A Framework for Understanding Decision Support Systems Evolution
David R. Arnott
School of Information Management & Systems
Monash University
Melbourne, Australia
Email: david.arnott@sims.monash.edu.au
Abstract
Terms such as ‘adaptive’ and ‘evolutionary’ capture the organic nature of the
development of a decision support system (DSS). However, they are rarely defined in DSS
research and their meaning varies widely in the research literature. The aim of this paper
is to contribute to decision support systems theory by clarifying the nature of the
evolutionary process of a DSS. Using insight from the theory of evolution and prior DSS
research, a framework for understanding DSS evolution is developed based on the
aetiology, lineage and tempo of evolution. The aetiology of DSS evolution is discussed in
terms of cognitive and environmental triggers, an important distinction for managing DSS
projects. The lineage of DSS evolution is viewed as occurring within an application and
between applications. In terms of the time pattern of evolution, DSS are considered
capable of continuous, punctuated, and quantum evolution. The descriptive validity of the
framework is demonstrated by using it to classify evolution in a number of published DSS
studies.
Keywords
Evolutive Design, IS Development Methods and Tools, DSS, Research Frameworks.
INTRODUCTION
Decision support systems (DSS) are computer-based information systems that are designed
with the express purpose of improving the process and outcome of decision making.
Evolutionary development has been central to the theory of decision support systems since the
inception of the field. The functionality of DSS is thought to evolve over a series of
development cycles where both the client and the systems analyst are active contributors to the
shape, nature and logic of the system. Developers of DSS need to adopt an evolutionary
approach because they generally address ill-structured management decisions. It is almost
impossible a priori to specify the system requirements in such an environment and the initial
versions of the system will help to clarify these requirements. The environment of DSS is
subject to significant change and so even if the system requirements have been specified with
some accuracy at the start of the project they are likely to change significantly over time.
This paper presents research that is best described as a conceptual study (Galliers, 1992). The
aim of this paper is to contribute to decision support systems theory by clarifying the nature of
the process of evolution within a DSS. This clarification is based on adapting theories from
scientific disciplines including biology, palaeontology, geology and physiology. The paper is
structured as follows: first, scientific theories of evolution that may be relevant to DSS are
discussed. Following this, current theories of DSS evolution are reviewed. Evolutionary and
DSS theories are then used to develop a framework for understanding DSS evolution. Case
studies of DSS development from the research literature are fitted to the framework to
demonstrate its descriptive validity. Finally, some concluding comments are made that may be
of value to both researchers and practising systems analysts.
EVOLUTIONARY THEORY AND DSS
Insight into the nature of DSS evolution can be gained from evolutionary theory in a number of
sciences In biology, evolution is “change in the properties of populations of organisms that
transcend the lifetime of a single individual” (Futuyma, 1986, p 7). Biologists make a
distinction between ontogeny, the changes in an individual over its lifetime, and evolutionary
change over generations. Much of what is termed evolution in DSS is clearly ontogeny
although cross generational change is also common. DSS are not capable of sexual
reproduction but in a sense they do progress through generations where some aspect of the
decision logic (which may be thought of as analogous to a genotype) is passed to the next
generation. In the language of information systems, a change in generation is a major version
change (e.g. version 1.0 to 2.0). Adaptation of individual DSS can be viewed as a minor
version change (e.g. version 1.1 to 1.2). The difference between minor and major version
changes, or evolution and ontogeny, is a matter of systems analyst and user perception.
Darwin (1859/1996) conceptualised evolution as the cumulative result of a very large number
of very small changes that occur over a very long period of time. This gradual continuous
process is driven by the blind process of natural selection. Biological evolution is not a goal
driven process, and an adaptation only becomes successful if the individuals concerned are able
to reproduce and pass the feature to later generations who in turn are able pass the feature to
their descendants. In Darwin’s words: “.. natural selection acts by life and death, - by the
preservation of individuals with any favourable variation, and by the destruction of those with
any unfavourable deviation of structure” (Darwin, 1859/1996, p 159). Budd & Coates (1992)
found that evolutionary changes in one time period may be reversed in the next and coined the
term non-progressive evolution to characterise this phenomenon. Non-progressive evolution is
convincing evidence for the lack of any evolutionary goal other than survival. The modern
approach to evolution represents a synthesis of many disciplines but still maintains natural
selection as an important mechanism. Selection needs variation between individuals to work
and it is well accepted that the major source of biological variation is gene mutation. Random
genetic drift and geographical isolation are also important factors in variation (Price, 1996).
DSS are man-made systems that are created from a design process and therefore any evolution
of a DSS is produced by artificial rather than natural selection. Normally, in an artificial
selection process the designers have some goal or desirable state in mind. They consciously
and deliberately select those features that support or promote the goal between generations. An
example of biological artificial selection is the breeding of thoroughbred race horses. Although
DSS are the product of a process of artificial selection, they are the class of information system
whose development pattern is closest to natural selection. Because of the unstructured nature of
managerial judgement, a DSS does not have a goal that is determined to the same degree that an
animal breeder has when attempting to breed a derby winner. Further, the goal of the DSS
project is likely to change as a result of the development and use of the system. This means that
although DSS evolution is to some extent goal driven, the goal itself is subject to evolutionary
processes. This system-goal evolution may even be subject to radical change. Many DSS may
have no goal other than to support a particular manager.
Adaptation can be best viewed as an mechanism of evolution. It is the process of change
whereby a feature of the population is improved in relation to some function. In some sciences,
for example physiology and psychology, it is an individual rather than a population effect, and
can be described as a phenotypic adjustment to a change in the environment. In a similar way
DSS adaptation occurs during the life of a system version. Another important evolutionary
agent is speciation, the development of one or more new species from a common stock. In
taxonomy, the discipline of scientific classification, a species may not be classified in the same
manner as in evolutionary biology and may be determined by other factors, the most important
of which is morphology. Evolutionary taxonomy allocates organisms into a classification
hierarchy of species, genera, families, orders and classes. Alter’s taxonomy of decision
support systems (Alter, 1980, chap. 2) classifies DSS applications at the genera level with
decision support systems as a type of information systems at the family level. This taxonomy is
shown in Table 1. It has been widely used in DSS research and although formulated in the late
1970s, it remains relevant as attested by a recent empirical validation (Pearson & Shim, 1994).
The names of Alter’s taxa are literally the generic names of decision support systems and each
DSS taxon will contain a number of different DSS species. For example, in the
representational models taxon a particular decision task may be supported either by a
spreadsheet or a financial modeling package. DSS based on each technology may be regarded
as different species and system evolution that involves transferring and changing the decision
logic from one platform to another may be thought of as DSS speciation.
Table 1.
Alter’s Taxonomy of Decision Support System
Taxa
Description
File Drawer Systems
allow immediate access to data items
Data Analysis Systems
allow manipulation of data by tailored or general
operators
Analysis Information Systems
provide access to a series of databases and small
Accounting Models
calculate the consequences of planned actions on
using accounting definitions
Representational Models
estimate the consequence of actions without using
or partially using accounting definitions
Optimisation Models
provide guidelines for action by generating an
optimal solution
Suggestion Models
provide processing support for a suggested decision
for a relatively structured task
The theory of evolution also contributes to the understanding of the tempo of DSS evolution.
Many DSS evolve in a continuous fashion, similar in some ways to that proposed by Charles
Darwin. This cumulative effect of numerous small changes to an application captures the
organic nature of many DSS development projects. It is the tempo of evolution addressed by
Courbon et al. (1978) and Keen (1980). The major difference between biological and DSS
evolution in this respect is the time scale of the evolution. Biological evolution takes tens of
thousand, often millions, of years whereas DSS evolution can take place over thousands of
minutes. While gradual speciation is central to Darwin’s theory the empirical evidence indicates
other evolutionary tempos. Saltational change, or change by leaps, was first articulated by
Aristotle. Under this thesis evolution can occur in a radical fashion, in some cases associated
with the emergence of new functionality (for example, flight in birds) and frequently it creates a
higher taxon. Simpson (1944) termed such rapid substantial change, quantum evolution. Some
biologists debate the existence of quantum evolution and have offered gradualist neo-Darwinist
explanations for the emergence of major functionality (Dawkins, 1996, chap. 4). On the other
hand, Lewis (1962) produced strong evidence of quantum evolution in the plant genus Clarkia.
In palaeontology, Gould & Eldredge (1977) proposed the theory of punctuated equilibria
where, rather than evolving through gradual continuous change, species exist in a relatively
stable state for considerable time periods and evolution is concentrated in brief periods of rapid
change (some as small as 10,000 years). Evolution in the form of punctuated equilibria is also
common in DSS. Keeping in mind the difference in time scale discussed above, a DSS may be
static in terms of the logic and operation of the system for some months and then a rapid period
of change occurs.
It is clear that both gradualist and saltational theories are required to explain the empirical
evidence in both biology and decision support systems. Some evolutionary lines are clearly
subject to gradual speciation, while some are subject to rapid, even quantum changes over
relatively short time periods. For decision support systems, the common property of each form
of system evolution is that they represent a change in how the user perceives the nature of the
decision process.
CURRENT APPROACHES TO DSS EVOLUTION
The notion that a decision support system evolves through an iterative process of systems
design and use has been central to the theory of decision support systems since the inception of
the field. Evolutionary development in decision support was first mentioned by Meador and
Ness (1974) and Ness (1975) in their description of middle-out design. This was a response to
the top-down versus bottom-up debate of the time that was concerned with the development of
transaction processing systems. Courbon et al. (1978) provided the first general statement of
DSS evolutionary development. In what they termed an “evolutive approach”, development
processes are not implemented in a linear or even in a parallel fashion, but in continuous action
cycles that involve significant user participation. As each evolutive cycle is completed the
system gets closer to its final or stabilised state. Courbon argued that the evolutive cycles
should be continuous and as rapid as possible as DSS exists in an environment of continuous
change.
USER
middle-out
design
user
learning
personalised
use
SYSTEM
facilitates
implementation
pressure for evolution
ANALYST
evolution of system function
Figure 1. Keen’s Adaptive Design Framework
Keen (1980), building on Courbon’s work, developed a model for understanding the dynamics
of decision support systems evolution. His approach was termed “adaptive design”, although
adaptive development is a more accurate term as the approach comprises development
processes other than design. The importance of this work was to give the concept a larger
audience. Keen (1980) remains the most cited and thereby the most influential description of
the evolutionary approach to DSS development. Keen’s model comprises three major elements:
the builder or systems analyst, the user and the system. These elements influence each other in
complex ways during the development process. Three major iterative loops are identified in this
model, namely the System-User, the User-Analyst and the System-Analyst loops. The model is
depicted in Figure 1 with slight changes made to the terminology used by Keen.
In the System-User loop, the link from the system to the user indicates that by using the system
the user gains better understanding of their problems and in some way improves their decisionmaking process. The link from the user to the system indicates that the system provides support
for the user’s personal needs. Ideally, the way that the system physically operates is
personalised to the needs of the individual manager/user. In the User-Analyst loop, the user to
systems analyst link represents the analyst learning about the user’s decision-making process
and usage patterns. At the same time, the systems analyst to user link reflects the user’s
discovery of the capabilities of the analyst and possibilities of the system development project.
This loop assumes a much closer relationship between client and developer than in most other
information systems applications. In the System-Analyst loop, the system to systems analyst
link shows the pressure placed on the analyst to modify and add new functionality to the
system. The systems analyst to system link, on the other hand, involves the systems analyst
actually enhancing the system. The evolution of the final decision support system is a result of
the cyclic operation of the various loops. Courbon (1996, p 119) describes these cycles as
sequences of “action - whenever the designer implements a new version and the user works
with it and ... reflection i.e. the feedback where the user and the designer think about what
should be done next based on the preceding active use.” Courbon argues that these
action/reflection cycles are similar to Piaget’s learning processes of accommodation and
reflective abstraction (Shaffer, 1989) and concludes that DSS evolution can be best seen as a
learning process.
The organisational influences on DSS development can also be considered in terms of Keen’s
three adaptive loops. Organisational procedures such as control, communication and reward
systems may limit user discretion and behaviour. Also, users can exert pressure to change
procedures which constrain organisational learning. In a similar manner, the technology
available within the organisation limits the capabilities of the system. Other organisational
issues include the charter and location of the development staff. Keen’s model is notable for its
consideration of environmental factors on DSS evolution.
Sprague and Carlson (1982) in an analysis of system adaptation and evolution identified four
levels of DSS flexibility: the flexibility to solve a problem in a personal way; the flexibility to
modify a specific DSS to handle different problems; the flexibility to adapt to major changes
that require a new specific DSS; and the flexibility to evolve with changes in technology. They
believed that these levels exist in a hierarchy with evolution at the top. They argued that “DSS
must evolve or grow to reach a ‘final’ design because no one can predict or anticipate in
advance what is required. The system can never be final; it must change frequently to track
changes in the problem, user, and environment because these factors are inherently volatile”
(Sprague and Carlson, 1982, p132). Sprague and Carlson’s ROMC design method was
designed to provide this flexibility in DSS development. A number of cases have reported the
use of the ROMC approach for evolutionary development (eg. Igbaria et al., 1996) .
There have been numerous other contributions to the understanding of the evolution of DSS.
Keen & Gambino (1983) provided an important case study of evolutionary development that
was influential in the development of DSS methodologies, particularly with regard to their
finding that evolution occurred at the sub-task rather than the task level. Stabell (1983) placed
evolutionary development in a decision theoretic framework by suggesting that the evolution of
a DSS should take place in a tension between the descriptive and prescriptive views of the
target decision (Bell, Raiffa & Tversky, 1988). Stabell suggested that early in a DSS project the
analyst should describe the current decision process and define an appropriate normative
solution. He argued that DSS evolution should be planned along a continuum between these
two definitions. Each version of the DSS should creep towards the normative solution. Young
(1989) presented a three stage DSS methodology whose final stage of iterative use, refinement
and assessment is an example of evolutionary development. Sage (1991, chap. 5) outlined a
seven stage systems design methodology where the stages were sequenced in an iterative
manner. Sage noted that information requirements determination exists in all stages of the DSS
development process and that this is likely to be the driver of system evolution. Arinze (1991)
developed a research model for DSS methodologies that was based on evolutionary principles.
He saw DSS methodologies as a tool for reducing the “unstructuredness” of managerial
decision making. Silver (1991) embraced the evolutionary philosophy in his framework for
DSS research and practice. He extended evolutionary theory by considering how DSS restrict
or limit decision making processes and how DSS can guide or direct a user’s approach to the
operation of the system. Fitzgerald (1991) in a survey of executive information systems
development practice reported that all the developers who were interviewed used an
evolutionary approach. Keen’s adaptive design model has been extended to executive
information systems (Suvachittanont et al., 1994). Evolutionary development has also been
used in group decision support systems (Shakin, 1991).
The discussion of current approaches and theories of evolutionary development illustrates the
importance of the concept of evolution for DSS theory and practice. Existing work on the
development of decision support systems emphasises a process whereby the final system
results from an adaptive process of user/analyst learning and system change. However, in
practice user/analyst learning can be seen as intermediate factor in adaptation. What is, or
should, be of more interest to a systems analyst are the factors that trigger, enable or force this
learning to take place. Another shortcoming of the current approach is that evolution is treated
in a rather homogeneous way, stressing or assuming a gradualist approach. From the
discussion of evolutionary theory it is arguable that other tempos of evolution are likely to
occur. The next section addresses these two aspects of DSS theory: causes or triggers of
adaptive and evolutionary processes, and the tempo or dynamics of evolution.
A FRAMEWORK FOR UNDERSTANDING DSS EVOLUTION
The aim of this section is to develop a framework for understanding DSS evolution that is
founded on the theories and evidence discussed in the previous sections. This framework is
structured around the aetiology, lineage and tempo of DSS evolution. Aetiology refers to the
causes of evolution, lineage to whether evolution occurs within an application or between
applications, and tempo relates to the pattern of evolution over time.
The lineage that develops as a result of DSS evolution can be conceptualised at the application
level; an application is a computer-based information system that supports an aspect (sometimes
all) of the decision task. Evolution can be thought of as either occurring in a lineage within a
type of application, or as occurring as a branching lineage between different applications. An
example of within-application evolution is substantial change in the decision logic of a
spreadsheet system such that the change is so great that the current system is indistinguishable
from the first version. Evolution between applications is likely to have a more significant effect
on systems development and will probably involve a new set of system initiation tasks,
including technology selection, initial requirements analysis and application budgeting. An
example of this form of evolution is a project that starts out as a data oriented system using EIS
software and over time moves to one focused on complex financial modeling.
Another way of viewing DSS evolution is to consider its aetiology, in particular the forces or
factors that trigger the adaptive processes that lead to evolution. These factors may be
exogenous or endogenous with respect to the decision maker. DSS aetiology is summarised in
Table 2. Endogenous, or cognitive triggers have been emphasised in many research studies
(Courbon, 1996; Keen, 1980; Keen & Gambino, 1983; Sprague & Carlson, 1982; Valusek,
1994). The most common expression of these evolutionary triggers is when managers learn
more about the decision task by using the system and interacting with a systems analyst.
However, other forms of cognitive trigger are possible. A manager may think of a new system
requirement during a conversation with a fellow manager. The idea for a change in the logic of
a system could also come from a consultant other than the DSS analyst, especially if the
consultant is a domain expert rather than an information systems professional. Attendance at
conferences, seminars and training courses could also provide cognitive triggers for DSS
evolution. Finally, just simply thinking about the DSS and the decision task (say while driving
to work) could lead to ideas that trigger evolutionary changes to the system.
Table 2.
The Aetiology of Decision Support Systems Evolution
Cognitive Triggers
Environmental Triggers
System use
Technology change
Analyst interaction
Personnel change
Peer interaction
Internal organisational change
Consultant interaction
Merger/Takeover
Training courses
Industry changes
'Idle' thought
Coevolution
Environmental triggers have not been addressed by DSS researchers to the same extent as
cognitive triggers. Angehrn and Jelassi (1994) in a review of DSS research and practice saw
systems evolution as necessary to cope with constant environmental change. The most obvious
environmental trigger is a change in the technology available to the systems analyst. The
emergence of personal computers was a major factor in the evolution of many DSS in the
1980s. Changes in data base software, particularly multi-dimensional models, and the internet
have had a similar effect in the 1990s. Another environmental trigger of system evolution is a
change in the user of the DSS. The new user may have a different conceptualisation of the task
and different cognitive abilities, and will wish the system to reflect their personal approach.
Internal organisational change may trigger evolution of the decision task level, and as a
consequence, the DSS. These changes include divisional and departmental restructuring,
downsizing, and outsourcing. Wholesale organisational changes such as merger and takeover
can lead to a change in organisational procedures which will trigger DSS evolution. External
events such as change to the industry structure and changes in government regulations may also
require fundamental changes to a DSS. The final environmental trigger is coevolution, which
occurs when a major change in the decision logic of one application or evolutionary line
triggers change in another.
Combining different lineages and aetiology yields the framework of DSS evolution presented in
Figure 3. The framework identifies four major classes of DSS evolution, namely within
application with a cognitive trigger, within application with an environmental trigger, between
application with a cognitive trigger, and between application with an environmental trigger. As
argued above, DSS evolution can follow a number of different patterns over time. DSS may
evolve in a continuous fashion, be subject to punctuated equilibrium, and may exhibit quantum
evolution. These tempos may be determined by aetiology and lineage. The likely allocation of
these tempos to the classes of DSS evolution is shown in Figure 3. Evolution becomes more
discontinuous moving from the upper left cell to the lower right cell of Figure 3.
Cognitive
Trigger
Environmental
Trigger
Figure 3.
Within
Application
Between
Application
Continuous Evolution
Punctuated Equilibrium
Continuous Evolution
Punctuated Equilibrium
Continuous Evolution
Punctuated Equilibrium
Quantum Evolution
A Framework for Decision Support Systems Evolution
To illustrate the descriptive validity of the framework, Figure 4 presents some examples of the
different classes of DSS evolution from the research literature. It is difficult to analyse much of
the reported research in evolutionary terms because many papers simply state that an
evolutionary approach was used without further elaboration. Few mention the causes or
triggers of system changes and even fewer mention the time frame of development.
Nevertheless, one can find many illustrations of cognitive triggered within-application
evolution. Igbaria et al. (1996) described the development of a DSS for transport managers in a
New Zealand dairy company where the development began with a replication of current practice
on the computer and further changes to the systems were mainly triggered by managerial
learning. Courbon (1996) outlined a DSS that was developed to schedule nurses on various
shifts over a four week period. The system did not use a normative scheduling algorithm and
was modified as the nurses used the system and learnt more about the problem. Keen &
Gambino (1983) described in great detail the development of a DSS to support decisions related
to public school financing. This development project is a clear example of continuous evolution
that is triggered by manager and analyst learning.
Within
Application
Cognitive
Trigger
Environmental
Trigger
Figure 4.
Between
Application
Courbon (1996)
Igbaria et al. (1996)
Keen & Gambino (1983)
Botha et al. (1997)
Hurst et al. (1983)
Janson & Smith (1985)
Nandhakumar (1996)
Niehaus (1995)
Rigby et al. (1995)
Gunter & Frolick (1991)
Jirachiefpattana et al. (1996)
Suvachittanont et al. (1994)
Examples of
Decision Support Systems Evolution
Examples of within-application evolution with environmental triggers are also common.
Niehaus (1995) described punctuated evolution within a human resource planning application
in a large naval dockyard. The triggers for the evolution of this system were improvements in
technology and the major organisational change associated with restructuring and downsizing.
Rigby et al. (1995) detailed a case of punctuated evolution in an oil industry application. The
evolution of this DSS was enabled by the new functionality provided by advances in
information technology. Nandhakumar (1996) in a detailed description of the evolution of a
system to support inventory performance found that within-application evolution of the system
was caused by a major change in organisational reporting procedures. The evolution of this
system is best characterised as punctuated equilibrium.
Examples of between-application evolution are more difficult to find in the research literature
but are not necessarily less common in practice. Botha et al. (1997) presented an example of
this form of evolution in a case from the South African National Defence Force. The system
comprised six modeling applications and a supporting database application. Hurst et al. (1983)
in their case describing a DSS to support production and finance decisions in a health and
beauty products company, reported on continuous between-application evolution with a strong
element of user learning triggered by system use and analyst learning. Janson and Smith (1985)
reported on a case study of the development of a DSS to support marketing research and
corporate planning in an insurance firm. The system began as a data analysis application and
then progressed to representational models. Each aspect (application) of the system evolved
through a number of versions. Gunter and Frolick (1991) described three generations of
executive support at a power utility where the availability of new technology enabled new
applications to be developed. Jirachiefpattana et al. (1996) in their Energy Company case found
that the system underwent between-application evolution as a result of significant changes to
the business of the corporation. Suvachittanont et al. (1994) in a case study of evolutionary
development of an executive information system in a manufacturing company, related how
between-applications evolution occurred when the development team had spare capacity. Each
new application was prioritised according to the seniority of the manager requesting the new
application.
While these research studies have been allocated exclusively to one class of DSS evolution or
another in the framework, it is likely that many projects will exhibit a number of these
evolutionary classes over the various generations of the DSS.
CONCLUDING COMMENTS
This paper contributes to decision support systems research by further clarifying the nature of
DSS evolution. The main differences between biological and DSS evolution were argued to be
the time frame for evolution, the use of artificial rather than natural selection in DSS, and the
subjective nature of DSS, in particular problem and generation definition. Despite these
differences many of the aspects of the theory evolution are relevant to decision support
systems. Using insight from a number of sciences and prior DSS research, a framework for
understanding DSS evolution was developed. This framework is based on the aetiology,
lineage and tempo of evolution. The aetiology of DSS evolution was discussed in terms of
cognitive and environmental triggers, an important distinction that has not been common in
published DSS research. The lineage of DSS evolution was considered as occurring within an
application and between applications. The time pattern of evolution was viewed as a
consequence of lineage and aetiology. DSS were considered capable of continuous,
punctuated, or quantum evolution. The descriptive validity of the framework was demonstrated
by applying it to published DSS studies.
While the classes of evolution identified in the framework have obvious interdependencies, it is
clear that each form of evolution places different demands on the systems analyst and in turn
the user. Evolutionary development is an uncertain and often stressful process. Better theories
of DSS evolution may help systems analysts predict what may happen next in the development
processes and help them in deciding which techniques and tools are likely to succeed with each
class of evolution. It is important to identify which class of evolution is occurring in order to
effectively manage the evolutionary processes during a DSS development project. It seems that
between-application evolution is more difficult to manage than within-application evolution.
Further research, especially intensive case studies, is needed on these matters. The notion that
DSS evolution is best viewed in terms of aetiology, lineage and tempo gives structure to this
further work. In particular, further investigation of the triggers of DSS evolution is likely to be
of significant practical relevance.
REFERENCES
Alter, S.L. (1980). Decision support systems: Current practice and continuing challenges.
Addison-Wesley, Reading, Massachusetts.
Angehrn, A.A., & Jelassi, T. (1994). DSS research and practice in perspective. Decision
Support Systems, 12, 257-275.
Arinze, O.B. (1991). A contingency model of DSS development methodology. Journal of
Management Information Systems, 8 (1), 149-166.
Bell, D.E., Raiffa, H. & Tversky, A. (1988). Descriptive, normative and prescriptive
interactions in decision making. In D.E. Bell, H. Raiffa and A. Tversky (Eds), Decision
Making: Descriptive, Normative and Prescriptive Interactions (pp 9-30). Cambridge
University Press.
Botha, S., Gryffenberg, I., Hofmeyer, F.R., Lausberg, J.L., Nicolay, R.P., Smit, W.J.,
Uys, S., van der Merwe, W.L. & Wessels, G.J. (1997). Guns or butter: Decision
support for determining the size and shape of the South African National Defence Force.
Interfaces, 27 (1), 7-28.
Budd, A.F., & Coates, G.C. (1992). Non-progressive evolution in a clade of Cretaceous
Montastraea-like corals. Paleobiology, 18, 425-446.
Courbon, J-C. (1996). User-centered DSS design and implementation. In P. Humphreys, L.
Bannon, A. McCosh, P. Milgliarese & J-C. Pomerol (Eds), Implementing systems for
supporting management decisions: Concepts, methods and experiences (pp 108-122).
Chapman and Hall, London.
Courbon, J-C., Grajew, J. & Tolovi, J. (1978). Design and implementation of interactive
decision support systems: An evolutive approach. Unpublished Manuscript. Institute
d’Administration des Enterprises, Grenoble, France.
Darwin, C. (1996). The origin of species. Oxford, UK: Oxford University Press. (Original
work published 1859)
Dawkins, R. (1996). Climbing mount improbable. Viking, London.
Fitzgerald, G. (1991). Executive information systems and their development in the UK: A
research study. International Information Systems, 1 (2), 1-35.
Futuyma, D.J. (1986). Evolutionary biology. Second Edition. Sinauer, Sunderland,
Massachusetts.
Galliers, R.D. (1992). Choosing information systems research approaches. In R.D. Galliers
(Ed.), Information systems research: Issues, methods and practical guidelines (pp.
144-162). London: Blackwell Scientific.
Gould, S.J., & Eldredge, N. (1977). Punctuated equilibria: The tempo and mode of evolution
reconsidered. Paleobiology, 3, 115-151.
Gunter, A., & Frolick, M. (1991). The evolution of EIS at Georgia Power Company.
Information Executive, 4 (4), 23-26.
Hurst Jr., E.G., Ness, D.N., Gambino, T.J., & Johnson, T.H. (1983). Growing DSS: A
flexible, evolutionary approach. In J.L. Bennett (Ed.), Building decision support
systems (pp 111-132). Addison-Wesley, Reading, Massachusetts.
Igbaria, M., Sprague Jr., R.H., Basnet, C. & L. Foulds, L. (1996). The impacts and benefits
of a DSS: The case of FleetManager. Information and Management, 31, 215-225.
Janson, M.A., & Smith, L.D. (1985). Prototyping for systems development: A critical
appraisal. MIS Quarterly, December, 305-315.
Jirachiefpattana, W., Arnott , D.R., & O’Donnell, P.A. (1996). Executive information
systems development in Thailand, In P. Humphreys, L. Bannon, A. McCosh, P .
Milgliarese & J-C. Pomerol (Eds), Implementing systems for supporting management
decisions: Concepts, methods and experiences (pp 203-224). Chapman and Hall,
London.
Keen, P.G.W. (1980). Decision support systems: A research perspective. Data Base, 12
(1/2), 15-25.
Keen, P.G.W., & Gambino, T.J. (1983). Building a decision support system: The mythical
man-month revisited. In J.L. Bennett (Ed.), Building decision support systems (pp.
133-172). Reading, MA: Addison-Wesley.
Lewis, H. (1962). Catastrophic selection as a factor in speciation. Evolution, 16, 257-271.
Loewenstein, G. (1996). Out of control: Visceral influences on behaviour. Organisational
Behaviour and Human Decision Processes, 65 (3), 272- 292.
Meador, C.L., & Ness, D.N. (1974). Decision support systems: An application to corporate
planning. Sloan Management Review, 15 (2), 51-68.
Nandhakumar, J. (1996). Design for success?: Critical success factors in executive information
systems development. European Journal of Information Systems, 5, 62-72.
Ness, D.N. (1975). Interactive systems: Theories of design. Joint Wharton/ONR
Conference on Interactive Information and DSS, The Wharton School, University of
Pennsylvannia, Philadelphia.
Niehaus, R.J. (1995). The evolution of strategy and structure of a human resource planning
DSS application. Decision Support Systems, 14, 187-204.
Pearson, J.M. & Shim, J.P. (1994). An empirical investigation into decision support systems
capabilities: A proposed taxonomy. Information and Management, 27, 45-57.
Price, P.W. (1996). Biological evolution. Harcourt Brace, Orlando, Florida.
Rigby, B., Lasdon, L.S., & Waren, A.D. (1995). The evolution of Texaco’s blending
systems: From OMEGA to StarBlend. Interfaces, 25 (5), 64-83.
Sage, A.P. (1991). Decision support systems engineering. John Wiley & Sons, New York.
Shaffer, D.R. (1985). Developmental psychology: Theory, research and applications.
Brooks/Cole, Monterey, California.
Shakun, M.F. (1991). Airline buyout: evolutionary systems design and problem restructuring
in group decision and negotiation. Management Science, 37 (10), 1291-1303.
Silver, M. S. (1991). Systems that support decision makers: Description and analysis.
Wiley, Surrey, UK.
Simpson, G.G. (1944). Tempo and mode in evolution. Columbia University Press, New
York.
Sprague Jr., R.H., & Carlson, E.D. (1982). Building effective decision support systems.
Prentice-Hall, Englewood Cliffs, New Jersey.
Stabell, C.R. (1983). A decision oriented approach to building DSS. In J.L. Bennett (Ed.),
Building decision support systems (pp 221-260). Addison-Wesley, Reading,
Massachusetts.
Suvachittanont, W., Arnott, D.R., & O’Donnell, P.A. (1994). Adaptive design in executive
information systems development: A manufacturing case study. Journal of Decision
Systems, 3 (4), 277-299.
Valusek, J.S. (1994). Adaptive design of DSS: A user perspective. In P. Gray (Ed.),
Decision support and executive information systems (pp 78-86). Prentice Hall,
Englewood Cliffs, New Jersey.
Young, L.F. (1989). Decision support and idea processing systems. William Brown,
Dubuque, Iowa.
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