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DOI: 10.1177/1548051812467208

2013 20: 62 originally published online 27 November 2012Journal of Leadership & Organizational Studies

Robert L. Porter and Gary P. Latham

The Effect of Employee Learning Goals and Goal Commitment on Departmental Performance

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DOI: 10.1177/1548051812467208

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In his review of the literature, Yukl (2012) noted that

“leadership is the process of influencing others to under-

stand and agree about what needs to be done and how to

do it, and the process of facilitating individual collective

effort to accomplish shared objectives” (p. 7). Thus,

among the most important tasks for leaders is motivating

and guiding the people who report to them. This is particu-

larly true during environmental and organizational uncer-

tainty when company resources may be even scarcer than

during times of relative stability. In an uncertain environ-

ment, setting appropriate employee goals and motivating

employees to commit to these goals is likely to be more

important than during times of resource abundance (Latham,

2004). Consequently, in this study, the relationship of three

types of goals, and employee commitment to these different

types of goals with department performance was examined.

No previous study has examined the relationship of these

three goals to performance in organizational settings.

Achieving departmental performance expectations in

times of uncertainty may require leaders and their employ-

ees to adapt in response to emerging threats or opportunities.

Yukl and Mahsud (2010) argued that organizational adapta-

tion typically involves a change in goals and the redirection

of key human resources. The role the leader plays in choos-

ing these goals and motivating employees to commit to these

goals may be one of the key adaptations required of a leader.

Motivating employees to pursue goals, whether the goals are

new or existing, has been shown to be a fundamental require-

ment in realizing desired performance outcomes.

Goal-Setting Theory

Locke and Latham’s (1990, 2002) goal-setting theory states

that a specific high goal leads to higher performance than urg-

ing people to do their best. Three of the four mediators are

primarily motivational, namely choice, effort, and persis-

tence. A fourth mediator is primarily cognitive. A goal cues an

individual to recall extant knowledge/skills necessary to

attain the goal. More than 1,000 studies in laboratory and

field settings, involving myriad tasks, performed by indi-

viduals as well as groups have provided empirical support

for this aspect of the theory (Mitchell & Daniels, 2003).

This is because a specific goal is a regulatory mechanism

for individuals to monitor, evaluate, and adjust their

behavior. Moreover, a specific goal often provides a

“strong situation” as to requisite behavior (Mischel, 1968).

Because the primary focus of goal-setting theory is

motivation, the tasks used in testing it are typically those

that an individual or group have already mastered. There is

little or no uncertainty or ambiguity on how to perform

them (Locke & Latham, 1990). With few exceptions, the

467208JLO20110.1177/1548051812467208Journal

of Leadership & Organizational StudiesPorter and Latham

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University of Central Florida, Orlando, FL, USA

University of Toronto, Toronto, Ontario, Canada

Corresponding Author:

Robert L. Porter, University of Central Florida, College of Business,

Management Department, 4000 Central Florida Boulevard, Orlando,

Florida, 32816-1400, USA

Email: [email protected]

The Effect of Employee Learning Goals

and Goal Commitment on Departmental

Performance

Robert L. Porter

and Gary P. Latham

Abstract

The relationship between employee goals at the individual level and firm performance at the department level was examined

across a variety of industries. Specifically, three types of employee goals—learning, performance, and do-your-best—were

studied with regard to department-level performance. Employee learning goals were related to higher levels of department-

level performance than were performance or do-your-best goals. The relationship between the level of employee goal

commitment and department level performance was also examined, and found to be positive and significant. The theoretical

and practical significance of these findings for leaders in an economically turbulent environment are discussed.

Keywords

learning goals, performance goals, goal commitment, department-level performance

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Porter and Latham 63

goals studied by behavioral scientists have been specific

performance outcomes to be attained. Several laboratory

experiments, however, have shown that when people lack

the knowledge/skill to perform at a specified high level, set-

ting a vague rather than a specific goal, namely urging them

to do their best, results in higher performance than specify-

ing a high outcome to be attained (e.g., Kanfer & Ackerman,

1989; Winters & Latham, 1996). This is because a specific

high performance goal imposes greater attentional demands

on people when they are in a learning mode than is the case

when people are given a vague or abstract goal, such as to

“do-your-best.” Effective performance on a task that is

complex for an individual requires, in addition to effort, the

discovery of the appropriate strategies for performing effec-

tively. Mone and Shalley (1995), using a task where people

lacked the knowledge to perform it, found that the dysfunc-

tional effect of a specific high performance goal increased

over a 3-day period while the performance of those with a

“do-your-best” goal became increasingly better. Rather

than searching systematically for effective strategies, those

with a specific high performance goal to attain appeared to

be mindlessly switching from one strategy to another in

order to attain it.

Wood and Locke (1990) found that the effect size of

goals is usually smaller on complex than simple tasks. The

beneficial effects of goal setting, they said, are often delayed

on complex tasks because learning is required. Hence,

Winters and Latham (1996) hypothesized that a high perfor-

mance outcome goal should be set only when the person or

group has the ability to perform the task effectively. Ability

is a moderator variable in goal-setting theory (Latham &

Locke, 2007; Locke & Latham, 2002).

Of the 1,000 or more studies on goal setting, only 8 have

been conducted on the effect of a learning goal on perfor-

mance (Kaplan, Erez, & Van-Dijk, 2004; Kozlowski &

Bell, 2006; Latham & Brown, 2006; Latham, Seijts, &

Crim, 2008; Noel & Latham, 2006; Seijts & Latham, 2001;

Seijts, Latham, Tasa, & Latham, 2004; Winters & Latham,

1996). A learning goal enables a leader to focus employees’

attention on acquiring the knowledge/ability for performing

a task effectively rather than relying on the knowledge/skill

employees already possess (Seijts & Latham, 2005).

Consistent with empirical research on performance goals

(Locke & Latham, 1990), research on learning goals has

been proceeding inductively. Winters and Latham (1996)

began research on learning goals by addressing two ques-

tions: Does a learning goal, as is the case for a performance

goal, have a positive effect on subsequent task performance?

Does the type of task, where a person has/has not the ability

to perform effectively moderate the effect of both learning

and performance goals? The answer to both questions was

shown in their study to be yes. A performance goal only

increases task performance when a person has the requisite

knowledge/ability. When ability is lacking, a vague goal

increases task performance relative to a performance goal.

But, a specific high-learning goal increases task perfor-

mance significantly more than either a do-your-best or per-

formance goal. This is because a learning goal draws

attention away from the end result and emphasizes the

importance of understanding the task. The focus is on dis-

covering/developing a plan for performing it effectively

(Seijts & Latham, 2005). This finding has been replicated in

laboratory settings by Drach-Zahavy and Erez (2002) and

Kozlowski and Bell (2006). Latham et al. (2008) found that,

as is the case with a performance goal, the higher the learn-

ing goal the higher a person’s performance.

In an educational setting, Latham and Brown (2006)

found that first-year MBA students who self-set a specific

high learning goal regarding ways to make their education

meaningful to them had a significantly higher grade point

average at the end of the academic year than those who

either set a specific high distal performance goal or were

urged by the Dean to “do-your-best” to obtain a meaningful

education. Furthermore, satisfaction with the MBA pro-

gram was highest for those people in the learning goal

condition.

Model and Hypotheses

The hypothesized model that was tested in this study is

shown in Figure 1. A major limitation of the paucity of

studies of learning goals is that the majority of them have

been conducted in a laboratory (e.g., Latham et al., 2008),

a simulation (e.g., Seijts et al., 2004), or an educational set-

ting (Latham & Brown, 2006). Thus, the generalizability of

these findings to organizational settings has yet to be exam-

ined. The present study is based on field data that spanned

a variety of industries, and hence addresses this limitation.

The environment in which this study was conducted was

one of high economic and employment uncertainty. As the

Financial Times (Freeland, 2009) noted, the global eco-

nomic crisis had bankrupted century-old institutions and

brought down once-mighty industrial organizations (e.g.,

AIG, General Motors, Lehman Brothers). Unemployment in

the United States was relatively high. Hence, it was hypoth-

esized that a leader’s choice of goal type has a significant

Employee Goal Type:

• Learning

• Performance

• Do-your-best

Department

Performance as

Perceived by

Supervisor

H1+

Employee Goal

Commitment

H2+

Figure 1. Theoretical model

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64 Journal of Leadership & Organizational Studies 20(1)

effect on a department’s performance. As Frese and Zapf

(1994) observed, high performance is not always because of

sheer effort or persistence. It is also because of cognitive

understanding of the tasks. This is likely imperative in an

unstable economic environment. A specific high-learning

goal, unlike a performance or do your best goal, may increase

the probability that a correct process or procedure will be

discovered. Thus, the first hypothesis tested was,

Hypothesis 1: The relationship between learning goals

for employees and their department’s performance

is significantly higher than that for a specific per-

formance or a vague “do-your-best” goal.

Goal commitment is defined as one’s determination to

attain the goal (Locke & Latham, 1990; Locke, Latham, &

Erez, 1988). Thus, the commitment a leader elicits from

employees to a department’s goals is critical for high perfor-

mance. Klein, Wesson, Hollenbeck, and Alge (1999) found a

direct effect of commitment to a performance goal on per-

formance. Seijts and Latham (2011), using a complex busi-

ness simulation, also found a direct effect of commitment to

a learning goal on performance. This is because “It is virtu-

ally axiomatic that a goal a person is not really trying for is

not really a goal and therefore cannot have much effect on

subsequent action” (Locke & Latham, 1990, p. 124).

As a result of this research conducted in laboratory set-

tings, where the dependent variable was an individual’s per-

formance, the following hypothesis was tested in the

workplace:

Hypothesis 2: There is a significant, positive relation-

ship between employee goal commitment and a

department’s performance.

In summary, the two following hypotheses were tested.

First, the relationship between employee learning goals and

the employees’ departmental performance is significantly

higher than that for a specific employee performance goal

or a vague employee goal, namely to a “do-your-best” goal.

Second, the relationship between employee commitment to

a learning goal and a department’s performance is signifi-

cant and positive.

Method

Sample and Procedure

A survey was conducted on three different types of

employee goals that were being set by leaders in industry

(e.g., financial, technology, and manufacturing) in the

southeastern United States. The survey was administered to

404 leaders and their employees. The survey was com-

pleted anonymously.

Of the 404 leaders with 5 or more subordinates con-

tacted, 174 and 5 of their employees (n = 870) responded

(43.1% response rate for managers and 5 employees).

Their respective mean ages were 37.12 years (SD = 11.17)

and 29.37 years (SD = 8.22). Their respective tenure with

their employing organization was 17.1 years (SD = 6.10)

and 3.02 years (SD = 2.69).

Measures

Goal type. The employees responded to a 6-item, 5-point

Likert-type questionnaire adapted from Seijts et al. (2004)

and Seijts and Latham (2005) for assessing learning (e.g.,

“goals set for me by my manager are based on specific

learning objectives, such as gaining knowledge or learning

a new skill”), performance (e.g., “. . . are based on specific

performance outcomes or results I need to achieve”), or

goals that are vague/abstract (e.g., “. . . are most likely to be

‘do-your-best’ goals rather than specific goals”).

The items were factor analyzed using varimax rotation.

The items loaded on three separate factors with an eigen-

value greater than 1.0, consistent with the three types of

goals. The Cronbach coefficient alphas for these three types

of goals were .82, .74, and .81, respectively.

Goal commitment. Hollenbeck and Klein (1987) noted a

large number of measurement problems in many of the

studies of goal commitment that led to inconsistent results.

Subsequently, Klein, Wesson, Hollenbeck, Wright, and

DeShon (2001) developed a reliable and valid self-report

scale for measuring goal commitment. This 5-point Likert-

type scale was used in the present study. A factor analysis

revealed that all items loaded above 0.60 on a single factor

with an eigenvalue of 2.03. The Cronbach coefficient alpha

was .78. Sample items include the following: “I am very

committed to completing the goals given to me by my man-

ager” and “I work hard to complete the goals given to me by

my manager.”

Performance. A department’s (n = 174) performance was

assessed by the respective manager on a 7-item, 5-point

Likert-type questionnaire developed by Delery and Huselid

(1998). A factor analysis revealed that all 7 items loaded

above 0.60 on a single factor with an eigenvalue of 4.04. The

Cronbach coefficient alpha was .84. Sample items include

the following: “How would you compare your department’s

performance with other departments that do the same kind of

work in terms of . . . quality of products?,” “. . .services or

performance?,” “. . . satisfaction of customers or clients?”

Controls. Age and tenure of a respondent, as well as the

size of the department and goal orientation, were controlled

in this study.

Goal orientation. Goal orientation (Dweck, 1986) is typi-

cally assessed as a trait. People with a learning-goal orienta-

tion (vs. a performance or avoiding orientation) typically

choose tasks where they can enhance their knowledge and

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Porter and Latham 65

skills. Although Seijts et al. (2004) found that goal setting

as a state masks goal orientation on a dynamic task, people

with a learning goal orientation performed better than those

with a performance goal orientation, a predisposition to

choose tasks where they can be seen as competent in the

eyes of others, in the “do-your-best” condition, weak situa-

tion (Mischel, 1969). Employees in the present study were

assessed with a 13-item, 7-point Likert-type scale devel-

oped by VandeWalle (1997). The items were factor ana-

lyzed using varimax rotation. The items loaded on three

separate factors with an eigenvalue greater than 1.0, consis-

tent with the three types of goal orientation. The Cronbach

coefficient alphas for a learning goal orientation, perfor-

mance goal orientation, and an avoiding goal orientation

were .85, .79, and .83, respectively.

Results

Descriptive Statistics and Correlations

Table 1 shows the descriptive statistics, intercorrelations,

and reliability measures for the study variables.

Tests of Hypotheses

The results of the analysis of the model are displayed in

Table 2. Regression analysis was performed for Hypotheses

1 and 2 (Aiken & West, 1991; Cohen, Cohen, West, &

Aiken, 2003).

For Hypothesis 1, effects are introduced across the col-

umns as the model is developed, indicated as numbered in

columns Model 1 and Model 2. Estimates of control vari-

ables are included in Model 1. The estimates for all three

types of employee goals are introduced in Model 2. For

Hypothesis 2, the estimate for employee goal commitment

is introduced in Model 3.

As noted earlier, each department’s performance was

determined by the supervisor on a departmental level, which

was typically composed of five employees.

A linear regres-

sion model was used with conditions of restricted maximum

likelihood estimation, using the SAS Version 9.1.3 and the

SAS procedure PROC.

Hypothesis 1 was supported. Learning goals explained

significantly more of the variance in a department’s perfor-

mance than did a performance or “do-your-best” goal. The

results for Model 2, shown in Table 2, indicate that the main

effect of learning goals was positive and significant (β =

0.25, p < .01). The performance goal effect, and do-your-

best goal effect were not significant (β = −0.02, p = ns) and

(β = −0.13, p = ns), respectively.

The results of this study also provide support for

Hypothesis 2. The hypothesized relationship of employee

goal commitment to departmental performance was found

to be significant and positive (β = 0.18, p < .05). In keeping

with the concept of testing the full model of interest, all the

goal type variables were kept in the regression analysis. It is

noteworthy that the effect of learning goals remained sig-

nificant and positive (β = 0.24, p < .01).

Discussion

Yukl and Mahsud (2010) argued that flexible, adaptive

leadership is essential for organizational effectiveness

when times are uncertain. This adaptation may be largely

dependent on the clarity and accuracy of the information

the leader receives, and the leader’s correct interpretation

of the implications for their department’s performance. The

goals leaders subsequently set for their respective teams

should reflect this interpretation. The goals serve as the

guided motivation the leaders strive to impart to their orga-

nization’s employees.

As argued in this study, the type of goal used to motivate

employees can take several forms. The results of this study

suggest that a goal designed to focus an employee on learn-

ing the processes and procedures needed by the employee to

achieve the goal is significantly related to a department’s

Table 1. Summary Statistics and Zero-Order Correlations

Variable M SD 1 2 3 4 5 6 7 8 9 10 11

1. Department Performance 3.98 0.59

2. Employee Learning Goal (LG) 3.54 0.49 .27*** (.72)

3. Employee Performance Goal (PG) 3.61 0.49 .21** .59*** (.74)

4. Employee Do-Your- Best Goal (BG) 2.89 0.61 −.09 .07 −.20** (.71)

5. Employee Goal Commitment (GC) 3.97 0.45 .26*** .30*** .52*** −.33*** (.78)

6. Employee Learning Goal Orientation 3.91 0.50 .21** .31*** .40*** −.23** .70*** (.78)

7. Employee Proving Goal Orientation 3.52 0.50 .14

†

.23** .19* .23** .015* .28*** (.82)

8. Employee Avoiding Goal Orientation 2.92 0.69 .00 .14

†

.00 .50*** −.35*** −.27*** .43*** (.72)

9. Department Size 17.9 29.3 −.01 −.05 −.06 −.02 −.15

†

−.10 −.02 .03 —

10. Supervisor Tenure 6.10 6.70 .04 .07 −.02 .10 .07 .08 .03 .12* .04 —

11. Employee Tenure 3.02 2.69 .03 −.09 −.02 −.07 .08 −.01 .03 −.03 .03 .01 —

Note. N = 174 for supervisors, 870 for employees. Reliability (α) estimates are listed on the diagonal in parentheses.

†p < .10. *p < .05. **p < .01. ***p < .001.

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66 Journal of Leadership & Organizational Studies 20(1)

performance. Defining learning goals may take more fore-

thought on the part of the leader than setting other types of

goals. For instance, performance goals may be the tradi-

tional type of goals used by an organization. In the absence

of a clearly set goal, the default unspoken goal is typically

interpreted as a vague “do-your-best” goal. Therefore, to

achieve the benefits argued for in this study, a leader needs

to consciously define employee goals in terms of discover-

ing processes and procedures to implement to improve a

department’s performance.

For instance, a traditional performance goal for a loan

officer in a bank might be stated as, “Increase commercial

loan production by 20% this quarter compared with last

quarter.” A learning goal for another loan officer might be,

“Determine five processes to increase commercial loan

production.”

This study also evaluated the effect of employee com-

mitment to the goals they were assigned. In a simulation

experiment, Seijts and Latham (2011) found that an assigned

learning goal and commitment to the goal were both signifi-

cantly related to student performance in the simulation.

These findings suggest that the assignment of a learning

goal, and commitment to that goal, act independently in

regard to a department’s performance. This finding suggests

that leaders should focus on both a learning goal and the

commitment by employees to the goals as separate actions.

That is, it is not enough to presume that assigning a learning

goal will translate into a commitment to that goal.

One way leaders can increase employee commitment to

goals is to take into account findings by Klein et al. (1999).

They reported there are strong positive overall relationships

between goal commitment and the antecedents of attrac-

tiveness of goal attainment, expectancy of goal attainment,

and motivational force. Locke et al. (1988) found that peo-

ple must understand the logic and rationale for why a goal

is set before they will commit to it.

Summary

The contribution of the present findings to the goal-setting

literature is at least fourfold. First, this is the only study to

assess the relationship of a learning goal to performance in

industrial organizations. Second, this is the only study to

examine the relationship of having one or more of three

goals, namely learning, performance, and “do-your-best”

with departmental performance. Third, this is the first study

to examine the relationship of learning goals with a macro

rather than a micro performance variable, namely, a depart-

ment’s performance rather than an individual’s. Fourth, the

present study integrated the motivational effects of goal

setting with previous research findings on leadership,

namely affecting employee goal commitment. In doing so,

the role of goal commitment on departmental performance

was examined as a direct effect.

These findings are of practical as well as theoretical

importance for the following reasons. First, the results show

that two types of goals, namely exhortations by a leader for

employees to “do-your-best” and setting a performance goal

were not significantly related to a department’s performance.

The fact that a “do-your-best” and a and a performance goal

were not related to performance likely reflects the turbulent

economic environment when this study was conducted.

There was extensive television coverage of people losing

their jobs because of companies shutting the doors and going

out of business. Hence, it is likely that employees were

uncertain as to what to do to ensure their department contrib-

uted to the employing organization’s survival.

Second, the relationship between a learning goal and a

department’s performance was high in this study. Again, this

may reflect the environmental turbulence when the study

was conducted. Falling back on extant knowledge and skills

to attain specific high performance goals may have been

necessary but not sufficient for the survival of many of those

companies. Hence, employees and their managers were

likely searching for new strategies/procedures that would

enhance their department’s competitiveness/survival.

Arguably, an important finding was the examination of a

commonly suggested variable in goal-setting theory, that is,

commitment to the assigned goal. The finding that this vari-

able acted independent from the assigned goal suggests that

it deserves considerable attention by leaders. This suggests

that leaders should spend adequate time understanding how

Table 2. Results of Regression Analysis for Hypothesis 1,

Hypothesis 2, and Hypothesis 3

Predictor Model 1 Model 2 Model 3

Controls

Department size (no. of

people)

0.01 0.01 0.01

Supervisor tenure (years) −0.04 −0.03 −0.02

Employee education (years) 0.03 −0.02 −0.03

Employee tenure (years) 0.07 0.02 0.02

Learning goal orientation 0.13 0.11 0.10

Performance goal

orientation

0.08 0.03 0.03

Avoiding goal orientation 0.02 0.01 0.01

Main effects

Employee learning goal (LG) 0.25** 0.24**

Employee performance goal

(PG)

0.02 −0.06

Employee do-your-best goal

(BG)

−0.13 −0.08

Employee goal commitment

(GC)

0.18*

Adjusted R

.01 .05 .07

Change in adjusted R

.04 .03

a. Standardized coefficients are reported.

†p < .10. *p < .05. **p < .01. ***p < .001.

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Porter and Latham 67

to motivate their employees to commit to assigned goals in

addition to the strategic intent of the organization when they

derive the goals.

Limitations and Future Research

Ideally, the present data, collected in a turbulent worldwide

economic crisis, would have been compared with data col-

lected in a relatively stable environment. No such data

exist. Hence, this study should be replicated if and when

relative economic stability is experienced in the United

States and elsewhere.

Determining the direction of causality among the vari-

ables is not possible with correlational data. Perhaps high-

performing departments are those who set high learning goals.

And high-performing departments may engender goal com-

mitment from employees. Despite the inability to draw casual

inferences, the present results, viewed in conjunction with the

six laboratory experiments and the field experiment in an edu-

cational setting that preceded it, suggest that organizations, in

times of uncertainty, consider setting specific learning goals

for their department, and taking steps to motivate goal com-

mitment for their respective department’s employees.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect

to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support

for the research, authorship, and/or publication of this article:

This study was funded in part by a grant to Gary P. Latham by the

Social Sciences and Humanities Research Council, Canada.

Notes

1. Managers with five or more employees in their department

were surveyed. A response was considered complete when a

manager survey and five corresponding employee surveys

were completed.

2. This type of relationship, namely, multiple employees in one

department, represents nested or multilevel data. This is a form of

multilevel data that is sometimes best handled with a mixed effect

model, also referred to as a random coefficient model (RCM) or

hierarchical linear model (HLM). Therefore, a mixed effect model

with conditions of restricted maximum likelihood estimation, using

the SAS version 9.1.3 and the SAS procedure PROC MIXED. The

fit of this model was compared using the SAS procedure PROC.

No additional insight was provided using the RCM approach;

therefore, regression was used for the sake of parsimony.

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Bios

Robert L. Porter is a visiting lecturer with the University of

Central Florida, College of Business in the Department of

Management. His areas of research include leadership, entrepre-

neurship, and strategy. He is a former founding director and Chief

Operating Officer for a community bank, a Chief Technology

Officer for a successful high-technology startup, and currently

consults as a Director for Management Insights.

Gary P. Latham is the Secretary of State Research Professor in

the Rotman School of Management, University of Toronto. He is

a past president of the Canadian Psychological Association and

the Society for Industrial-Organizational Psychology; he is

President-elect of Division 1, Work and Organizational

Psychology of the International Association for Applied

Psychology. He serves on the Board of the Society for Human

Resource Management.

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