

and 99th percentile ranking of goals. An equivalent

story emerges where increasing Six Sigma Tools/

Method results in lower performance when goal

difficulty is low. However, when goal difficulty is high,

increasing Six Sigma Tools/Method results in higher

performance.

The conditional effects plots (Figs. 3 and 4) and the

statistical significance of the interaction term in Table 2

support Hypothesis 2. That is, empirical evidence

indicates that Six Sigma Tools/Method positively

moderates the effect of challenging goals on project

performance.

We are unable to fully support Hypotheses 1 and 3 as

stated, since there is a significant interaction term in

model 4 that makes it difficult to interpret the main

effects. This is because the main effects are conditioned

by their interaction with another variable. That is, the

effect of goals is conditioned on the level of Six Sigma

Tools/Method and visa versa. The conditional effects plot

in Fig. 3 indicates partial support for Hypothesis 1.

Increasing goals linearly improves performance when

teams make extensive use of Six Sigma Tools/Method.

However, we found no support for the quadratic

relationship between goals and performance as suggested

in Fig. 2. Possibly all Six Sigma projects sampled in this

survey did not have goals that were viewed unrealistic

and unattainable, which in turn could have reduced

motivation and lowered performance outcomes.

The conditional effects plot in Fig. 4 also gives

support to Hypothesis 3. The use of Six Sigma Tools/

itioned on Six Sigma.


Fig. 4. Six Sigma conditioned on goal difficulty.

Method results in higher performance except when

goals are low. One reviewer noted the anomaly that

extensive use of Six Sigma Tools/Method actually

resulted in lower performance when goals are low.

There could be an explanation for this outcome. In

discussions with Six Sigma consultants, they note that

sometimes organization inappropriately apply the Six

Sigma Tools/Method. As the adage goes ‘‘if you have a

hammer, everything looks like a nail.’’ Teams can waste

valuable time and effort rigorously applying Six Sigma

Tools/Method when the solution is obvious. In this

situation, the excessive use of Six Sigma Tools/Method

can actually reduce performance. As suggested by one

consultant, the solution method should fit the problem.

This consultant advocates the use of a Do-It project

when the solution is obvious and the focus is on

implementation. (A Do-It project means take action

immediately, there is no need for a root cause analysis.)

This consultant argues that a Six Sigma project should

only be used when the solution is not clear (that is, root

cause analysis is required). Possibly projects that have

low goals should have been Do-It projects rather than

Six Sigma projects, which would be consistent with the

results in Fig. 4.

5. Discussions and conclusions

5.1. Theoretical implications

This study addresses the role of goals in Six Sigma

project improvement teams. To the extent that Six

Sigma reflects quality-management practices in gen-

eral, this research should be generalizable to other

quality-management approaches. We found empirical

support that goals do in fact affect project performance.

However, analysis shows a positive interaction effect

between Goals and Six Sigma Tools/Method with

performance. This supports the perspective advocated

by some quality-management authorities. As Deming

(1994, p. 41) said, ‘‘. . . a goal that lies beyond the means

of its accomplishment will lead to discouragement,

frustration, demoralization. A numerical goal accom-

plishes nothing. What counts is the method—by what

method?’’ The results of this research are consistent

with Deming’s perspective. Goals can have a positive

effect on performance when used with Six Sigma Tools/

Method. As Carson and Carson (1993) note, ‘‘Deming

and traditional theorists are often arrayed on opposing

sides. The truth, though, is somewhat different.’’ This

research provides empirical support that Deming and

goal theory can be reconciled.

Little empirical research has been done using goal

theory in operations management and quality manage-

ment in particular (Linderman et al., 2003), a surprising

fact given that goal theory is a well-established

management theory (Locke and Latham, 1990; Pinder,

1984; Miner, 1980). Incorporating theories from

organizational behavior can help inform the practical

consequences of implementing operations management

practices. In this setting, we find that behavioral theories

interact with technical tools and method in interesting

ways; that is, the use of technical tools and motivational

factors must be managed jointly rather than in isolation.

More broadly, operations management should not be

understood as a purely technical problem but must be

considered simultaneously with behavioral underpin-

nings. Recently operations management scholars have

recognized that ‘‘incorporating human behavior into

OM models will yield more realistic insights’’

(Boudreau et al., 2003). Some scholars have begun to

examine the behavioral implications of technical tools

in operations management (Doerr et al., 1996;


Rungtusanatham, 2001; Schultz et al., 1998). Our

research helps advance this effort.

This research also contributes to goal theory.

Although goal theory is well established for individuals,

it is still developing for groups (Locke and Latham,

1990; Weingart and Weldon, 1991; Weldon et al., 1991;

Weingart, 1992; O’Leary-Kelly et al., 1994). Little goal

theory research has considered different types of groups

(O’Leary-Kelly et al., 1994). Most studies consider

work-teams where group membership is stable and

employees work together on a daily basis. To our

knowledge, this is the first study to consider problem-

solving groups (Six Sigma teams) and goals. In this

setting, problem-solving teams are formed to solve a

problem and then disbanded upon completion of the

project. We also consider the influence of using

problem-solving tools and method on goals, which

other goal theory studies have not considered.

5.2. Managerial implications

Quality-management practices can often become the

latest management fad (Abrahamson, 1996), which can

also create distortions between the rhetoric and reality

of what is practiced (Zbaracki, 1998). Managers may set

high goals when deploying quality but may not follow

through on fully implementing the prescribed techni-

ques. Based on this research, setting challenging goals

without fully utilizing the tools and method can lead to

sub-optimal results. Organizational leaders need to

make sure that quality-improvement teams are, in fact,

using the tools and method espoused by quality theorists

and practitioners. For example, MFG developed a

Project Evaluation System that performs a pre- and

post-project review of the improvement projects to

assess the appropriate use of the tools and method. The

Project Evaluation System helps make sure that

improvement teams use the tools and methods in the

prescribed fashion. Using these deployment techniques

not only ensures proper use of tools and method but also

facilitates achievement of project improvement team

goals.

Recently, Steve Kerr, Chief Knowledge Officer at

General Electric, noted ‘‘Most organizations do not

have a clue about how to manage stretch goals. It is

popular today for companies to ask their people to

double sales or increase speed to market threefold. But

then they do not provide their people with the

knowledge, tools, and means to meet such ambitious

goals’’ (Sherman, 1995, p. 231). This research supports

the belief that challenging goals must be supplemented

with tools and method to solve difficult problems. Kerr

further suggested that giving employees challenging

goals without a means to achieve them is immoral

(Sherman, 1995). In the quality context, leaders need to

be cognizant of the importance of training and

supporting the use of problem-solving tools and

methods. Otherwise, challenging improvement goals

could demoralize employees involved in Six Sigma

projects.

Organizational leaders pursuing quality practices

often take a rational approach to improvement of the

organizational system (Kast and Rosenzweig, 1972).

Improvement of rational systems (Scott, 1987) is

governed by both knowledge and motivation. Without

knowledge, improvement only occurs by chance events

that are rarely understood. In Six Sigma, the creation of

knowledge occurs through intentional or explicit

learning that employs formal improvement methods.

Intentional learning requires regulation of actions taken

by organizational members. Goals serve as regulators of

human action by motivating project improvement

teams. Thus, improvement goals motivate teams to

engage in intentional learning activities that create

knowledge (Linderman et al., 2003). As a result,

organizational leaders that make effective use of goals

can regulate how much organizational knowledge is

created through Six Sigma.

Also, organizations often use heuristics when setting

goals for project improvement teams. One common

heuristic used in Six Sigma is the 10� rule, which sets

an improvement goal of reducing defects by a factor of

10 (Harry and Schroeder, 2000). However, from a goal

theory perspective, such heuristics may not be effective.

If managers do not ensure teams are trained and use the

appropriate problem-solving tools, challenging goals

might actually lead to lower performance.

5.3. Limitations and conclusions

Despite the interesting results of this study, several

limitations need to be emphasized. First, the study

considered data from a single firm. Although this does

help control for confounding factors like organizational

culture, it lacks external validity. Further replications of

this study are required to fully test the theory.

Second, factors such as group cohesion (Levine and

Moreland, 1990) and social loafing (Price, 1987) were

not considered. These contextual variables could

influence a team’s ability to achieve goals; however

to date, they have not been tested (O’Leary-Kelly et al.,

1994). As research on goal theory in a group setting

matures, we should better understand contextual

variables in teams that influence goal achievement.


Third, other types of improvement projects beyond

Six Sigma were not considered. Possibly the type of

improvement project employed could effect the

relationship between goals and performance. For

example, the Do-It project discussed earlier could have

a different effect on the relationship between goals and

performance. Other improvement approaches such as

Lean were also not considered. Possibly using alter-

native improvement tools/methods has a different effect

on relationship between goals and performance.

Fourth, the role of national culture is not considered.

Possibly, different national cultures affect how goals

and Six Sigma Tools/Method affect performance. One

reviewer noted that this relationship could be investi-

gated by examining the interaction effects of the

Domestic variable with all other variables in the model.

However, this created significant multicollinearity

problems that made interpretation of the coefficients

impossible. Future research can study the effect of

national culture on these relationships in more depth.

Finally, project size and duration, prior group project

experience, or project group size could also affect

performance. Variation of these variables at MFG were

somewhat limited since they typically scoped projects

to last between 4 months and half a year, and each

project had typically four to six team members. In

addition, they had a 2-year rotation system for Black

Belts, which meant that Black Belts had a 2-year

assignment in Six Sigma and then were re-integrated

back into the regular organization. This would control

somewhat for prior experience with respect to Black

Belts. As a result, these variables were somewhat fixed

at MFG. Future research could investigate how changes

in these variables influence performance.

Despite the limitations, this study makes several

important contributions. Notably, it helps to clarify the

controversial relationship that goal theory has had with

quality-management authorities. It shows that goals can

be effective when used with quality tools and method.

This is particularly important for Six Sigma, which has

a strong goal orientation toward improvement (Linder-

man et al., 2003). In addition, very little research in

operations management has considered goal theory

(Boudreau et al., 2003). This is surprising given that

goal theory is well established in the management

literature (Locke and Latham, 1990). This research

helps illustrate that operations management is not just a

technical problem but also requires behavioral con-

sideration. Future research in operations management

should consider theories from organizational behavior.

Organizational behavior theories might serve to

enhance future research in operations management.

Or, this research has demonstrated the usefulness of

organizational behavior theories for future operations

management inquiries.

Acknowledgement

This research was supported in part by a National

Science Foundation Grant NSF/SES-0080318

Appendix A. Measurement scales

All responses range from 1-strongly disagree to 7-

strongly agree

Team project goals

1
We found it very difficult to achieve the project goals
2
It was relatively easy to achieve the project goals
(reverse scaled)

3
The project goals were challenging to us
Six Sigma Tools/Method

1
The project strictly followed the sequence
of DMAIC steps

2
Each step in DMAIC was faithfully completed
3
There was an emphasis on applying various
analysis tools wherever applicable in this project

4
This team frequently used Six Sigma tools to analyze
data and information

Team performance

1
We met or exceeded customers’ expectations in this project
2
This team had superb results on this project
3
This team did not meet the project goals (reverse scaled)
4
The cost savings or strategic impact of the project
were significant

5
The project was effective in improving the process or product
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