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551
European Journal of Marketing,
Vol. 34 No. 5/6, 2000, pp. 551-575.
# MCB University Press, 0309-0566
Received May 1998
Revised October 1998
Computer-based support for
marketing strategy
development
Shuliang Li
Cloucestershire Business School, Cheltenham & Gloucester College of
Higher Education, Cheltenham, UK
Russell Kinman and Yanqing Duan
Luton Business School, University of Luton, UK and
John S. Edwards
Aston Business School, Aston University, Birmingham, UK
Keywords Marketing strategy, Information systems, New technology, Marketing mix
Abstract Reports on an investigation of computer-based support for developing marketing
strategy. First, on the basis of a literature study, the nature of marketing strategy development is
clarified. The needs for computer-based support are identified. The status and underlying
problems of existing computer-based information systems for developing marketing strategy are
then examined. Finally, a mail questionnaire survey on computer-based support for developing
marketing strategy is discussed. The major issues addressed in the survey were: managers' needs
for computer-based support in developing marketing strategy; the current provision of computerbased
information systems; and the gaps between the identified needs and current provision. It is
believed that this was the first study to use a large-scale questionnaire survey to explore these
issues. The findings provide important evidence and implications for both researchers and
designers of computer-based support for marketing strategy development.
Introduction
Sound and robust marketing strategy is essential to the survival and success
of any business in the increasingly complex, hostile and competitive
environment in which companies operate (McDonald, 1996; Creveling, 1994).
Nevertheless, developing a sound and timely marketing strategy is not an
easy task. Many companies are experiencing difficulties with strategic
marketing planning. There are a number of barriers to effective marketing
strategy development (McDonald, 1992b). In order to help managers
formulate good marketing strategy, computer-based information systems
have been applied to support the process of marketing strategy development
and strategic marketing planning in different ways. However, such efforts as
have been made still exhibit a disappointing degree of success. Current
information systems developed for this purpose are still in the embryonic
phase.
The research register for this journal is available at
http://www.mcbup.com/research_registers/mkt.asp
The current issue and full text archive of this journal is available at
http://www.emerald-library.com
The authors wish to thank Dr Brian Mathews at Luton Business School for his suggestions on
the questionnaire design conducted in this study. The authors are very grateful to the editor
and the two anonymous reviewers whose valuable comments have helped improve this
manuscript.
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This paper deals with computer-based support for developing marketing
strategy. The opening section of the paper examines the nature of marketing
strategy development, and this leads to a consideration of the needs for
computer-based support in developing marketing strategy. The next section
outlines the status and underlying problems of existing computer-based
information systems for developing marketing strategy according to the
literature. There follows an account of a questionnaire survey on managers'
perceived needs for computer support, current provision of computer-based
information systems, and the gaps between the needs and current provision.
The final section presents the conclusions of the paper and an outline of related
work.
The nature of marketing strategy development
Marketing strategy has been a major focus of academic research since the
1980s (Schnaars, 1991). There are many definitions of marketing strategy in the
marketing literature, reflecting differing points of view. However, most of the
definitions agree that marketing strategy provides the means of utilising the
company's skills and resources to achieve marketing objectives. Generally,
marketing strategy is concerned with the four major elements of the marketing
mix: product, price, place and promotion (Morris and Pitt, 1993; Kotler, 1991;
Wind and Robertson, 1983; Day, 1984; McDonald, 1996; Fifield, 1992; Hooley
et al., 1992; Varadarajan and Clark, 1994). Essentially, marketing strategy
evolves as a consequence of an interplay of four major strategic inputs and the
processes which act on them. The inputs are related to the company's
customers, its competition, its resources and environmental forces. The task of
marketing strategy formulation involves determining the strength, direction
and interaction of these strategic forces in a given situation (Jain and Punj,
1987). The purpose of marketing strategy development is to establish, build,
defend and maintain competitive advantage (McDonald, 1992a).
Many academic researchers conceive the formulation of strategy as a
formalised, analytical process. Porter (1979, 1987, 1980a, 1980b) suggests a set
of analytical techniques for developing strategy with strong emphasis on
strategic thinking. Day (1984), Aaker (1984), Brownlie (1985), McDonald (1989b,
1992a, 1996) and Brooksbank (1990) individually emphasise step-by-step
systematic processes for strategic marketing planning. Cravens (1988) states
that if a manager follows a logical sequence of analyses and decisions,
corporate and marketing management will be better able to select winning
strategy. However, arguments have been presented that the strategy making
process must emphasise line managers' intuition and judgement, for it is line
managers who are responsible for implementing strategy, and it is they who
are closest to the markets concerned (Mintzberg, 1994a, 1994c). Managerial
judgement is essential in dealing with ambiguity and uncertainty in strategic
marketing (Brownlie and Spender, 1995). While researchers cannot seem to
agree on a single, best approach, managers have to get on with planning as best
as they can, using a combination of experience, intuition and hope (McDonald,
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1992a). Mintzberg (1976, 1994b, 1994c) points out that the value of both
intuition and analysis should be respected in the process of strategy making.
The final choice is often the result of strategic analysis and management's
intuitive sense of the strategic fit of the various strategies under consideration
(Cravens, 1988). Generally speaking, marketing strategy development has the
following characteristics (Li, 1998):
. It is particularly concerned with devising the means by which the
company can effectively differentiate itself from its competition, by
capitalising on its strengths to provide better value to its customers over
time (Jain and Punj, 1987).
. It is a complex process that usually involves complex decision-making
activities by managers, and requires both comprehensive analysis of
changing environments and a synthesis of useful information
(Mintzberg, 1994a, 1994c).
. It requires a wide range of relevant strategic information (Mintzberg,
1994c; Taylor et al., 1992).
. It is domain knowledge intensive (McDonald and Wilson, 1990;
Dubelaar et al., 1991).
. It involves a high degree of uncertainty and ambiguity (Brownlie and
Spender, 1995).
. It involves managers' experience, intuition and judgement (Little, 1970;
Mintzberg, 1976, 1994a, 1994c; Brownlie and Spender, 1995; McIntyre,
1982; Alpar, 1991).
The needs for computer support in developing marketing strategy
Arriving at a sound and timely marketing strategy is not a easy task. Because
of bitter competition, uncertainty, high stakes, and the complexity and fast
pace of change in the environment, strategic marketing planning presents a
real challenge to managers. There are many barriers preventing managers
from developing an effective strategic marketing plan (McDonald, 1992b).
Many external and internal factors interact in a bafflingly complex way to
affect the manager's ability to achieve successful marketing (McDonald, 1989b,
1996). The question must be asked, therefore: ``what are managers' needs for
computer-based support?'' As strategy makers, managers possess many unique
strengths (Mintzberg, 1994c). First, they have the authority to make strategy.
Secondly, they hold, and have the connections needed to acquire, critical ``soft''
information (Mintzberg, 1994a, 1994b). Thirdly, they have the flexibility to
respond to strategic issues directly, and in a dynamic way. Finally, and
perhaps most importantly, managers have experience, intuition and judgement
about markets. As strategy makers, managers may, of course, have some
weaknesses (Mintzberg, 1994a, 1994b, 1994c): their information processing
abilities are constrained by cognitive capacity; their domain knowledge is
limited by experience and background; many will lack the analytical skills to
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undertake systematic strategic analysis; it is well recognised that busy
managers usually lack time to focus intensively and at length upon strategic
issues. Further, the assumption that judgement is a manager's greatest asset
must be considered. Intuition and judgement require intimate knowledge of the
domain, and this may take many years to develop; it can be limited by narrow
experience and constrained by culture and tradition. Not all managers will have
good intuition and judgement (Li, 1998).
Although effective managers can think intuitively about various strategic
dimensions, managers' ability to understand the relationships is ultimately
limited without support tools that can help them examine these relationships
interactively (Fredericks and Venkatraman, 1988). It is evident that managers
need a wide range of relevant strategic information, domain knowledge, and
assistance with systematic strategic analysis to overcome their inherent
limitations in the process of developing strategy. It is also believed that these
needs can be met, to a great extent, by suitable uses of effective computer-based
systems (Wilson and McDonald, 1994; Frolick et al., 1997). Information systems
have demonstrated the ability to gather, filter, and analyse data and
subsequently communicate information (Frolick et al., 1997). Appropriate
computer-based systems can help managers learn about strategic concepts,
facilitate strategic thinking, and, in some instances, can be used to validate
their strategies (Belardo et al., 1994; Li, 1998).
Existing computer-based information systems for developing
marketing strategy
For the purpose of this study, a computer-based information system is defined
as a system for the gathering, storage, processing and communication of
information (Molloy and Schwenk, 1995) in direct support of marketing
strategy development or strategic marketing planning. The relevant computerbased
information systems concerned in the study include database systems,
spreadsheets, marketing information systems, executive information systems,
decision support systems, artificial neural networks, fuzzy logic and other
computer-based information systems. In addition, in this study, computerbased
support is defined as the use or application of computer-based
information systems in direct support of marketing strategy development or
strategic marketing planning (Li, 1998).
The importance of computers in marketing was first highlighted by Kotler
(1966). In recent years, effort has been devoted to the introduction of computerbased
information systems to improve the effectiveness and efficiency of
marketing decision making. Surveys (Higgins and Opdebeek, 1984; Morris et al.,
1989; Hirst, 1991a, 1991b; Higby and Farah, 1991) reveal that the computerbased
systems most widely used in marketing functions are general IT tools
such as spreadsheets, graphics packages, word processing, electronic mail,
database systems and statistics packages. Existing computer-based support
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for strategic marketing planning and strategy formulation is in the main
restricted to the provision of relevant information from operational systems
(Wilson and McDonald, 1994).
A marketing information system (MkIS) is an essential tool for translating
raw data to useful information (O'Brien et al., 1995). The first definition of
marketing information systems was presented by Cox and Good (1967).
Conventionally, a MkIS was seen as a set of procedures and methods for the
regular planned analysis and presentation of information for use in making
marketing decisions. During the past decades, many authors have presented
models for MkIS (e.g. Kotler, 1991; Proctor, 1991; Li et al., 1993). According to a
study conducted by McLeod and Rogers (1985), over three-quarters of the
Fortune 1,000 firms utilise marketing information systems for decision making.
However, these systems do little more than process information mechanically
and presumably at considerable expense (Bessen, 1993). The large volumes of
information presented by such systems do not serve management needs well
(Amaravadi et al., 1995).
Decision support systems (DSSs) are interactive computer-based
information systems designed to help decision making by utilising data and
models to solve unstructured problems (Sprague and Carlson, 1982; Turban,
1995). A marketing DSS is a co-ordinated collection of data, systems, tools, and
techniques with supporting software and hardware by which an organisation
gathers and interprets relevant information from business and environments
and turns it into a basis for marketing action (Little, 1979). Marketing DSSs
have been developed to assist with strategic marketing decision making
(Arinze, 1990; Wilson and McDonald, 1994; Choffray and Lilien, 1986; Green
et al., 1983; Moormann and Lochte-Holtgreven, 1993). In addition to DSSs,
executive information systems (EISs) have also been introduced to support the
decision making of senior management (Watson et al., 1991; Cottrell and
Rapley, 1991). An executive information system is a data retrieval and
information handling system developed specifically for the executive (Molloy
and Schwenk, 1995; Watson et al., 1991; Turban, 1995). Occasionally there are
business models in the system, which enable executives to explore ``What if?''
decision making options (Cottrell and Rapley, 1991). In general, however, these
systems are as yet little used successfully to support strategic planning (Wilson
and McDonald, 1994). Although DSSs have been shown to be valuable in the
achievement of competitive advantage, many companies have been
disappointed. The systems so far developed in support of marketing decision
making are limited in their managerial support capabilities (Amaravadi et al.,
1995). Further, even with the significant technological advances of the past
decade, research has found that many current systems are still in rudimentary
stages of development (O'Brien et al., 1995). A major limitation of conventional
DSSs is that they rely on limited quantitative models and cannot handle
situations where complete information is not available (Amaravadi et al., 1995).
Neither EISs or conventional DSSs are very applicable to the many cases where
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managers make decisions based largely upon their own beliefs, experience and
expertise, rather than using complete data, information and quantitative
models.
Strategy development needs much expert judgement and extensive domain
knowledge (McDonald and Wilson, 1990; Dubelaar et al., 1991). Since expert
systems (ESs) can embody organised domain knowledge and can perform as
skilful and cost-effective experts to generate reasoned advice or instructions
(Moutinho et al., 1993; McDonald, 1989a), they may have a role to play in
developing strategy. For some years, ESs have been developed to support
managers in conducting strategic marketing (Alpar, 1991; McIvor et al., 1992;
Moutinho et al., 1993; Curry et al., 1992; Borch and Hartvigsen, 1991; McDonald
and Wilson, 1990; Carlsson and Walden, 1996). However, almost all of the
reported expert systems in the domain of strategic marketing are either
prototype or experimental (Duan and Burrell, 1995; Dubelaar et al., 1991).
Progress towards applying expert systems to this field has been slow
(McDonald and Wilson, 1990), and it seems probable that such systems are
likely always to be severely limited in capability and application (Duan and
Burrell, 1995). An ES is restricted to a structured and narrow domain, and only
well-defined situations can be modelled. In addition, a conventional ES cannot
learn from or adapt to environmental changes (Goonatilake, 1995; Goonatilake
and Khebbal, 1995; Medsker, 1994; Osyk and Vijayaraman, 1995).
More recently, with the emergence of artificial neural networks (ANNs),
some research has been carried out to try to apply ANNs to the analysis of
market share using the PIMS database (Poh, 1994). ANNs are distributed
information-processing systems typically composed of computational elements
interacting across weighted connections (Hinton, 1992; Venugopal and Baets,
1994). ANNs have salient features that are important in modelling and
forecasting non-linear systems; they are seen to have advantages over current
statistical methods. They have proven to be particularly successful in
forecasting, and learning patterns from noisy or incomplete data where experts
are unavailable or where clear rules cannot be easily formulated. A main
limitation of ANNs, however, is that they lack explanation capabilities. They
are thus not suitable for applications where explanation of reasoning is critical
(Goonatilake, 1995; Goonatilake and Khebbal, 1995; Medsker, 1994; Osyk and
Vijayaraman, 1995).
Fuzzy logic (Zadeh, 1988) is another technique which is designed to deal
with imprecise ``linguistic'' concepts or fuzzy terms. It aims at modelling the
imprecise modes of reasoning that play an essential role in the remarkable
human ability to make rational decisions in an environment of uncertainty and
imprecision (Zadeh, 1988). Systems based on fuzzy logic allow users to provide
input in imprecise terms and receive either fuzzy or precise advice. In recent
years, fuzzy logic has been applied to address semi-structural and fuzzy issues
in developing customer-oriented decision support systems (Levin et al., 1995).
Researchers have also utilised fuzzy logic to handle imprecision, such as
ambiguous facts, subjective analyses, and imperfect decision rules in modelling
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the market entry decision (Levy and Yoon, 1995). Potential limitations of fuzzy
logic are that the membership functions and rules have to be specified
manually. Further, fuzzy systems cannot adapt automatically to changes in the
environment (Goonatilake, 1995; Goonatilake and Khebbal, 1995; Medsker,
1994).
The strengths and weaknesses of different types of computer-based systems
for developing marketing strategy are summarised in Table I.
In recognition that each of the range of techniques has its own strengths and
limitations, efforts have been made to build a hybrid system that integrates the
advantages of a DSS and an ES, in order to enhance the ability to aid strategic
marketing planning (Duan and Burrell, 1995). The research is, however, limited
to the integration of an ES and the Analytic Hierarchy Process approach
(Saaty, 1980) and the development of a prototype demonstration system. A
more detailed literature review on existing computer-based information
systems for developing marketing strategy can be found in Li (1998).
In general, attempts to apply computers to aid decision-making in strategic
marketing have not met with much success so far. The focus has been more
upon information systems that provide routine and operational functions to
Table I.
Different types of
computer-based
systems for developing
marketing strategy
Type of system Function Strength(s) Weakness(es)
Marketing
Information
System (MkIS)
Regular planned
analysis and
presentation of
information
Widespread, therefore
familiar; information
mainly about the past
``Mechanical'';
expensive; can result
in information
overload
Decision Support
System (DSS)
Providing data and
models to support
unstructured problem
solving
Can explore many
options quickly;
calculates quantitative
decisions
Mainly quantitative;
cannot handle missing
data or situations
requiring expertise and
experience
Executive
Information
System (EIS)
Data retrieval,
information handling,
and business models
for senior executives
Information handling
and presentation;
specifically aimed at
senior management;
easy to use
As for DSS, but these
weaknesses are more
severe for executive
decisions
Expert System
(ES)
Making/advising on
decisions in a specific
domain
Can share expertise of
others; enables
consistent decision-
making
Only works within
very narrow, well
structured domain;
human expert(s)
needed for system to
be built
Artificial Neural
Network (ANN)
Forecasting;
recognising patterns in
data
Can cope with noisy or
incomplete data
No explanation
facilities
Fuzzy Logic Making/advising on
decisions in a specific
domain
Can use imprecise
input; output may be
either fuzzy or precise
Membership functions
and rules specified
manually, therefore
slow; still experimental
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support marketing activities (Wilson and McDonald, 1994). The support
abilities of current information systems are still rather limited (Amaravadi et al.,
1995). O'Brien et al. (1995) suggest that such systems as do exist are in an early
stage of development.
Li (1998) argues that a primary reason for the lack of success in developing
computer-based decision support is the complexity of marketing strategy
development problems. The process of decision making is not fully understood.
It commonly involves much uncertainty, and relies heavily upon the
experience, knowledge and judgement of the decision maker. Further, strategic
marketing decisions usually have to be made quickly because of rapid change
in the external environment, and often on the basis of limited and incomplete
data (McDonald, 1996).
There is also a suggestion (Li, 1998; Li et al., 1998) that a preponderance of
researchers from systems backgrounds (rather than with a deep knowledge of
marketing) has resulted in a tendency to emphasise the technology and its
capabilities at the expense of a good match with the real needs of decisionmaking
managers. The critical importance of capturing managerial judgement
in making strategic decisions has not always been recognised. Nor have the
systems allowed capitilisation upon the strengths of individual decision
makers and the minimisation of their weaknesses (Hoch and Schkade, 1996;
Mintzberg, 1994a, 1994c).
In much of the previous reported work in this domain, attempts have been
made to apply a specific technique or technology (such as MkISs, DSSs, ESs,
ANNs or fuzzy logic) to the problems of marketing strategy formulation. It is
unlikely to be the case, however, that a single technique or technology will be
suitable for each of the wide range of complex problems inherent in strategy
formulation, or appropriate to each of the many different organisations
engaged in the process. The strengths and weaknesses of individual support
techniques and technologies render them effective in some circumstances, less
so in others. A single support technique often fails precisely to fit the decisionmaking
parameters, and to meet the wide range of information, modelling and
conceptual needs of marketing strategy development (Li, 1998; Goonatilake and
Khebbal, 1995).
It is suggested that it would also be meaningful to embody a variety of
marketing strategic analysis models (such as Porter's five forces model and the
directional policy matrix) in one support system. Each model has its
peculiarities and may not readily adapt to specific circumstances; it has
advantages and disadvantages that vary depending upon context (McDonald,
1992a; Li, 1998). Computer support has the potential benefit of aiding the
decision maker to combine the range of strategic analysis models, or choose the
most appropriate one for his or her purpose.
Finally, previous support systems seem to have done very little in helping
managers deal with uncertainty and ambiguity in developing marketing
strategy. If the problem of handling fuzziness and uncertainty in strategy
formulation can be overcome, there is likely to be considerable benefit (Li, 1998).
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The mail questionnaire survey
The mail questionnaire is an impersonal survey method. It has such
advantages as low cost of data collection, less interviewer bias, a high degree of
anonymity, and wide access to geographically dispersed samples. In order to
strengthen and extend the findings gained from the literature study, a largescale
mail questionnaire survey was conducted by Li (1998) to explore the
following key issues:
. Managers' needs for computer-based support in developing marketing
strategy.
. Current provision of computer-based information systems for
developing marketing strategy.
. The gaps between managers' needs and current provision of computerbased
systems.
It is believed that this was the first study to use a large-scale questionnaire
survey to explore the above issues in large manufacturing companies in the
United Kingdom. The manufacturing industry was selected because of its
importance to the national economy, and its direct concern with products and
markets. The questionnaire used consisted of three major parts, with 31
questions in all. The first set of 14 questions asked about managers' potential
needs for computer-based support in the process of developing marketing
strategy. In this part, the nature of marketing strategy development, managers'
difficulties in developing marketing strategy, and managers' perceived needs
for computer-based support were investigated. The second section comprised
13 questions. These were used to explore the gaps between managers'
perceived needs and the current provision of computer-based systems. In
particular, some factors contributing to managers' dissatisfaction and the
factors holding companies back from using (or making further use of)
computer-based systems were explored. The third component of the
questionnaire was designed to establish which companies would be interested
in evaluating a prototype of a hybrid intelligent support system.
Methodology and responses
The questionnaire sample consisted of manufacturing companies
geographically located in the United Kingdom. These manufacturing
companies are principally involved in the manufacture and sale of industrial
goods. Only companies with more than 500 employees were included.
Companies not providing employee numbers or turnover are excluded. A total
of 900 companies were randomly selected from the FAME (Financial Analysis
Made Easy) CD-ROM database. The characteristics of the population of British
manufacturing companies with more than 500 employees are shown in Table II.
The stratified sample composition of the 900 selected sample companies is
given in Table III.
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During the Autumn of 1997, a questionnaire survey package was mailed to the
marketing director of each company (or managing director if the name of
marketing director was not available). The package consisted of a covering
letter, the questionnaire and a prepaid envelope for return mailing. A follow-up
procedure was employed after a four week period. A total of 104 usable
responses were received, which gave a usable response rate of 11.5 per cent.
The characteristics of the 104 respondent companies are shown in Table IV.
The low response rate can probably be ascribed to two main reasons. First,
Table II.
Characteristics of the
population of UK
manufacturing
companies with more
than 500 employees
Number of employees
Range Number of companies Percentage
500 to 1000 739 48.0
1001 to 2000 410 26.6
2001 to 3000 143 9.3
3001 to 5000 119 7.7
Over 5000 129 8.4
Total sample 1540 100
Turnover
Range (£m)
Under 10 11 0.7
10.001 to 20 32 2.1
20.001 to 20 338 22.0
50.001 to 100 427 27.7
Over 100 732 47.5
Total sample 1540 100
Note: Companies not providing employee number or turnover are excluded
Table III.
Sample composition of
the 900 randomly
selected companies
Number of employees
Range Number of companies Percentage
500 to 1000 297 33
1001 to 2000 297 33
2001 to 3000 108 12
3001 to 5000 99 11
Over 5000 99 11
Total sample 900 100
Turnover
Range (£m)
Under 10 9 1.0
10.001 to 20 12 1.3
20.001 to 50 140 15.6
50.001 to 100 235 26.1
Over 100 504 56.0
Total sample 900 100
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many companies now have a policy of not taking part in any surveys; 24
companies replied to that effect here. Secondly, the questionnaire was long (31
questions) and needed to be answered by a senior member of the organisation.
However, as the survey was conducted to strengthen and extend the authors'
literature review findings, the total of 104 usable responses was felt to be
sufficient.
Statistical comparisons have been made to test the differences in some
characteristics between the 900 sample companies and the population of
manufacturing companies with more than 500 employees. T-tests have also
been conducted to compare the characteristics of the 104 respondent companies
with the population. The t-test results are given in Tables V, VI, VII and VIII. It
can be seen from Table V that there is a significant difference in the average
number of employees between the 900 selected sample companies and the
population of manufacturing companies with more than 500 employees. Table
VI shows that there is also a difference in the average turnover between the 900
chosen companies and the population. These differences may be mainly due to
Table IV.
Characteristics of the
104 respondent
companies
Number of employees
Range Number of companies Percentage
500 to 1000 49 47.0
1001 to 2000 28 27.0
2001 to 3000 12 11.6
3001 to 5000 8 7.7
Over 5000 7 6.7
Total sample 104 100
Turnover
Range (£m)
Under 10 2 1.9
10.001 to 20 2 1.9
20.001 to 50 16 15.4
50.001 to 100 30 28.9
Over 100 54 51.9
Total sample 104 100
Table V.
Comparison of the
average number of
employees of the 900
sample companies with
that of the whole
population
95% confidence interval
of the difference
n Mean t df
Sig.
(2-tailed)
Mean
difference Lower Upper
No. of
employees 900 3704.17 3.264 899 0.001 944.47 376.65 1512.30
Notes: Alpha = 0.05. Test value = 2759 (The average number of employees of the
population of manufacturing companies with more than 500 employees)
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the fact that more larger companies were sampled in the survey. Some
differences will also be noticed in Tables II and III. However, it can be seen from
Table VII that the average number of employees of the 104 respondent
companies is not significantly different from that of the population. Moreover,
Table VIII indicates that the average turnover of the 104 respondent companies
is also not significantly different from the mean of the population. Therefore,
according to these t-test results, it is evident that the 104 respondent companies
are representative of large manufacturing companies in the UK. Nevertheless,
because of the low response rate, care is still needed in generalising the survey
findings to the population of large British manufacturing companies. The
results reported here are exploratory and tentative.
Table VII.
Comparison of the
average number of
employees of the 104
respondent companies
with that of the
population
95% confidence interval
of the difference
n Mean t df
Sig.
(2-tailed)
Mean
difference Lower Upper
No. of
employees 104 2422.76 ± 0.592 103 0.555 ± 336.93 ±1465.75 791.88
Notes: Alpha = 0.05. Test value = 2759 (The average number of employees of the
population of the manufacturing companies with more than 500 employees)
Table VI.
Comparison of the
average turnover of the
900 sample companies
with that of the
population
95% confidence interval
of the difference
n Mean t df
Sig.
(2-tailed)
Mean
difference Lower Upper
Turnover 900 404418 3.210 899 0.001 103516.8 40218.2 166815.0
Notes: Alpha = 0.05. Figures are expressed in thousands of £s. Test value = 300901.01
(The average turnover of the population of manufacturing companies with more than 500
employees)
Table VIII.
Comparison of the
average turnover of the
104 respondent
companies with that of
the population
95% Confidence interval
of the difference
n Mean t df
Sig.
(2-tailed)
Mean
difference Lower Upper
Turnover 104 363454 0.661 103 0.510 62552.61 ± 125176.00 250282.00
Notes: Alpha = 0.05. Figures are expressed in thousands of £s. Test value = 300901.01
(The average tunover of the population of the manufacturing companies with more than 500
employees)
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The content and reliability of the questionnaire survey were validated
according to guidelines given by Straub (1989) and Yoon et al. (1995). Most of
the questionnaire measurement items were derived from the relevant literature
and have been previously used. Before the first mailing, the questionnaire was
reviewed and validated by several experienced researchers. Based on the 104
usable replies, the internal consistency reliability coefficients were also
computed. The calculated Cronbach's Alpha values for the constructs (and
alphas if individual items are deleted) are given in Tables X, XI and XV,
respectively, in the following section. As shown in these Tables, the internal
consistency reliability coefficients (alpha) are all well above the level acceptable
for exploratory studies (Nunally, 1978). In addition, in order to check for late
response bias, the means of the major measurement items from the 63
responses to the first mailing were compared with those for the 41 follow-up
responses by means of t-tests. No significant differences between the means of
these two different sets of samples were found.
Findings
The mail questionnaire survey results were analysed using the SPSS statistical
package and Microsoft Excel.
Managers' needs for support in developing marketing strategy. First of all, a
question was asked to identify which individuals have primary responsibility
for developing marketing strategy. This question was adapted from Fletcher
and Hart (1990). It was designed to clarify who needs computer-based support
in developing marketing strategy. Respondents were invited to choose from
listed management functional specialisms.
Marketing directors were most frequently cited as having principal
responsibility for developing marketing strategy, often jointly with managing
directors, sales directors and other relevant business managers (see Table IX).
In order to explore the nature of marketing strategy development and
managers' difficulties in developing strategy, respondents were asked to judge
whether or not each of the listed statements describes the situation in their own
companies, using a modified five point scale derived from O'Keefe (1989). The
statements were derived from the relevant research (Moutinho et al., 1993;
Curry et al., 1992; McIvor et al., 1992; Wilson and McDonald, 1994; McDonald,
Table IX.
Principal responsibility
for developing
marketing strategy
Position Percentage
Marketing director 71
Managing director 41
Sales director 25
Other 21
Notes: Respondents were invited to choose from listed managers. Some respondents gave
more than one response. The sample size n = 102.
European
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1989a, 1992a; Brownlie and Spender, 1995; Mintzberg, 1994a, 1994b; Porter,
1980b,1987). In particular, the nature of marketing strategy development was
measured by using five component items (see Table X). Managers' difficulties
in developing marketing strategy were measured by using seven items (see
Table XI). The responses and calculated Cronbach's alpha values are given in
Tables X and XI, respectively.
As may be seen in Table X, in regard to the nature of marketing strategy
devlopment, the majority of respondents strongly agreed or somewhat agreed
with the following statements:
. Strategic thinking is essential to developing good marketing strategy.
. Managers need a wide range of relevant information to develop
marketing strategy.
. Managers' intuition and judgement are important in developing
marketing strategy.
. Systematic analytical approaches are helpful for developing marketing
strategy.
. Marketing strategy development involves a high degree of uncertainty
and ambiguity.
Moreover, as shown in Table XI, a majority also agreed that: managers lack
time to focus intensively and at length on strategic marketing issues.
Table X.
The nature of
marketing strategy
development
Item
Strongly
agree
(%)
Somewhat
agree
(%)
Neither agree
nor disagree
(%)
Somewhat
disagree
(%)
Strongly
disagree
(%)
Alpha if
item deleted
Strategic thinking is essential to
developing good marketing
strategy (n = 103) 65.3 28.7 2.0 2.0 2.0 0.7260
We need a wide range of
information to develop
marketing strategy (n = 104) 61.8 28.4 3.9 4.9 1.0 0.8281
Our intuition and judgement are
important in developing
marketing strategy (n = 104) 31.4 51.0 8.8 6.9 2.0 0.7390
Systematic analytical approaches
are helpful for developing
marketing strategy (n = 104) 29.4 51.0 14.7 4.9 0 0.7681
Marketing strategy development
requires managers to deal with
issues that involve a high
degree of uncertainty and
ambiguity (n = 41) 23.1 38.5 20.5 15.4 2.6 0.7786
Note: Cronbach's alpha for the overall measure is 0.8088. n denotes the sample size
Computer-based
support
565
A substantial minority of the respondents also agreed that they have
difficulties in:
. analysing relevant strategic information;
. gaining proper insights into their own competitive position;
. acquiring knowledge and skills in developing marketing strategy;
. gaining a proper perspective of the alternative strategies;
. understanding various factors affecting the development of marketing
strategy.
A rather smaller minority (19.6 per cent) felt that they have difficulty in:
understanding various competitive forces.
It does appear from Table XI that many respondents claim they do not have
difficulties with some aspects of strategy development. This might not
necessarily imply that managers do not have these difficulties. It could be that
some managers may not realise the difficulties or may be reluctant to admit
their problems.
Table XI.
Managers' difficulties
in developing
marketing strategy
Item
Strongly
agree
(%)
Somewhat
agree
(%)
Neither agree
nor disagree
(%)
Somewhat
disagree
(%)
Strongly
disagree
(%)
Alpha if
item deleted
We lack time to focus intensively
and at length on strategic issues
(n=104) 23.5 42.2 12.7 13.7 7.8 0.8559
We have difficulties in analysing
relevant strategic information
(n=104) 7.8 38.2 15.7 26.5 11.8 0.8303
We have difficulties in gaining
proper insights into our
company's competitive position
(n=104) 6.9 31.4 13.7 27.5 20.6 0.8503
We have insufficient knowledge
and skills in developing
marketing strategy (n=104) 3.9 30.4 13.7 24.5 27.5 0.8428
We have difficulties in gaining a
proper perspective of the
alternative strategies (n=104) 6.9 25.5 22.5 32.4 12.7 0.8335
We have difficulties in
understanding various factors
affecting the development of
marketing strategy (n=103) 1.0 34.7 19.8 28.7 15.8 0.8294
We have difficulties in
understanding various
competitive forces (n=104) 1.0 18.6 21.6 43.1 15.7 0.8343
Note: The overall measure has a Cronbach coefficient alpha of 0.8593. n denotes the sample size.
European
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When the respondents were asked, in open-ended question format, what key
information they needed in developing marketing strategy, 75 managers
answered the question. Content analysis was used to analyse these responses.
There were 280 occasions (phrases). These were classified into seven different
content categories as shown in Table XII. Two observers coded the answers
independently. The inter-observer agreement was measured according to the
guidelines in Robson (1993). First, a two-dimensional matrix (``confusion''
matrix) was used to identify the pattern of agreements and disagreements
between the two observers. Secondly, the proportion of agreement was
calculated, and found to be 0.867. Thirdly, the proportion expected by chance
was computed. The total chance proportion for all seven codes was found to be
0.289. Finally, Cohen's Kappa was obtained, which in this case was 0.813.
According to Fliess (1981), a Kappa value of above 0.75 indicates excellent
reliability. The findings are presented in Table XII.
When the respondents were asked, in an open-ended question, what
computer-based support they needed most in developing marketing strategy,
67 managers replied. Similarly, these responses were analysed by content
analysis. Managers' answers were allocated into four different content
headings. There were 145 occasions (phrases) in the answers. The phrases were
coded independently by two observers. The proportion of agreement was found
to be 0.917. The proportion expected by chance was 0.334. The value of Cohen'
Kappa was found to be 0.875. The findings are listed in Table XIII.
Current provision of computer-based information systems. To investigate the
current provision of computer-based systems, respondents were asked whether
or not their companies currently use computer-based systems to support the
development of marketing strategy. Of the 104 responding companies, 86
companies (85 per cent) answered ``yes'' to this question. In order to investigate
Table XII.
Key information
managers most need
Content category
Frequency of observed
agreement
Percentage
agreement
Market size, structure, segments, channels, growth
rate, trends and profit potential 86 30.7
Competitors' market share, strategies and activities 51 18.2
Customers' requirements for products, prices and
services 34 12.1
Political, economic, social and technological
environment and trends 17 6.0
The company's competitive strengths and
weaknesses including image, product, price,
market share, resources, etc. 42 15.0
External opportunities and threats 7 2.5
Corporate plan, objectives and strategies 6 2.1
Note: The number of respondents is 75. There are 280 occasions (phrases). Cohen's Kappa
is 0.813
Computer-based
support
567
what types of computer-based systems are currently used in their companies
for developing marketing strategy, the respondents were also asked to select
from listed computer-based systems. The replies are summarised in Table XIV.
As Table XIV shows, the most common support is provided by general IT
tools; spreadsheets and database systems are used by the vast majority of
respondents. Nearly half of the respondents reported currently using marketing
information systems, which is a greater proportion than might have been
anticipated from the literature. Executive information systems (25 per cent) and
decision support systems (15.5 per cent) have a reasonable presence, but only 6
per cent of the respondents reported currently using expert systems in support
of marketing strategy development.
When the respondents were asked about the provision of the systems
currently used, the information in Table XV was obtained. As shown in the
table, seven items were used to measure the current provision of computerbased
systems. The items for measuring system provision were designed on
the basis of previous relevant work (Molloy and Schwenk, 1995; Wilson and
McDonald, 1994; Mintzberg, 1994a, 1994b, 1994c; Brownlie and Spender, 1995).
The responses all took the form of a five-point scale adapted from Molloy and
Schwenk (1995), where 1 = no help at all, 3 = moderately helpful, and 5 =
completely helpful. It appears that current systems fail to provide even
Table XIII.
Computer-based
support managers most
need
Content category
Frequency of observed
agreement
Percentage
agreement
Appropriate computer-based systems for
marketing strategy development 70 48.3
Strategic analysis tools and models 15 10.3
Provision of relevant strategic information 23 15.8
Strategic analysis knowledge and skills support 25 17.2
Note: The number of respondents is 67. There are 145 occasions (phrases). Cohen's Kappa
is 0.875
Table XIV.
Computer-based
systems used in
developing marketing
strategy
Systems Percentage
Database 91.7
Spreadsheets 90.5
Marketing information 48.8
Executive information 25.0
Decision support 15.5
Expert 6.0
Other computer-based 6.0
Note: Respondents were asked to select from listed systems. Many respondents gave more
than one response. The sample size n = 86
European
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moderate help for most requirements, except perhaps for ``meeting information
needs''. The calculated Cronbach's alpha for the construct (and the alphas if
individual items are deleted) are given in Table XV.
The gaps between managers' needs and current provision of computer-based
systems When the respondents were asked how satisfied they are with the
computer-based systems currently used in developing marketing strategy, the
results in Table XVI were obtained. All the questions took the form of a
modified five-point scale adopted from Molloy and Schwenk (1995), where
1 = very dissatisfied, 2 = dissatisfied, 3 = moderately satisfied, 4 = satisfied,
and 5 = very satisfied.
Table XVI shows that the majority (55 per cent) of the responding managers
are dissatisfied or very dissatisfied with the computer-based systems currently
used in developing marketing strategy. Only 9 per cent of the managers are
satisfied by the current provision, and no one is very satisfied. This is
consistent with the results in Table XV. It does appear that there are gaps
between managers' needs and the current provision of computer-based
systems.
To explore what factors might lead to this dissatisfaction, respondents were
invited to select from listed factors. These factors were developed based upon
Table XV.
Current provision of
computer-based
systems
Item Mean score
Standard
deviation
Alpha if item
deleted
Meet information needs (n = 84) 3.06 0.83 0.8616
Help couple analysis with judgement (n = 83) 2.60 1.01 0.8197
Provide strategic analysis assistance (n = 84) 2.55 0.98 0.8242
Help strategic thinking (n = 84) 2.54 0.95 0.8301
Help understand marketing strategy factors (n = 82) 2.13 0.90 0.8418
Cope with uncertainty (n = 40) 2.18 1.07 0.8319
Improve strategy formulation practice (n = 84) 2.51 0.93 0.8257
Note: Measuring scale: 1 ± no help at all, 2 ± somewhat helpful, 3 ± moderately helpful,
4 ± very helpful, 5 ± completely helpful. Cronbach's alpha for the overall measure is 0.8544.
n denotes the sample size
Table XVI.
The extent of
managers' satisfaction
Very dissatisfied 8.2%
Dissatisfied 47.1%
Moderately satisfied 35.3%
Satisfied 9.4%
Very satisfied 0
Mean score 2.46
Standard deviation 0.78
Note: Measuring scale: 1-very dissatisfied, 2-dissatisfied, 3-moderately satisfied, 4-satisfied,
5-very satisfied. The sample size n = 86.
Computer-based
support
569
the work of McDonald (1992a), Mintzberg (1994a, 1994b, 1994c), Belardo et al.
(1994) and Brownlie and Spender (1995). The principal factors contributing to
dissatisfaction are shown in Table XVII. Limited support capabilities and the
inability to couple analysis with managers' intuition and judgement were
mentioned most frequently.
To investigate which factors hold companies back from using (or making
further use of) computer-based systems to support the development of
marketing strategy, respondents were asked to choose from listed factors. The
listed factors were derived from the literature (Wilson and McDonald, 1994;
Cottrell and Rapley, 1991; Mintzberg, 1994a, 1994c). The findings are given in
Table XVIII.
As Table XVIII indicates, the main factors which hold companies back are a
lack of appropriate computer-based systems, a lack of necessary data and
information, and a lack of knowledge and skills in developing or using
computer-based systems.
Many managers of large companies who replied to the questionnaire survey
would like to be further involved in this study. When the respondents were
asked whether or not they would be willing to evaluate a prototype system for
developing marketing strategy, 41 responded positively.
Table XVII.
Principal factors
contributing to
managers'
dissatisfaction
Factor Percentage
The individual computer-based system used is limited in support capabilities for
developing strategy (n = 70) 54.3
The computer-based systems used cannot couple strategic analysis with
managers' judgement and intuition (n = 70) 53.6
The particular strategy analysis models employed are limited in function and
scope (n = 70) 38.6
The computer-based systems we have used do not match managers' real needs
(n = 70) 32.9
The computer-based systems cannot deal with uncertainty and ambiguity
(n = 41) 22.9
The computer-based systems are difficult to use (n = 70) 10.0
Note: Respondents were asked to select from listed factors. Most respondents gave more
than one response. n denotes the sample size
Table XVIII.
Main factors holding
companies back from
using computer-based
systems
Factor Percentage
Lack of appropriate computer-based systems 61.6
Lack of required data and information 55.8
Lack of knowledge and skills in computer-based systems 34.9
Note: Respondents were invited to choose from listed factors. Most respondents gave more
than one response. The sample size n = 86
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Conclusion and further work
This research sought to explore computer-based support for developing
marketing strategy. In this paper, the needs for computer-based support in
developing marketing strategy have been clarified. The current state of
computer-based support for marketing strategy formulation has also been
examined. In particular, the strengths, weaknesses and underlying problems of
relevant support systems have been identified. It is evident that most current
computer-based systems for developing marketing strategy exhibit a
disappointing degree of success.
The mail questionnaire survey found that, in most instances, marketing
directors and managing directors have primary responsibility for developing
marketing strategy, and thus are the persons for whom support should be
provided. It is also clear that, from the point of view of the surveyed managers:
strategic thinking is essential to developing good marketing strategy;
managers' judgement is important in developing marketing strategy;
systematic analytical approaches are helpful for developing marketing
strategy; marketing strategy development involves a high degree of
uncertainty and ambiguity. Moreover, the computer-based support managers
need most includes: appropriate computer-based information systems; the
provision of relevant strategic information; marketing strategy analysis
models and tools; and strategic analysis knowledge and skills assistance.
The mail questionnaire survey also revealed that many companies currently
use computer-based systems to support the development of marketing
strategy. Database systems and spreadsheets are almost universal; marketing
information systems are used by approximately half of the respondents'
companies. Some companies currently use executive information systems and
decision support systems to support marketing strategy development, but few
use expert systems. It is also evident that most current systems fail to provide
even a moderate level of help in most aspects of the process of developing
marketing strategy.
Not surprisingly, therefore, the survey results also show that most
responding managers are dissatisfied or very dissatisfied with the computerbased
systems currently used in developing marketing strategy. It appears that
the five principal factors contributing to managers' dissatisfaction are: the
individual system used is limited in support capabilities; the system used
cannot couple strategic analysis with managers' judgement; the particular
strategy analysis models employed are limited in function and scope; the
systems used do not match managers' real needs; and the systems used cannot
deal with uncertainty. In addition, three main factors holding companies back
from using or making further use of computer-based systems seem to be a lack
of appropriate computer-based systems, a lack of required data and
information to drive the systems, and a lack of knowledge and skills in the use
of computer-based systems.
A major limitation of this study is the low response rate. However, according
to the t-test results, there are not significant differences in the size and turnover
Computer-based
support
571
between the 104 respondent companies and the population of UK
manufacturing companies with more than 500 employees. Even so, care is still
needed in generalising the survey results to the whole population of UK large
manufacturing companies. Even more caution needs to be exercised in any
attempt to generalise more widely beyond manufacturing, although the issues
explored are not specific to the manufacturing sector. The evidence presented
here, therefore, is exploratory and tentative.
Despite the limitations, this study represents a first attempt to explore
managers' information and computer-based support requirements for
marketing strategy development; the provision of current computer-based
systems; the gaps between the identified needs and current provision; and
causes of managerial dissatisfaction. The results of the study provide
important evidence for both researchers and designers of computer-based
support for developing marketing strategy. The findings reported here provide
useful guidance and suggest implications for further research in this field.
Having recognised managers' needs for computer-based support in
developing marketing strategy and the gaps between those needs and the
current provision of computer-based systems, it should be possible to develop
effective computer-based systems to better meet manager's identified needs for
support, and to fill the existing gaps. To progress this study, a framework for a
hybrid intelligent system in support of marketing strategy development has
been established (Li, 1998; Li et al., 1998). Furthermore, a hybrid intelligent
system for developing marketing strategy, which integrates the strengths of
different support techniques and technologies, has been developed by the first
named author to assist strategic analysis; to support the coupling of strategic
analysis with managers' judgement; to help strategic thinking; to help
managers cope with uncertainty; and to support the assessment of strategic
marketing factors based on the opinions of a board of directors. Field work has
also been undertaken by the first named author to test and evaluate the hybrid
intelligent system in five large companies in the United Kingdom.
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