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Management Research News
Vol. 29 No. 10, 2006
pp. 632-651
#Emerald Group Publishing Limited
0140-9174
DOI 10.1108/01409170610712335
Managing information technology
infrastructure: a new
flexibility framework
Anote Chanopas
School of Management, Asian Institute of Technology, Klong Luang,
Pathumthani, Thailand
Donyaprueth Krairit
School of Management, Asian Institute of Technology, Klong Luang,
Pathumthani, Thailand, and
Do Ba Khang
School of Management, Asian Institute of Technology, Klong Luang,
Pathumthani, Thailand
Abstract
Purpose – The purposes of this study are to present an operational definition of information
technology (IT) infrastructure flexibility and to provide a framework for assessing its components.
Design/methodology/approach – A comprehensive review of the relevant literature was
conducted along with expert interviews to determine what experts considered to be the
characteristics of IT infrastructure flexibility. A questionnaire was then developed, and 388 IT
personnel with a wide range of experience verified the proposed framework. Factor analysis was
conducted to reveal the common aspects of IT infrastructure flexibility.
Findings – The results expand on the four recognized components (connectivity, compatibility,
modularity and IT personnel competency) from the literature by revealing five further components
(scalability, continuity, rapidity, facility and modernity).
Research limitations/implications – The issue of external validity should be a concern because
the samples were collected only from IT personnel in the financial service industry in Thailand. The
improvement of the instrument to fit additional contexts is recommended.
Practical implications – Practitioners may now consider their IT infrastructure profiles and
determine which components need more attention. Researchers mayexpand on this paper’s results by
conducting further investigations with other organizational measurements.
Originality/value – This study is the first to provide empirical evidence from the context of a
developing country, which fills a significant gap in the literature. Although this study reports
different findings from the literature, the results still complement rather than contradict the existing
research framework.
Keywords Information industry, Flexible organizations, Financial services
Paper type Research paper
Introduction
As the global business environment has become more dynamic and complex,
competition among companies has become increasingly intense amid ever tighter
budget constraints. This tension has forced organizations to make the management of
all its resources a priority. The improvement of productivity, cycle times, customer
service and responsiveness has become ever more critical. At the same time, business
executives are expected to make quick but careful decisions that will take advantage of
emerging opportunities. Therefore, they are beginning to realize the importance of
information technology (IT) and understand its role in changing and improving the
way businesses operate.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0140-9174.htm
Managing IT
infrastructure
633
Although IT is an important tool in attaining the desired growth and
competitiveness of today’s businesses, it may also constitute a major portion of an
organization’s capital investment (Alshawi et al., 2003; Kumar, 2004; Huang et al., 2006).
As reported by Cuneo (2005), average IT spending among the companies in
InformationWeek 500 during 2001-2005 was approximately US$ 300 million per year.
Moreover, IT spending in the US economy has increased by more than 200 per cent
since 1970 (Mistry, 2006). IT investment and its payoffs have always been important to
executives but now there is another issue which is increasingly concerned under ever-
changing business environments. The question is, with a large investment, how can IT
infrastructure be managed to best achieve today’s business goals as well as future
demand? The simple answer is that IT infrastructure must be flexible enough to
handle changes. However, there are two questions that must be answered first: what is
‘‘IT infrastructure flexibility’’ and what characteristics of IT infrastructure are
considered ‘‘flexible’’?
This study will answer the aforementioned questions based on the following
three rationales. Firstly, IT is constantly evolving and change happens very quickly.
Improved IT products and services are released every day. In most cases, it is
difficult for organizations to implement new IT systems without a large re-investment
and without affecting regular business operations. Secondly, IT infrastructure is a
long-term asset, a long-term shareholder value and it represents the long-term
options of an organization (Weill and Broadbent, 1998). Since IT infrastructure
involves a large investment and affects the entire organization, it is difficult to change
in a short period of time. Therefore, it must be able to support change without having
to start from scratch every time a new development is introduced because that costs
too much and takes too long to implement (Robertson and Sribar, 2002; Schalken
et al., 2005). Thirdly, although some research has been conducted concerning
IT infrastructure flexibility, the concept itself is still vaguely understood and not
fully developed. Therefore, the issue of IT infrastructure flexibility needs to be
explored more deeply. This paper aims to address this need by focusing on two
research objectives.
(1) To present an operational definition of IT infrastructure flexibility.
(2) To provide a framework for assessing the components of IT infrastructure
flexibility.
Research methodology
In order to provide an operational definition of IT infrastructure flexibility and to
provide a framework for assessing its components, the research methodology is based
on a comprehensive review of the relevant literature, interviews with industry experts
and a field survey. This study relies on many sources of data because no single source
offers a complete advantage over all the others (Yin, 1994).
Although the literature offers much insight, some of the predictions and
expectations presented are weak due to inherent biases. Some researchers also tend to
rely too much on documents as opposed to testing conclusions in practical contexts.
Expert interviews can shed light on these areas since they are based on real-world
situations that can be missing from the literature. However, biases must be kept in
mind in these situations too, since even though experts offer frameworks for the real
world, they may rely only on their own experience. Additional biases may creep in
regarding the particular organizational culture and environment at the time of the
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interview. A field survey with a large number of respondents can address these
concerns by statistically verifying a framework that supports both the conclusions
from the literature and the experts. Even though a field survey may also contain some
error and biases, the outcome of the survey should be able to clarify and support other
research approaches. In summary, data from various sources is highly complementary
and the balance of different sources reduces possible biases during the research
process. The research methodology of this study is illustrated in Figure 1.
Literature review
The term ‘‘IT infrastructure’’ became popular in the mid 1990s. The definition of IT
infrastructure is quite consistent in the literature (e.g. Keen, 1991; Niederman et al.,
1991; Duncan, 1995; Brancheau et al., 1996; Byrd and Turner, 2000; Robertson and
Sribar, 2002; Schalken et al., 2005). Accordingly, in this study, IT infrastructure is
defined as a set of shared IT resources which is a foundation for both communication
across the organization and the implementation of present/future business
applications. IT infrastructure is composed of two broadly defined infrastructures:
technical and human. Technical infrastructure includes hardware, software, the
network, telecommunications, applications and tangible IT resources. Human
infrastructure refers to the knowledge and skills required to manage IT resources
Field
Survey
Literature Review
Definitions,
Characteristics and
Measurements
Factor Analysis
Expert Interviews
Data Collection
A New Definition
and Framework
Instrument
Development
Reliability Analysis
Figure 1.
Research methodology
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635
within an organization. A comprehensive picture of IT infrastructure was
demonstrated by Weill and Broadbent (1998) and is shown in Figure 2.
The issue of flexibility has long been a concern to researchers and practitioners in
the management literature (e.g. Boynton and Victor, 1991; De Leeuw and Volberda,
1996; Golden and Powell, 2000; Patten et al., 2005). Flexibility generally gives
organizations more options to diversify their products and services, allowing them to
handle a greater variety of market needs and customers. The term ‘‘flexibility’’ is used
in many areas of management including finance (e.g. Mason, 1986), automation (e.g.
Adler, 1988), manufacturing (e.g. Slack, 1990), health care (Jack and Powers, 2006) and
human resources (e.g. Tu
¨selmann, 1996). In each area, the definition is different.
Since flexibility is important in every field of management, and businesses are
increasingly dependent on IT, flexibility in IT infrastructure has become a major
concern of management teams. The challenge of the investment decision is to choose
an IT infrastructure that is able to support both present and future applications.
Flexible IT systems help develop cost-effective new products and services (Weill and
Broadbent, 1998). Growth and competitiveness depends on the flexibility of IT
infrastructure because it allows organizations to develop new initiatives quickly
(Murray and Lynn, 1997; Bhatt, 2000). Conversely, inflexibility is the difficulty that
developers often encounter with user demands that require IT to do things it was not
designed to do (Duncan, 1995). An infrastructure that does not support a business can
immediately lead to lost sales and goodwill (Robertson and Sribar, 2002).
Subsequently, many researchers (e.g. Sauer and Willcocks, 2003; Schiesser, 2003;
Bailey and Turner, 2005; Palanisamy, 2005; Patten et al., 2005; Akhavan et al., 2006) also
delineated the features of ‘‘adaptive/responsive/flexible/dynamic IT infrastructure’’
which has provided the foundation for a new IT management style. Nevertheless, early
Local Applications
Shared/Standard IT Applications
Shared IT Services
Human IT Infrastructure
IT Components
Local Applications
Shared and Standard IT
Applications
Shared IT Services
Human IT Infrastructure
IT Components
IT Infrastructure
The top piece of the picture is the fast
changing local business applications
such as bank loan applications, customer
support systems or insurance claim
processing. This layer is not considered
as IT infrastructure.
This is a set of services that users can
understand and use in businesses. They
are services which are stable over time
such as shared customer databases,
PC/LAN access or intranet.
This layer consists of knowledge, skills
and experiences of IT personnel to bind
IT components into services.
At the base of infrastructure are IT
components such as computers,
printers, routers, operating systems and
other devices.
This layer consists of applications
which change less regularly such as
accounting, budgeting or human
resource management.
Source: Weill and Broadbent, 1998
Figure 2.
IT infrastructure
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studies often described the benefits of a flexible IT infrastructure but they did not
actually define it. The exact definition of IT infrastructure flexibility is seldom found in
IT literature. Byrd and Turner (2000) offered the following definition:
IT infrastructure flexibility is the ability to easily and readily diffuse or support a wide
variety of hardware, software, communication technologies, data, core applications, skills,
competencies, commitments, values within the technical physical base and the human
component of the existing IT infrastructure.
Later, Byrd (2001) posited an even more comprehensive definition:
IT infrastructure flexibility is the ability of the infrastructure to support a wide variety of
hardware, software and other technologies that can be easily diffused into the overall
technological platform, to distribute any type of information (data, text, voice, image, video)
to anywhere inside of an organization and beyond, and to support the design, development
and implementation of a heterogeneity of business applications.
Table I presents the derivation of IT infrastructure flexibility from the literature.
Duncan (1995) and Byrd and Turner (2000) produced two seminal studies in this area.
Duncan (1995) stated that an understanding of IT infrastructure flexibility must begin
with the degree to which IT resources are sharable and reusable. One way to describe
IT infrastructure flexibility more precisely is through the qualities of connectivity,
compatibility and modularity. She also mentioned that organizations that have high
connectivity, compatibility and modularity are considered to have high flexibility in IT
infrastructure. Byrd and Turner (2000) provided statistical evidence to support the
significance of four key components: connectivity, compatibility, modularity and IT
personnel competency. In short, connectivity is the ability of the hardware and
software to make internal and external electronic linkages. Compatibility is the ability
to share any type of information (text, voice, image and video). Modularity is the ability
to easily reconfigure hardware, software and data. IT personnel competency includes
both the skills and experience required of IT personnel to perform IT activities.
Although some researchers in Table I did not refer to flexibility directly, their
concepts and items were still relevant to flexibility. For example, Keen (1991)
introduced the concept of ‘‘reach and range’’ which has since been developed into the
concept of ‘‘connectivity and compatibility’’. Lee et al. (1995) mentioned IT personnel
skills in general, but their work was also used as a foundation for understanding
flexibility in human infrastructure. Table II illustrates the components and definitions
from the literature.
Expert interviews
The previous section focused on an overview of the relevant literature. Although a
number of studies have been completed, they tend to be inapplicable to the context of
this study. Previous studies were conducted, for example, in the life/health insurance
industry in the US (Duncan, 1995), across industries in Fortune 1000 companies in the
US (Byrd and Turner, 2000) and across industries in the US/Canada (Chung et al., 2003).
This study, however, was conducted in the financial service industry in Thailand,
which is a developing country. Hence, the results from previous research may not be
applicable to this study due to technological and cultural differences.
The financial service industry was selected for this study primarily because it is an
information-intensive industry in which IT plays a strategic role (Sager, 1988;
Broadbent and Weill, 1993; Swierczek and Shrestha, 2003). Organizations in this
industry are heavy IT investors and users because IT is the means to deliver services
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Tabl e I.
Derivation of IT
infrastructure flexibility
from the literature
Literature Connectivity Compatibility Modularity IT personnel competency
Keen (1991) Reach Range
Gibson (1993) Communications
connectivity
Computing
compatibility
Data
transparency
Applications
functionality
IT planning, organization and control
Duncan (1995) Connectivity Compatibility Modularity
Lee et al. (1995) Technology
management
skills
Business
functional
skills
Interpersonal
skills
Technical
specialty
skills
Bhatt (2000) Network
infrastructure
Data integration
Schwager et al. (2000) Connectivity Compatibility Modularity Technical
management
knowledge
Business
functional
knowledge
Interpersonal and
management
knowledge
Technical
specialty
knowledge
Byrd and Turner (2000);
(2001a); (2001b)
Connectivity Compatibility Modularity IT personnel flexibility
Bhatt and Stump (2001) Network
connectivity
Network flexibility
Byrd (2001) Connectivity Compatibility Modularity
Chung et al. (2003); (2005) Connectivity Compatibility Modularity IT personnel
Byrd et al. (2004) Connectivity Compatibility Modularity IT personnel skills
Bhatt and Troutt (2005) Network
connectivity
Data integration Network flexibility
Egyedi and
Verwater-Lukszo (2005)
Compatibility Reusability
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(McFarlan et al., 1983; McFarlan, 1984; Ghorab, 1997). They are critically dependent on
IT, which is the core engine that processes daily operations (Porter and Millar, 1985;
Weill and Broadbent, 1998). Although the industry is mature, it is consistently in a
state of flux, and the ability to implement new products and services with new
technologies on existing IT architecture is the key to competitiveness (Mudie and
Schafer, 1985; Frei et al., 1999). Consequently, IT infrastructure flexibility is important
to this industry.
Expert interviews were conducted to determine how experts and practitioners
defined IT infrastructure flexibility and what they considered to be its core
characteristics. As shown in Table III, 11 ITexperts from a wide range of organizations
were interviewed to collect facts and opinions that offered a range of themes for this
research. Their expertise was determined according to their position and experience in
the organizations. They all had between 7 and 20 years of IT experience in Thailand’s
Table II.
Components and
definitions of IT
infrastructure flexibility
Component Definition
Connectivity The ability of any technology component to attach to any of the other
components inside and outside the organization environment
Compatibility The ability to share any type of information across any technology component
Modularity The ability to add to, modify and remove any software, hardware or data
component of the infrastructure with ease and no major overall effect
IT personnel
competency
The ability of IT personnel to possess relevant skills and experiences to
effectively perform IT activities
Sources: Byrd and Turner, 2000; Byrd, 2001; Byrd et al., 2004
Table III.
Profiles of IT experts
Expert Position
a
Typ e
b
Experience
c
Experience
d
1 Associate professor Academic institution – 31
2 Associate professor Academic institution – 20
3 SVP, IT Asset management
company
720
4 Director of data security,
IT division
Commercial bank 18 20
5 IT consultant Credit foncier company 14 17
6 AVP, IT department Finance company 20 20
7 Manager, planning and system
analysis development
Insurance company 7 7
8 AVP, technology and
information system department
Securities company 10 15
9 VP, computer and technology Securities company 12 12
10 SVP, IT Specialized financial
institution
18 30
11 AVP, IT department Specialized financial
institution
12 16
Notes:
a
Position titles may be different in each organization (AVP - Assistant Vice President,
SVP - Senior Vice President, VP - Vice President);
b
Except for academic institutions, organization
types are categorized by the Bank of Thailand, the Office of the Securities and Exchange
Commission and the Department of Insurance at the Ministry of Commerce;
c
Years of experience
in IT careers in the financial service industry;
d
Years of experience in IT careers in work-life
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financial service industry. Two experts were from academic institutions. Six experts
were heads of IT functions who were responsible for implementing key IT activities
such as planning, budgeting and decision-making. Two experts were assistants to the
head of IT functions and one was an IT consultant. The focused interview was the
selected interview format because it could be conducted effectively within a short
period of time (Yin, 1994). Moreover, since most key interviewees held senior
management positions and were very busy, the focused interview was particularly
appropriate for this study. The interviews employed semi-structured and open-ended
questions and were completed within 30 minutes. The experts were encouraged to
share their experience in IT infrastructure. Relevant information was extracted from
the conversations and the results were then examined. An overview of the interview
results can be seen in Table IV which also provides a guideline for questionnaire
development.
Field survey
Instrument development
In order to identify common characteristics of IT infrastructure flexibility,
questionnaires were developed to collect data from large samples. Forty-one items were
generated from IT infrastructure flexibility literature, IT-related literature and
interview results. Respondents were asked whether these items were characteristics of
IT infrastructure flexibility using the likert scale (1 ¼strongly disagree, 5 ¼strongly
agree). Since the target population was IT personnel, initial questionnaires were sent to
ten IT personnel with different IT backgrounds (less than 1 year and up to 11 years of
IT experience). The respondents were asked to complete the questionnaire and offer
any suggestions about the existing items for vocabulary, understandability and
ambiguity. All comments were integrated into the questionnaire. Then, the resulting
questionnaires were pilot-tested with 36 IT personnel in the financial service industry.
Data collection
One difficulty of this study was that unit of analysis was IT personnel, not the
organization. Nonetheless, a directory of IT personnel does not exist in Thailand.
Table IV.
Characteristics of IT
infrastructure flexibility
reported by IT experts
Expert A B C D E F G H I
1****** *
2***** *
3**** *
4****
5**** *
6*****
7****
8****** *
9***** *
10 * * * * * *
11 * * * * *
Notes: A¼Connectivity; B ¼Compatibility; C ¼Modularity; D ¼IT personnel competency;
E¼Able to scale, upg rade and expand; F ¼Able to recover from disaster, continue IT operations
without interruption; G ¼Able to support high speed link and high data volume; H ¼Able to use
easily; I ¼Able to survive in the future, based on trends of technologies
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The best possible way is to select at the organization level. Therefore, 20 organizations
were randomly selected from the following three directories: the Bank of Thailand, the
Office of Securities and Exchange Commission and the Department of Insurance at the
Ministry of Commerce. Five IT outsourcing companies which were responsible for IT
operations in financial service organizations were also selected by judgment sampling
to cover aspects from outsourcing entities, which have never been included before.
Questionnaires were distributed in IT departments in these organizations with the
number of respondents ranging from just one up to 74 from each organization. A
total of 388 returned questionnaires ranged from programmers to vice presidents in
IT. Tables V and VI show the profiles of these respondents.
Factor analysis
Exploratory factor analysis was conducted to examine common aspects and reveal
certain characteristics of IT infrastructure flexibility. As a minimum requirement,
sample size should be at least five times the total number of variables (Hair et al., 1998).
This study’s sample easily met that requirement because it was about nine times the 41
variables. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.935, which
was well above the acceptable 0.50 level. In addition, the Bartlett’s Test of Sphericity
indicated that there was the presence of correlations among variables (approximately
Chi-Square ¼8,180.246, df ¼820, Significant 0.000). Therefore, the collected data was
suitable to conduct factor analysis.
The extraction method used in this study was Principle Component Analysis with
equamax rotation. The factor solution explained 63.046 per cent of total variance,
slightly below two-thirds. All factor loadings were above 0.30, which was significant
for a sample size of 350 or greater (Hair et al., 1998). No item was dropped and only one
item had communality below 0.50. As a result, items were grouped into nine
components, which signified additional dimensions compared to those mentioned in
the published literature. The Eigenvalue of each component was above 1.0. Table VII
reports the results.
Tabl e V.
Respondents by years of
experience in IT careers
in work-life
Experience Frequency Percent
Less than 1 15 3.9
1-3 95 24.5
4-6 62 16.0
7-9 68 17.5
10-12 73 18.8
13-15 42 10.8
More than 15 33 8.5
Table VI.
Respondents by
organization type
Type Frequency Percent
Commercial bank 199 51.3
IT outsourcing company 80 20.6
Insurance company 77 19.8
Finance company 21 5.4
Securities company 11 2.8
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Table VII.
Factor analysis results
Factor
loading Communality
IT personnel competency: Eigenvalue ¼3.594, variance
explained ¼8.766%, ¼0.893
IT personnel understand policies and goals of the organization 0.620 0.654
IT personnel are able to plan for future technological challenges 0.611 0.630
IT personnel are able to be IT project leaders 0.608 0.577
IT personnel are able to quickly learn and apply new technologies 0.604 0.648
IT personnel are eager to learn new technologies 0.604 0.655
IT personnel are able to interpret business problems and develop
appropriate technical solutions 0.586 0.621
IT personnel are knowledgeable about environmental constraints
within the industry 0.568 0.689
IT personnel are able to work cooperatively with users in a cross-
functional team 0.539 0.563
IT personnel are skilled in multiple technologies and tools (e.g.
programming languages, operating systems) 0.511 0.557
Scalability: Eigenvalue ¼3.283, variance explained ¼8.007%,
¼0.904
Hardware/software can be easily upgraded on existing IT
infrastructure 0.655 0.740
Hardware/software can be easily scaled on existing IT
infrastructure 0.603 0.666
Hardware/software can be easily and quickly adapted for changing
needs and standards 0.594 0.713
Hardware/software can support business growth in the future 0.563 0.713
Hardware/software can be added to, modified or removed from
existing IT infrastructure with no major overall effect 0.538 0.661
Continuity: Eigenvalue ¼3.258, variance explained ¼7.946%,
¼0.848
Disaster planning and recovery are ready to launch 0.719 0.735
Data backups are adequately kept 0.686 0.696
IT personnel in any positions can be easily replaced 0.612 0.683
Hardware/software can be concurrently used by a large number
of users 0.550 0.674
Compatibility: Eigenvalue ¼2.811, variance explained ¼6.856%,
¼0.813
Applications can be used across multiple operating systems 0.632 0.530
Data can be shared across applications and operating systems 0.598 0.647
Data across applications and operating systems has consistency
and integrity 0.543 0.637
The organization provides multiple data types (e.g. text, voice,
multimedia) for data sharing 0.512 0.648
Data can be shared across departments and organizational
boundaries 0.504 0.508
The organization provides multiple interfaces (e.g. website, call
center) for data sharing 0.399 0.469
Connectivity: Eigenvalue ¼2.732, variance explained ¼6.663%,
¼0.737
Authorized data can be accessed by external parties through IT
networks, regardless of location 0.798 0.678
(Continued)
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Reliability analysis
Cronbach’s alpha (), a measure of internal consistency, was calculated to confirm the
reliability of each component. Reliability refers to the extent to which a scale produces
consistent results if repeated measurements are made. Also, it can be defined as the
extent to which the measures are free from random error. Cronbach’s alpha obtains
value from 0 to 1, with the higher the value the better. It is a commonly used index to
assess the reliability of the scales with multiple items as well as to explore the potential
for scale purification (Churchill, 1979; Nunnally and Bernstien, 1994).
According to Hair et al. (1998), although an acceptable Cronbach’s alpha is above
0.70, it may decrease to 0.60 in exploratory research. As shown in Table VIII, seven
components in the analysis had coefficient alphas above 0.70 and only two (rapidity
and facility) had alphas below 0.70 but above 0.60. Furthermore, three scales could be
improved if one poorly fit item was deleted. The first one was IT personnel competency
with a potential increasing alpha of 0.001. The second one was rapidity with
Table VII.
Factor
loading Communality
Authorized data can be accessed by internal users through IT
networks, regardless of location 0.750 0.687
All external parties (e.g. customers, suppliers) are electronically
linked with the organization through IT networks 0.638 0.581
Conferences within the organization can be held through IT
networks, regardless of location 0.571 0.566
All departments and branches are electronically linked together
through IT networks 0.418 0.536
Rapidity: Eigenvalue ¼2.595, variance explained ¼6.329%,
¼0.661
IT components (e.g. hardware, software, database) are standardized
throughout the organization 0.738 0.611
Speed of communication through IT networks is satisfactory for
internal users 0.697 0.672
Compared to rivals within the industry, the organization has the
foremost IT networks 0.620 0.532
Modularity: Eigenvalue ¼2.590, variance explained ¼6.318%,
¼0.704
Data is separated from applications 0.642 0.567
Legacy systems within the organization do not restrict the
development of new applications 0.594 0.573
Reusable subsystems or modules are widely used in system
development (e.g. login module is reused in many applications) 0.496 0.522
Data captured in one part of the organization are immediately
available to everyone in the organization 0.424 0.514
Facility: Eigenvalue ¼2.504, variance explained ¼6.107%, ¼0.606
Single terminal can be used to operate on different operating
systems 0.834 0.755
Applications are user-friendly (e.g. web-based, menu-driven) 0.582 0.587
Non-IT personnel can use applications without intensive training 0.385 0.578
Modernity: Eigenvalue ¼2.482, variance explained ¼6.054%,
¼0.776
Hardware/software are based on well-known products 0.878 0.808
Hardware/software are based on current technological trends 0.827 0.768
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an additional alpha of 0.003. These two cases were not worth changing the structure of
the factor solution, so they remained unchanged. The last one was facility. The alpha
could increase to 0.676 if the variable ‘‘single terminal can be used to operate on different
operating systems’’ was taken out. However, since this item had a high factor loading of
0.834 and a high communality of 0.755, it was retained. Therefore, the resulting
components had sufficient reliabilityand were the best factor solution for this sample.
Discussion
This research was based on the multi-method approach to define and assess the
flexibility of IT infrastructure. As summarized in Table IX, the study started with
relevant literature which identified four components of IT infrastructure flexibility.
Expert interviews were then conducted to determine the characteristics of IT
infrastructure flexibility in the view of practitioners. One notable result from the
interviews was that IT infrastructure flexibility turned out to be a new concept in
Thailand. None of the experts were familiar with it. However, when they were
encouraged to identify what they look for in order to determine flexibility, most of the
issues raised in the literature were familiar to them, although some of the points were
completely new. The results from the interviews added five more aspects as shown in
Table IX.
In order to confirm both the literature and interview results, a field survey was
carried out and factor analysis was performed. According to the empirical evidence,
Table VIII.
Reliability analysis
Variable
Number
of items
Cronbach’s
alpha
Improvement
opportunity
IT personnel competency 9 0.893 0.894
Scalability 5 0.904 None
Continuity 4 0.848 None
Compatibility 6 0.813 None
Connectivity 5 0.737 None
Rapidity 3 0.661 0.664
Modularity 4 0.704 None
Facility 3 0.606 0.676
Modernity 2 0.776 None
Table IX.
Components of IT
infrastructure flexibility
from the multi-method
approach
Literature review Expert interviews Field survey
Connectivity Connectivity Connectivity
Compatibility Compatibility Compatibility
Modularity Modularity Modularity
IT personnel competency IT personnel competency IT personnel competency
– Able to scale, upgrade and expand Scalability
– Able to recover from disaster, continue
IT operations without interruption
Continuity
– Able to support high speed link and
high data volume
Rapidity
– Able to use easily Facility
– Able to survive in the future, based on
trends of technologies
Modernity
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nine components were extracted. Four components were similar to those described in
the literature, while five were additional components of IT infrastructure flexibility.
The issue of construct validity was then examined. Assessing the construct validity
through factor analysis is useful in determining convergent and discriminant validity
(Rotchanakitumnuai, 2004). Convergent validity is the extent to which the measure
correlates highly with other methods designed to measure the same construct
(Churchill, 1979). Convergent validity is also satisfied if two independent
methodologies lead to similar ends (Nunnally and Bernstein, 1994). In this study,
the results from qualitative work (expert opinions in Table IV) were consistent with
quantitative work (empirical results in Table VII) indicating adequate convergent
validity. Another construct validity is discriminant validity. Discriminant validity is
the extent to which the measure is indeed novel and not simply a reflection of some
other variable measures (Churchill, 1979). In a simple way, it is the degree to which a
concept differs from other concepts. That means, in factor analysis, each item should
have no high factor loading on more than one factor (see Appendix). Again, the factor
model demonstrated acceptable discriminant validity.
This study’s results differ from previous research in this area for three major
reasons. Firstly, as mentioned, the context was different. Previous studies were
conducted across industries in developed countries. This study, however, was
conducted in the financial service industry in Thailand, which is a developing country.
Secondly, the respondents were different. Most of the respondents in other studies were
upper middle management and top IT executives. Our respondents represented a wider
range of experience and expertise. Lastly, time and technologies have changed, and the
perceptions of fast-changing technologies, such as IT, have evolved over time.
Although this study reports different findings from the literature, the results still
complement rather than contradict the existing research framework.
Conclusion
This paper was an exploratory study, which provided a new way to define IT
infrastructure flexibility as a concept. Based on the review of literature, interviews and
a survey taken in this study, the qualitative results were well-verified with the
quantitative evidence. The survey results offer a comprehensive understanding of IT
infrastructure flexibility that integrates the most relevant concerns raised in both the
literature and expert interviews. As a result of the three-pronged approach, an
operational definition for IT infrastructure flexibility emerges.
IT infrastructure flexibility is the ability of existing IT infrastructure to adapt to change from
both within and outside the organization in order to facilitate information sharing, system
development and continuity of IT operations with minimal effort and time.
Nine core components were found to be a new framework to describe the
characteristics of IT infrastructure flexibility which aim to enable a rapid response to
necessary changes from either opportunities or threats. The higher the degree of each
component will result in a higher degree of infrastructure flexibility. It is also
noteworthy that there could be some tradeoffs among components. For example,
software that has a high degree of modernity would probably have a low degree of
compatibility. Table X defines each component.
Managing IT
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645
Implications for managers
From a practical perspective, the findings contribute to a more thorough
understanding among top management of the importance of IT infrastructure
flexibility. On one hand, a large investment in IT infrastructure flexibility beyond
current needs can allow a system to better cope with future changes. Flexible
IT infrastructure characteristics allow managers the freedom to take full advantage
of an IT system because it is able to accommodate and respond to new initiatives
more efficiently. If the IT infrastructure is not flexible, extra money, effort and time
for either new IT implementation or other business operations will result. That is,
the lack of flexibility reduces strategic responsiveness and slows time-to-market. On the
other hand, if the flexibility of IT infrastructure is not utilized to its full potential
or a firm invests in the wrong infrastructure, it adds redundant costs with an
inadequate return. Flexibility is not free. Building IT infrastructure requires a large
investment, but building a flexible IT infrastructure adds even more to the total cost
(Byrd and Turner, 2000). It is therefore necessary to maintain a delicate balance
between over-investing in IT infrastructure and allowing for potential delays that
could result from the need to accommodate important changes (Broadbent et al., 1999;
Weill et al., 2002).
Although IT components are commodities, the ability to combine them to create
the optimal flexibility for a particular enterprise is a much scarcer resource.
Accordingly, before embarking on any IT infrastructure investment, IT executives
are encouraged to assess their IT infrastructure profiles. This new framework
offers them a practical way to review the current flexibility of IT infrastructure in their
organizations, identify the potential to meet future demands and make more careful
decisions on IT infrastructure investment. The 41-item instrument with its 0-100
scoring system, reported in Table VII, can be used to assess the flexibility of IT
infrastructure and determine which components have low scores and need more
attention. These measurements can be used to compare organizations as well.
When this analysis is complete, IT executives can then create an investment plan
Table X.
Components and
definitions of IT
infrastructure flexibility
Components Definition
IT personnel competency The degree to which IT personnel possess relevant skills and
experience to effectively perform IT activities
Scalability The degree to which hardware/software can be scaled and upgraded
on existing infrastructure
Continuity The degree to which hardware/software/data/IT personnel can
seamlessly serve the users in an organization without disruption
Compatibility The degree to which hardware/software can share any type of
information both inside and outside the organization
Connectivity The degree to which hardware/software can connect to others both
inside and outside the organization
Rapidity The degree to which hardware/software can deliver information
whenever it is needed
Modularity The degree to which hardware/software/data can be separated and
recombined to support new system development
Facility The degree to which hardware/software can be used with ease
Modernity The degree to which hardware/software are based on well-known
products and technological trends
MRN
29,10
646
that fills the gaps. Companies with a low IT infrastructure flexibility can identify
their weak points and make immediate changes to keep up with the competition.
Practical implications of enhancing flexibility of IT infrastructure are presented in
Table XI.
Table XI.
Practical implications of
enhancing flexibility
of IT infrastructure
Components Implications
IT personnel
competency
Explain policies, goals, current business status and environmental constraints
of the organization to IT personnel
Provide budgets for IT personnel to gain new knowledge (e.g. seminars, IT
professional certificates)
Develop a program to enhance managerial skills of IT personnel (e.g. leadership role)
Set the goals and evaluate the performance of IT personnel at least once a year
Offer an incentive or a reward to IT personnel who can successfully introduce
and apply new technologies to solve business problems
Scalability Design overall IT infrastructure to handle an increase in users, workload and
transactions, for example, use hub or switch that supports ten per cent more
connections than the number of staff
Develop a plan for expanding and upgrading hardware/software at least for the
next six months
Continuity Backup data daily
Keep data in a safe place in addition to the office
Create a disaster plan for recovery (e.g. in case of fire, power outage)
Rotate IT personnel to learn different jobs every six months, for example, rotate
a system analyst to be a project manager
Compatibility If possible, use the same operating system throughout the organization
If not, ensure the interoperability among applications and operating systems
using middleware, especially with legacy systems
Implement web services platform (e.g. Microsoft’s .NET or Sun’s Java 2
Enterprise Edition) to interact with other applications using open standards
Make use of extensible markup language documents
Apply electronic business using extensible markup language framework to
exchange data with trading partners
If all of above cannot be done, provide multiple data types (e.g. text, voice,
multimedia) and interfaces (e.g. website, call center) to a wide range of internal
and external users
Connectivity Create electronic linkages among departments and branches as well as external
parties (e.g. customers, suppliers)
Allow authorized users to access data through intranet or virtual private
network with encrypted technology (e.g. IP security, secure sockets layer),
regardless of location
Rapidity Use digital subscriber line technology or leased-line channel to create external
linkages that support a high speed link and a high data volume
Implement network topology (e.g. bus, ring, star) that fits the internal usage
Monitor the bottlenecks within the networks every month and find possible remedies
Modularity Separate and secure data from applications using database management systems
Break down a large program into individual modules that can be programmed
and tested independently
Combine repetitive process in multiple applications into a single reusable
module (e.g. credit authorization process)
Utilize reusable technology (e.g. object-oriented programming)
Facility Build user-friendly applications (e.g. web-based, menu-driven)
Create manual or documentation for each hardware/software
Modernity Implement hardware/software that are reputable or based on current
technological trends
Managing IT
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647
Implication for researchers
From a theoretical perspective, this study is the first to provide empirical evidence
from the context of a developing country, which fills a significant gap in the literature.
This paper also improves on the existing model of IT infrastructure flexibility by
developing a new set of reliable measurements.
Businesses in the information age rely heavily on IT, and as a result, the IT
infrastructure on which organizations depend must be flexible enough to respond to
ever-changing business environments. However, it is difficult for IT executives to
justify a large IT infrastructure investment because it is often questionable whether the
expected goal or target can be reached (Broadbent and Weill, 1997; Bhatt and Stump,
2001; Kumar, 2004; Lee, 2004; Huang et al., 2006). Thus, determining whether IT
infrastructure adds value to the organization is an important management question.
Researchers may use the measurements proposed in this study to conduct further
investigations with other organizational measurements. For example, the study of IT
infrastructure flexibility and revenue or customer retention can be explored. The issue
of managing IT infrastructure flexibility via outsourcing is also an interesting and
important topic for future research. The outcomes will be valuable for policymakers
(e.g. IT executives, business executives) who are looking for guidelines to integrate IT
with an organization’s strategy.
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Appendix. Major factor analysis loadings
Table AI.
Rotated component matrix
12345 6 7 89
IT personnel competency (¼0:893)
Item 1 0.620
Item 2 0.611 0.306
Item 3 0.608
Item 4 0.604 0.314
Item 5 0.604 0.318
Item 6 0.586
Item 7 0.568 0.305 0.307
Item 8 0.539 0.338
Item 9 0.511 0.354
Scalability (¼0:904)
Item 1 0.655 0.325
Item 2 0.603 0.333
Item 3 0.594 0.358
Item 4 0.563 0.418
Item 5 0.538 0.305 0.352
Continuity (¼0:848)
Item 1 0.719
Item 2 0.686 0.343
Item 3 0.612
Item 4 0.425 0.550
Compatibility (¼0:813)
Item 1 0.632
Item 2 0.598 0.303
Item 3 0.543 0.372
Item 4 0.379 0.512 0.411
Item 5 0.504
Item 6 0.372 0.399
(Continued)
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About the authors
Anote Chanopas is a doctoral student at the School of Management, Asian Institute of Technology.
He holds a B.B.A. in Management Information Systems and a M.S. in Information Technology.
Before commencing his current degree, he worked as an IT analyst in a multinational consulting
company. His current research focuses on IT infrastructure and its implementation.
Donyaprueth Krairit holds a Ph.D. from Massachusetts Institute of Technology (MIT) in
Telecommunications Technology and Policy. She was a consultant in telecommunications and a
guest speaker at Harvard and Tufts University. Her research has been in the field of
telecommunications and sustainable development, economic and policy implications of
technologies and E-commerce, with particular focus on Asian countries and the US. She is now
an assistant professor at the School of Management, Asian Institute of Technology. She had
publications in the Information and Security Journal, an electronic version of the Journal of
Information Technologies and International Development and International Journal of the
Computer, the Internet and Management. Donyaprueth Krairit is the corresponding authour and
can be contacted at: donya@ait.ac.th
Do Ba Khang is currently an associate professor at the School of Management, Asian
Institute of Technology. He holds a M.S. in Mathematics, another in Industrial Engineering and
Management, and a Ph.D. in Industrial Engineering and Management. His research interests
include quantitative modeling in operations and project management.
Rotated component matrix
12345 6 7 89
Connectivity (¼0:737)
Item 1 0.798
Item 2 0.750
Item 3 0.638
Item 4 0.361 0.571
Item 5 0.313 0.319 0.418
Rapidity (¼0:661)
Item 1 0.738
Item 2 0.697
Item 3 0.620
Modularity (¼0:704)
Item 1 0.642
Item 2 0.345 0.594
Item 3 0.380 0.496
Item 4 0.424 0.372
Facility (¼0:606)
Item 1 0.834
Item 2 0.582
Item 3 0.320 0.369 0.385
Modernity (¼0:776)
Item 1 0.878
Item 2 0.827
Variance explained (%) 8.766 8.007 7.946 6.856 6.663 6.329 6.318 6.107 6.054
Cumulative variance
explained (%) 8.766 16.772 24.719 31.575 38.237 44.566 50.884 56.992 63.046
Note: Items are listed in order of Table VII and factor loadings below 0.30 are suppressed Table AI.
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