The use of system dynamics to examine the
relationship amongst quality, value, price and
profitability
Gerard King Gus Geursen PhD
Faculty of business and Law School of Marketing
Deakin University University of South Australia
Geelong Adelaide
Australia, 3217 Australia
61 3 5227 2743 61 8 8302 0402
gerardk@deakin.edu.au gus.geursen@unisa.edu.au
Abstract
This paper argues that the positivist approach adopted by many studies into the
relationship between customer satisfaction and revenue is limited; the ontology
associated with positivism permits surface relationships only to be determined. What is
required is a method grounded in a more realistic ontology that allows for a deeper
investigation. The paper proposes that system dynamics is one such method. The
differences between the positivist approach and a systems approach are expounded,
and, though the positivist approach is not completely rejected (on the contrary, it is
defended), its limitations, particularly when applied to a social environment, are
apparent. In particular, the paper expands on the differences between studies in the
natural sciences and those in the social sciences. It is these contrasts that make
extremely suspect an effective translation of the methodology applied so successfully to
the natural sciences, across to the social environment, and which demands an
alternative methodology. This paper presents one such methodology and posits system
dynamics clearly within that methodology.
Introduction
The issue of managing customer satisfaction has been a core of marketing since Drucker
(1954) commented that firm revenue is driven by customer need satisfaction; and later
that ‘to satisfy the customer is the mission and purpose of every business.’ (Drucker
1973) Drucker’s comments link three customer issues; Firstly the immediate revenue
possibilities, secondly the longer term opportunities for revenue and thirdly the question
of the interrelationships required for these opportunities to be realised. These aspects are
critical if the linkage between revenue activity and profitability are to be realised. It is
the purpose of this paper to explore these issues.
The linkage between customer retention and profitability was demonstrated by
Reichheld (1996). In a study extending over a period of more than ten years, Reichheld
and his colleagues found that what distinguished the unusually successful companies
from their competitors was ‘a measurable advantage in customer and employee loyalty.’
Each company with outstanding loyalty also delivered superior value to its customers
and employees, and generated strong cash flows. Reichheld maintained that increasing
customer retention rates was a more effective way of improving profits than a strategy
focused on attracting new customers. Whilst this strategy may be questioned for large
markets where customers do not have direct links to the firm, for example fast moving
consumer goods (Sharp, Riebe et al 2002), its appropriateness is pertinent in business to
business and markets with small customer bases.
Development of greater loyalty, or greater customer retention rates, has been claimed to
have a significant positive effect on market share (Fornell and Wernerfelt 1988; Rust
and Zahorik 1993; McGahan and Ghemawat 1994), on firm profits (Dawkins and
Reichheld 1990; Reichheld and Sasser 1990; King and Rickard 1994; Rust, Zahorik et
al. 1995; Colgate, Stewart et al. 1996), and therefore to be a significant determinant of a
firm’s long-term financial performance (Jones and Sasser 1995). In summary, these
literatures argue that increasing customer satisfaction and customer retention leads to
lower marketing expenditure, positive word-of-mouth, and improved profits (Reichheld
1996; Heskett, Sasser et al. 1997).
A considerable amount of the research literature has focused on service quality as the
prime determinant of customer satisfaction (Oliver 1980; Bearden and Teel 1983;
LaBarabera and Mazursky 1983; Parasuraman, Berry et al. 1988; Anderson and
Sullivan 1993; Taylor and Baker 1994; Johnson 1995). Other possible determinants of
customer satisfaction have received less attention: for example, price (Ravald and
Gronroos 1996); value (Woodruff 1997; McDougall and Levesque 2000); and equity
(Hellier and Geursen 2003). Research effort has been mainly directed towards
identifying attributes of service quality that may be improved, resulting, it is hoped, in
greater customer satisfaction and firm revenue. The linkages suggested by the literature
may be depicted as in Figure 1, which provides a simple diagrammatic representation of
the postulated, and often researched, linkage from customer-satisfaction attributes to
firm revenue.
Figure 1: Linkage from customer satisfaction to firm profitability
Much of the literature follows the linear and positivist approach suggested by Figure 1.
In assuming linearity, it implicitly accepts the validity of the sequence suggested by the
questions raised earlier: certain attributes of satisfaction are manipulated to improve
customer satisfaction; and these improvements translate directly and immediately into
increases in customer retention and revenue. In adopting positivism, it implicitly
assumes that the resultant model provides a general law-like statement for the customer
satisfaction-revenue relationship. But the customer satisfaction/revenue relationship
exists within a social world, while positivism was originally applied to investigations of
the natural world. Thus, is it appropriate to investigate, for example, the customer
satisfaction/profitability linkage as if it was a natural science study? That is, is it
legitimate to investigate social objects in the same way as we would natural objects?
Research philosophy
The main premise of the method of science is that the world is characterised by
phenomena that are ordered and regular, and that ‘laws of nature’ can explain these
regularities. It is these laws of nature that the research study attempts to discover. In
seeking to explain and predict these phenomena, science constructs theories to account
for the regularities observed under experimental conditions of closure. The laws
explaining these regularities are then subjected to empirical testing, as observation of
experimental results is regarded as the sole source of scientific knowledge (empiricism).
The basis of the scientific method was thus established as: observation and
experimentation, deductive reasoning and, where possible, mathematical representation.
One last addition was required before the ‘scientific method’ was firmly established and
this came from an influential contribution by Rene Descartes (1596-1650). In his
‘Discourse on Method’, Descartes provided four rules for conducting research. His
second rule, which introduced the concept of ‘reductionism’, underpinned much of the
intellectual and scientific development over the next two centuries. In it he states
(Sutcliffe 1968):
The second was to divide each of the difficulties that I was examining into as
many parts as might be possible and necessary in order best to solve it.
In other words, by decomposing the whole into its component parts; explaining the
operation of each part; and viewing the operation of the whole as the sum of its parts,
the laws governing any entity, regardless how complex, may be derived.
We thus arrive at a method of science characterised by a trinity of reductionism,
repeatability and refutation, and one that still dominates much contemporary scientific
practice and pedagogy (Checkland 1999). Reducing complexity permits an investigator
to examine what Ree calls ‘simple natures’ (Ree 1974); results from the investigation
can be validated by the repeatability of the investigation; and, knowledge about the
particular area of interest is advanced through on-going process of refutation or
validation of hypotheses.
Social science is an attempt to explain social phenomena, that is, to find explanations or
theories of the social world, within the limits of available evidence (Lewins 1992). The
method of science, up until the creation of the new discipline ‘sociology’, was applied
to the natural world, but the founders of social science were in no doubt that it was
equally applicable to the social world. Comte’s (1798-1857) aim was to create a
naturalistic science of society, which would enable the study of society to follow the
same scientific approach as that of nature. Thus, the new science adopted the empirical
methods and epistemological underpinnings of the natural sciences, and sought, through
its scientific study to improve the society of the new industrial era.
This Naturalist approach accepts the unity of method between the natural and the social
sciences, adhering to positivist principles that centre around and are underpinned by the
Humean conjunction of empirical regularities with causality. An anti-naturalist view
posits that, because of the composition of society—a composition that includes humans,
human activities and relations—application of positivist principles to the study of the
social sciences is not merely inappropriate, but fundamentally incorrect. Rather, the
anté naturalist approach entails interpretation or elucidation of social events, using ideas
originating in classical hermeneutics (Outhwaite 1975).
In recent years, a critical realist philosophical approach to social enquiry, that provides a
common approach to the study of the natural and social sciences, has emerged,
invigorated by developments in the philosophy of science. While Roy Bhaskar has
been particularly influential in the development of this approach (Bhaskar 1978;
Bhaskar 1979), it has been applied in many fields: linguistics (Pateman 1987); feminism
(Assiter 1996); marketing (Hunt 1991); and economics (Lawson 1989), (see(Outhwaite
1987; Collier 1994).
In the experimental activity associated with the natural sciences, constant conjunctions
of events are produced under closed conditions and the laws deduced are then applied in
open world systems. There, as in social systems, the object of interest is not the
constant conjunction of events, but rather the generative mechanisms underlying the
deduced causal laws. In the social world, the unperceivability of the objects of interest
is not the impediment to knowledge of those objects. The main epistemological limit to
the application of the natural method to the social enquiry is the fact that, in the social
world, the object of interest can only be observed in an open system, where invariant
empirical regularities cannot be manifest.
In short, social systems cannot ever be experimentally closed. Thus, unlike in natural
systems, theories cannot be tested since a closed system is never available for such test
purposes. The import of this is that in social science, theories must be explanatory
rather than predictive (Bhaskar 1979)
In summary, the type of studies generally undertaken into customer satisfaction, while
useful in that they identify correlative relationships that are worthy of further study, do
not, and cannot meet the objectives set out earlier: that is, the development of a generic
model of customer satisfaction/revenue relationship that permits necessary adjustment
in satisfaction attributes to achieve maximum profit/revenue. Rather, what must be
recognised is that each situation needs to be investigated anew, guided by the results of
quantitative studies indicating likely factors that might be considered as influences on
the satisfaction-profitability relationship.
It has been argued that methodologies that rely solely on the manufacture of
experimental conditions of closure to identify constant conjunctions of events are
limited in their ability to produce realistic explanations about the social world.
Furthermore, such empirical realist approaches, depending as they do on human
observation of constant conjunctions and metal abstractions of those conjunctions, deny
the autonomous existence and operation of causal structures that may not be readily
apparent. Critical realism, on the other hand, regards the objects of knowledge as ‘the
structure and mechanisms that generate phenomena’ and these objects are ‘neither
phenomena (empiricism) nor human constructs imposed upon the phenomena
(idealism), but real things and structures which endure independently of our knowledge,
and the conditions which allow us access to them.’ (Bhaskar 1979). Critical realists,
recognising that reality exists independently of investigators and their perceptions of it,
attempt to become informed about the objects of knowledge through a layered ontology
comprising three domains:
1. the empirical, consisting of perceptions and experiences;
2. the actual, consisting of events and behaviour; and
3. the real, consisting of underlying structure and generative mechanisms.
In the first domain, knowledge is obtained only through perception, and since
perception will reveal only events and constant patterns, relationships, as causal powers,
can be obtained only through logical deduction. An example, associated with customer
satisfaction, might be: whenever the value associated with a service increases, customer
satisfaction increases. Now, most empiricists will move beyond this limited view of
reality to include the domain of the actual, and will admit to hidden casual mechanisms
that are revealed through the constant conjunctions of observable events. Such events,
however, are observed under specific condition of experimental closure, and knowledge
gained from this is inductively applied, as law-like statements, beyond the experimental
closure conditions to the outside world. Again, an example associated with customer
satisfaction might be: all other things being equal (the ceteris paribus assumption),
whenever value increases, customer satisfaction increases; or, for this specific group,
whenever value increases, customer satisfaction increases. In drawing such
conclusions, the investigator identifies events and empirical experience of those events
as the underlying mechanisms and structures. The domains of the empirical and actual
are ‘fused’ with the domain of the real. This two-domain approach usually begins with
the identification of some empirical phenomena, followed by the establishment of
conditions of experimental closure in order to develop an explanation which might
account for the phenomena. It is not necessary that the explanation or theory be
plausible, only that it is coherent and can predict future regularities. It is, in other
words, accepted on the basis of its instrumentality.
In the third or real domain, it is recognised that the constant conjunctions of events that
occur under conditions of experimental closure are not necessarily to be found in the
open world. Empirical experiences may be out of phase with actual events, which, in
turn, may be out of phase with underlying mechanisms and structures; mechanisms may
act in tandem or in opposition, or act transfactually across domains without causing
events. The power associated with mechanisms and structures, that may be exercised
without realising observed events, is referred to as tendencies by critical realists. The
attribution of tendencies with events and entities provides the realist’s statements of
law.
In summary, the empirical and actual domains provide what might be referred to as
‘surface’ accounts of observed phenomena. Underlying such accounts are the deeper
and more coherent explanations found in the domain of the real. The domains are said
to be stratified, in that surface accounts identified in the empirical provide a guide to the
investigation of underlying causal powers and mechanisms within the more complex
domains of the actual and the real. When one structure or mechanism is identified, it
becomes the entity to be explained.
In the social world, reality is constructed by people, interacting through associated
beliefs, values and language. Thus, to investigate a social world, for example an
organisation, is to investigate an entity that both socialises the participants and is
transformed by the participants. Constant conjunctions of events that might apply to
one organisation might not be evident in another, or, if evident, may be attributable to
other structures or mechanisms. From this viewpoint, therefore, no one model will suit
all organisations; each organisation has its own social world. Each organisation must be
investigated as a single entity. The empirical domain, the first layer of investigation,
still reveals regularities that then may be explained by human constructs. These
accounts then became the object of the next stage of the deepening investigation.
System dynamics allow for this ever-deepening process for advancing knowledge of the
objects being investigated. At first glance, SD may look like the ‘hard’ approach
associated with systems engineering, and may not appear to lend itself to a critical
realist investigation. Lane explores this criticism, and addresses each of four main
interpretations of the criticism from a perspective of social theory and systems science.
His conclusion is a firm no: SD is not necessarily a hard approach (Lane 2000). While
the system dynamics literature certainly states that a study must have a specific purpose,
the purpose is not an optimisation objective, as in OR; rather the purpose of the model is
to probe mental models associated with agreed problem situations with a view of
improving understanding of the system. Participants are involved with the process, and
the model must act not as a ‘coercive’ process, but as a ‘negotiative’ process (Eden and
Sims 1979). Through use of causal loop diagrams, participants in the SD investigation
can probe structures, identifying those that require immediate attention, while allowing
for the possibility of probing deeper into the structure at a later time. SD, by examining
causal structures and dynamic behaviour, both permits the dynamic response of systems
to be more readily viewed and enables various polices to be checked for, what Lane
(2000) calls, dynamic coherency.
System dynamics
System Dynamics (SD) was developed in the late 1950s at the Massachusetts Institute
of Technology’s Sloan School of Management by Jay Forrester (Forrester 1961),
initially as a way of explaining industrial behaviour. His method was then called
industrial dynamics. Later, the method was applied to social systems; for example,
Forrester (1971) developed a SD model of world dynamics, including world population,
global economy, natural resources and physical environment. The purpose of his model
was ‘to investigate effects of population and economic growth as human activity
approaches the carrying capacity of earth’ (Sterman 2000). Other examples of the
application of SD include: energy modelling (Meadows, Behrens III et al. 1974;
Sterman, Richardson et al. 1988); heroin imports to U.S. (Gardiner and Shreckengost
1987); community alcohol problems (Holder and Blose 1987); healthcare (Lane,
Monefeldt et al. 1999); organisational learning (Senge 1990) and quality improvement
(Sterman, Repenning et al. 1997).
A system approach to the relationships between customer
satisfaction and revenue
From the literature on customer satisfaction, the following model of the relationship
between attributes of customer satisfaction (quality, price, value) and revenue was
developed (see Figure 2).
Figure 2 Basic model relating attributes of customer satisfaction and revenue
The following simulations were developed as part of a consultancy with a smallish real
estate firm. The firm was quite successful, but felt that performance could be improved,
without having any specific changes in mind. This case was chosen as being suitable
for a customer satisfaction investigation (relating satisfaction to revenue), as staff at the
firm were intuitively aware of the need to satisfy customers, in order to further enhance
the good reputation of the firm; to initiate a steady stream of revenue; and to develop
additional clients through referrals from satisfied customers. That this was so was
clearly evident from discussions in the later workshops. The simulations below were
run to demonstrate the difference between taking a positivist approach and a systems
approach. Later, a more pertinent simulation was developed for their real estate
environment, but this is not reported here.
Simulation 1: the basic simulation
The basic model of Figure 2 is derived from the model outlined in Figure 1. Customer
satisfaction is directly and linearly related to customer retention, which in turn is
directly and linearly related to revenue. New customers come on board at a steady rate
per time period. The simulation tracks the total number of customers and the net value
of a single customer (see Figure 3).
Sustomer
Figure 3 Basic simulation of the customer satisfaction/revenue model
Figure 4 displays the dynamics of Total customers and Revenue per year, for a retention
rate (and customer satisfaction) of 0.7. The steady state Total customers equal 333 and
the steady state Revenue per year is 4333.
&® +: Revenue per year 2: Total customers
As 5000.00:
— 2 2
2 400.00
ns
200.00
A: 5000.00
2 0.004
0.00 15.00 30.00 45.00 60.00
ge2F Graph 3 (Untitled) Years 8:54PM Thu, 30 Sep 2004
Figure 4 Dynamics of Total customers and Revenue per year from simulation 1
If customer satisfaction (and hence customer retention) are increased, the total number
of customers and revenue increase linearly. This is the response assumed by much of
the customer satisfaction research.
Simulation 2: simulation 1 plus disconfirmation as a driver of customer
Satisfaction
The second simulation includes Oliver’s (1977) expectancy-disconfirmation paradigm,
wherein customer satisfaction is driven by disconfirmation and perceived quality.
Disconfirmation is taken here as the difference between perceived quality and
expectations; expectations lags perceived quality (implying that customer expectations
are based on previous perception of quality); and, perceived quality lags actual quality —
customers need some time to track an increase or decrease in quality (they track a
decrease in quality quicker than an increase.); actual quality is constant. Figure 5
displays the simulation.
Net Present Value
Initial cost
Total revenue Profit per customer
Revellye per year
Discount rate
Customer satisfaction
New customers Defecting customers
Total customers Nominal CS
Change in CS
chg in pevd quality 2__ Perceived Quality &
Disconfirmation
Disconfirmed CS
Perceived Quality
S chg in Boud expectations \ Expectations
delay in adjusting perceived quality
Simulation 2 - Simulation 4
with Disconfirmation
driving customer
satisfaction.
Actual quality is constant.
Expectations tracks
perceived quality with
delay.
‘Actual quality
delay in adjusting perceptions \
Figure 5 Simulation 2: simulation 1 plus disconfirmation
Expectations can initially be less than or greater than perceived quality, but will adjust
to the perceived quality value over time. Customer satisfaction reflects the value of
perceived quality, but positive disconfirmation will increase customer satisfaction and
negative disconfirmation will decrease customer satisfaction. Also, negative
disconfirmation will decrease customer satisfaction more than the increase from the
equivalent value of positive disconfirmation.
If actual quality, perceived quality and expectation are all set equal to 70, customer
satisfaction assumes a value of 70 and a dynamic response equal to that in simulation 1
would be obtained. What is more likely is that expectations have been either raised or
decreased in response to perceived quality and actual quality.
Case 1
Actual quality = 85; Perceived quality = 70 and Expectation = 70.
Customer satisfaction has an initial value of 70 reflecting the initial value of perceived
quality (70); the initial retention rate is 0.7. Figure 6 displays the dynamic output.
Perceived quality and expectations gradually adjust to match actual quality; that is,
although people initially have lower expectations, their expectations are gradually
increased by their perceptions of the actual quality. Figure 6 displays the constant value
of actual quality; shows perceived quality tracking actual quality, and expectations
tracking the value of perceived quality. Also customer satisfaction tracks perceived
quality, for a final value of 85, but is increased by another 5 points to 90 as a result of
the initial positive disconfirmation. The defection rate decreases from its initial value of
0.3 to a final value of 0.1, mirroring the increase in customer satisfaction.
&® +: sctual quality 2: Perceived Quality 3: Expectations 4:Defection rate 5: Customer satis.
1 10.00%
a
3
4 0.50
5 100.00
4 eee ra
osens
1
gheons
g
g
8
0.00 16.00 30.00 45.00 60.00
aaF* Graph 1 (Untitled) Years 8:21AM Fri, 1 Oct 2004
Figure 6 Simulation 2 output for initial actual quality greater than expectations
What happens to customer numbers and revenue? Figure 7 below displays their
responses.
® 1: Total customers 2: Revenue per month
‘ 1500.00™
2 40000.00
é 750.00, La
2 17500.00'
1 0.00
2 -5000.00
0.00 15.00 30.00 45.00 60.00
a BF Graph 2 (Untitled) Years 8:21AM Fri, 1 Oct 2004
Figure 7 Simulation 2 outputs for Total customers and Revenue per year with initial
actual quality greater than expectations
The steady state Total customers are just over 1000 and the steady state revenue per
year is about 32500. The steady state values for Total customers and revenue per
year are consistent with the result from simulation 1 with a value of just over 90 for
customer satisfaction.
This simulation allows examination of various changes in actual quality: a steady
increase over time; or, a sudden increase at a particular time.
Case 2
Actual quality = 60; Perceived quality = 70; and Expectation = 70.
Customer satisfaction has an initial value of 70, reflecting the value of perceived
quality. The retention rate has an initial value of 0.7. Figure 8 displays the response for
actual quality, perceived quality, expectations, customer satisfaction and retention rate.
Expectations gradually adjust to match actual quality; that is, although people initially
have higher expectations, their expectations are gradually decreased by their perceptions
of the actual quality. The graph below indicates this. The final customer satisfaction
value is about 56.6 reflecting both the final perceived quality of 65 and the initial
negative disconfirmation.
B® +: pctuat quatty 2: Perceived Quality 3: Expectations 4: Defection rate 5: Customer satis.
1 70.00%
2
3:
4 0.50
5 70.00 a—|
1
2 60.00 =
5: ———
4 0.25
5: 60.00
1
2
3 50.00
4 0.00
5 50.00
0.00 15.00 30.00 45.00 60.00
aeaF Graph 4 (Untitled) Years 6:25PM Thu, 30 Sep 2004
Figure 8 Simulation 2 outputs for initial actual quality less than expectations
Total customers and revenue per year are shown in the Figure 9.
B® 1: Total customers 2: Revenue per month
1 1500.00
2: 4000.00
1 750.00 J
2: 17500.00
7
0.00
2: -5000.00:
0.00 15.00 30.00 45.00 60.00
3 BF Graph 2 (Untitled) Years 6:25PM Thu, 30 Sep 2004
Figure 9 Simulation 2 outputs for Total customer and Revenue per year with initial
actual quality less than expectations
The steady state total customers are now about 230 and the steady state Revenue per
year is about 207.
While the results from simulation Case | and Case 2 are consistent with results from
simulation 1 with customer satisfaction values equal to the final value of customer
satisfaction for Case 1 and Case 2, these cases differ from the simulation 1 in their
transient response; that is, in the values for the years leading to the steady state
condition. The table below shows the growth of Total customers and Revenue per year
for the first ten years for the basic simulation and for simulation 2, Case 2 (the same
applies to simulation 2, Case 1).
Table 5.2 Total customers and Revenue per year
Simulation 3: simulation 2 with a quality improvement program included
Simulation 3 is the same as simulation 2 with a quality improvement program included.
The quality improvement program is driven by revenue directed to it, but with
diminishing returns. The simulation is now as shown in Figure 10.
Net Present Value
Graph 1 Graph 2 Graph
Discount rate
Customer satisfaction
New customer:
‘otal customers
Detecting customers Change in Nominal CS
cchg in pevd quality Perceived Quality _Disconfirmgfion
Perceived Quality
cchg in expectat
Expectations
ting perceived quali
eng Nectual qualibelay in adjusting expectations
Actual qual
Simulation 3 - simulation 2 with actual quality
driven by a quality improvement prodram
dependent upon revenue.
quality improvemfat
delay in adjusting actual quality
Revenue per year
revenue to quality
quality increase 2
Figure 10 Simulation 3: simulation 2 plus a quality improvement program
The quality improvement program is funded by a constant proportion of revenue per
year for five years. Initial values are: actual quality = 65; perceived quality = 60;
expectations = 80; quality improvement =65 (quality program just starting).
Because of the initial low value of perceived quality, customer satisfaction starts off at a
value of 60. Expectations are initially high (having been raised, say, by marketing
associated with the latest release), but they quickly drop, as the actual quality is
perceived to be much lower than expectations. Immediately, cash begins to flow into
the quality improvement program, increasing the actual quality, and, after a short delay,
the perceived quality. As perceived quality increases, expectations again increase. The
retention rate is initially only 60% but increases to a steady state value of about 78% as
perceived quality and positive disconfirmation both apply. Customer satisfaction is
increased from the initial value of 60 to a final value of 78. Figure 11 displays the
output for actual quality, perceived quality, expectations and customer satisfaction.
&® +: expectations 2: Perceived Quality 3: Actual quality 4: Customer satisfaction
1 80.00%
2:
3:
4
1
2
‘ 65.00.
4
1 I
2
3:
4 50.00.
0.00 25.00 50.00 75.00 100.00
aeaF Graph 2 (Untitled) Years 8:29 AM Fri, 1 Oct 2004
Figure 11 Simulation 3 outputs for actual and perceived quality, expectations and
satisfaction
The graph of Total customers and Revenue per year is displayed in Figure 12.
&® 1: Total customers 2: Revenue per year
1 500.00%
2 410000.00
Le
1 250.00 |
0.00
1 0.00
2: -10000:00.
0.00 25.00 50.00 75.00 100.00
aeaF Graph 4 (Untitled) Years 8:29 AM Fri, 1 Oct 2004
Figure 12 Simulation 3 output for Total customers and Revenue per year
The final value of Total customers is about 440, consistent with a value of 78 for
customer satisfaction.
Simulation 4: simulation 3 plus value and ability to satisfy
Simulation 4 expands on simulation 3 by including two additional factors influencing
customer satisfaction:
e The first is a measurement of value, included as the difference between price
disutility and quality utility. Price disutility is a non-linear function of price, and
quality utility is a non-linear function of quality;
e The second is a measure of the firm’s ability to continue to satisfy customers as
customer number increase.
The simulation is now as displayed in Figure 13.
Disconfirmation Profit per customer
Price Disutilty
chg in pevd quali
Expectations
PD minus QU
dklay in g@justing perceived quality
Quality utility
delay in adjusting expectations
Perceived Quality
SS
ary nner \ |
delay in adjusting actual quality
Simulation 5- Simualtion 3 plus a
Value driver for CS. Value
depends on perceived quality
which lags actual value.
quality increase
Revenue per year
Figure 13 Simulation 4: simulation 3 plus a value variable and an ability to satisfy
variable
The initial values are the same as in simulation 3: actual quality = 65; perceived quality
= 60; expectation = 80. Again, it is assumed that expectations have been raised by the
firm in their public announcements. The usual graph of quality, expectations and
customer satisfaction is shown in Figure 14.
a 1: Actual quality 2: Perceived Quality 3: Expectations 4: Customer satisfaction
80.00%
ONS
s
2 si)
2 1
- 65.0044
4
—
ae eet
1
2
3:
4 50.00;
0.00 10.00 20.00 30.00 40.00
aeaF Graph 4 (Untitled) Years 9:17 AM. Fri, 1 Oct 2004
Figure 14 Simulation 4 outputs for actual and perceived quality, expectations and
satisfaction.
Actual quality, and in turn, perceived quality, are increased through the quality
improvement program. Expectations, initially high, rapidly fall to match the perceived
quality, but then increase, tracking the increase in perceived quality. The final value of
actual quality and expectations is about 73, as in simulation 3. Customer satisfaction
decreases after an early increase, due to the increased influence of the ‘ability to satisfy’
variable, but as the increase in quality takes effect, customer satisfaction increases,
though only to a value of 62, compared with the value of 78 in simulation 3. Total
customers and revenue per year are shown in Figure 15.
9 1: Total customers 2: Revenue per year
1 300.00%
z 2000.00
we
1 150.00}
2: -1500:00
0.00
2 -5000:00
10.00 20.00 30.00 40.00
3 2. Graph 2 (Untitled) Years 9:32 AM Fri, 1 Oct 2004
Figure 15 Simulation 4 outputs for Total customers and Revenue per year
Now, consider the following scenario. As the quality improvement program takes
effect, thereby increasing actual and perceived quality, management makes the decision
to increase their profit margin to compensate for the fall in revenue per year after year 4.
Previously, the profit margin was set at $40 per year. Now, it is increased to $60.
Quality improves as before, but customer satisfaction goes into a decline after year 12,
rather than increase as before, as shown in Figure 16.
B® + actual quaiiy 2: Perceived Quality 3: Expectations 4: Customer satisfaction
1 80.00%
Z
3:
4
bee |
2 _-
2: Li /
- 65.004
4
1
2:
3:
4 50.00 : =
0.00 10.00 20.00 30.00 40.00
aeaF Graph 4 (Untitled) Years 9:55 AM Fri, 1 Oct 2004
Figure 16 Simulation 4 outputs for actual and perceived quality, expectations and
satisfaction with profit margin increase after t=4
Total customers and revenue per year are shown in Figure 17.
9 1: Tota! customers 2: Revenue per year
1 300.004
z 4000.00
SS
i"
4 — SSS
1 150.00}
2 -500.00
1 0.00
2 5000.00:
0.00 10.00 20.00 30.00 40.00
aeaF Graph 2 (Untitled) Years 9:55 AM Fri, 1 Oct 2004
Figure 17 Simulation 4 outputs for Total customer and Revenue per year with a
profit margin increase in year 5
Revenue per year initially increases following the profit margin increase, but after a
period of time it begins to decrease again. The values of total customers and revenue
per year after 20 time periods are shown in Table 3.
Table 3 Total customers and revenue per year for profit margins of $40 and $60
These simulations demonstrate clearly the effect of interactions among variables. The
interconnectivities, the nonlinearities and the delays make it impossible for humans to
even attempt to estimate the dynamics of such complex structures. SD permits not only
the viewing of the dynamics, but also the examination of possible strategies on the
operation of the overall system. It allows for the ‘digging deeper’, consistent with the
critical realism methodology. Simulation 4, in particular, reveals the counterintuitive
behaviour associated with improvement in actual service quality. The positivist
approach suggests that this will result in improved customer satisfaction, customer
retention and revenue. Contrary to this projection, customer satisfaction decreases, as
does revenue.
Conclusions
The simulations in this paper demonstrate that the relationships amongst quality, value,
price and revenue (especially net revenue which is profit) are complex and need to be
modelled as such. Here, we have underpinned the need for a systems approach by
examining methodological issues; argued that a social environment requires a
methodology that permits deeper examination than that offered by the natural sciences
method; briefly described one such methodology (critical realism); and, finally, applied
system dynamics to the customer satisfaction/revenue relationship. The ability to
examine the behaviour and impacts of such links has to the best of our knowledge not
been demonstrated using other research means. We argue that this paper indicates that
system modelling can become a fundamental tool useful for the examination and
modelling of these relationships.
References
Anderson, E. W. and M. W. Sullivan (1993). "The Antecedents and Consequences of
Customer Satisfaction For Firms." Marketing Science Vol 12(No. 2): 125-43.
Assiter, A. (1996). Enlightened women. London, Routledge.
Bearden, W. O. and J. Teel (1983). "Selected Determinants of Consumer Satisfaction
and Complaint Reports."
Bhaskar, R. (1978). A Realist Theorey of Science. Brighton, Harverster Press.
Bhaskar, R. (1979). The Possibility of Naturalism. Brighton, Sussex, The Harvester
Press Ltd.
Checkland, P. (1999). Soft Systems Methodology:a 30-year retrospective. Chichester,
W.Sussex, John Wiley.
Colgate, M., K. Stewart, et al. (1996). "Customer Defection: A Study of the Student
Market in Ireland." International Journal of Bank Marketing Vol. 14(No. 3): 23-
29.
Collier, A. (1994). Critical Realisim: An Introduction to Roy Bhasker's Philosophy.
London, Verso.
Dawkins, P. and F. Reichheld (1990). "Customer Retention as a Competitive Weapon."
Directors & Board(Summer): 42-7.
Drucker, P. F. (1954). The Practice of Management. New York, Harper.
Drucker, P. F. (1973). Management. New York, Harper & Row.
Eden, C. and D. Sims (1979). "On the nature of problems in consulting practice."
Omega 7: 119-27.
Fornell, C. and B. Wernerfelt (1988). "A Model for Customer Complaint Management."
Marketing Science 7(No. 3): pp.287-298.
Forrester, J. W. (1961). Industrial Dynamics. Cambridge, MA., MIT Press.
Forrester, J. W. (1971). World Dynamics. Waltham, MA., Pegasus Communications.
Gardiner, L. and R. Shreckengost (1987). "A system dynamics model for estimating
heroin imports into the United States." System Dynamics Review 3(1): 8-27.
Hellier, P. K. and G. M. Geursen (2003). "Customer Repeat Purchase Intentions: A
General Structural Equations Model." Euopean Journal of Marketing 37(N0.11-
12): 1762-1800.
Heskett, J. L., W. E. J. Sasser, et al. (1997). The Service-Profit Chain. New York, NY,
Free Press.
Holder, H. and J. Blose (1987). "Reduction of community alcohol problems: Computer
simulation experiments in three counties." Journal of Studies on Alcohol 48(2):
124-35.
Hunt, S. (1991). Modern Marketing Theory: Critical Issues in the Philosophy of
Marketing Science. Cincinnati, South-West Publishing Company.
Johnson, R. (1995). "The Zone of Tolerance: Exploring the Relationship Between
Service Transactions and Satisfaction with Overall Service." International
Journal of Service Industry Management 6(2): 46-61.
Jones, T. O. and W. E. Sasser (1995). "Why satisfied customer defect." Harvard
Business Review 73(November-December): 88-99.
King, G. and J. Rickard (1994). The Impact of Customer Retention on Firm
Performance. Implementation Strategies For TQM, Queensland University of
Technology, QUT.
LaBarabera, P., A. and D. Mazursky (1983). "A Longitudinal Assessment of Consumer
Satisfaction/Dissatisfaction: The Dynamic Aspect of the Cognitive Process."
Journal of Marketing Research Vol. XX(November): 393-404.
Lane, D. C. (2000). "Should System Dynamics be Described as a 'Hard' or
‘Deterministic’ Systems Approach?" Systems Research and Behavioral Science
17: 3-22.
Lane, D. C., C. Monefeldt, et al. (1999). "Looking in the wrong place for healthcare
improvements: a system dynamics study of an accident and emergency
department." Journal of the operational Research Society.
Lawson, T. (1989). "Abstraction, Tendencies and Stylised Facts: A Realist Approach to
Economic Analysis." Cambridge Journal of Economics Vol. 13.
Lewins, F. (1992). Social Science Methodology. Melbourne, Australia, MacMillan
Education Australia Pty Ltd.
McDougall, G. H. G. and T. Levesque (2000). "Customer satisfaction with services:
putting perceived value into the equation." Journal of Services Marketing 14(5):
392-410.
McGahan, A. M. and P. Ghemawat (1994). "Competition to Retain Customers."
Marketing Science 13(No. 2): pp. 165-176.
Meadows, D. H., W. W. Behrens III, et al. (1974). The Dynamics of Growth in a Finite
World. Cambridge, MA., The Wright-Allen Press.
Oliver, R., L. (1980). "A Cognitive Model of the Antecedents and Consequences of
Satisfaction Decisions." Journal of Marketing Research 17(November): 460-69.
Oliver, R., L. (1977). "Effects of Expectations and Disconfirmation on Postexposure
Product Evaluations." Journal of Applied Pschology 62(April): 246-50.
Outhwaite, W. (1975). Understanding Social Life: The Method called Verstehen.
london, Allen and Unwin.
Outhwaite, W. (1987). New Philosophies of the Social Science: Realism, Hermeneutics
and Critical Theory. London, Macmillan.
Parasuraman, A., L. L. Berry, et al. (1988). "SERQUAL: a multiple-item scale for
measuring customer perceptions of service quality." Journal of Retailing
64(Spring): 2-40.
Pateman, T. (1987). Language in Mind and Language in Society. Oxford, Clarendon
Press.
Ravald, A. and C. Gronroos (1996). "The value concept and relationship marketing."
European Journal of Marketing 30(2): 19-30.
Ree, J. (1974). Descartes. London, Allen Lane.
Reichheld, F., F. and E. Sasser, W. (1990). "Zero Defections: Quality Comes to
Services." Havard Business Review 68(September/October): 105-11.
Reichheld, F. F. (1996). The Loyalty Effect. Boston, Massachusetts, Harvard Business
School Press.
Rust, R. T. and A. J. Zahorik (1993). "Customer Satisfaction, Customer Retention and
Market Share." Journal of Retailing 69(Summer): 145-56.
Rust, R. T., A. J. Zahorik, et al. (1995). "Return of Quality (ROQ): Making Service
Quality Financially Accountable." Journal of Marketing Vol. 59(No. 2): 58-70.
Senge, P. (1990). The fifth Discipline: The Art and Practice of the Learning
Organisation. New York, Doubleday.
Sterman, J., D. (2000). Business dynamics: Systems Thinking and Modeling for a
Complex World. N.Y., Irwin McGraw-Hill.
Sterman, J., G. Richardson, et al. (1988). "Modeling the estimation of petroleum
resources in the United States." Technology Forecasting and Social Change
33(3): 219-49.
Sterman, J. D., N. Repenning, et al. (1997). "Unanticipated side effects of successful
quality improvement programs: exploring a paradox of organisational
improvement." Management Science 43: 503-21.
Sutcliffe, F. E. (1968). Translation of Descartes' Discourse on Method. Harmondsworth,
Penguin Books.
Taylor, S. A. and T. L. Baker (1994). "An Assessment of the Relationship Between
Service Quality and Customer Satisfaction in the Formation of Consumers!
Purchase Intentions." Journal of Retailing 70(2): 163-178.
Woodruff, R. B. (1997). "Customer Value: The Next Source for Competitive
Advantage." Journal of the Academy of Markeing Science 25(2): 139-153.
