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TitleMass Customization for Personalized Communication Environments: Integrating Human Factors
LanguageEnglish
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Table of Contents
                            Title
Table of Contents
Preface
Acknowledgment
The Life Event Cycle:A Special Management Tool for Mass Customization of Services
Optimizing Consumer Responses to Mass Customization
Resource Implications of Manufacturer-Customer Interactions in Mass Customization
A Multi-Agent System for Recommending Customized Families of Products
Developing Interoperability in Mass Customization Information Systems
Mass Customization with Configurable Products and Configurators: A Review of Benefits and Challenges
A Dynamic User Centric Framework for Enhancinge Services Effectiveness Aiming at Mass Customization
Adaptive Interaction for Mass Customisation
Personalizing the TV Experience: Vocomedia – A Case Study from Interactive TV
Affective Human Factors Design with Ambient Intelligence for Product Ecosystems
Technological and Psychological Fundamentals of Psychological Customization Systems: An Example of Emotionally Adapted Games
Expected and Realized Costs and Benefits from Implementing Product Configuration Systems
Usability and User Experience Evaluation Methods
Effective Product Customization on the Web: An Information Systems Success Approach
Compilation of References
About the Contributors
Index
                        
Document Text Contents
Page 2

Mass Customization for
Personalized
Communication
Environments:
Integrating Human Factors

Constantinos Mourlas
National & Kapodistrian University of Athens, Greece

Panagiotis Germanakos
National & Kapodistrian University of Athens, Greece

InformatIon scIence reference

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Adaptive Interaction for Mass Customisation

The groups of systems which use these types of
techniques are known as adaptive presentation
systems and adaptive navigation support systems
respectively. Techniques which provide adaptation
based on the content can be adapted to various
details, difficulty, and media usage to satisfy us-
ers with different needs, background knowledge,
interaction style and cognitive characteristics.
Techniques which provide adaptation based on
links provide direct guidance, adaptive hiding or
re-ordering of links, link annotation, map adapta-
tion, link disabling and link removal (Kinshuk &
Lin, 2003). The introduction of hypermedia and
the Web has had a great impact on adaptive web
systems but there are some limitations of AHS.
De Bra (De Bra, 2000) states, that if prerequisite
relationships are omitted or are just wrong, the
user may be directed to pages that cannot be
understood because the lack of necessary prior
knowledge in the domain. Other issues include
users interacting with a different interface due to
the adaptation of the user model which may lead
to confusion.

data Mining for Mass Customisation

The importance of data mining approaches for
mass customization has been recognised in recent
years. Data mining techniques can be used for
predicting the customers purchasing behaviour,
preferences and needs. These patterns can be
useful in analysing the varying customers which
may fall into different purchasing groups, this
information can be utilised in the designing
and manufacturing products for specific group
of customers. Utilising data mining algorithms
in this manner makes it possible for vendors to
practice more individualised marketing. In this
section we present the data mining approaches
which may be used to determine customer needs
for one-to-one marketing.

Fuzzy Systems

A number of fuzzy classification (Meirer & Werro,
2007) approaches have been proposed in the
marketing literature. Hruschka (Hruschka, 1986)
proposed a segmentation of customers using fuzzy
clustering methods.

Fuzzy systems deal with representation of
classes whose boundaries are not well defined.
The key idea is to associate a membership function
with the elements of a class. The function takes
values in the interval [0, 1] with 0 corresponding
to no membership and 1 corresponding to full
membership. Membership values between 0 and
1 indicate marginal elements in the class.

Fuzzy systems have been very popular in the
analysis of consumer habits in the marketing
literature. Hsu’s Fuzzy Grouping Positioning
Model (Hsu, 2000) allows an understanding of
the relationship between consumer consumption
patterns, and the company’s competitive situation
and strategic positioning. The modelling of fuzzy
data in qualitative marketing research was also
described by Varki (Varki et al 2000). Finally, a
fuzzy Classification Query Language (fCQL) for
customer relationship management was proposed
by Meier et al. (Meier et al. 2005). Most of the cited
research literature applies fuzzy control to clas-
sical marketing issues. Up to now, fuzziness has
not yet been adapted for e-business, e-commerce,
and/or e-government. In (Meirer & Werro, 2007)
the power of a fuzzy classification model is used
for an electronic shop. On-line customers will no
longer be assigned to classical customer segments
but to fuzzy classes. This leads to differentiated
on-line marketing concepts and helps to improve
the customer equity of on-line shop users. A
prototype system has been implemented on the
Internet that demonstrates the proposed fuzzy
mass customization concept. Through examples
of wine glass and furniture design, it can be seen
that the proposed system is effective for prod-
ucts of simple shape or when only a few critical
parameters of a complex product are frequently

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Adaptive Interaction for Mass Customisation

customized. In (Chen et al, 2001) a new design
approach, namely fuzzy mass customization,
which allows most household consumers, who
are not familiar with both mechanical design and
sophisticated CAD software, to customize some
parameters of a product using preferred linguistic
information such as small, normal, big, very big,
and so on. A family of products is represented using
a set of parameters that is divided into two types:
user-defined parameters and deduced parameters.
All parameters are defined as fuzzy variables.
The user-defined parameters are input by a user.
The deduced parameters are determined by the
user-defined parameters using fuzzy reasoning.
A prototype system (Chen et al, 2001) is imple-
mented on a web client/server architecture, namely
CyberFGC, which consists of a fuzzy geometric
customization (FGC) program, Virtual Reality
Modelling Language (VRML), and common
gateway interface (CGI) programs. In this system,
household consumers can customize products
using their preferred linguistic description such
as big, small, normal, etc., over the World Wide
Web. Here a fuzzy model is proposed for the
classification of on-line customers. With fuzzy
classification, an on-line customer can be treated
as a member of a number of different classes at the
same time. Based on these membership functions,
the on-line shop owner can devise appropriate
marketing programs for acquisition, retention,
and add-on selling.

Clustering Algorithms

Clustering algorithms are important for deter-
mining patterns within consumer purchasing
habits. They can be used to cluster consumers
into groups based on their purchasing behaviour.
In e-commerce clustering techniques are used
to analyse shopping basket history, click stream
data etc. They function by clustering the instances
together based on their similarity. The clustering
algorithms can be divided into hierarchical and
non hierarchical methods. Hierarchical methods

construct a tree where each node represents a
subset of the input items, where the root of the tree
represents all the items in the item set. Hierarchi-
cal methods can be divided into the divisive and
agglomerative methods. Divisive methods begin
with the entire set of items and partition the set
until only an individual item remains. Agglomera-
tive methods work in the opposite way, beginning
with individual items, each item is represented as
a cluster and merging these clusters until a single
cluster remains. At the first step of hierarchical
agglomerative clustering (HAC) algorithm, when
each instance represents its own cluster, the simi-
larities between each cluster are simply defined
by the chosen similarity method rule to determine
the similarity of these new clusters to each other.
There are various rules which can be applied
depending on the data; some of the measures are
described below:

Single-Link: In this method the similarity of
two clusters is determined by the similarity of the
two closest (most similar) instances in the different
clusters. So for each pair of clusters Si and Sj,

sim S S d d d S d S
i j i j i i j j

( ) max{cos( , ) , }
,

Complete-Link: In this method the similarity of
two clusters is determined by the similarity of the
two least similar instances of both clusters. This
approach can be performed well in cases where
the data forms the natural distinct categories,
since it tends to produce tight (cohesive) spherical
clusters. This is calculated as:

sim S S d d
i j i j

( ) min{cos( , )}
,

Average-Link or Group Average: In this
method, the similarity between two clusters is
calculated as the average distance between all pairs
of objects in both clusters, i.e. it’s an intermediate
solution between complete link and single-link.
This is unweighted, or weighted by the size of the
clusters. The weighted form is calculated as:

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297

Index

R
radar diagram 220, 221
recommender systems 35, 36, 38, 39, 46,
133, 136, 137
recommender systems, collaborative 135, 136,

137, 144, 145, 146, 147
recommender systems, content-based

135, 136, 137
recommender systems, hybrid 136, 137
relationship marketing 1, 3, 7, 8
resource interactions 24, 25
re-work 30

S
SDAI level 62
self-expressive value 12, 14
semantic ontologies 168, 169, 173, 175
service complexes 2
service delivery 1
service factories 2
service management 1
service-oriented architecture (SOA) 50, 58
service-oriented environment (SOE) 58
service providers 1
service shops 2
services, kinds of 1
service stores 1, 2, 5, 7
service tasks 1
simple object access protocol (SOAP) 58
smarTag framework 108, 110, 119, 120, 121,

122, 123, 124, 130
smart Web objects 120, 121
standard-based framework 49
standard data access interface (SDAI) 55, 61
stereotypes 136
strategic resources 23, 24, 25, 28, 32, 33
supplier relationship networks 24, 32
sustainable competitive advantage 24, 32
SWOOP Web authoring tool 118
system quality 247

T
target experience 185, 194, 195, 196, 197,

208, 209
technology for enhanced learning (TEL) envi-

ronments 186

three-dimensional perceptual preferences model
109

TinyMCE Web authoring tool 119
TIVO system 151
total constant commonality index (TCCI) 165

U
ubiquitous computing 133, 134, 141, 145

57, 65
unique product 10, 15, 17
universal description, discovery, and integration

(UDDI) registry 58
usability 232, 233, 234, 235, 236, 240, 24

1, 243
usability evaluation methods

233, 235, 236, 240
usability evaluation methods, expert-oriented

234, 235
usability evaluation methods, inspection-orient-

ed 234, 243
usability tests 234
user experiences, customization of 182, 183,

184, 185, 186, 187, 189, 192, 194,
195, 196, 197, 199, 202, 205, 206,

208, 209, 210
user experience (UX) 232, 233, 235, 236,
237, 238, 239, 240, 241, 243
user satisfaction 245

V
vector space representation 136
Video on Demand (VoD) 155, 156

152, 158
virtual reality (VR) 169, 173, 177
visual working memory span (VWMS) 109
vocomedia 239, 240
Vocomedia system 149, 150, 154, 158, 160
voice of customer (VoC) 164

W
Web-based architecture 97
Web-based decision support systems 245
Web personalization 108, 110, 111, 129, 130,

131, 133, 134, 135
Web services description language (WSDL) 58

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298

Index

Web Services Interoperability Organization
(WS-I) 59

working memory (WM) span
114, 115, 128, 129

World Wide Web Consortium (W3C) 58

X

XML metadata interchange (XMI) 65

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