iBusiness, 2013, 5, 55-58
http: //dx.doi.org/10.4236/ib.201 3.53B012 Published Online September 2013 (http://www.scirp.org/journal/ib) 55
A System Dynamics Model for the Evaluation of the
Productivity of Knowledge-intensive Services
Alexander Rannacher1, Robert Stranzenbach1, Flavius Sturm2, Susanne Mütze-Niewöhner1,
Christopher M. Schlick1
1RWTH Aachen University, Institute of Industrial Engineering and Ergonomics, Aachen, Germany; 2University of St uttga rt, Inst itute
for Human Factors and Technology Management, Stuttgart, Germany.
Received June, 2013
ABSTRACT
This paper presents an approach to develop a system dynamics model for the evaluation of the productivity of knowl-
edge-intensive services. This model is based on the results of a case study as well as on literature research. At first, this
paper gives a short introduction about knowledge-intensive services and the system dynamics method. The identified
variables as well as their causal relationship s will b e presented in the following sections. A recapitulation of the findings
and a prospect on further research conclude the paper.
Keywords: System Dynamics; Productivity; Knowledge-intensive Services
1. Introduction
For manufacturing companies it ha s become a priority to
offer a range of services along with their tangible prod-
ucts. In a growing number of industries services account
for a significant share of turnover and often generate a
higher margin. These services can often be considered as
knowledge-intensive, as they require frequent interaction
with the customer and the processing of a large amount
of information and knowledge (e.g. for consulting ser-
vices). Due to their complexity and the high level of
customization, these services are difficult to standardize,
and consequently, it is somewhat difficult to evaluate
their productivity.
In accordance with Djellal and Gallouj, the authors
claim that determining productivity requires a flexible
evaluation system that integ rates multiple criteria instead
of using a single productivity ratio [1]. To develop such
an evaluation system, it is necessary to identify the es-
sential variables affecting productivity as well as to find
out how these variables in teract with each other. As there
are certainly many variables that impact service provi-
sion, the illustration of the interactions would be of im-
mense value for both academics and practitioners. In
2012 the authors investigated the case of the implement-
tation of a performance measurement model in the ‘2nd
level technical support unit’ of a multinational company
[2]. This technical support service can be considered as
rather knowledge-intensive, as it solves rather complex
problems that occur at the customer’s site which cannot
be solved by the customers themselves or the field ser-
vice of the company. Based on the results of the case
study supplemented by a literature research the authors
set up a company-specific system dynamics model [2].
Grounding on this specific model, additional studies
and further literature research, the authors have devel-
oped a general system dynamics model for the evaluation
of the productivity of knowledge-intensive services. This
general model includes variables of the company-specific
model. Other variables had to be universalized and ex-
tended to transfer them. This model will be presented in
the following.
2. Developing a System Dynamics Model
2.1. System Dynamics
System dynamics models are used to depict and analyze
dynamic systems. These models were originally devel-
oped at the Massachusetts Institute of Technology in the
1950s and published in the article ‘Industrial Dynamics:
A Major Breakthrough for Decision Makers’ by Jay W.
Forrester in 1958. He analyzed relationships and proc-
esses in industry [3] and in cities [4]. In the meantime,
system dynamics models have been built and applied in
many disciplines and in many contexts.
Dependencies in a dynamic system can be described
by using single or multiple feedback loops. A feedback
loop is a fixed path, which connects the decision that
controls an action, the condition of the system and all

A System Dynamics Model for the Evaluation of the Productivity of Knowledge-intensive Services
56
information regarding to the condition, being reported at
the decision point [5]. Therefore, system dynamics mod-
els are suitable to identify th e effects of a single action in
the course of a process, allowing the detection of delays
within processes, caused by a badly designed flow of
information. Therefore, a reshaping of the information
flows is possible [6].
To depict feedback loops, so-called Causal Loop Dia-
grams are used to describe the causal dependencies be-
tween system elements, as well as the polarity of the de-
pendencies. The polarity of the feedback loop may be
either reinforcing (positive polarity) or balancing (nega-
tive polarity). Hence nonlinear feedback systems can be
generated. The systematic variation of information rela-
tionships, interaction loops and dependencies in system
dynamics models help to clarify the performance of a
system in its entirety.
2.2. Structure of the System Dynamics Model
The system dynamics model presented in Figure 1 shows
all variables influencing the productivity of knowledge-
intensive services. Con nections between variables d epict,
which ones are influencing other variables. The spear-
head indicates the direction of the influence. In addition,
the spearhead shows the kind of influence as a ‘+’ for
proportional influence and a ‘–’ for inversely propor-
tional influence. The evaluation, whether the influence is
proportional or inversely proportional, requires the sim-
plified assumption that all other variables stay constant,
even though this is actually impossible due to the feed-
back. The dotted variables are input variables. These
variables are not influenced by any other variables. The
blank variables are the so called state variables. All state
variab les combin ed are ch aracterizin g the curren t state of
the complete system. The presented system dynamics
model can be divided into four feedback loops. The nota-
tion of these loops is defined in the legend of Figure 1.
2.3. Variables of the System Dynamics Model
The service capacity is an existential potential of a ser-
vice company to be able to provide their services. In-
creasing the two input variables number of employees
and average working time causes a positive service ca-
pacity. Knowledge management (i.e. the creation, sharing,
capturing and distribution of knowledge) is one of the
main input variables on the productivity of knowl-
edge-intensive services. The most important resource for
these services is, of course, knowledge [7]. Providing
more knowledge management increases the level of
qualification of each employee [8] and effectively re-
duces service capacity [9] at the same time. Because
knowledge management is not influenced by any other
variable, it can be seen as an input variable for the ser-
vice provider. The number of trainings is another sig-
nificant input variable, which can be influenced by the
service provider. On the one hand trainings reduce ser-
vice capacity, but on the other hand they increase the
qualification of the employees. Knowledge-intensive
employees should frequently attend trainings to improve
their qualification [10,11]. So only a reasonable level of
qualification enables employees to perform their work
(working ability) sufficiently.
Another input variable which can be influenced by the
company is equipment. Equipment is obviously neces-
sary to provide any kind of service. If there is no func-
tional equipment, the employees need more time to per-
form their tasks properly. In addition, using the right
equipment increases the working ability of the employees
[12].
The autonomy of the employees is another input vari-
able. It signifies that employees are allowed to manage
their work by themselves (e.g. autonomous sequencing of
tasks). A higher degree of autonomy may have a positive
effect on their motivation [13]. Motivated employees
have a higher performance readiness and a lower ab-
sence [14]. Combining the performance readiness with
the working abilit y leads to the performance [10,15]. The
performance is the ability of a service provider to per-
form a service matching the customer’s requirements on
a specific level [16]. The performance contains the ability
of the service provider as well as the willingness to per-
form the service. The customer performance as an input
variable describes the p erformance or the activities that a
customer is supplying in order to achieve a specified ser-
vice outcome.
The performance on the one hand combined with the
service capacity on the other hand influence the service
rate in a proportional way. The service rate is the average
number of orders that can be served in a specific time.
The difference between service capacity and demand
for services is the so called quantity gap. Consequently,
the service provider always attempts to keep the qu antity
gap small. When there is not enough demand for the
given service capacity, a service is not profitable [17].
The bigger the quantity gap is, the more the workload of
the employees and the orders in process rise. The work-
load has a main influence on the employee satisfaction.
Additionally, more orders in process lead to more com-
pleted orders, which extend the experience. Queued or-
ders are those orders, which cannot be started immedi-
ately, because the demand is high er than the service rate.
Unfortunately, queued orders increase the waiting time
and therefore lower the perceived process quality.
In addition to the quantity gap there is also a quality
gap. Comparing the expected outcome quality with the
perceived outcome quality the customer will normally
have a mismatch. Secondary this gap is generated by the

A System Dynamics Model for the Evaluation of the Productivity of Knowledge-intensive Services
57
Figure 1. System dynamic s mode l for the evaluation of the produc tivity of know le dge -intensive services.
difference between the expected process quality and the
perceived process quality [18]. Mostly the customer’s
initial expectation on the quality is higher than the re-
ceived one. Because of this, there is a quality gap. When
the gap increases, the customer satisfaction is getting
worse. It is necessary for a company to have satisfied
customers, as customer retention will create new demand
for services in the long run. Also the exogenous demand
depending on the market share has an influence on the
demand for services.
A first step towards finding ways to deal with the
complexity of knowledg e-intensiv e serv ices is to un cov er
the main cause-effect relationships. This paper presented
an attempt to develop a general system dynamics model
for the evaluation of the productivity of knowledge-in-
tensive services. Due to the complexity and diversity of
knowledge-intensive services the presented model is just
qualitative. Nevertheless, it offers a good starting point to
select a well-balanced set of variables for measuring the
productivity of knowledge-intensive services. More stud-
ies are required to improve the quality of the model and
to generalize the findings.
To sum up, the system dynamics model, which is
shown in Figure 1, is not finalized, but it is a first step to
decompose the service provision process and to identify
the main drivers and barriers fo r service productivity. 4. Acknowledgements
This research has been carried out within the project
AESTIMO (03FL10066) in the research program “Inno-
vation with Services”, which is funded by the German
Federal Ministry of Education and Research (BMBF) and
supervised by the Project Management Agency of the
German Aerospace Center (PT-DLR). The authors would
3. Conclusions and Further Research
Measuring the productivity of knowledge-intensive ser-
vices is an intricate problem because the process and the
outcome of knowledge-intensive services are to a certain
degree unpredictable.

A System Dynamics Model for the Evaluation of the Productivity of Knowledge-intensive Services
58
like to express their gratitude for the support given.
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