The Impact of Goal-setting on Worker Performance - Empirical Evidence from a Real-effort Production Experiment

Abstract

This paper examines the influence of goal-setting on worker performance in an industrial production process. For empirical examination, we conducted a real-effort experiment at the Training Factory for Energy Productivity at the Technische Universität München. The participants’ performance was measured by checking for quantity and quality of the assembled products and furthermore by recording the consumed compressed air per finished good. In total four groups were defined, each group in a different experimental setting. This experiment is the first one ever conducted related to goal-setting in an industrial production setting and thus adds valuable results to academia and practitioners in the field of sustainable manufacturing. The major results are that even without financial incentives goal-setting improves worker performance by 12 to 15% compared to the situation where no goals were defined. This holds true for the groups which had to maximize either output quantity or output quality, as well as for the group which was obliged to be as energy efficient as possible.

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Procedia CIRP 26 ( 2015 ) 127 – 132
Available online at www.sciencedirect.com
2212-8271 © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universität Berlin.
doi: 10.1016/j.procir.2015.02.086
ScienceDirect
12th Global Conference on Sustainable Manufacturing
The impact of goal-setting on worker performance - empirical
evidence from a real-effort production experiment
Sven Asmusa*, Florian Karlb, Alwine Mohnena, Gunther Reinhartb
aChair of Corporate Management, TUM School of Management, Technische Universität München, Arcisstrasse 21, 81369 Munich, Germany
bInstitute for Machine Tools and Industrial Management, Technische Univer sität München, Boltzmannstrasse 15, 85748 Garching, Germany
* Corresponding author. Tel.: +49 89 289 24074; fax: +49 89 289 24075. E-mail address: sven.asmus@tum.de
Abstract
This paper examines the influence of goal-setting on worker performance in an industrial production process. For empirical
examination, we conducted a real-effort experiment at the Training Factory for Energy Productivity at the Technische
Universität München. The participants’ performance was measured by checking for quantity and quality of the assembled
products and furthermore by recording the consumed compressed air per finished good. In total four groups were defined,
each group in a different experimental setting. This experiment is the first one ever conducted related to goal-setting in an
industrial production setting and thus adds valuable results to academia and practitioners in the field of sustainable
manufacturing. The major results are that even without financial incentives goal-setting improves worker performance by 12
to 15% compared to the situation where no goals were defined. This holds true for the groups which had to maximize either
output quantity or output quality, as well as for the group which was obliged to be as energy efficient as possible.
© 2014 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universität Berlin.
Keywords: goal-setting; sustainable manufacturing; industrial production
1. Introduction
Every relationship between a principle and an agent is
characterized by information asymmetry [1,2]. Since the
employer is not able to monitor the effort of the employees
permanently, certain measures to motivate the staff have to be
offered to the workforce. One suitable way to motivate is to
set goals [3]. The implementation of goals in everyday work
is manifold starting with sales goals for marketing managers
or production goals for blue collar worker in industrial
production [4]. From a research perspective, the impact of
goal-setting on human behavior is an interesting and already
well-examined topic. Starting in the late 1960’s [5,6], studies
focused on the effect of consciously set goals on the
performance of individuals with a major focus on the number
of solved tasks or produced units within a certain time frame.
Thereby most of the experimental studies focused on daily
office tasks like telephone services [7] or typing [8].
What has not been observed so far, is the effect of goal-setting
on real production tasks. Furthermore there is only little
knowledge about the impact of setting goals related to other
goal dimensions than output quantity. Given the above, the
presented study is examining if the findings of previous
studies about goal-setting are applicable to a real industrial
production setting and furthermore transferable explicitly to
other goal dimensions, in this case output quality and energy
efficiency as two of the major goals in modern production.
2. Literature and Hypotheses
The goal-setting theory is strongly affected by Ryan’s [9]
assumption that human behavior is driven by objectives, so-
called goals [10]. Taking this relationship into account,
primarily Latham and Locke [10,11,12], among others,
focused their research on the link between consciously set
goals and the observable task performance of individuals.
Thereby their major interest was on explaining and
forecasting human performance on several types of tasks and
furthermore on affecting someone’s performance by different
© 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universität Berlin.

128 Sven Asmus et al. / Procedia CIRP 26 ( 2015 ) 127 – 132
types of measures [10]. The findings of numerous
experimental studies show that goal-setting strongly
influences human behavior and thus leads to changes in
performance [13,14,15,16]. This holds true for varying tasks,
different groups of participants and diverse experimental
settings. However, so far goal-setting in industrial production
processes is not well-examined. Based on the results of
previous experimental studies in non-production settings
hypothesis 1 is as follows:
H1: Goal-setting improves task performance in an industrial
production setting.
Our study not only aims to examine the effects of goal-
setting in industrial production, but moreover to distinguish
sharply the effects of goal-setting on different kind of goal
dimensions. The main focus of previous studies was on the
measure of the impact of goal-setting on task quantity, mostly
defined as the time needed to perform a certain task, either
doing calculations [5], specific reading tasks [14] or reacting
on a signal light [17]. The effects of goal-setting on other
important goal dimensions like output quality have not been
neglected. Energy consumption as an important factor for
industrial production processes, so far has not been examined
within a goal-setting situation. Hence, to strengthen the
explanatory power of goal-setting theory in a production
process, the presented experiment focuses on the impact of
goal-setting on the three described goal dimensions, output
quantity, output quality and energy consumption. Therefore
the sub-hypotheses of H1 are as follows:
H1a: Goal-setting increases the output quantity.
H1b: Goal-setting increases the output quality.
H1c: Goal-setting decreases the consumed energy per output
unit.
In 1990, Locke & Latham introduced the High
Performance Cycle (HPC) [18] which integrates the essential
elements of goal-setting theory as shown in figure 1.
Fig. 1. The High Performance Cycle (HPC).
While specificity and difficulty as the goal core
dimensions as well as certain moderators and mechanisms
influence the performance, the potential achievements e.g.
productivity or cost improvement have a strong impact on the
individuals’ satisfaction and the willingness to cope with
challenging tasks and objectives in the future [10,18]. Both
difficulty and specificity have been investigated extensively.
Several empirical studies show that the higher the difficulty of
a goal is, the higher is the performance of the individuals that
executed the task [13,16]. Notably, a performance increase is
only possible, until the limit of a persons’ ability is reached
[19]. Experiments show that specific goals lead to greater
achievements compared to non-specified or vague defined
goals [13,17]. Therefore so-called ‘do-your-best-goals’ are
not sufficient to generate optimal performance [12,16].
However, since specific goal-setting is not always easy to
implement, for instance due to a lack of information about the
potential range of performance outcomes, even broadly
formulated goals like maximizing the output or minimizing
the input can lead to significantly improved performance. Due
to the design of this experiment, our formulated goals are not
specified further than maximizing the output quantity,
maximizing the number of assembled goods having a defined
quality level and using as little energy as possible to perform
the task.
As one of the four mechanisms, persistence is integrated as
a potential mediator on task performance in the High
Performance Cycle [10,18]. In this regard, persistence can be
interpreted as an indicator for the development of task
performance over time or as a potential proxy how people
change their effort level due to certain time constraints while
performing a task. As already empirically proven, people
extend their effort when having tight time constraints
compared to situations with loose deadlines [5,11].
Based on the power curve model of Wright [20] and
innumerable other approaches to show and explain learning
curves, it is known that people are able to improve their
performance over time when doing repeated tasks.
Furthermore Dar-el et al. [21] found out that cognitive and
motorial elements occur in different stages when learning
industrial tasks through repetition. So far no clear evidence
exists on the interaction of goal-setting and learning effects on
task performance. Therefore it is of high interest, if goal-
setting leads to changes in the development of task
performance over time when performing repeated tasks due to
occurring learning effects. To examine the described
relationship, hypothesis 2 is as follows:
H2: Goal-setting intensifies learning effects and leads
therefore to additional task performance improvement
when doing repeated tasks.
3. Method
3.1. Experimental setting
Between July and August 2013 the presented experiment
was conducted at the Training Factory for Energy
Productivity at the Technische Universität München, Munich,
Germany. Since the production line of the training factory
used as the setting for this experiment is quite similar to real
industrial production, this experiment combines elements of
economic laboratory and field experiments. Because

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Sven Asmus et al. / Procedia CIRP 26 ( 2015 ) 127 – 132
comparability of participants’ performance should be as high
as possible, we chose the final step of the whole production
chain as the setting for the experiment, where participants
were asked to assemble gearboxes. This process step ensured
the highest controllability of external parameters.
3.2. Participants
We invited 120 subjects, who were assigned to four
different treatment groups. More than 95% of the participants
were regular bachelor and master students of the two major
Munich universities with an average age of 24 years. To
ensure high comparability of the subject pools of the four
groups, the share of females was held equal between 20.00
and 23.33% in each group. Besides the set gender distribution,
participants were randomly assigned to the groups. With
about 92% of all subjects, the vast majority was right-handed.
Furthermore it was possible to adjust the used pneumatic
screwdriver for left or right-hand-use. Therefore no
distinction respectively special allocation to the groups based
on the handedness was made.
3.3. Task and goals
All participants were provided with the same workplace.
To assemble the gearboxes, the workplace was equipped with
a pneumatic screwdriver to screw bolts into the gearboxes (six
bolts per gearbox), 16 unassembled gearboxes, a large number
of bolts, a flow meter which displayed the accumulated
consumed compressed air, a pressure balancer to control the
pressure level of the pneumatic screwdriver, as well as a
digital torque wrench to check the torque level of the bolts in
the assembled gearboxes.
For evaluating participants’ performance, the number of
screwed bolts, the consumed compressed air, as well as the
average torque reach level of each assembled gearbox were
measured and noted down after each round. Based on the
treatment group participants were assigned to, they were
asked upfront to achieve certain goals as already described in
detail:
Group 1 (Control Group) - No goal.
Group 2 (Quantity Group) – Quantity maximization
Group 3 (Quality Group) – Quality maximization.
Group 4 (Energy Group) – Energy consumption minimization.
As it can be seen above, group 1 as the control group, had
no goal to achieve, while group 2, 3 and 4 each had one
defined goal. The goal of group 2 was the maximization of the
output, measured through the number of assembled gearboxes
and the thereby installed bolts. The goal of group 4 was the
minimization of the input factor compressed air while
performing the assembly task. The goal of group 3 was more
complicated to formulate for that experiment, because the
potential measures for quality in production are both manifold
and difficult to implement. Finally the quality goal was set as
reaching an average bolt torque of 2.0 to 2.5 Nm for every
assembled gearbox and its six bolts. This level was set for two
reasons: on the one hand side to avoid potential releasing of
the bolts after a certain operating time due to a too low bolt
torque, on the other hand side to avoid potential damages to
the bolts and gearboxes due to a too high bolt torque.
3.4. Procedure
As already described, the presented part of the experiment
consisted of four different experimental groups. As it can be
seen in figure 2, the sequence of the experiment was equal to
all participants.
Fig. 2. Sequence of the experi ment.
First of all a presentation and a video with a detailed
description of the task to perform, the workplace and the
available equipment had been shown. Subsequently a trial
round of five minutes started, in which participants should
become acquainted to the task. After that trial round again a
presentation was shown to familiarize participants even more
with the equipment available, to evaluate first own
experiences of the trial round and to clarify open questions.
Subsequently, the first round with a duration of five minutes
started, followed by a break of one minute. After that, the
same procedure as in round 1 started with the beginning of the
second round, followed again by a one minute break and
finally followed by the third and last round of the experiment.
While participants had the chance to use the breaks to relax,
the experimentator wrote down the results of all three goal
dimensions of the previous period on a scoring sheet.
After the final round all participants were provided with a
questionnaire asking for demographics and task-related
information. After finishing this questionnaire, participants
got paid their fixed compensation of 9€. Based on how fast
participants clicked through the presentations and filled out
the questions at the end, the total duration of the experiment
was between 40 and 55 minutes.
By showing presentations and videos to the participants
instead of letting the content be explained by the
experimentator, a highly standardized process for all
participants was secured, avoiding for instance influences
resulting from even unintentionally change of experimentator

130 Sven Asmus et al. / Procedia CIRP 26 ( 2015 ) 127 – 132
behavior from one participant to another and securing that
every participant got every information presented from the
same perspective and in the same sequence. The only
difference between group 1, having no goal and the other
three groups each having one goal defined, was that in the
presentations before every round, except of the trial round,
one slide explicitly mentioned the particular goal of that
group. No goal was mentioned to group 1, maximizing the
output to group 2, reaching the particular average bolt torque
for as many as possible assembled gearboxes to group 3 and
minimizing the used compressed air per assembled gearbox
respectively per bolt to group 4.
4. Results
4.1. The influence of goal-setting on task performance
For testing the first hypothesis about the general influence
of goal-setting on task performance, three two-sample t-tests
with equal variances had been conducted to test for significant
differences of the performance outcomes between the groups.
In the following, the results regarding the three sub-
hypotheses of H1 will be described and visualized for the
corresponding treatment group compared to the control group
(group 1).
In case of output quantity as the first goal dimension, the
results of group 1 are compared to group 2, as the group with
the goal of assembling as many gearboxes as possible in the
given time. With t(58) = -3.1862, p < .01, group 2, as the one
with the quantity objective, shows a significantly higher
performance than group 1 with no objective. Figure 3
visualizes the total number of bolts subsumed over all three
rounds comparing both groups. While the participants of
group 1 reached on average a total number of 88.6 bolts,
group 2 generated with 101.1 bolts more than 15% more
output in the three rounds.
Fig. 3. Comparison of the means of the total number of bolts over three
rounds for group 1 and 2.
Comparing the influence of goal-setting on task
performance regarding output quality, group 1 with no
objective and group 3 as the one with the quality objective are
taken into account. With t(58) = -0.5001, p > .05 no
significant difference between the groups does exist. With
about 24.1% target achievement, the group with the quality
objective reached a slightly higher share compared to the
control group with 21.5%, but not on a significant level.
To check whether or not goal-setting has a significant
influence on the participants’ performance regarding the
minimization of used compressed air for executing the task,
the consumed compressed air per bolt is compared for group 1
and group 4. With t(58) = 5.9636, p < .001 the difference is
highly significant. Group 4, as the group with the energy
saving objective, consumed with 6.23 liter per bolt
significantly less than group 1 which used on average 7.00
liter compressed air per bolt. The graphical results can be seen
in figure 4, where the average consumed compressed air level
per bolt over three rounds for group 1 and group 4 are shown.
Fig. 4. Comparison of the means of consu med compressed air per bolt over
three rounds for group 1 and 4.
Summarizing the results of hypothesis 1 and its sub-
hypothesis, goal-setting in the given industrial production
setting leads to a higher performance when subsuming the 3
experimental rounds. This counts for all three one-objective
groups compared to the control group. Regarding the
objectives output quantity and energy consumption, the
differences are very, respectively highly significant, while for
the quality goal the difference is not significant.
4.2. Goal-setting as an intensifier of learning effects when
performing repeated tasks
We now examine if goal-setting intensifies learning effects
and if therefore the change in task performance over the three
rounds is different if a goal is given or not. For that reason, we
ran panel regression analyses analyzing the performance
change of the control group and the respective comparison
group for every goal dimension.
First of all the change in produced quantity over the three
experimental rounds had been compared between group 1 as
the control group and group 2 as the group with the goal to
maximize the output quantity. With F(1,59) = 7.71, p < .01,
the F-statistics of the panel regression analysis shows that the
control group increased the output quantity from round to

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round significantly, with a coefficient of 1.42. With F(1,59) =
49.91, p < .001 group 2 increased the quantity as the
considered performance indicator significantly too, having a
much higher coefficient of 2.42. These results are visualized
on the left part of figure 5 where the rounds are displayed on
the x-axis while the number of bolts is shown on the y-axis. It
becomes clear that regarding the quantity, goal-setting works
as an intensifier of learning effects. Additionally is has to be
mentioned that the model’s validity for group 2 is with R² =
.4583 much higher than for group 1 with R² = .1156.
Fig. 5. Quantity and quality development over the three rounds comparing
group 1 and group 2 for quantity and group 1 and group 3 for quality.
Figure 5 shows on the right hand side the development of
the quality performance comparing group 1 and group 3. The
rounds are again displayed on the x-axis and the target
achievement in percent on the y-axis. It becomes clear that the
control group had a less steep learning curve from round 1 to
round 2 compared to the objective group, but both groups
decreased in their performance from round 2 to round 3.
Fig. 6. Energy consumption development over the three rounds comparing
group 1 and group 4.
Taking a closer look on the performance development of
group 1 and 4 related to the energy consumption, group 4 as
the group with the objective to use as little compressed air as
possible started in round 1 on a much lower consumption
level compared to the group 1 and had stronger learning
effects from round 1 to round 2 and from round 2 to round 3.
Nonetheless the learning effects of both groups are not
significant as it is illustrated in figure 6. This figure displays
the results of the panel regression with the rounds on the x-
axes and the energy consumption per bolt on the y-axes.
To prove whether or not goal-setting leads to higher
performance due to enhanced learning effects for the
objectives quality and energy too, panel regression analysis
for group 1 and the objective groups had been performed.
Contrary to output quantity, no significant improvements
based on learning effects over the three rounds did occur,
neither for output quality nor for the energy consumption.
Furthermore the validity of the models had been comparable
low with R² = .0156 for the panel regression of group 1
regarding quality and R² = .0161 for group 3 regarding quality
and R² = .0146 for the panel regression of group 1 related to
energy and R² = .0535 for group 4.
5. Discussion
This experiment aimed to show the effect of goal-setting
on human behavior. While this was already examined
extensively in several experimental studies [13,15] mostly for
output quantity as the measure of interest, other goal
dimensions were not included extensively. Analyzing the
results of the current study it becomes clear that our
hypothesis suggesting that goal-setting improves task
performance can be fully supported for the goal dimension
output quantity. With a significant higher output for the
quantity-objective group compared to the control group, the
results are in accordance with the existing literature
[22,23,24].
Regarding the goal dimension output quality, the results of
the experiment go into the same direction. Even the
performance differences between the control group and the
quality-objective group are not significant, the objective
group outperformed the control group by a 12% higher target
achievement seen in relative numbers. Even though the target
achievements are with 21.5% for the control group and 24.1%
for the objective group comparable low, the relative
performance difference between both groups is not negligible.
Of eminently importance, not only for the field of
sustainable manufacturing, are the findings regarding the
energy consumption goal. Comparing the results of group 1 as
the control group and group 4 having the objective to use as
less compressed air as possible to assemble the gear boxes,
the objective group used with 6.23 liter compressed air per
bolt significantly less than the control group with 7.00 liter
per bolt. The results for all three different observed goal
dimensions show that the goal-setting theory based on Latham
& Locke [10,11,12] is also applicable to other goal
dimensions than output quantity and works as well in an
industrial production setting.
Analyzing the results of hypothesis 2 that goal-setting
works as an intensifier of learning effects, a somewhat similar
statement can be derived for all three goal dimensions. For the
objective groups the learning curves of the participants appear
steeper compared to the control group. While the performance

132 Sven Asmus et al. / Procedia CIRP 26 ( 2015 ) 127 – 132
difference regarding quantity is significant comparing the
control group and the quantity-objective group, the
differences in the learning curves of energy consumption have
been demonstrated clearly in a figure, but are not statistically
significant. The same counts for the quality goal. While
control group and quality-objective group both improved their
level of task achievement from round 1 to round 2, their
performance dropped down from round 2 to round 3. Even the
performance increase to the second round was larger and the
decrease in performance from the second to the third round
smaller for the quality-objective group compared to the
control group, the differences are not statistically significant.
For all three goal dimensions it can be concluded that goal-
setting works as an intensifier of learning effects. The
strongest reinforcement has been measured for the quantity
goal.
6. Conclusion
First, this experiment aimed to assess if goal-setting theory
is applicable to other goal dimensions than output quantity.
Therefore output quality and energy consumption as further
relevant goal dimensions where integrated into the
experiment. Second, the experiment took place in a real
industrial production setting to figure out, if positive effects of
goal-setting are reproducible outside conventional
experimental laboratories. From the results we can conclude
that setting goals is one promising way to improve workers’
performance in industrial workplaces and that goal-setting
theory is of high relevance even far beyond conventional set
goals, being deployable to output quality and environmental
objectives, too. Due to scarce resources and increasing market
prices for energy, these results are of high relevance not only
for producing companies but more globally seen for the well-
being of our society. If the effects of goal-setting either on
traditional or currently emerging goals can be intensified by
incentives, has to be assessed in future research.
A further aim of the present study was about the
interaction of goal-setting and learning effects and its impact
on task performance. The results for all three goal dimensions,
output quantity, output quality and energy consumption
clearly indicate that goal-setting works as an intensifier of
learning effects. This counts especially for output quantity,
since the differences between control group and objective
group have been statistically significant. Future research
should further estimate the sustainability of goal-setting as an
intensifier of learning effects by conducting experimental
studies with extended task repetition.
The results furthermore show that goal-setting had
different impacts on the examined goal dimensions output
quantity, output quality and energy consumption. The reasons
for that can be manifold, e.g. related to the specificity of a
goal, the way a goal is framed or socio-demographic
characteristics of the people who are confronted with certain
goals. Even though it became clear that different goal
dimensions should not be treated equally, this field has to
undergo further research.
For the purpose of that study, the goals have not been
specified further than maximizing the output quantity,
reaching a certain quality level for as many as possible
assembled goods and using as little energy as possible to
perform the task. Specified goal level for every of the
dimensions should allow further statements about the concrete
impact of goal-setting.
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