A strategy-based process for effectively determining system

Information and Software Technology 51 (2009) 1308–1318
Contents lists available at ScienceDirect
Information and Software Technology
journal homepage: www.elsevier.com/locate/infsof
A strategy-based process for effectively determining system requirements
in eCRM development
Ing-Long Wu a,*, Ching-Yi Hung b
a
b
Department of Information Management, National Chung Cheng University, 168 University Road, Ming-Hsiung, Chia-Yi 621, Taiwan
Chung-Hwa Telecommunication Inc., 21-3 First Section Sin-Yi Road, Taipei, Taiwan
a r t i c l e
i n f o
Article history:
Received 15 April 2008
Received in revised form 12 March 2009
Accepted 18 March 2009
Available online 7 April 2009
Keywords:
eCRM
Relationship marketing
Consumer behaviors
Requirements engineering
a b s t r a c t
Customer relationship management (CRM) is an important concept to maintain competitiveness at ecommerce. Thus, many organizations hastily implement eCRM and fail to achieve its goal. CRM concept
consists of a number of compound components on product designs, marketing attributes, and consumer
behaviors. This requires different approaches from traditional ones in developing eCRM. Requirements
engineering is one of the important steps in software development. Without a well-defined requirements
specification, developers do not know how to proceed with requirements analysis. This research proposes
a strategy-based process for requirements elicitation. This framework contains three steps: define customer strategies, identify consumer and marketing characteristics, and determine system requirements.
Prior literature lacks discussing the important role of customer strategies in eCRM development. Empirical findings reveal that this strategy-based view positively improves the performance of requirements
elicitation.
Ó 2009 Elsevier B.V. All rights reserved.
1. Introduction
Business over the Internet presents unprecedented opportunities for building sales and increasing revenue streams by expanding geographical scope, enhancing channel coordination, and
improving supply chain efficiency. To be successful at e-commerce,
companies have to rethink their business focus. Their business
model has to evolve from production-centric to customer-centric.
As products have become more commoditized and pricing differences more slight, the great differentiator today is delivering
customer value. Customer value is what leads to increased loyalty,
sales, and retention rates. Customer relationship management
(CRM) is a strategic and management concept in creating customer
value [10,27,28]. Basically, the idea of CRM is founded on relationship marketing. Relationship marketing is mainly to build a longterm association, characterized by purposeful cooperation and
mutual dependence on social, as well as structural, bonds [41].
Examples of the well-defined frameworks include Neumann [43]
and Stone et al. [58], which are based on the assumption of considering a customer’s consumption of products or services through
the customer life cycle.
Thus, CRM concept involves a number of compound components on product designs, marketing characteristics, and customer
behaviors. It is relatively complex and dynamic in nature. Without
* Corresponding author. Tel.: +886 5 2720411x34620; fax: +886 5 2721501.
E-mail address: [email protected] (I.-L. Wu).
0950-5849/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.infsof.2009.03.004
the support of information technology, in particular, Internet and
communication technology, it is difficult for firms to implement
CRM practice. The issue of electronic CRM (eCRM) has increasingly
become the identification of the success of CRM implementation
[11,62]. ECRM is a business and technology discipline that helps
firms acquire and retain the most profitable customers while meeting their requirements. With this trend, too many organizations
are rushing to implement a website and take a ‘‘build it and they
will come” attitude. However, the results were less than what were
expected. Estimates of eCRM projects failing to achieve their objectives range from 60% to 80% [26,54,64]. One of the reasons for
unsatisfactory outcomes is focusing solely on technological aspect
rather than on marketing aspect for the use of marketing personnel
[44,65].
As application software has become more complicated and
large scale, developing eCRM thus requires the following different
approaches from traditional approaches [1] Software requirements
engineering is one of the most important phases in software
development process [48,50]. Requirements engineering basically
consists of the understanding and representation of the problem
to be solved and the requirements elicitation for the problem
[51,57]. The main purpose lies in defining a well-written requirements specification to effectively manage user requirements
[51,57]. This study emphasizes on the issue of requirements
engineering in eCRM development and proposes an approach to
address this issue. Requirements elicitation requires relatively difficult communication interface to be made, particularly between
I.-L. Wu, C.-Y. Hung / Information and Software Technology 51 (2009) 1308–1318
users’ social world and analysts’ technical world. Improving a communication fit between the constraints of the two worlds is fraught
with complexity by virtue of the interplay of human, organizational processes, and technical issues. As a developer, it is absolutely critical that we recognize human and organizational
aspects in our communication with customers [20,21]. Specifically,
requirements elicitation is closely dependent on the understanding
of CRM domain in this study.
While a successful CRM approach builds on a strategic vision for
effectively integrating organizational processes with technologies
[35], a strategic view on requirements elicitation can provide a
clear picture to predefine the complex process and further, simplify
the complexity of requirements elicitation [13,39,49]. Therefore,
this research proposes a strategy-based process for effectively
determining requirements in eCRM development. The first is to define a well-planned, unique customer strategy for a firm in the
market, which conceptually considers its particular customer
needs and marketing objectives, to further direct CRM practice
[12,54]. Next, the fundamental to customer strategies requires further understanding consumer and marketing stimuli in order to
analyze CRM practice [8,11]. Accordingly, user requirements in
eCRM development can be effectively determined based on the
previous analyses.
In summary, this planning process comprises three steps: define customer strategies, identify consumer and marketing characteristics, and determine system requirements. Little research has
discussed the important role of customer strategies in eCRM development. Moreover, prior literature on this implementation process
has only reported partially of these steps rather than integratively.
In addition, the techniques used for analyzing these steps are also
discussed. Finally, this framework is empirically examined to
understand the practical validity.
2. Literature review
2.1. CRM and eCRM
The new Internet Economy presents numerous challenges to
marketers along with the significant opportunities it offers. Customers are smart, powerful, and highly informed. Customers are
demanding that every interaction a company has with them leave
them more than satisfied. Customer satisfaction is therefore considered as the most important and new performance metric, soon
to rival revenue and profit [46]. Today, any advantage based on
product or service is short lived. Instead, forging long-term relationships with customers is the key to stability in an increasingly
dynamic market [36,45]. This implies that traditional marketing
approach no longer fulfills the needs of the Internet era.
Although CRM is a recent concept, its tenets have been around
for some time. Similarly, the concept of mass customization has
been in the literature for nearly a decade [15]. The original focus
of CRM is to forge closer and deeper relationships with customers, ‘‘being willing and able to change your behavior toward an
individual customer based on what the customer tells you and
what else you know about the customer” [8]. The premise is that
existing customers are more profitable than new customers; that
it is less expensive to sell an incremental product to existing customers; and that customer retention would be maximized by
matching products and levels of services more closely to customer expectation. The central objective of CRM is thus to maximize the lifetime value of a customer to the organization
[8,47,62].
However, all have remained essentially theoretical concepts,
aspirations rather than a practical or commercial reality. It has
been very difficult for firms to develop eCRM due to the complex-
1309
ity of CRM process. The great advancement in information technologies recently has provided tremendous resources for
facilitating the fulfillment of eCRM. Information technologies such
as database, data warehouse, and data mining allow companies to
track and manage customer profitability, behavior, and satisfaction at a reasonable cost [6]. Moreover, the fast growing Internet
network and communication technologies provide services such
as e-mail, online interaction, and personal website to become
an important resource for a company to achieve one-on-one relationship, customer value analysis, and mass customization
[15,63].
A joint study by the Economist Intelligence Unit and Andersen
Consulting in the year 2005 investigated eCRM deployment at
more than 200 companies worldwide [33]. They argued that companies have identified the need to break down geographical, functional, and organizational barriers to customer information and
interactions. For the next five years, the percentage of the companies organized around the issues of customer segmentation and
profitability analysis is expected to increase by more than 170%.
Seventy percent of the companies plan to make major changes
in the design of their eCRM development process, focusing on
overall outcomes as opposed to individual tasks. This implies that
the design of eCRM should be approached from a strategic
perspective.
2.2. Relationship Marketing and its strategies
The underlying determinants of CRM are basically from the discipline of relationship marketing [19,42]. Relationship marketing is
a common term with different definitions. Stone et al. [58] defined
it as the use of a wide range of approaches, including marketing,
sales, communication, service, and customer care, to identify a
company’s named individual customers, to create a relationship
between the company and its customers which stretches over
many transactions, and to manage the relationship in order to benefit the company and its customers. Relationship is how to find
customers, get to know customers, keep in touch with customers,
and ensure what customers want from the company in every aspect [47]. Thus, relationship marketing needs further elaboration
for a sound theoretical base in developing eCRM.
Perhaps, the earliest model for analyzing relationship marketing can be traced to the customer resource life cycle (CRLC) model
proposed by Ives and Learmonth [24]. The CRLC focuses on the
relationship or linkage between a company and its customers. This
framework is based on four stages: requirements, acquisition,
stewardship, and retirement, each comprising several steps. The
assumption is that a customer’s consumption of products and services (resources) goes through this life cycle. It provides managers
with customer’s perspective in evaluating strategic potentials of IT
applications to achieve competitive advantage. The development
of buyer–seller relationship is mapped out in a five-phase model:
awareness, exploration, expansion, commitment, and dissolution
[46]. Within such a framework, one might easily characterize a
marketing relationship as a marriage between a seller and a buyer
(the dissolution phase being a ‘‘divorce”). The use of the marriage
metaphor emphasizes the complexity as well as some affective
determinants of the quality of a relationship. This process takes
place in the long run, results in some bilateral benefits, and rests
on an in-depth understanding of customer needs and
characteristics.
Stone et al. [58] summarized the relationship as a series of
stages to help companies plan the use of IT in managing their customers. These stages are: recruitment, welcoming, getting to know,
account management, intensive care, potential divorce, divorce,
and winback. Companies can employ this model to identify which
customers to acquire, retain, and which to discourage as well as to
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further develop marketing, service and IT policies to create the
right relationships with different types of customers. While CRM
is conceived as a strategy for an organization deciding to build
long-term relationship with customers [12,54,64], there is a need
to provide some guidance for defining relationship strategies in order to carry out CRM practice. Furthermore, these defined strategies will facilitate an understanding of CRM domain and in turn,
eCRM development. Based on a comprehensive literature review,
three similar models have been proposed to define customer relationship strategies in e-commerce [8,25,62].
The first defined three stages: acquisition, enhancement, and
retention [25]. Each has a different impact on the customer relationship and each can more closely tie the company to the customer’s life and value.
(1) Acquiring new customer. You acquire new customers by promoting product and service leadership that pushes performance boundaries with respect to convenience and
innovation. The value proposition to the customer is the
offer of a superior product backed by excellent service.
(2) Enhancing profitability of existing customers. You enhance the
relationship by encouraging excellence in cross-selling and
up-selling. This deepens the customer relationship. The
value proposition to the customer is an offer of greater convenience at low cost (one-stop shopping).
(3) Retaining profitable customers for life. Retention focuses on
service adaptability – delivering not what the market wants,
but what customers want. The value proposition to the customer is an offer of a proactive relationship that works in his
or her best interest.
The second investigated 1500 organizations to understand what
is the pinnacle of customer care to which most organizations
should aspire [8]. All organizations seem to fall within one of three
distinct stages of customer care: customer acquisition, customer
retention, and strategic customer care. The descriptions of stage I
and stage II are similar to those of Kalakota and Robinson’s model
[25]. Stage III organizations recognize that customer relationships
that are in essence strategic partnerships allow them to increase
own benefits by focusing on enhancing the customer’s profits. Finally, Gartner Group described a relationship marketing model
similar to Brown’s discussion [60]. It divides customer interaction
into four categories. These are customer selection, customer acquisition, customer retention, and customer extension. The descriptions of these stages and their relationships are shown in Fig. 1.
In the next section on theoretical model development, we will
make a summary from these three models for defining the types
of customer strategies adopted in this study.
2.3. Consumer behaviors
As discussed previously, customer strategies are defined as a
high-level concept, and consumer behaviors consist of the basis
of the concept and have a profound impact on the way that eCRM
is developed. At this time, we need to discuss the concept of consumer behaviors [8,11]. Market researchers have been trying for
decades to understand consumer behaviors. Schiffman and Kanuk
[52] comprehensively discussed six models of consumer behaviors: the Nicosia model [4], the Howard–Sheth model [23], the Engel–Kollat–Blackwell model [17], the Sheth family decisionmaking model [55], the Bettman information-processing model
[5], and the Sheth–Newman–Gross model [56]. These models are
basically defined in terms of input–process–output structure. The
input component consists of two sets of stimulus characteristics,
consumer and marketing characteristics. The process component
deals with the search for the relevant information and evaluation
"What criteria determines
who will be our most
profitable customers?"
Customer
Acquistion
Customer
Selection
Relationship
Marketing
"How can we increase
the loyalty and the
profitability of this
customer?"
Customer
Extension
"How can we acquire this
customer in the most
efficient and effective
way?"
Customer
Retention
"How can we keep this
customer for as long as
possible?"
Fig. 1. Relationship marketing model (adapted from Turban et al. [62]).
Personal
Characteristics
External
Characteristics
Age, Gender, Income,
Education, Lifestyle,
Psychological,
Occupation, Values,
Personality, Marital
Social, Community,
Family
Stimuli
Marketing
Price
Promotion
Product
Quality
Environmental
Economical
Technology
Political
Cultural
Buyers' Decisions
Decision Making
Process
Buy or Not
What to Buy
Where(Vendor)
When
How Much to spend
Repeat Purchases
Vendors' Controlled
Systems
Logistic Technical
Customer
Support
Support
Service
Payments, Web Design, FAQ,
Delivery Intelligent
E-Mail,
Agents
Call Centers,
One-to-One
Fig. 2. Consumer behavior model of e-commerce (adapted from Turban et al. [62]).
of the firm’s brand in comparison with alternative brands. The output component describes that the consumer’s motivation toward
the firm’s brand results in purchase of the brand from a specific retailer (decision making).
Moreover, a model of buyer behavior indicates a similar stimulus–response structure [32]. This model illustrates marketing and
environmental stimuli entering buyer’s ‘‘black box” and producing
buyer’s responses. In comparison with the previous models, an
exception to this model is that buyer’s characteristics are defined
as part of buyer’s black box, rather than as outside stimuli. In addition, a consumer behavior model in e-commerce describes marketing stimuli, personal characteristics, external characteristics, and
vendors’ controlled systems entering decision-making process
and producing buyers’ decisions, as indicated in Fig. 2 [62]. This
model includes an additional set of vendors’ controlled systems
as compared with the previous models. Basically, this component
plays a technological and supporting role for the other three components in e-commerce environment.
2.4. IT planning and requirements engineering
The main reason for application software not meeting user
expectation is the failure to obtain a complete and correct set of
user requirements in development [22]. Software requirements
engineering concerns all the activities, including understanding
of the problem, analysis of user requirements, documentation of
the requirements as a specification, and validation of the documented requirements against the actual users’ needs [51,57]. In essence, it involves an understanding of the specific problem and its
I.-L. Wu, C.-Y. Hung / Information and Software Technology 51 (2009) 1308–1318
context in which the requirements are embedded. Specifically, it
needs to construct a clear, high level specification of the problem
that is to be solved. Accordingly, requirements elicitation, as part
of the requirements engineering, can be started for modeling, gathering and extracting information about required functionality,
data, and interface of a proposed system by system developers
[14,20,34]. This will ensure that a solution is being developed for
the problem and that the solution is feasible. More importantly, a
good requirements engineering approach is able to build a common vision of the problem and the conceptualized system solution
between users and developers [50,51].
Previous research has presented a large number of approaches
to effectively manage requirements elicitation. Most of the approaches are from methodology-oriented rather than from organization-oriented perspective, such as content management, use case
based, scenario, and classification [3,31,38,57]. For example, classification approach is mainly used for effectively developing complicated software while most requirements in larger projects are
obtained from various stakeholders located in wide and different
regions [31]. The initial requirements analysis must be classified
by various topics prior to analysis phase in order to be usable as input for regular requirements analysis methods. Other approaches
have discussed the importance of communication-related methods, such as viewpoint-oriented requirements definition [2,14].
This technique allows structuring of requirements in terms of different
viewpoints
and
defining
detailed
requirements
specifications.
Basically, the reasons for difficulties in requirements determination can be summarized from relevant literatures [3,14,16,31]
as follows: (1) cognitive issues resulting from constraints on humans as information processors and problem solvers; (2) problem
unstructuring issues resulting from the variety and complexity of
information requirements; (3) communication issues resulting
from the complex patterns of interaction among users and analysts
in defining requirements; and (4) political and behavioral issues
resulting from unwillingness of some users to provide requirements. Problem structuring issues are the most apparent to system
analysts in defining requirements [2,3,57,59]. While CRM domain
is with complex structure, as indicated above, this research intends
to alleviate the impact of problem unstructuring issues and finds a
new approach in effectively defining requirements for eCRM
development.
Prior literature has proposed a number of models for the development of information system plan (ISP) and information architecture. These models are mainly based on understanding the
problems of requirements analysis in system development. A
three-stage model for ISP suggests that a strategic alignment effort
as the first stage should precede organizational information
requirements as the second stage and resource allocation as the
third stage [7]. The stimulus for ISP is typically an organizational
problem [9,37]. The problem may be effectively analyzed in terms
of an organizational structure with certain hierarchical levels, e.g.,
organizational strategies at the top, business processes at the second, tasks at the third, and information at the bottom [9,37]. In
summary, these models indicate an important fact that an effective
approach for defining requirements should be rooted at appropriate choice of strategies. The hierarchical structure may lay a foundation on developing a strategy-based process for effectively
eliciting requirements. Many researchers have proposed similar
strategy-based concepts for helping requirements elicitation
[29,40,51].
3. Theoretical framework
As discussed in Sections 2.2–2.4, while problem domain is
unstructural in nature, this approach to effectively define user
1311
requirements should be based on the thinking of a hierarchical
organizational structure, such as organizational strategies at the
top, tasks at the next, and information at the bottom [7,9,34,37].
Moreover, eCRM domain is essentially complex in nature and its
scope is difficult to be defined for determining requirements. Much
research has argued that eCRM concept is not simply a software
application but is a strategy. It concerns the creation of stakeholders’ values through the understanding of market changes and the
building of long-term relationships with key customers and customer segments [11,12,54,64]. While considering both eCRM domain with a strategy focus and a hierarchical structure for
defining requirements, an approach can be made for developing
a strategy-based process for defining requirements based on customer strategies. In conclusion, this research thus proposes a customer strategy-based process for defining requirements in eCRM
development, as indicated in Fig. 3. It consists of three steps: define
customer strategy, identify customer and marketing characteristics, and determine system requirements. Little research has discussed the important role of customer strategies in requirements
elicitation for eCRM development.
The following first defines the three steps based on prior literature and then, develops hypotheses. As discussed in Section 2.2,
there are three similar models defined for customer strategies,
i.e., Kalakota and Robinson [25], Brown [8], and Turban et al.
[62]. All the three models generally involve a marketing cycle of
the relationship building between companies and customers.
Comparatively, Brown’s model basically covers the primary features of the other models, adopted to define customer strategies
in this step, i.e., customer acquisition, customer retention, and
strategic customer care. This model also indicates that achieving
all three stages well is a difficult proposition, even for the best
of companies. The reason for this may be because it is difficult
for companies to compete well in wider customer strategies in
terms of resource limitation. Companies often have to choose
which one of these dimensions will be their primary focus and
have to master very well. The decision is important because it
will dictate the infrastructure strategy of technology [8]. As discussed in Section 2.3, both input–process–output and stimulus–
response structures in defining consumer behaviors commonly
describe the input component as consumer/personal and marketing/environmental characteristics [32,62,65]. There are two sets
of stimulus characteristics in defining consumer behaviors, i.e.,
consumer and marketing characteristics. For determining requirements, there is a need to assess its performance to understand
whether it is executed satisfactorily. A study has widely defined
a measurement system to include five items: accuracy and free
bias, completeness, time reduction in performing requirements
Define
customer strategies
Identify
consumer & marketing
characteristics
Determine
system requirements
Fig. 3. Theoretical framework.
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determination, usefulness of output information, and ease of use
in output information [61].
Next, hypothetical linkages are developed for the research
framework. Many studies have discussed the importance of customer strategies in directing the process of developing and implementing eCRM [12,40]. Furthermore, while a new type of customer
relationship is well developed based on customer strategies for a
firm, the fundamentals to the particular relationship are hereafter
identified, i.e., consumer behaviors [13,48]. Another study defines
CRM as an integrated customer strategy to effectively manage
customers by providing customized products and maximizing customers’ lifetime values [30]. This also indicates the relationship between customer strategies and consumer behaviors. Accordingly,
we can argue that there is a linkage between customer strategies
and consumer behaviors. While there are two sets of stimulus
attributes, consumer and marketing characteristics, Hypotheses 1
and 2 are thus developed for the two relationships. While eCRM
has been replete with high failure rate, mainly failing to meet
CRM objectives, user requirements should be defined in terms of
their strategies and consumer behaviors [49,64]. As a result, user
requirements can be effectively elicited while consumer behaviors
are well predicted in advance. Accordingly, we can argue that there
is a linkage between consumer behaviors and requirements elicitation. Hypotheses 3 and 4 are thus developed for the two
relationships.
H1: The choice of customer strategies is positively related to the
identification of consumer characteristics.
H2: The choice of customer strategies is positively related to the
identification of marketing stimuli.
H3: The identification of consumer characteristics is positively
related to the performance of requirements elicitation.
H4: The identification of marketing stimuli is positively related
to the performance of requirements elicitation.
4. Research design
A survey is used to collect empirical data. The design of research
is described as below.
4.1. Instrument
The instrument contains a four-part questionnaire as indicated
in Appendix A. The first part uses a nominal scale, while the rest
use 7-point Likert scales.
4.1.1. Basic information
Here, information is collected about the organizational characteristics including industry, annual revenue, number of employees,
and experience of eCRM, together with the respondent’s characteristics including education, age, experience, and position.
4.1.2. Customer strategy
This part is adapted from a self-assessing instrument for defining customer strategies of a firm [8], comprising 12 items in all.
Basically, the measure includes example attributes such as Internet
applications, differentiated services, customer segmentation, and
customers’ profitability in order to discriminate the adoption of
customer strategies in a firm.
4.1.3. Consumer and marketing characteristics
This part is adapted from two constructs in Turban et al. [62], as
indicated in Fig. 2, including 10 attributes for consumer characteristics: age, gender, income, education, lifestyle, psychological state,
occupation, values, personality, and marital as well as 4 attributes
for marketing stimuli: price, promotion, quality, and product.
4.1.4. Performance of requirements elicitation
This part is adapted from the instrument for assessing requirements elicitation [61]. Basically, the measure includes five items:
accuracy and free bias, completeness, time reduction in performing
requirements determination, usefulness of output information, and
ease of use in output information.
4.2. Sample design
Since this study mainly explores the development of eCRM, the
focus is on B2C commerce for analysing consumer purchasing
behavior. Service, and finance and banking industries would have
more experience on this. Moreover, the qualified firms would need
to have experience on strategic planning and massive IT investment. It is assumed that larger firms would be more likely to have
this experience. A study sample, including 645 service firms and
200 finance and banking firms, was thus selected from the year
2006 listing of service, and finance and banking sectors published
by the Taiwan Stock Exchange Corporation. Based on this sample,
CIOs or IS top managers were selected as the respondents. This is
because this study focuses on the understanding of eCRM development and involves the analysis of customer strategies. Therefore,
CIOs are more likely to be the managerial personnel who have best
knowledge regarding all these topics. Their experiences should be
properly reflected on the responses of the questionnaire. In addition, in order to improve survey return, a follow-up procedure by
phone or letter is carried out for the non-respondents after 2–3
weeks.
Initially, pretest was conducted for the scale. The scale was
carefully examined by selected practitioners and academicians in
this area, including translation, wording, structure, and content.
Content validity of the scale should be acceptable. After the questionnaire was finalized, 159 replied, with 4 incomplete responses
deleted, resulting in a total sample of 155 respondents for an
18% response rate. The responding sample contains 115 service
firms (74.2%) and 40 finance and banking firms (25.8%). The sector
distribution of the responding firms is approximately similar to
that of the sample frame. The seemingly low response rate raises
the concern about non-response bias. To check non-response bias,
the responding sample was divided into two subsamples, i.e., early
and late subsamples with 100 and 55 respondents, respectively.
The two groups were compared on various demographic characteristics for their correlation with t-test, including annual revenue,
number of employees, and experience. All of them (t value = 1.21,
1.56, and 1.84) reveal no significant differences at 0.05 level. This
indicates no systematic non-response bias for the responding sample. Accordingly, we can infer that the responding sample is well
representative of the sample frame.
4.3. Scale validation
Confirmatory factor analysis (CFA) was used to analyze scale
validation, as described below. Firstly, a measurement model
should be assessed for a goodness-of-fit. The literature suggested
that, for a goodness-of-fit, chi-square/degrees of freedom (v2/df)
should be less than 3, adjusted goodness-of-fit index (AGFI) larger
than 0.8, goodness-of-fit index (GFI), normed fit index (NFI), and
non-normed fit index (NNFI) greater than 0.9, and root mean
square error (RMSE) less than 0.10 [21,53]. Secondly, two criteria
are used for scale validation, reliability and construct validity.
Reliability is the extent to which a measuring device yields the
same result on repeated trials. Without the agreement of yielding
consistent results from measuring device, researchers would be
unable to satisfactorily draw conclusions, formulate theories, or
make claims about the generalizability of their research. Construct
validity seeks an agreement between a theoretical concept and a
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Table 1
Reliability and validity.
Construct
Item
loading
Composite
reliability
AVE
Squared correlation
Cs
Cc
Customer strategy
Consumer
characteristics
Marketing stimuli
Performance of
requirements
elicitation
0.85–0.88
0.77–0.83
0.92
0.83
0.82
0.71
–
0.38
–
0.82–0.84
0.88–0.90
0.87
0.93
0.76
0.85
0.32
0.39
0.40
0.38
Ms
Pe
–
0.38
–
Consumer strategy (Cs), consumer characteristics (Cc), marketing stimuli (Ms),
performance of requirements elicitation (Pe).
specific measuring device. Construct validity is further divided
into two sub-categories: convergent validity and discriminant
validity. Convergent validity is the actual general agreement
among ratings of measuring indicators where measures should
be theoretically related. Discriminant validity is the lack of a relationship among measures where they should not be theoretically
related.
Reliability is evaluated by the index of composite reliability.
Convergent validity is assessed by three criteria, item loading (k)
for an item at least 0.7 and significance, composite reliability at
least 0.8, and average variance extracted (AVE) for a construct larger than 0.5 [18]. Finally, discriminant validity is assessed by the
measure that AVE for a construct should be larger than the
squared correlation between the construct and other constructs.
The testing results of the measurement model indicate all the
indicators above the criteria and consequently, a goodness-of-fit
model. The indicators for reliabilities and validities are reported
in Table 1. Item loadings are all greater than 0.7, composite reliabilities are all larger than 0.8, and average variances extracted
(AVE) are all higher than 0.5. Thus, reliabilities and convergent
validities are all above the criteria. Moreover, AVEs for a construct
are all larger than the squared correlation between the construct
and other constructs. Discriminant validities are in an acceptable
level.
5. Analysis and findings
This section proceeds with the sequence of the three steps in
this framework.
5.1. Define customer strategies
An exploratory analysis first verifies Brown’s model by empirical data. Cluster analysis with hierarchical and nonhierarchical
procedures in combination is used to group the sample firms based
on the 12 variables, as defined in customer strategies questionnaire. The hierarchical procedure with Ward’s algorithm first identifies the appropriate number of clusters and nonhierarchical
procedure with K-means further adjusts or fine-tunes the results
Table 2
Validation results from discriminant analysis.
Discriminant analysis
Cluster analysis
Cluster 1
Cluster 2
Cluster 3
Cluster 1
Cluster 2
Cluster 3
19 (93.9%)
0 (0%)
0 (0%)
0 (0%)
67 (94.7%)
0 (0%)
1 (6.1%)
3 (5.3%)
65 (100%)
from the hierarchical procedure. The analysis finally suggests that
three-cluster solution is the appropriate choice, with cluster sizes
of 20, 70, 65 firms for clusters 1, 2, and 3, respectively. Furthermore, discriminant analysis is used to validate the solution, as indicated in Table 2. This indicates 97.4% of clustered firms correctly
classified. Next, while Brown’s model theoretically argues defining
the three stages of customer strategies in terms of their relative
size on these variables, Scheffe method for multiple comparisons
among the three clusters reveals clusters 2, 3, and 1 in an ascending order for most of these variables. Clusters 2, 3, and 1 can be reasonably identified as the strategies of customer acquisition (45.2%),
customer retention (41.9%), and strategic customer care (12.9%),
respectively.
From the percentage distribution, some facts can be discussed
for practice. There are almost half of the sample firms considering
customer acquisition as their major customer strategies. Obviously, traditional marketing approach is still playing the important role while CRM approach is currently in the early stage for
most firms in Taiwan. Nevertheless, the adoption of the other
two strategies adds up to occupy a high percentage (54.8%). This
implies that a trend of moving toward CRM approach is under
developing as the Internet has increasingly played more important role in supporting the business activities of customer retention and strategic customer care. In particular, the firms
adopting the middle stage of retention strategy would begin separating customers into different categories using the information
collected in the initial stage of acquisition strategy and gradually
establish a strong customer base. More specifically, only 12.9% of
the sample firms are in the final stage of strategic customer care.
The firms in this stage have built the relationships of strategic
partnership with customers. However, customers are smart,
sophisticated, and highly informed for demanding good quality
of products in the Internet era. Building partnership relationship
with customers may require an extended time and much effort
to achieve [47].
5.2. Identify consumer and marketing characteristics
The first discusses the relationship between customer strategies
and consumer characteristics. Their relationship can be defined in
terms of consumer characteristics as 10 dependent variables and in
terms of customer strategies as one independent variable with
three levels/stages. Moreover, dependent variables are of metric
attributes and independent variable is of non-metric attribute.
MANOVA is used to analyze this relationship structure. The testing
results show that Wilks’ k = 0.72, and equivalent F = 20.45 and
p < 0.01. Thus, Hypothesis 1 is accepted. More specifically, univariate F statistics are further examined to understand the relative
Table 3
Univariate tests for consumer attributes across customer strategies.
Consumer
characteristics
F
P-value
Age
Gender
Income
Education
Lifestyle
Psychological
Occupation
Values
Personality
Marital
7.83
6.43
13.23
10.12
5.12
7.34
6.64
6.38
9.23
5.89
0.012*
0.031*
0.000*
0.001*
0.041*
0.071
0.029*
0.033*
0.009*
0.064
Customer strategies
1
2
3
3.58
2.84
4.98*
3.21
2.31
3.03
2.96
3.01
3.18
3.11
5.01*
5.16*
5.69*
4.82*
3.84
3.31
5.39*
3.68
5.23*
3.70
5.93*
5.26*
6.43*
6.25*
4.51*
4.01
6.23*
5.23*
5.41*
4.01
Group mean
differences
3>1*; 2>1*
3>1*; 2>1*
3>1*
3>1, 2*; 2>1*
3>1*
3>1*; 2>1*
3>1, 2*
3>1*; 2>1*
–
1: customer acquisition; 2: customer retention; 3: strategic customer care.
*
P < 0.05.
1314
I.-L. Wu, C.-Y. Hung / Information and Software Technology 51 (2009) 1308–1318
importance of the ten consumer characteristics across the three
customer strategies, as indicated in Table 3. Eight of them reveal
significant differences across the three customer strategies and
two of them do not. In general, this implies certain facts for practitioners. While the eight consumer attributes are more important
in supporting customer strategies for CRM practice, requirements
analysis for eCRM development should place more emphasis on
understanding these consumer attributes in order to effectively
reach the goal. Besides, eCRM development requires massive resources while it involves wide and complex components. However,
organizational resources for competitive business activities are
limited for calling for effective management. Consumer characteristics such as psychological state and marital status are not significant in building customer relationships and may be ignored in the
consideration of their impact on requirements elicitation. In summary, the quality of system development can be substantially
achieved while at the same time, a lower developing cost can be
maintained.
Next, while examining group mean differences among three
customer strategies for each of consumer characteristics given
in Table 3, a pattern that acquisition strategy is the least significant in identifying most consumer characteristics for building
customer relationships is indicated. This may be because this
strategy mainly focuses on expanding new customers rather than
on exploring existing customers. Thus, customer information required for this strategy may be relatively preliminary such as
customer name, telephone number, and address. As the firms
move to customer retention, the focus shifts to customer-centered and customers are initially considered as the important asset of the firms. Differentiated services for customers are critical
to sustaining their profits. Hence, consumer characteristics, such
as age, gender, income, education, occupation, and personality,
are the important information to develop customer relationships.
For the firms evolving to strategic customer care, the focus is on
identifying the most profitable customers and on further developing differentiated services for them. The objective of this strategy is to try to achieve one-to-one customer relationships. As a
result, most consumer characteristics, except for psychological
state and marital status, are significantly impacted by this
strategy.
In summary, while looking at the fourth, fifth, and sixth columns for different customer strategies given in Table 3, there are
one (income), six, and eight consumer characteristics which indicate significances for the different impacts of the strategies of
acquisition, retention, and strategic customer care. While CRM is
conceived as a high level of strategic concept and eCRM is development accordingly, this would provide insight for clearly predefining the particular scope of user requirements in eCRM
development. Accordingly, the requirements elicitation process
would be run in a smooth manner.
The second discusses the effect of customer strategies on marketing stimuli. Their relationship comprises marketing stimuli as
four dependent variables and customer strategies as one indepen-
dent variable with three levels/stages. Moreover, dependent variables are of metric attributes and independent variable is of nonmetric attribute. MANOVA is used to analyze the type of relationship structure. The testing results show that Wilks’ k = 0.52, and
equivalent F = 11.45 and p < 0.01. As a result, Hypothesis 2 is accepted. More specifically, univariate F statistics are further examined to understand the relative importance of the four marketing
stimuli across the three customer strategies. They are all significant
differences, as indicated in Table 4. In general, this implies that
requirements analysis for marketing components should be limited on the four stimuli regardless of the choice of customer
strategies.
Next, while examining group mean differences among three
customer strategies for each of the marketing stimuli given in Table 4, a different pattern from consumer characteristics that
acquisition strategy plays the most significant role in identifying
marketing stimuli for building customer relationships is indicated.
This is because this strategy is primarily at expanding new customers for a wider customer base and thus, the focus is on product attributes for attracting new customers. The four marketing
stimuli are basically defined in terms of the product attributes.
While customer strategies evolve to latter stages, the correlation
with the four attributes becomes weaker and weaker. For example, there are four, two, and one marketing attributes which indicate significances for the different impacts of the three customer
strategies, as indicated in Table 4. As indicated above, CRM is conceived as a high level of strategic concept and accordingly, eCRM
development does. The findings have the important implications
in requirements analysis. This will help system analysts to more
clearly define the particular scope of eCRM domain in advance
and as a result, requirements elicitation can be achieved
successfully.
5.3. Determine system requirements
This step discusses the performance of requirements elicitation
after the analyses of the previous two steps. First, Hypotheses 3
and 4 are tested. Their relationship structure is defined in terms
of the performance of requirements elicitation as one dependent
variable and consumer characteristics as 10 independent vari-
Table 5
Regression results for customer characteristics and requirements elicitation.
Source of variation
df
SS
MS
F
P
Regression
Residual
Total
9
145
154
35.78
128.07
163.85
3.99
0.88
4.53
0.00
Table 6
Impacts of customer strategies on requirements elicitation.
Table 4
Univariate tests for marketing stimuli across customer strategies.
Marketing
attributes
F
Price
Promotion
Quality
Product
5.33
4.43
4.23
4.12
P-value
0.028*
0.031*
0.039*
0.041*
Customer strategies
1
2
3
6.31*
6.01*
5.75*
5.83*
5.25*
3.31
4.29
4.82*
4.83*
3.16
4.23
4.16
Group mean
differences
1 > 3*
1 > 2, 3*
1 > 2, 3*
1 > 3*
1: customer acquisition; 2: customer retention; 3: strategic customer care.
*
P < 0.05.
Customer strategy
Emphases on consumer/marketing
characteristics
Performance
Customer acquisition
Income
Price, Promotion, Quality, Product
Age, Gender, Income, Education,
Occupation, Personality
Price, Product
Age, Gender, Income, Education,
Occupation, Values, Lifestyle,
Personality
Price
5.71*
Customer retention
Strategic customer care
*
P < 0.01.
6.16*
6.42*
I.-L. Wu, C.-Y. Hung / Information and Software Technology 51 (2009) 1308–1318
ables. Both sets of variables are of metric attributes. Multiple
regression analysis is used to examine the type of relationship
structure. The testing results are reported in Table 5. Hypothesis
3 is accepted. By the same procedure, Hypothesis 4 is also accepted. Next, the performance of requirements elicitation can be
assessed using the five measuring items defined in the questionnaire for the three groups of sample firms with different strategies. Their average performances and significant tests with t
x ; x ¼ 4) in a 7-point Likert scale are indicated in
statistics (t ¼ x
psffi
n
Table 6.
Finally, we make a brief summary for guiding the understanding of the above analysis as a whole. For the proposed theoretical
framework, Hypotheses 1 and 2 were first examined for acceptance. Tables 3 and 4 are further presented. Hypotheses 3 and 4
were then examined for acceptance. Table 5 is further presented.
In addition, the emphases on consumer and marketing characteristics for significantly impacting requirements elicitation while
adopting different customer strategies are summarized in Table 6
based on the results listed out in Tables 3 and 4. This indicates that
requirements elicitation performs well while following the strategy-based process. In that, the adoption of different customer
strategies has different impacts on consumer behaviors and marketing attributes and further, provides insight to requirements elicitation in eCRM development.
6. Conclusions and suggestions
As the growth of customer base has become more saturated
and the price premium of products less differentiated, the need
for better CRM is justified. While the Internet offers a tremendous amount of communication resources to support eCRM
implementation, most organizations are rushing to move to
web-based CRM systems. However, a high percentage of firms
fail to achieve their CRM goals. While eCRM domain involves a
number of compound components on product design, market
characteristics, and consumer behavior, a strategy-based approach for requirements analysis can initially provide a clear picture for the domain. Therefore, this study proposes a three-step
process for requirement elicitation, i.e., define customer strategies, identify consumer and marketing characteristics, and determine system requirements. Important findings are discussed
below. While firms adopt different customer strategies in the
market such as acquisition, the emphases of consumer attributes
on building CRM practice such as income and price, are different
in many aspects. Accordingly, requirements elicitation is effectively carried out based on the particular customer segments or
marketing approaches. In summary, while software requirements
engineering tries to define a well-written requirements specification for system developers, the three-step process based on a
strategic perspective has shown a high level of performance
achievement in requirements elicitation in this study and can
be considered as a well-defined requirements specification
framework for eCRM development, as indicated in Fig. 3. In the
long term, we hope that the new approach has provided the
important guidance to the area of requirements engineering
1315
while it can be extended appropriately in various development
contexts.
The implications for practitioners are noted as below. In order
to effectively elicit requirements in eCRM development, system
developers should initially focus on understanding what are the
firm’s customer strategies for CRM practice, i.e., customer acquisition, customer retention, and strategic customer care. Alternatively, this drives the important consumer and marketing
attributes for clearly defining the particular scope of eCRM domain while different firms will have different emphases on their
attributes. The findings are summarized in Table 6. Accordingly,
user requirements are stabilized under the defined scope and
can be effectively determined. Besides, this approach also facilitates effective allocation of organizational resources and reduces
system development cost. The implications for researchers are
discussed as below. A strategy-based approach for requirements
elicitation is critical in eCRM development since it intends to
overcome developers’ and users’ inabilities in recognizing problem unstructuring issues. This is a common problem for developers in building complex softwares. Traditional method is an
operation-based consideration and often difficult to converge in
requirements determination process. Prior research has not discussed the important role of customer strategies in eCRM development and has only reported partially for the implementation
process. This research provides a new thinking for emerging research on this issue.
Furthermore, subsequent research could be based on this
foundation. First, this study is primarily at developing a theoretical framework to effectively determine requirements and further, to empirically validate it by survey data. Future research
could specifically conduct case studies longitudinally to understand the usefulness of this framework in building eCRM in
practice. Second, the empirical survey is sampled from a combination of industries, so the conclusions are more general and
comprehensive. Future research could be targeted toward the
particular industries, for instance, banking industry, to understand their differences and similarities. Third, IS managers or
CIOs (from developers’ perspective) are chosen as participants
in this survey since this study mainly involves the planning
work of system development. However, a complementary study
from the perspective of marketing personnel (from users’ perspective) would provide further insight to understand requirements analysis.
Finally, although this research has produced some useful results, a number of limitations may be inherent in it. First, the response rate (18%) is slightly lower than desirable, despite the
various efforts to improve it. This may be due to that CIOs currently
lack a strategy-based experience in eCRM development. However,
the sample data indicate no systematic non-response bias in the
responding sample and are well representative of the sample
frame. In fact, in comparison with many prior survey studies, the
response rate is quite acceptable. Next, CIOs from larger firms are
primarily chosen for the participants in the survey; however, some
of the questionnaires may have been completed by subordinates,
and as a result the data may be some biases.
1316
Appendix A. Questionnaire
I.-L. Wu, C.-Y. Hung / Information and Software Technology 51 (2009) 1308–1318
I.-L. Wu, C.-Y. Hung / Information and Software Technology 51 (2009) 1308–1318
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Ing-Long Wu is a professor in the Department of Information Management at
National Chung Cheng University. He gained a Bachelor in Industrial Management
from National Cheng-Kung University, a M.S. in Computer Science from Montclair
State University, and a Ph.D. in Management from Rutgers, the State University of
New Jersey. He has published a number of papers in Journal of American Society for
Information Science and Technology, Information & Management, Decision Support
Systems, International Journal of Human Computer Studies, Behavior & Information
Technology, Psychometrika, Applied Psychological Measurement, and Journal of
Educational and Behavioral Statistics. His current research interests are in the areas
of CRM, SCM, knowledge management, and e-commerce.
Ching-Yi Hung currently works as a senior researcher in Business and Marketing
Strategy Lab. at Chung-Hwa Telecommunication Inc. She gained a MBA in information management from National Yunlin University of Science and Technology.
She has been working many years in the areas of CRM and system development.