Proceedings of the 2007 Winter Simulation Conference
S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds.

USING INTELLIGENT AGENTS TO UNDERSTAND
MANAGEMENT PRACTICES AND RETAIL PRODUCTIVITY

Peer-Olaf Siebers
Uwe Aickelin

Helen Celia
Chris W. Clegg

School of Computer Science, Automated Scheduling,
Optimisation and Planning Research Group (ASAP)
University of Nottingham
Nottingham, NG8 1BB, UK

Centre for Organisational Strategy, Learning & Change,
Leeds University Business School
University of Leeds
Leeds, LS2 9JT, UK

ABSTRACT
Intelligent agents offer a new and exciting way of understanding the world of work. In this paper we apply agentbased modeling and simulation to investigate a set of
problems in a retail context. Specifically, we are working
to understand the relationship between human resource
management practices and retail productivity. Despite the
fact we are working within a relatively novel and complex
domain, it is clear that intelligent agents could offer potential for fostering sustainable organizational capabilities
in the future. The project is still at an early stage. So far
we have conducted a case study in a UK department store
to collect data and capture impressions about operations
and actors within departments. Furthermore, based on our
case study we have built and tested our first version of a
retail branch simulator which we will present in this paper.
1

INTRODUCTION

The retail sector has been identified as one of the biggest
contributors to the productivity gap that persists between
the UK, Europe and the USA (Reynolds et al. 2005). It is
well documented that measures of UK retail productivity
rank lower than those of countries with comparably developed economies. Intuitively, it is inevitable that management practices are inextricably linked to a company’s
productivity and performance. However, many researchers have struggled to provide clear empirical evidence using more traditional research methods (for a review, see
Wall and Wood 2005).
Significant research has been done to investigate the
productivity gap and the common focus has been to quantify its size and determine the contributing factors. Best
practice guidelines have been developed and published,
but there remains considerable inconsistency and uncer-

tainty regarding how these are implemented and manifested in the retail work place. Siebers et al. (submitted)
have conducted a comprehensive literature review of this
pertinent research area linking management practices to
firm-level productivity. Practices are dichotomized according to their focus, whether operationally-focused or
people-focused. The authors conclude that management
practices are multidimensional constructs that generally
do not demonstrate a straightforward relationship with
productivity variables. Empirical evidence affirms that
management practices must be context specific to be effective, and in turn productivity indices must also reflect a
particular organization’s activities.
Currently there is no reliable and valid way to delineate the effects of management practices from other socially embedded factors. Most Operational Research (OR)
methods can be applied as analytical tools once management practices have been implemented, however they are
not very useful at revealing system-level effects of the introduction of specific management practices. This holds
particularly when the focal interest is the development of
the system over time, like in the real world. This contrasts
with more traditional techniques, which allow us to identify the state of the system at a certain point in time.
The overall aim of our project is to understand and
predict the impact of different management practices on
retail store productivity. To achieve this aim we have
adopted a case study approach and integrated applied research methods to collect both qualitative and quantitative
data. In summary, we have conducted four weeks of informal participant observations, forty staff interviews
supplemented by a short questionnaire on the effectiveness of various management practices, and drawn upon a
variety of established informational sources internal to the
case study organization. Using this data, we are applying
Agent-Based Modeling and Simulation (ABMS) to try to

Siebers, Aickelin, Celia, & Clegg
devise a functional representation of the case study departments.
In this paper we will focus on the simulation side of
the project. In Section 2 we summarize the literature review we have conducted to find a suitable research tool
for our study. Section 3 describes the conceptualization,
design and implementation of our retail branch simulator.
In Section 4 we describe two experiments that we have
conducted as a first step to validate our retail branch
simulator. Section 5 concludes the paper and unveils our
future ideas.
2

WHY AGENT-BASED SIMULATION?

OR is applied to problems concerning the conduct and coordination of the operations within an organization (Hillier and Lieberman 2005). An OR study usually involves
the development of a scientific model that attempts to abstract the essence of the real problem. When investigating
the behavior of complex systems the choice of an appropriate modeling technique is very important. In order to
be able to make a choice for our project, we reviewed the
relevant literature spanning the fields of Economics, Social Science, Psychology, Retail, Marketing, OR, Artificial Intelligence, and Computer Science. Within these
fields a wide variety of approaches are used which can be
classified into three main categories: analytical approaches, heuristic approaches, and simulation. In many
cases we found that combinations of these were used
within a single model. Common combinations were
‘simulation / analytical’ for comparing efficiency of different non-existing scenarios, (e.g. Greasley 2005), and
‘simulation / analytical’ or ‘simulation / heuristic’ where
analytical or heuristic models were used to represent the
behavior of the entities within the simulation model (e.g.
Schwaiger and Stahmer 2003).
In our review we put a particular emphasis on those
publications that try to model the link between management practices and productivity in the retail sector. We
found a very limited number of papers that investigate
management practices in retail at firm level. The majority
of these papers focus on marketing practices (e.g. Keh et
al. 2006). By far the most frequently used modeling technique we found being used was agent-based modeling
employing simulation as the method of execution. It
seems to be the natural way of system representation for
these purposes.
Simulation introduces the possibility of a new way of
thinking about social and economic processes, based on
ideas about the emergence of complex behavior from relatively simple activities (Simon 1996). While analytical
models typically aim to explain correlations between
variables measured at one single point in time, simulation
models are concerned with the development of a system
over time. Furthermore, analytical models usually work

on a much higher level of abstraction than simulation
models. For simulation models it is critical to define the
right level of abstraction. Csik (2003) states that on the
one hand the number of free parameters should be kept on
a level as low as possible. On the other hand, too much
abstraction and simplification might threaten the homomorphism between reality and the scope of the simulation
model. There are several different approaches to simulation, amongst them discrete event simulation, system dynamics, micro simulation and agent-based simulation. The
choice of the most suitable approach always depends on
the issues investigated, the input data available, the level
of analysis and the type of answers to be sought.
Although computer simulation has been used widely
since the 1960s, ABMS only became popular in the early
1990s (Epstein and Axtell 1996). ABMS can be used to
study how micro-level processes affect macro-level outcomes. A complex system is represented by a collection
of individual agents that are programmed to follow simple
behavioral rules. Agents can interact with each other and
with their environment to produce complex collective behavioral patterns. Macro behavior is not explicitly simulated; it emerges from the micro-decisions made by the
individual agents (Pourdehnad et al. 2002). The main
characteristics of agents are their autonomy, their ability
to take flexible action in reaction to their environment and
their pro-activeness depending on motivations generated
from their internal states. They are designed to mimic the
attributes and behaviors of their real-world counterparts.
The simulation output may be potentially used for explanatory, exploratory and predictive purposes (Twomey
and Cadman 2002). This approach offers a new opportunity to realistically and validly model organizational characters and their interactions, to allow a meaningful investigation of human resource management practices. ABMS
is still a relatively new simulation technology and its
principle application has been in academic research. With
the appearance of more sophisticated modeling tools in
the broader market, things are starting to change (Luck et
al. 2005). Also, an ever increasing number of computer
games use the ABMS approach.
A detailed description of ABMS and thoughts on the
appropriate contexts for ABMS versus conventional modeling techniques can be found in WSC introductory tutorial on ABMS (Macal and North 2006). Therefore we provide only a brief summary of some of our thoughts.
Due to the characteristics of the agents, this modeling
approach appears to be more suitable than Discrete Event
Simulation (DES) for modeling human-oriented systems
(Siebers 2006). ABMS seems to promote a natural form
of modeling, as active entities in the live environment are
interpreted as actors in the model. There is a structural
correspondence between the real system and the model
representation, which makes them more intuitive and easier to understand than for example a system of differential

Siebers, Aickelin, Celia, & Clegg
equations as used in System Dynamics. Hood (1998) emphasized that one of the key strengths of ABMS is that the
system as a whole is not constrained to exhibit any particular behavior as the system properties emerge from its
constituent agent interactions. Consequently assumptions
of linearity, equilibrium and so on, are not needed. With
regard to disadvantages there is a general consensus in the
literature that it is difficult to evaluate agent-based models, because the behavior of the system emerges from the
interactions between the individual entities. Furthermore,
problems often occur through the lack of adequate empirical data. Finally, there is always the danger that people
new to ABMS may expect too much from the models,
particularly in regard to predictive ability.
3

MODEL DESIGN AND IMPLEMENTATION

The strategy for our project is iterative, creating a relatively simple model and then building in more and more
complexity. To begin with we have been trying to understand the particular problem domain, to generate the underlying rules currently in place. We are now in the process of building an agent based simulation model of the
real system using the information gathered during our
case study and will then validate our model by simulating
the operation of the real system. This approach will allow
us to assess the accuracy of the system representation. If
the simulation provides a sufficiently good representation
we are able to move to the next stage, and generate new
scenarios for how the system could work using new rules.
3.1 Modelling Concepts
Our case study approach and analysis has played a crucial
Customer Agent

role allowing us to acquire a conceptual idea of how the
real system is structured. This is an important stage of the
project, revealing insights into the operation of the system
as well as the behavior of and interactions between the
different characters in the system. We have designed the
system by applying a DES approach to conceptualize and
model the system, and then an agent approach to conceptualize and model the actors within the system. This
method made it easier to design the model, and is possible
because only the actors’ action requires an agent based
approach.
In terms of performance indicators, these are identical to those of a DES model. Beyond this, ABMS can offer further insights. A simulation model can detect unintended consequences, which have been referred to as
‘emergent behavior’ (Gilbert and Troitzsch 2005). Such
unintended consequences can be difficult to understand
because they are not defined in the same way as the system inputs; however it is critical to fully understand all
system output to be able to accurately draw comparisons
between the relative efficiencies of competing systems.
Our conceptual ideas for the simulator are shown in
Figure 1. Within our simulation model we have three different types of agents (customers, sales staff, and managers) each of them having a different set of relevant parameters. We will use probabilities and frequency
distributions to assign slightly different values to each individual agent. In this way a population is created that reflects the variations in attitudes and behaviors of their real
human counterparts. In terms of other inputs, we need
global parameters which can influence any aspect of the
system, and may for example define the number of agents
in the system. With regards to the outputs we always hope
to find some unforeseeable, emergent behavior on a

Visual Dynamic Stochastic Simulation Model

Customer Agent
Shopping need, attitudes,
Customer Agent
demographics etc.

Emergent behaviour on
macro level

Customer Agent

Understanding about
interactions of entities within
the system

Sales Staff Agent
Sales Agent
Attitudes, length of service,
competencies, training etc.

Identification of bottlenecks

Manager Agent
Leadership quality, length of
service, competencies,
training etc.

Global Parameters

Performance Measures

Number of customers, sales
staff, managers etc.

Staff utilisation, average
response time, customer
satisfaction etc.

Interface for User
Interaction during Runtime

Figure 1: Conceptual model for our simulator

Siebers, Aickelin, Celia, & Clegg
macro level. Having a visual representation of the simulated system and its actors will allow us to monitor and
better understand the interactions of entities within the
system. Coupled with the standard DES performance
measures, we hope to identify bottlenecks and help to optimize the modeled system.
For the conceptual design of our agents we have decided to use state charts. State charts show the different
states an entity can be in and also define the events that
cause a transition from one state to another. This is exactly the information we need in order to represent our
agents later within the simulation environment. Furthermore, this form of graphical representation is also helpful
for validating the agent design as it is easier for nonspecialists to understand.
The art of modelling pivots on simplification and abstraction (Shannon 1975). A model is always a restricted
copy of the real world, and we have to identify the most
important components of a system to build effective models. In our case, instead of looking for components we
have to identify the most important behaviours of an actor
and the triggers that initiate a move from one state to another. We have developed state charts for all the relevant
actors in our retail branch model. Figure 2 shows as an
example the state charts for a customer agent. The transition rules have been replaced by numbers to keep the
chart comprehensible. They are explained in detail in the
Section 3.2.
A question that can be asked is whether our agents
are intelligent or not? Wooldridge (2002) states that in order to be intelligent agents need to have the following attributes: being reactive, being proactive and being social.
This is a widely accepted view. Being reactive means responding to changes in the environment (in a timely manner), while being proactive means persistently pursuing
goals and being social means interacting with other agents
(Padgham and Winikoff, 2004). Our agents perceive a
goal in that they want to either buy something or return
something. For buying they have a sub goal; that they are

trying to buy the right thing. If they are not sure they will
ask for help. Our agents are not only reactive but also
flexible, i.e. they are capable to recover from a failure of
action. They have alternatives inbuilt when they are unable to perceive their goal, e.g. if they want to pay and
things are not moving forward in the queue they always
have the chance to leave a queue and continue with another action. They are responding in a flexible way to certain changes in their environment, in this case the length
of the queue. Finally, as there is communication between
agents and staff, they can also be regarded as being social.
3.2 Empirical Data
Often agents are based on analytical models or heuristics
and in the absences of adequate empirical data theoretical
models are employed. However, for our agents we use
frequency distributions for state change delays and probability distributions for decision making processes as statistical distributions are the best format to represent the
data we have gathered during our case study due to their
numerical nature. The case study was conducted in the
Audio and Television (A&TV) and the WomensWear
(WW) departments of a leading UK department store. As
mentioned earlier we have conducted informal participant
observations, staff interviews, and drawn upon a variety
of established informational sources internal to the case
study organization.
Our frequency distributions are modeled as triangular
distributions supplying the time that an event lasts, using
the minimum, mode, and maximum duration. Our triangular distributions are based on our own observation and
expert estimates in the absence of numerical data. We
have collected this information from the two branches and
calculated an average value for each department type,
creating one set of data for A&TV and one set for WW.
Table 1 lists some sample frequency distributions that we
have used for modeling the A&TV department (the values
presented here are slightly amended to comply with con-

Customer State Chart

Complaining

Using Aftersales

Enter
Seeking help

Contemplating

passive/active

(dummy state)

Browsing

Leave

Queuing for help

Being helped

Queuing at till

Being served
(at till)

Figure 2: Conceptual model for customer agent

Siebers, Aickelin, Celia, & Clegg
fidentiality restrictions). The distributions are used as exit
rules for most of the states. All remaining exit rules are
based on queue development, i.e. the availability of staff.
situation
leave browse state after …
leave help state after …
leave pay queue (no patience) after …

min
1
3
5

mode
7
15
12

max
15
30
20

Table 1: Sample frequency distribution values
The probability distributions are partly based on
company data (e.g. conversion rates, i.e. the percentage of
customers who buy something) and partly on informed
guesses (e.g. patience of customers before they would
leave a queue). As before, we have calculated average
values for each department type. Some examples for
probability distributions we used to model the A&TV department can be found in Table 2. The distributions make
up most of the transition rules at the branches where decisions are made with what action to perceive (e.g. decision
to seek help). The remaining decisions are based on the
state of the environment (e.g. leaving the queue, if the
queue does not get shorter quickly enough).

event
someone makes a purchase after browsing
someone requires help
someone makes a purchase after getting help

probability it occurs
0.37
0.38
0.56

Table 2 – Sample probabilities
Company data is available about work team numbers
and work team composition, varying opening hours and
peak times (to be implemented in future). Also financial
data (e.g. transaction numbers and values) are available
but have not been used at this stage.
3.3 Implementation
Our simulation has been implemented in AnyLogic™
which is a Java™ based multi-paradigm simulation software (XJ Technologies 2007). During the implementation
we have used the knowledge, experience and data gained
through our case study work. The simulator can represent
the following actors: customers, service staff (including
cashiers, selling staff of two different training levels) and
managers. Figure 3 shows a screenshot of the current customer and staff agent logic as it has been implemented in

Figure 3: Customer (left) and staff (right) agent logic implementation in AnyLogic™

Siebers, Aickelin, Celia, & Clegg
AnyLogic™. Boxes show customer states, arrows possible transitions and numbers satisfaction weights.
Currently there are two different types of customer
goals implemented: making a purchase or obtaining a refund. If a refund is granted, the customer’s goal may then
change to making a new purchase, or alternatively they
will leave the shop straight away. The customer agent
template consists of three main blocks which all use a
very similar logic. These blocks are ‘Help’, ‘Pay’ and
‘Refund’. In each block, in the first instance, customers
will try to obtain service directly and if they cannot obtain
it (no suitable staff member available) they will have to
queue. They will then either be served as soon as the right
staff member becomes available or they will leave the
queue if they do not wait any longer (an autonomous decision). A complex queuing system has been implemented
to support different queuing rules. In comparison to the
customer agent template, the staff agent template is relatively simple. Whenever a customer requests a service
and the staff member is available and has the right level
of expertise for the task requested, the staff member
commences this activity until the customer releases the
staff member. While the customer is the active component
of the simulation model the staff member is currently passive, simply reacting to requests from the customer. In future we planned to add a more pro-active role for the staff
members, e.g. offering services to browsing customers.
A service level index is introduced as a new performance measure. The index allows customer service satisfaction to be recorded throughout the simulated lifetime. The
idea is that certain situations might have a bigger impact
on customer satisfaction than others, and therefore differential weightings are assigned to events to account for
this. For example, in our model if a customer starts to
wait for a refund and leaves without one, then their satisfaction index decreases by 4 (see figure 3). We measure
customer satisfaction in two different ways derived from
these weightings; both in terms of how many customers
leave the store with a positive service level index value,
and the sum of all customers’ service level index values.
Applied in conjunction with an ABMS approach, we expect to observe interactions with individual customer differences, variations which have been empirically linked to
differences in customer satisfaction. This helps the analyst
to find out to what extent customers underwent a positive
or negative shopping experience. It also allows the analyst
to put emphasis on different operational aspects and try
out the impact of different strategies.
The simulator can be initialized from an Excel™
spreadsheet and supports the simulation of the two types
of departments we looked at during our case study. These
differ with respect to their staffing, service provision and
customer requirements, which we hope will be reflected
in the simulation results. WW customers will ask for help
when they know what they want whereas A&TV custom-

ers will ask for help when they do not know what they
want. WW makes a lot more unassisted sales than A&TV
and service times are very different; in WW the average
service time is a lot shorter than in A&TV. This service
requirement has a differential impact on the profile of
employee skills at the department level.
4

A FIRST VALIDATION OF OUR SIMULATOR

To test the operation of our simulator and ascertain face
validity we have designed and run 2 sets of experiments
for both departments. Our case study work has helped us
to identify the distinguishing characteristics of the departments, for example different customer arrival rates
and different service times. In these experiments we will
examine the impact of these individual characteristics on
the volume of sales transactions and customer satisfaction
indices. All experiments hold the overall number of staffing resources constant at 10 staff and we run the simulation for a period of 10 weeks. We have conducted 20
repetitions for every experimental condition enabling the
application of rigorous statistical techniques.
Each set of results are analyzed for each dependent
variable using a two-way between-groups analysis of
variance (ANOVA). Despite our prior knowledge of how
the real system operates, we were unable to hypothesize
precise differences in variable relationships, instead predicting general patterns of relationships. Indeed, ABMS is
a decision-support tool and is only able to inform us about
directional changes between variables (actual figures are
notional). Where significant ANOVA results were found,
post-hoc tests were applied where possible to investigate
further the precise impact on outcome variables under different experimental conditions.
During our time in the case study organization, we
observed that over time the number of cashiers available
to serve customers would fluctuate. In the first experiments we vary the staffing arrangement (i.e. the number
of cashiers) and examine the impact on the volume of
sales transactions and two levels of customer satisfaction;
both customer satisfaction (how many customers leave
the store with a positive service level index value) and
overall satisfaction (the sum of all customers’ service
level index values). In reality, we saw that allocating extra
cashiers would reduce the shop floor sales team numbers,
and therefore the total number of customer-facing staff in
each department is kept constant at 10. We therefore predict that for each of our dependent measures: number of
sales transactions (1), customer satisfaction index (2) and
overall satisfaction index (3):
• Ha: An increase in the number of cashiers will be
linked to increases in 1, 2 and 3 to a peak level, beyond which 1, 2 and 3 will decrease.
• Hb: The peak level of 1, 2 and 3 will occur with a
smaller number of cashiers in A&TV than in WW.

Siebers, Aickelin, Celia, & Clegg
Department Cashiers

A&TV

WW

Total

1
2
3
4
5
1
2
3
4
5
1
2
3
4
5

Number of Transactions
mean
std. dev.
4853.50
26.38
9822.20
57.89
14279.90
96.34
14630.60
86.19
13771.85
97.06
8133.75
22.16
15810.10
56.16
25439.60
113.66
30300.70
249.30
28894.25
195.75
6493.63
1661.19
12816.15
3032.61
19859.75
5651.89
22465.65
7937.00
21333.05
7659.04

Customer Satisfaction
mean
std. dev.
12324.05
77.64
14762.45
81.04
17429.70
103.77
17185.00
99.09
16023.20
82.66
18508.20
88.68
22640.40
92.00
28833.10
115.65
32124.60
230.13
30475.20
176.41
15416.13
3132.55
18701.43
3990.07
23131.40
5775.35
24654.80
7566.98
23249.20
7319.32

Overall Satisfaction
mean
std. dev.
9366.40
563.88
19985.20
538.30
28994.80
552.60
32573.60
702.64
27916.05
574.56
17327.95
556.03
42339.10
736.61
58601.10
629.68
74233.30
570.79
76838.65
744.31
13347.18
4069.20
31162.15
11337.24
43797.95
15003.13
53403.45
21104.67
52377.35
24781.61

Table 3: Descriptives for first experiments (all to 2 d.p.)
An ANOVA was run for each dependent variable,
and all revealed statistically significant differences (see
Table 3 for descriptive statistics). For 1 and 2, Levene’s
test for equality of variances was violated (p<.05) so a
more stringent significance level was set (p<.01).
For 1 there were significant main effects for both department [F(1, 190) = 356441.1, p<.001] and staffing
[F(4, 190) = 124919.5, p<.001], plus a significant interaction effect [F(4, 190) = 20496.37, p<.001]. Tukey’s post
hoc tests for the impact of staffing revealed significant
differences for every single comparison (p<.001).
There is clear support for Hla. We expected this to
happen because the number of normal staff available to
provide customer advice will eventually reduce to the extent where there will be a detrimental impact on the number of customers making a purchase. Some customers will
become impatient waiting increasingly long for service,
and will leave the department without making a purchase.
Hlb is not supported, the data presents an interesting contrast, in that 1 plateaus in A&TV around 3 and 4 cashiers,
whereas WW benefits greatly from the introduction of a
fourth cashier. Nonetheless this finding supports the
thinking underlying this hypothesis, in that we expected
the longer average service times in A&TV to put a greater
‘squeeze’ on customer advice with even a relatively small
increase in the number of cashiers.
For 2, there were significant main effects for both
department [F(1, 190) = 391333.7, p<.001], and staffing
[F(4, 190) = 38633.83, p<.001], plus a significant interaction effect [F(4, 190) = 9840.07, p<.001]. Post hoc tests
for staffing revealed significant differences for every single comparison (p<.001).
The results support both H2a and H2b. We interpret
these findings in terms of A&TV’s greater service requirement, combined with the reduced availability of advisory sales staff. These factors result in a peak in purchasing customers’ satisfaction with a smaller number of
cashiers (4) than in WW (5).
For 3, there were significant main effects for both
department [F(1, 190) = 117214.4, p<.001], and staffing

[F(4, 190) = 29205.09, p<.001], plus a significant interaction effect [F(4, 190) = 6715.93, p<.001]. Tukey’s post
hoc comparisons indicated significant differences between all staffing levels (p<.001).
Our results support H3a for A&TV, showing a clear
peak in overall satisfaction. H3a is only partially supported for WW, in that no decline in 3 is evident with up
to 5 cashiers, although increasing this figure may well expose a peak because the overall satisfaction appears to be
starting to plateau out. The results offer firm support in
favor of H3b.
The second experiment investigates employee empowerment. During our case study we observed the implementation of a new refund policy. This new policy allows cashiers to independently decide whether or not to
make a refund up to the value of £50, rather than referring
the authorization decision to a section manager. To simulate this practice, we vary the probability that cashiers are
empowered to make refund decisions autonomously. We
assess its impact in terms of two performance measures:
overall customer refund satisfaction and cashier utilization (a proportion of maximum capacity). The staffing arrangement is held constant, consisting of 3 cashiers, 5
normal staff members, 1 expert staff member, and 1 section manager. We hypothesize that:
• H4. Higher levels of empowerment will be linked
to higher refund satisfaction.
• H5. Higher levels of empowerment will be linked
to greater cashier utilization.
An ANOVA was run for each outcome measure (see Table 4 for descriptives). For refund satisfaction, there were
significant main effects for both department [F(1, 190) =
508.73, p<.001], and empowerment [F(4, 190) = 120.46,
p<.001], plus a significant interaction effect [F(4, 190) =
29.81, p<.001]. Tukey’s post hoc tests for the impact of
empowerment revealed significant differences between all
comparisons (p<.001), except for .00 with .75, and .25
with .50, where there were no significant differences.

Siebers, Aickelin, Celia, & Clegg
Department

A&TV

WW

Total

Empowerment
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00

Overall refund
mean
std. dev.
3130.70
242.58
3880.70
225.70
3876.50
181.47
3716.80
225.31
2991.60
245.15
3090.30
222.00
3116.00
266.70
3041.20
211.75
2716.80
217.79
2085.20
168.19
3110.50
230.43
3498.35
457.61
3458.85
465.61
3216.80
551.59
2538.40
503.70

Cashier utilization
mean
std. dev.
0.6286
0.00
0.6392
0.00
0.6488
0.00
0.6571
0.00
0.6623
0.00
0.6756
0.00
0.6737
0.00
0.6736
0.00
0.6722
0.00
0.6720
0.00
0.6521
0.02
0.6565
0.02
0.6612
0.01
0.6646
0.01
0.6672
0.01

Table 4: Descriptives for the third experiment (all to 2
d.p., except cashier utilization to 4 d.p.)
The data provides support for H4 between .00 and .25
levels of empowerment. However, as empowerment increases all of the results do not support our hypothesis,
and demonstrate a counterintuitive progressive decline of
refund satisfaction beyond the .25 level. Both departments
display the curvilinear relationship between these two
variables; refund satisfaction peaks at a middling level of
empowerment (.50 for A&TV, .25 for WW). These results suggest that some constraining factors are occurring
at the higher levels of empowerment. This may be linked
to the empowered employees adhering to a stricter refund
policy (resulting in less customer satisfaction), or the empowered employees taking longer to process the transaction.
For cashier utilization, there were significant main effects for both department [F(1, 190) = 2913.45, p<.001],
and empowerment [F(4, 190) = 126.37, p<.001], plus a
significant interaction effect [F(4, 190) = 190.64, p<.001].
Tukey’s post hoc tests for the impact of cashier utilization
confirmed significant differences between all comparisons (p=.01 between .75 and 1.00, p<.001 for all others).
The results support H5 for A&TV, but not for WW.
In WW, empowerment is significantly inversely related to
till utilization. Case study observations indicated that
A&TV cashiers, like A&TV sales staff, when they have a
higher level of empowerment they are motivated to work
more efficiently. Empirical evidence indicated that WW
cashiers may not be under the same time pressures to
work more quickly, however this data goes one step further and suggests an inverse relationship.
5

CONCLUSIONS AND FUTURE DIRECTIONS

In this paper we present the conceptual design, implementation and operation of a retail branch simulator used to
understand the impact of management practices on retail
productivity. As far as we are aware this is the first time
researchers have tried to use agent-based approaches to

simulate management practices such as training and empowerment. Although our simulator uses specific case
studies as source of information, we believe that the general model could be adapted to other retail companies and
areas of management practices that have a lot of human
interaction.
From what we can conclude from our current analyses, some findings are as hypothesized whereas others are
more mixed. Further experimentation is required to enable
rigorous statistical evolution of the outcome data and
identification of statistically significant differences.
Currently we are developing our agents with the intention of enhancing their intelligence and heterogeneity.
For this purpose we are introducing evolution and stereotypes. The most interesting system outcomes evolve over
time and many of the goals of the retail company (e.g.
service standards) are also planned long term. We are introducing an evolution of entities over time, including
product knowledge for staff. Moreover, the customer
population pool will be fixed to monitor customer agents
over time. This allows us to consider shopping experience
based on long-term satisfaction scores, with the overall
effect being a certain ‘reputation’ for the shop. Another
interesting aspect we are currently implementing is the
introduction of stereotypes. Our case study organization
has identified its particular customer stereotypes through
market research. It will be interesting to find out how
populations of certain customer types influence sales.
Overall, we believe that researchers should become
more involved in this multi-disciplinary kind of work to
gain new insights into the behavior of organizations. In
our view, the main benefit from adopting this approach is
the improved understanding of and debate about a problem domain. The very nature of the methods involved
forces researchers to be explicit about the rules underlying
behavior and to think in new ways about them. As a result, we have brought work psychology and agent-based
modeling closer together to form a new and exciting research area.
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AUTHOR BIOGRAPHIES
PEER-OLAF SIEBERS is a Research Fellow in the
School of Computer Science and IT at the University of
Nottingham. His main research interest is the application
of computer simulation to study human oriented complex
adaptive systems. Complementary fields of interest include distributed artificial intelligence, biologically inspired computing, game character behavior modeling, and
agent-based robotics. His webpage can be found via
<www.cs.nott.ac.uk/~pos>.
UWE AICKELIN is a Reader and Advanced EPSRC
Research Fellow in the School of Computer Science and
IT at the University of Nottingham. His research interests
are mathematical modeling, agent-based simulation, heuristic optimization and artificial immune systems. see his
webpage for more details <www.aickelin.com>.
HELEN CELIA is a Researcher at the Centre for Organisational Strategy, Learning and Change at Leeds University Business School. She is interested in developing ways
of applying work psychology to better inform the modeling of complex systems using agents. For more information visit <www.leeds.ac.uk/lubs/coslac>
CHRIS W. CLEGG is a Professor of Organizational
Psychology at Leeds University Business School. And the
Deputy Director of the Centre for Organisational Strategy,
Learning and Change. His research interests include: new
technology, systems design, information and control systems, socio-technical thinking and practice; organizational
change, change management, technological change; the
use and effectiveness of modern management practices,
innovation, productivity; new ways of working, job design, work organization. An emerging research interest is
modeling and simulation. His webpage can be found via:
<www.leeds.ac.uk/lubs/coslac>

