Measuring the evolution of microcomputer technology
| Pages | 262-268 |
| DOI | https://doi.org/10.1108/02635579810236724 |
| Published date | 01 September 1998 |
| Date | 01 September 1998 |
| Author | David K. Peterson,Phillip E. Miller |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
[ 262 ]
Industrial Management &
Data Systems
98/6 [1998] 262–268
© MCB University Press
[ISSN 0263-5577]
Measuring the evolution of microcomputer
technology
David K. Peterson
Lynchburg College, Lynchburg, Virginia, USA
Phillip E. Miller
East Tennessee State University, Johnson City, Tennessee, USA
People who are interested in
evaluating and rating micro-
computer technology need a
single, composite measure
which is rich enough to
enable comparing machines
of widely differing time peri-
ods, features and formats.
Traditionally, computers are
modeled by describing four
primary features – their CPU,
available memory, and
input/output (I/O) capabili-
ties. Applying this general
model to portable microcom-
puters, this paper uses a
scoring model methodology to
develop a composite measure
for the portable microcom-
puter marketplace and then
evaluates the model’s longitu-
dinal performance. The tech-
nological scoring model
methodology is a very prag-
matic and highly subjective
technique to derive a relative
measure for identifying long-
term technological trends and
rating/ranking individual
machines one with another.
Furthermore, employing the
scoring model offers some
unique challenges to the
technological forecaster.
However, the scoring model
does seem to be a useful
approach (at least for
portable microcomputer
technology) if used with due
caution.
Introduction
Since the 1970s, the rapid evolution of micro-
computer technology has resulted in a dra-
matic increase in desktop and portable micro-
computing capabilities. These capabilities,
packaged in a wide variety of shapes and
sizes, present consumers and manufacturers
alike with a sometimes bewildering array of
microcomputing alternatives. Furthermore,
the combinations and permutations of these
alternatives combined with the perpetual
march of technology makes direct compar-
isons between historic and contemporaneous
machines difficult at best. Often these com-
parisons are reduced to their lowest common
denominators, definable characteristics such
as: CPU, RAM, etc., rather than evaluating the
microcomputers as complete packages.
People who are interested in evaluating and
rating microcomputer technology need a
single, composite measure which is rich
enough to enable comparing machines of
widely differing time periods, features and
formats. This paper uses a scoring model
approach to develop such a measure for the
portable microcomputer marketplace and
then evaluates the model’s longitudinal per-
formance. This composite measure of techno-
logical advance is “intended to capture main-
stream trends” (Alexander and Nelson, 1973)
but cannot make fine distinctions between
different microcomputers. The composite
measure’s greatest applicability is in provid-
ing a general sense of the technology’s
advance and how a particular machine com-
pares to that trend which can then help in
setting research and development agendas or
building marketing programs (Alexander and
Nelson, 1973).
Background
What are technological forecasts?
As noted by Martino, technological forecasts
are made to serve as inputs to a decision
(1993b). Specifically, these forecasts can iden-
tify the performance limits of a technology or
a feasible rate of performance improvement
(Martino, 1993b). Typically, technological
forecasts estimate the value of a “parameter
which characterizes the performance of that
technology” (Martino, 1993a).
However, the essence of many complex
technologies cannot be adequately character-
ized by a single parameter (Martino, 1993a).
Frequently, these complex technologies are
characterized by multiple parameters. “More-
over, there may be tradeoffs among the para-
meters, such that at a given state of the art, an
increase in one parameter must be accompa-
nied by a decrease in one or more of the other
parameters” (Martino, 1993a). Unfortunately,
while a single parameter analysis may not
adequately model a given product’s complexi-
ties, there is also no “theoretical nor a practi-
cal formula for combining the several para-
meters into a single measure” (Martino,
1993a). In situations like this, a composite
scoring model approach is frequently the
next best alternative.
What is the scoring model?
When accounting for the possibility of trade-
offs among the technical parameters of a
complex product is particularly important,
methodologies such as the composite scoring
model can be very helpful. “The concept is
that at any given point in the state of the art,
the designer has the freedom to choose more
of one parameter at the expense of some other
parameter(s). ...[and that] several different
devices representing the same state of the art
may have different values for the various
parameters ....” (Martino, 1993b). This is espe-
cially true of microcomputers where,
although at any given point in time there is a
finite variety of hardware components to
choose from, these components can be inte-
grated and assembled into a much wider
range of alternatives in the final product
form.
Thus, to build a composite measure of tech-
nology necessitates identifying and concen-
trating on what function(s) the technology
performs (Martino, 1993b). “This is particu-
larly true when the forecaster is dealing with
a succession of technical approaches. Each of
these technical approaches can be put on a
common basis only in terms of the function
they all perform” (Martino, 1993b). In addi-
tion, these functions are commonly described
in terms of either “functional” or “technical”
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