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ALFRED
P.
WORKING PAPER
SLOAN SCHOOL OF MANAGEMENT
Institutional Variations in Problem Choice and
Persistence Among Pioneering Researchers
Kocnraad Debackere
Gent
Rijksuniversiteit
December 1991
Michael A. Rappa
Massachusetts Institute of
Technology
Sloan
WP # 3389-92
MASSACHUSETTS
INSTITUTE OF TECHNOLOGY
50 MEMORIAL DRIVE
CAMBRIDGE. MASSACHUSETTS 02139
1
Massachusetts Institute of Technology
INSTITUTIONAL VARIATIONS IN PROBLEM CHOICE AND
PERSISTENCE AMONG PIONEERING RESEARCHERS
Michael A. Rappa
Koenraad Debackere
Gent
Massachmnts
Rijksuniversiteit
Institute
of
Technology
December 1 99
1991
Sloan
WP # 3389-92
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Alfred P. Sloan School of
Management
Massachusetts Institute of Technology
50 Memorial Drive, E52-538
Cambridge,
MA
02139-4307
institutional variations in problem choice and
Persistence among Pioneering researchers
DEBACKERE
Kocnraad
and Michael A. RAPPA'''
Massachusetts Institute of Technology
December 1991
abstract
This paper examines institutional variations in the factors that influence
pioneering
scientists in their choice
and
persistence in
an area of research.
presented from an international survey of more than sevenhundred researchers working in the field of neural networks. Elaborating
Evidence
is
on previous
which find that pioneers
results,
their motivations to enter
and
differ from their peers in
their persistence to
remain in the field,
both
this
academic pioneers who merit special distinction. In
general compared with other scientists, academic pioneers are more deeply
influenced by the intrinsic intellectual appeal of the field and are less
study suggests that
it is
influenced by the social dynamics of the research community. Pioneers in
commercial and government institutions are not found to he dissimilar from
their colleagues
scientific
who
enter the field afier
it
gains legitimacy within the
community.
INTRODUCTION
The
question of
why
to understanding the
set
scientists
choose to pursue the topic of research they do
emergence of new
fields
central
is
of science and technology. By selecting one
of problems and not another, scientists can thereby steer the frontier of knowledge
new and
different directions.
The "problem of problem
sociologists of science, involves sorting
scientists
in
relative significance
it
is
Of
known
may
through the myriad of forces that
their decisions to pursue an area of research.
determining the
scientific
choice," as
in
to
influence
particular concern
is
of factors that are internal and external to the
community (Merton, 1938; Cole and Cole, 1973; Zuckerman, 1978;
1978; and Ziman, 1987). While some have suggested cognitive factors
as
Giery-n,
the principle
force guiding scientists' choices, others have pointed to the strength of social processes
within the research community, such
as the
competition for funding, recognition and
rewards (Hagstrom, 1965). Other explanations have focused on external faaors, such
influence of military and commercial interests, as well as other forces that
immediate realm of the
'
Koenraad Debickere
Rijltsuniversiteit
Sloan School,
is
scientific
beyond the
community.
a Fulbright post-doctoral fellow at
Gent, Belgium. Michael Rappa
MIT.
lie
as the
is
MIT
and
a research associate at the Vlcrick School,
an assistant professor
in
the behavioral and polic)' sciences area of the
One manner
in
which
approach the problem of problem choice
to
distinction between pioneering scientists
who
Pioneers are those scientists
and the
initiate
of practicing
large majority
and continue working
of new
fields
scientists.
in a field before
perceived as significant or perhaps even legitimate by their peers.
unconventional problem choices, pioneering
to recognize the
is
By
it
is
virtue of their
provide the impetus for the creation
scientists
of research. Therefore, deciphering the faaors that contribute to pioneers'
problem choices may hold
special relevance to
understanding progress
in
science and
technology.
In an earlier paper
we examine
enter and persist in a field (Rappa
the factors influencing pioneers in their decisions to
and Debackere, 1991). Comparing pioneers
who
networks research with their colleagues
the scientific
—
group
even
enter after the field becomes legitimate within
community, we show empirical evidence
when
conform more
for viewing pioneers as a distinct
controlling for age and professional experience. Briefly stated, pioneers
problem choice: they
closely to traditional explanations of
more by cognitive
in neural
forces
and
less
by
social forces
than their colleagues. For example,
the choice of neural networks as a problem area, pioneers are
intrinsic intelleaual appeal
of the
while
field;
at
are influenced
more influenced by
the same time pioneers are
by the aaions and opinions of other researchers, the
less
in
the
influenced
availability of funding,
and the
potential for financial rewards. Similarly, pioneers exhibit a far stronger attachment to the
field.
They
express
that might lead
less
them
concern than their colleagues do with respect to a host of factors
to leave the field, such as a lack
of funding for their research, and rapid progress
expect, pioneers espouse a longer term
By
of progress
in neural
networks, a lack
in alternative fields. Finally, as
commitment
to the field.
taking into consideration pioneers in the problem of problem choice,
show how both
cognitive and social processes can operate within a research
different times
and with different individuals. TTie
social processes in the
are less
problem choices of pioneers
one might
relative influence
is
we
are able to
community
of cognitive versus
a reasonable finding.
That pioneers
concerned with the external validation of their problem choices and are content
follow their
own judgment, no
matter
how misguided
it
might seem
conclusion that supports general perceptions of pioneering behavior.
at
to colleagues,
It
is
is
to
a
precisely this
willingness not to heed the criticisms of colleagues that enables pioneers to remain
steadfast in their
commitment
to their research interests.
concerned about colleagues' criticisms of their work are
mainstream
in order to
pioneer
new
fields.
Researchers
less likely to
who
are overly
depart from the
9
But what
made of
be
to
is
community? Although our previous study
scientists'
problem choices and persistence
from outside the
originate
that
factors
scientific
may
hints to the external forces that
influence
adequate consideration has yet to be
in a field,
given to this issue. Both indtistry and government are prominent players within the research
establishment,
in
whose influence cannot be ignored.
terms of the dollar
on
billion spent
amount of
basic
More than
research performed.
and applied research
two-thirds of the $45.
1989 was performed by non-academic
in
As might be expected, the presence of industry and government
institutions.
pronounced with respea
to applied research.
But even
so,
is
more
more than one-third of the $18.6
1989 on basic research alone was performed outside of university
spent in
billion
In the U.S., this caji be seen very clearly
laboratories (National Science Foundation, 1989).
The
be seen
potential influence of industry
and government on the direaion of science can
also
terms of employment. Figures for 1987 show that more than one-fifth of
in
doctoral scientists and engineers
non-academic
institutions.
who were engaged
who were engaged
in basic research
Furthermore, of the 806,200 doctoral
in research
and development aaivities
all
were employed by
and engineers
scientists
in 1987, a staggering
85 percent
of them were employed by industry (National Science Board, 1989).
Given the tremendous volume of research conduaed
laboratories
and the
examination of
military
scientists'
in
industrial
from
and
employed,
it is
problem choices must be considered
and other external
internal cognitive
when,
large percentage of doctorates
in industrial
and government
not surprising that any
in light
of commercial,
influences. Frequently, these interests can conflict with the
social
influences of the scientific
community.
Difficulties arise
order to preserve commercial advantage or national security, scientists in
and military laboratories need
their colleagues in the rest
to maintain a level
of secrecy that divides them
of the research commimity (Allen, 1977; Kornhauser, 1962;
Pclz and Andrews, 1966).
Numerous
historical studies
(1988) work on
Du
of industrial laboratories, such
Pont, have described
clash with scientists' perceptions of
Pruitt, 1990). It
the scientific
is
what
is
how
community and
scientists,
seek to adhere to
management. Whereas members of the
who
The
Swann, 1988; Graham and
see themselves as
members of
at
odds with
community value openness,
originality
its
scientific
also:
norms, find themselves
and professional autonomy, managers seek confidentiality,
control over the direction of research.
Hounshell and Smith's
the commercial concerns of managers can
important (see
not unusual for industrial
as
practicality
and corporate
confusion that can result from this dichotomy
was forthrighrly expressed to
Du
Wallace Carothers, who would
Font's
scientists. Criticizing the firm's policies for
research
is
young organic chemist,
the
become one of the
later
The only guide we have
management by
firm's
most distinguished
guiding research, Carothers wrote:
own
formulating and criticizing our
in
show
the rather desperate feeling that they should
a profit at
As a result, I think that our problems are being undertaken in a
of uncertainty and skepticism without any faith in a successful
outcome or even without any clear idea of what would create a successful
the end.
spirit
outcome (Hounsheli and Smith, 1988).
The
pressure to
make problem
choices in isolation from their colleagues and that reflect
the immediate profit goals of the firm,
is
as if to leave industrial researchers to
explore
the fi-ontier of science without a compass.
For some, the divergent
satisfactorily: the "industrial scientist"
Each
industrial.
is
of commerce and science can never be resolved
interests
a world onto
itself,
an oxymoron. Science
is
is
academic; technology
is
bridged together only tenuously by the passage of
students from one side to the other and by a rare breed of technologist called
"gatekeepers," for which Allen (1977) has provided extensive empirical support.
problem of problem choice then
is
an issue of concern not for the industrial technologist (n^
but for management.
scientist),
and industry
Alternatively for others, the connection between science
tenuous enough. Relatively young industries such
as
some
fields, scientists are readily
not nearly
if
not to a
fault.
aware of the pecuniary value of their work through
consulting activities, ownership participation in
of their research. Not to be
is
biotechnology and microelearonics are
heavily rooted in science, and scientists' commercial motives are obvious,
In
The
new venture
left-out, universities
firms,
and
industrial funding
have increasingly sought to exploit the
commercial potential of faculty research by patenting inventions and licensing them
industry.
To
the scientific purist, this
been led astray and
how
is
a clear demonstration of
how modern
to
science has
the problem of problem choice has been transformed into a profit-
maximizing funaion.
To what
constraints
influences
extent are the problem choices of scientists influenced by the institutional
under which they must work? Clearly, further investigation of external
on
substantially
scientists'
problem choices
is
warranted. Having
from those researchers who enter a
primary concern here
will be to
field after
it
shown
that pioneers differ
becomes legitimated, our
examine the influences on problem choice and persistence
across different kinds of institutions,
homogeneous group
and
across academic,
to ask, in panicular,
whether or not pioneers
commercial and government
are a
institutions.
PIONEERS IN NEURAL NETWORKS RESEARCH
We
which
selected as the basis for this examination the field of neural networks research,
is
one of nearly a dozen
concurrently.
The
of science and technology that
fields
we
arc studying
decision to examine neural networks holds no special significance other
than the opportunity to do so presented
we concluded
neural network field,
it
itself first.
would be
After a preliminary investigation of the
interesting to
condua
a
comprehensive
study with a primary focus on pioneering researchers.
A
of the
neural network
human
brain.
certain features that
is
a type of information processing system that
By using
make
it
example, a neural network
a biological
unique
is
in
model
in
its
is
inspired by models
design, a neural network system has
form and function from conventional computers. For
not programmed in the usual sense, but rather
it
is
trained
with data. This implies that the computational performance of a neural network improves
with experience:
as
processes
it
more and more information
becomes increasingly more accurate
in
parallelism in processing a task. Unlike a
its
in
performing a
response. Another feature
is
its
task,
it
degree of
normal computer with a single or small number
of sophisticated central processing units, a neural network has a very large number of
simple processing elements that operate simultaneously on a computational problem.
These features allow
it
perform certain tasks that otherwise might be very
to
difficult
using existing computer technology. Neural networks are also referred to as connectionist
systems, adaptive systems, or neurocomputers. For further details, refer to the recent repon
by DARPA (1988).
Neural networks have a long history of development, stretching back to theoretical
explanations of the brain and cognitive processes proposed during the 1940s. In the early
years, researchers
formulated and elaborated upon basic models of neural computing that
they then used to explore
random networks. By
the most
phenomena such
as
adaptive stimulus-response relations
the 1960s there were several efforts to implement neural networks,
nouble being the
single-layer "perceptron."
Among
neural network researchers the
perceptron was considered a watershed, but at the same time
for
criticism
intelligence.
from researchers more interested
The
in
in
it
served as a lightning rod
the burgeoning field of artificial
idea of neural networks, as exemplified by the perceptron, quickly
became seen
almost antithetical to the symbolic reasoning principles of
as
intelligence. Critical analysis
to
of the perceptron led some highly respeaed AI researchers
proclaim that the concept was fundamentally flawed, and
researchers to waste
neural networks
larger
much
may have
casting doubt as to
inappropriate for
legitimacy, antagonists of
its'
cffeaively dissuaded other researchers from entering the field in
controversy surrounding neural networks notwithstanding,
the early 1970s with perhaps no
Undeterred
in their belief
more than
light
by researchers
a few
work continued during
hundred researchers worldwide
in the
of the potential of neural networks, their persistence over
the next decade eventually paid-off.
new
By
effort on.
as such,
numbers (Minsky and Papert, 1988).
The
field.
artificial
By
the 1980s, neural networks began to be viewed in a
of disciplines, such that the
in a variety
community.
position of legitimacy within the scientific
A
field
soon achieved
a
professional society for neural
network researchers was formed, specialized journals and books were published, and the
first
in a series
why
exactly
of international conferences were held. While
it
is
difficult to explain
the perception of the field changed so dramatically, at least four important
technical events can be discerned: (1) the evolution of the single-layer perceptron into a
multi-layer system; (2) the rapid development of related technologies that enabled
researchers to develop, simulate,
and diagnose neural networks of greater sophistication;
(3) significant progress in theoretical
understanding of neuro-biological processes; and
(4)
the contributions of researchers pursuing the idea of parallel distributed processing, the socalled
PDP-group. In
became widespread, such
expanded
rapidly.
By
from a few hundred to
The
of these developments,
light
that the
number of
emerging
we have found
that
several
fields in
it
researchers
working on neural networks
the end of the decade the size of the field swelled in
is
membership
thousand researchers worldwide.
evolution of the neural network research
typical of
as well as others, interest in the field
some of
fairly
its
common
community
is
not unusual and
sociological characteristics.
for
new
fields to lack
may
From our
even be
research,
widespread acceptance for
long periods, sometimes attracting controversy, other times simply being ignored by
researchers.
But when they do catch on,
fields
tend to grow rapidly. This pattern has
occurred, to greater or lesser extent, in each of the dozen fields
Given the recent experience within the
excellent opportunity to
relative to large
examine
numbers of
field
of neural networks,
in greater detail the
researchers
who
we have examined
this case presents us
so far.
with an
behavior of pioneering researchers
follow in their footsteps.
METHOD
Through an
we
conference proceedings for the two-year period from 1988 to 1989,
identified
we were
Given the scope of the research community,
questionnaire was determined to be the most appropriate
method of
activities, (b) their decision to
begin working on neural networks,
might lead them to cease their neural network research
interaaion with the
demographic
Additional
characteristics.
tests
among
arising
rest
(a) their
in
those respondents for
A
neural
faaors that
(c)
of another problem area,
of the neural network research community, and
The
were conduaed
in favor
a survey
investigation.
twelve-page questionnaire in English was sent to researchers inquiring about
(d) their
this
able to determine the exact address for each of 2,037 researchers in
thirty-five different countries.
network
more
From
than 3,000 researchers worldwide working on the subject of neural networks.
material,
and
analysis of published sources, including books, journal anicles,
(e) their
questionnaire was pretested in the United States.
Europe to reduce potential interpretation^
whom
English
Since there were thirty-seven researchers with
is
a
difficulties
second language.
more than one address during
the time
period considered, a total of 2,074 questionnaires were mailed in February 1990. After the
third
week of data
collection,
on computer bulletin boards
we mailed
a follow-up letter
to alert neural
and posted e-mail messages
network researchers of the survey.
questionnaires, 162 were returned as undelivered by the post office.
seven researchers with
more than one address were represented
Of the
None of
in
2,074
the thirty-
the undelivered
questionnaires. At the completion of the data colleaion period approximately ninety days
later,
720 of the 1,875 questionnaires presumed
to be delivered
returned, yielding a final response rate of 38.4 percent.
Some of the
were completed and
faaors that
may have
affected the response rate include: the length of the questionnaire, the global scope of the
survey,
and the
institutional mobility
of researchers.
DATA
Validity checks
In order to rule out apparent self-seleaion biases,
made
to
determine whether the respondent sample depans significantly from the survey
population. First, a geographic comparison was
and the survey population into four
Middle
demographic comparisons were
East.
Of
the
made by
clustering the respondent sample
regions: the Americas, Europe, the Far East,
720 respondents, 63 percent
reside in the
Americas
(all
and the
but a few
percent in the U.S.), 25 percent in Europe, ten percent in the Far East, and about two
p>erccnt in the
A
second
Middle East (x^=5.24,
test
d.f.=3, n.s.).
compared the respondent sample and survey population with
respect to
the type of institutional affiliation. Respondents were classified into three categories:
universities,
commercial firms, or other types of institutions (mostly government funded
Among
laboratories that are not university-based).
720 respondents,
the
percent) are affiliated with academic laboratories,
no
reveals that
statistically significant
sample and the survey population (x^=5.6l, d.f =2,
A
compared the
final, albeit less precise, test
452 (63
177 (25 percent) are employed
commercial firms, while 91 (12 percent) are engaged
comparison
a total of
in other types
of institutions.
in
The
departure exists between the respondent
n.s.).
disciplinary
background of the sample
respondents with those of the survey population. Although respondents indicated their
disciplinary backgrounds, for the survey population
disciplines fi-om their postal address
when
a
we were only
departmental
affiliation
careful inspection of the survey population, the disciplinary
researchers were found. Using this data,
when comparing respondents with
among respondents
percent),
we were unable
the survey population.
for
about 1,500
to find a significant difference
The
disciplines
most represented
include electrical engineering (36 percent), physical sciences (19
computer science (18 percent),
biological sciences
and engineering
(7 percent),
(5 percent).
characteristics
The 720 respondents
purpose of
this analysis,
are
we
employed
classified
universities
at
in
383
different institutions worldwide. For the
respondents by their institutional affiliation into
three categories: academic, commercial,
based
was provided. Upon
background
mathematics (7 percent), and psychology and cognitive science
Sample
able to infer researchers'
and government. Academic
institutions include
and federally-funded research and development laboratories (FFRDCs) that
universities.
Commercial
operate for-profit, ranging from
category, government,
of the survey.
It
is
institutions include public
new
and private firms that
ventures to multinational corporations.
somewhat more
are
The
last
diverse in charaaer given the international scope
includes laboratories that are run as an
arm of government agencies (such
as
the military), government funded laboratories unaffiliated with universities, non-profit
organizations, and quasi-public firms associate with military research.
classifying such a
The
difficulties in
wide variety of institutions should not be minimized; and though
it
is
—
certainly possible to devise a
more
numbers
elaborate classification scheme, the small cell
that result will ultimately render statistical procedures inappropriate.
A number
of steps were taken
in
order to ensure the comparability of the three types of
institutions in the present analysis. First, given their special status
experience at the time of the survey,
wc omincd doaoral
of 1990) from the analysis. Since our focus
make
allow us to
scientists.
who
member,
faculty
title a5 a
who
academic
typically
omission
this
who
report their formal position
few respondents
report position appointments in
scientists
who hold secondary
number of respondents
more than one type of institution
positions as managers or industrial
affiliated
with organizations that do not
within the domain of the proposed topology were dropped from the analysis.
As a
result
of these
steps, the
institutional distribution:
(11
The academic
in
employed by a
single firm.
industrial respondent
in
is
represent
is
laboratories, with
Tukey-HSD and
Schefifif tests in
|ii=|lj (for all
ANOVA
an
i,
a=.05).
j,
find any significant differences across institutions with respect to
(ANOVA
of years of professional experience, which
is
elapsed since the respondent last graduated,
for
32
39.4 years old (s.d.=8.9 years), the average
39.3 years old (s.d.=9.3 years).
we could not
9.9
The
37.2 years old (s.d.=8.4 years), and the average government
the respondent's professional experience
years),
ten.
any single organization being seven.
(F=2.58, p=.08) do not allow us to rejea the null-hypothesis that
Similarly,
174 different
any single university being
The government respondents
average academic respondent
is
in
in
industry are employed in 56 different firms, with fourteen of them
most respondents employed
respondent
employed
researchers are
with the most respondents employed
respondents
The
sample was reduced to 452 cases with the following
286 (63 percent) academic, 115 (25 percent) commercial, and
percent) government.
universities,
the
those respondents
scientist or engineer. TTierefore, the relatively
consultants. TTiird, a small
51
on understanding the significance of
report their primary positions as managers or consultants were omitted, as well as
those respondents
fall
(as
between academic and non-academic
a clearer comparison
we included only
Second,
students and recent graduates
on the problem choice and persistence of pioneers,
institutional affiliation
will
is
and lack of professional
F=1.16, p=.31,
defined as the
is
The
number of
average
years),
and 11.9
number
years that have
10.8 for academic respondents
commercial respondents (s.d.=7.4
respondents (s.d.=9.0 years).
n.s.).
for
(s.d. =
8.7
government
10
The number of years
that respondents have been involved in the field of neural networks
Academic respondents have on average 7.5
differs significantly across institutions.
(s.d.=6.9 years) in the field,
commercial respondents have 4.5 years (s.d.=4.5
government respondents have
Tukey-HSD and
Scheff(f tests
and commercial
instit-utions.
6.1
years (s.d.=5.8 years):
ANOVA
years
years),
and
F=9.l4, p<.001. Both
with a=.05 reveal a significant difference between academic
Idmtifying pioneers
Since the focal point of this study
is
on understanding
problem choice motives of pioneering researchers and
identifying exaaly
who
the pioneers are
respondent sample should be
a critical issue.
is
classified as a
pioneer
is
institutional variations in the
their persistence in the field,
Our
determination of who
when
based upon
in the
a respondent enters
the field of neural networks. As previously discussed, the evolution of the neural network
community
is
marked by
a period of rapid
by the advent of a professional
growth
in
membership during the 1980s and
society, specialized journals
and books, and an international
conference. These events serve as indicators of the establishment of neural networks as a
legitimate field of research within the broader scientific
community. Thus,
demarcation for separating pioneering researchers from the
somewhere
a point of
of the sample should
rest
lie
in this period.
The cumulative
that the field
distribution of the entry year for each of the
grew most rapidly
entering the field each year was
ft-om
much
of the respondents entered the
about 1984 onwards.
720 respondents
The number of
indicates
respondents
lower during the 1960s and 1970s. About 25 percent
field prior to
1984; whereas about 75 percent of them
entered from 1984 to 1990. Both the historical overview of neural network research and the
entry pattern suggest that the point of demarcation for identifying pioneers
pioneers, while placing the remaining
control group (x2=18.15; p<.001).
contrasting the responses of pioneers.
seCTor
is
shown
in
Table
1.
who
we
entered the field by 1983
as
324 (73 percent) who entered
The
The
after
1983 into the
control group provides us with a
distribution of pioneers
As we have previously reported, a
the soundness of using 1983 as a cut-off year (see
somewhere
in this analysis,
between 1980 and 1985. As a consequence, for 441 (of 452) cases used
decided to classify 117 respondents (27 percent)
is
means
for
and controls within each
sensitivity analysis confirms
Rappa and Debackere, 1991).
11
12
13
14
15
16
number of
difference in the
lowest
N
cell
cases per cell in the
rwo-faaor
about 17:1). Nonetheless, the
is
supported by those previously reported
in
ANOVA
of the two-factor
results
Tables 2 and
A
4.
statistically significant interaction effcCTs further illustrates this
items that are meant to capture the influence of
(the ratio highest-to-
communal
ANOVA
are
closer inspection of the
On
argument.
interaction
two of the
on the respondent's
entry decision (the positive opinions of leading researchers and the successes of other
researchers),
commercial pioneers more closely resemble the controls than they resemble
their fellow pioneers in other institutions (refer to
the 3
Tabic 2
for details
of the
means
cell
in
X 2 ANOVA).
Given the time-dependent nature of
this analysis,
create a potential bias in the results. Specifically,
were employed
in a different type
presently. Since
we
are
making
it
some complications
may
in interpreting the results if in fact the respondents
Migration
among
the respondents
percent) academic respondents, 31
field
may
present a problem
moved between
different types of
fairly
is
common. For example, 129
The
difference across institutions
we can determine whether
p=.001). Although
(47
(27 percent) commercial respondents and 17 (34
work on
percent) government respondents have changed institutions since starting
networks.
then they are
comparisons of entry decisions based upon
present affiliations, the past institutional migration of respondents
affiliations.
may
be the case that some respondents
of institution when entering the
institutional
arise that
is
statistically significant
neural
(x^=13.50, d.f=2,
or not a respondent has changed institutions
since entering the field, unfortunately the survey does not allow us to identify the exact
type of institution in which these respondents were previously employed.
In order to rule-out the existence of bias in the subsequent analysis
migration,
who
same
we performed
institution over the entire period.
who
to institutional
the following tests comparing the answers of those respondents
migrate between institutions after entering the
respondents
due
are pioneers,
it
is
field
with those
who remain
with the
Given the disproportionate number of migrant
necessary to control for differences that
may
exist
between them and the control group. Thus, within each type of institution, we made
comparisons between migrant and non-migrant pioneers and migrant and non-migrant
controls.
Using discriminant analysis on academic (both pioneers and controls),
commercial (controls only) and government (controls only) respondents, we were unable
discriminate between migrants and non-migrants
for each
on the
of the four discriminant functions were well
number of pioneers
in
commercial and government
basis
to
of the entry items (p-vaJues
in excess
institutions,
of
.10).
Due
we were unable
to the
to
low
perform
17
a similar discriminant analysis for migrants
and non-migrants
we compared migrant and non-migrant
using t-tcsts on each entry item separately,
no
pioneers. In each case,
Second,
we
significant differences could be found.
repeated the analysis reported in the previous tables, only this time using
who
just those respondents
network
did not migrate between institutions since initiating their neural
research. Again, the analysis largely confirms the results
entire sample.
Undoubtedly, knowing the
they enter the
field
tests
those scaors. Instead,
in
is
most
we have reponed
institutional affiliation for each
for the
respondent when
preferable. In absence of this information, however, the previous
enable us to conclude that, regardless of whether a respondent has migrated between
institutions, the results arc basically the
same
for
both pioneers and the control group.
Persistence in the field
In our previous analysis,
we show
from their
that pioneers differ significantly
colleagues with regard to the faaors that might influence
them
to leave the field. In
almost every respect, pioneers express a more passionate commitment to their chosen
of research.
results
When
viewed
in
conjunction with the analysis of problem choice behavior, the
provide a coherent picture of pioneers
cognitive perceptions of the field and
community. Here, we
variations that
may
revisit
exist
Table 6 shows the
among
results
pronounced
as in case
less
as researchers
who
more influenced by
influenced by the social dynamics of the research
pioneers.
of a oneway
ANOVA
field,
now
of the factors that might influence the
taking into consideration institutional
observed across institutions for pioneers and controls are not
as
Many
similarities
both pioneers and controls. Tukey-HSD and Scheffd
tests reveal
of the respondent's decision to enter the
exist across institutions for
are
the question of persistence in light of the institutional
respondent's decision to leave the
affiliations. TTie differences
field
significant differences for pairwise
field.
comparisons of means with respea to only two items: a
lack of financial rewards and the discontinuance of neural networks research at the
respondent's organization (Table 7). Financial rewards are
more important
to commercial
respondents in the control group than they are to their control group peers in academic and
government
institutions.
As might be expected, the discontinuance of neural networks
research at the respondent's organization
institutions than
it
is
to those in
is
more important
academic institutions
for
to those in
commercial
both pioneers and the control
group. Within the control group, government respondents also consider discontinuance of
neural networks by their organization
more important than academic respondents.
18
19
PIONEER
CONTROL
GROUP
ITEMS
McOM
lack of financial rewards
^ M^ACAD
J^OM* ^^VT
discontinuance at organization
TABLE
7:
^^OM
Pairwise comparisons on
* M-ACAD
mean
l^OM
* ^^ACAD
mX)VT
* M-ACAD
scores for factors that
might influence the respondent's decision
field,
The
using
Tukey-HSD and
were further investigated using
institutional differences
and controls within each scaor (Table
to leave the
Scheffif tests.
t-tests to
These comparisons strongly suppon the main
8).
finding firom our analysis of a respondent's decision to enter the
difference between pioneers and the control group occurs
institutions.
With respea
to respondents in
compare pioneers
commercial
field;
namely, the
most strongly within academic
institutions, except in
one instance,
there are no statistically significant differences between pioneers and the control group.
The
picture with
government respondents
examined interaction
muced. Using a two-factor
ANOVA, we
between institutional type and the pioneer/control
effects
independent variables (Table
is
9).
The only
"rapid progress in alternative areas."
Upon
item to attain a significant interaction was
closer inspection,
we found
that pioneers in
commercial institutions are more similar to the controls, regardless of institutional type,
than to their pioneering colleagues
expect, the
main effeas
again, caution
relatively
is
in
academic and government laboratories. As we should
are consistent with the results reported in Tables 6
required in interpreting the results of the two-faaor
high difference in the
Lastly, the results
number of cases
of our inquiry into
how
and
ANOVA
8.
Once
due to the
p)cr cell.
long respondents are willing to work on
neural networks, given current progress, are reported in Table 10. Using Kruskal-Wallis
non-parametric
unable to find
tests,
we compared
statistically significant differences
across the three seaors.
type using
less
pioneers and controls across institutions and were
We
also
among
the pioneers and the controls
compared pioneers and controls within each
Mann-Whitney non-parametric
tests
(Table
1
1).
Once
support the overall picture that has emerged thus
far:
institutional
again, the results
more or
academic pioneers are
20
21
22
in the control
from their peers
significantly different
group (p<.001). While government
pioneers are also significantly different from their peers in the control group, the difference
between commercial pionecn and the control group
statistically
is
weak (p=.048).
DISCUSSION
A
comparative analysis of institutions shows that significant variations
factors influencing pioneers' decisions to enter
from
persist in a field.
The
results of
problem choices indicate that academic pioneers are
investigation of pioneers'
different
and
exist in the
their peers in that they are
our
distinctly
most influenced by cognitive faaors and
least
influenced by social faaors in the research community. In contrast, pioneers in commercial
and government laboratories
more
are
similar to control
their pioneering colleagues in universities.
pioneering researchers
we
group respondents than they are
to
Thus, we conclude that the charaaeristics of
previously found to exist are, in
fact, largely
associated with
pioneers in universities.
When we
field,
we
compare pioneers
across institutions in terms of the decision to enter the
employed
find that those
in firms are
more influenced than those
in universities
by the opinions of leading researchers, the successes of other researchers, and the
community. Thus, beyond the expeaed
firm,
result
of having a greater interest
commercial pioneers are more greatly influenced by the internal
the research
community.
Similarly, with respect to the factors that
respondent's decision to leave the
field,
size
in building their
social
dynamics of
might influence a
commercial pioneers are almost identical
attitudes to their peers in the control group, with evidence of only
one weakly
in their
significant
difference being the diminished intellectual challenge of neural networks having
relevance to pioneers.
We
Exploring these results
express attitudes
more
their intentions for
in
like those
like
are
more
like
is
in the field.
generally find that commercial pioneers
of control respondents
show some
ability to
in
firms than they do other
contrast with peers in the control group,
commercial pioneers
academic pioneers. However, the
government pioneers
remaining
more depth, we
pioneers. Furthermore, although they
government pioneers
more
only one weak difference between commercial pioneers
also find
and controls when analyzing
of the
draw
in their attitudes
solid conclusions
than they are
with respect to
considerably more difficult due to the relatively small
respondents (15 pioneers, 34 controls) with which to
make comparisons.
A
number of
larger
number
of cases might change the overall picture regarding government pioneers in either direction.
23
24
In general, the findings from our analysis point to the special significance of academic
institutions in fostering pioneering behavior.
To
the extent that researchers are provided the
freedom to interact with their colleagues and pursue their
interests
determination, pioneers are perhaps more likely to be found
in
with single-minded
an academic context.
Our
finding that commercial pioneers (and to lesser degree, government pioneers) are not
unlike other researchers
who
on non-academic
constraints placed
persistence in the field.
Be
significant differences can be
Having
new
field.
in light
this as
students
academic institutions
when
group from pioneering
new
fields
interesting to ask
fostering pioneers.
more
in
—
is
it
is
pioneering a
in
wonh
revisiting
them
it,
interesting to see if pioneering students
as
is
often implied in the literature
it
problems by faculty?
that students play a
Or
more formidable
(e.g.
— and
role in
by leading the way for faculty?
in
pioneering
new
fields,
whether or not certain institutions have a greater propensity
Does the
overall research prestige of a university matter?
Although there
more
are they
where they might have
research agendas?
note that very few
our previous study, a disproportionate
are directed to their research
more abundant? Or
institutions,
of problems and
of students
this analysis,
would be
it
likely to begin their research at
relatively
the special
being a rather unique group,
as
Furthermore, given the importance of academic institutions
is
how
entering the field of neural networks. Given the
faculty. Is
more challenging thought
pioneering
also interesting to
is
Although students were omitted from
Ziman, 1987), that students
a
affect their choice
to be to investigate the role
importance of academic institutions,
is
it
be an indication of
found across institutions within the control group.
would seem
number of pioneers were
this
may,
of the present findings. As we noted
differ as a
may
researchers
it
identified pioneers within
the next logical step
may
enter after them,
is
prestigious institutions,
more
likely to
relatively greater latitude in
much
for
Are pioneers
where resources
emerge from
it
less
are
prestigious
pursuing non-mainstream
anecdotal evidence in support of both views, there
has yet to be any systematic examination of the relationship between institutional prestige
and pioneering behavior.
One
caveat in drawing conclusions from our analysis relates to our inability to classify
pioneers according to the institution where they
the difficulties that can occur
when examining
with a cross-sectional survey.
pioneers,
in the
who we
same one
first initiate
longitudinal
The problem does
not so
their research.
phenomena, such
much
lie in
This
is
one of
as pioneering,
evaluating academic
are fairly ceruin stan their research in universities (albeit, not necessarily
as
where they
are currently employed), but in evaluating
commercial and
25
government pioneers. Nevertheless, the
statistical
pioneers enable us to be confident that, even with
checks of migrant and non-migrant
full
information, the overall results will
not change. Moreover, despite the analytical problems with respea to entry, a consistent
picture emerges
when studying
a respondent's decision to enter together with their decision
to persist in the field.
CONCLUSION
Elaborating on an earlier study of pioneers in neural networks, in which
factors that influence their decisions to enter
institutional
variations
in
and
persist in the field, this
we examine
the
paper reports on
pioneering behavior. Comparing pioneers in academic,
commercial and government laboratories, we find that
it is
only academic researchers
who
demonstrate the kind of behavior we have previously found to be associated with pioneers.
In their choice of a
problem area and
more influenced by
their cognitive perceptions of
their persistence in the field,
what
is
academic pioneers are
an interesting problem and
less
influenced by the actions and opinions of others in the research community. In contrast,
commercial and
colleagues
who
(to
a lesser extent)
enter the field after
government pioneers
are
more
similar to their
has gained legitimacy than they are to fellow
it
pioneers in universities.
Pointing-out a paradox of emerging
fields.
Carver Mead, a prominent
neural networks, has remarked: "In a field like neural networks, one
optimistic in the
shon
run, but one
is
who seem
of persisting long enough
in their research for the reality
enthusiasm they have for
it.
laboratory they can
fostering the
call
of a
to have mastered the
field to
an
catch-up with the
the extent that universities can provide pioneers with a
home, academic
emergence of new
usually too
never optimistic enough over the long run" (DARPA,
1988:281). Pioneers are those relatively few individuals
To
is
scientist in
fields
institutions will continue to play a special role in
of science and technology.
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28
APPENDIX
Items inquiring into the respondent's decision to enter the field of neural networks:
How
important were each of the following factors
in influencing
your research agenda (respondents were asked
important) -scale with midpoint 4 (somewhat important)):
networks
in
to circle
your
on a
initial
decision to include neural
I (not at all
important) to
7 (very
compelling nature of neural networks
1.
intellectually
2.
lack
3.
availability
4.
potential for financial rewards
5.
potcndal for recognition by peers
6.
dissatisfaction
7.
positive opinions
8.
successes of other researchers with neural networks
9.
opportunity to build a commercial enterprise
10.
opportunity to solve an important societal problem
of other promising research topics
of funding for neural networks research
with a previous research agenda
of leading researchers
in the field
Items inquiring into what might lead the respondent to leave the field of neural networks:
How
important would each of the following factors be
network
activities (respondents
were asked
to circle
on a
in
diminishing your current interest in neural
1 (not at all important) to
midpoint 4 (somewhat important)):
1.
slow progress in solving technical problems in neural networks
2.
lack of funding for your neural
3.
diminished interest
4.
rapid progress in alternative areas of research
5.
opinions of leading researchers unfavorable to neural networks
6.
negative opinion of your supervisor
7.
discontinuance of neural net activities at your organization
8.
lack of financial rewards
9.
diminished intellectual challenge of neural network research
10.
increased financial cost of conducting neural network research
1
overcrowding in terms of the number of neural network researchers
1
12.
difficulty in
among
network research
other researchers in neural networks
keeping up with
(if
any) toward neural networks
new developments
55U
in neural
037
networks
7
(very important) -scale with
IRARIF.S
3
TDflO
DIlPl
DD7Sbfll3
1
Date Due
JAN.
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