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Subject: AEJ 94 HerlingT CTP Resistance to adoption by faculty
From: Elliott Parker <[log in to unmask]>
Reply-To:AEJMC Conference Papers <[log in to unmask]>
Date:Fri, 19 Aug 1994 13:21:55 EDT
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                  RESISTANCE TO THE ADOPTION OF COMPUTER
             COMMUNICATION TECHNOLOGY BY COMMUNICATION FACULTY
 
 
 
 
 
 
                                    by
 
 
 
 
 
 
                             Thomas J. Herling
                           University of Oregon
                   RESISTANCE TO THE ADOPTION OF COMPUTER
             COMMUNICATION TECHNOLOGY BY COMMUNICATION FACULTY
 
 
     The National Information Infrastructure (NII) or  "information
superhighway" has
been referred to in White House press releases as something that will "help
unleash an
information revolution that will change forever the way people live, work, and
interact
with each other."  A great deal of debate has focused on who will provide these
information resources that will reach the nation's schools, offices and
factories and what
content they will carry.  However, little has been said about whether these new
information resources will actually be used once they get there.
     This study looks at adoption of computer communication technology by
faculty in
a sample of schools of communication in which online database services and
electronic
mail were made available to individual faculty members without cost or access
barriers.
This resulted in a quasi-experimental situation in which a high rate of adoption
was
expected under such ideal conditions.
     Once these new communication technologies were made readily available to
faculty, a high rate of adoption was expected for several reasons.
Communication
professors have easily realizable rewards for using them, including the benefits
of more
efficient lesson preparation and improved research.  They are motivated by a
variety of
pressures to keep current with developments in the field.  Financial barriers to
adoption
are low since most professors can get the necessary equipment, such as computers
and
modems, at no cost to themselves.  Finally, these individuals are in an
environment that
is oriented toward the acquisition of new skills and includes knowledgeable
people that
could be consulted for training.
Computer Communication and Mass Communication: Practitioners and Educators
     With the increasing computerization of information resources, the need to
know
about new information technology has become crucial to professional
communicators.
Miller considers computer databases the principal innovation that has
revolutionized
access to information, which he calls the "retrieval revolution."  The use of
online
database technology has grown explosively since its inception only some 30 years
ago.
Currently, the full texts of over 100 United States newspapers and nearly 1,000
trade
publications and magazines are available electronically from commercial database
vendors such as Mead Data Central's Lexis/Nexis system, Knight-Ridder's VU/Text,
Dow-Jones News Retrieval Service, and others.
     The ability to send and receive information using electronic mail is also
becoming
more important.  Email messages can be sent to almost any location in the world
at
virtually no cost to the user through Internet, a network of computers that
spans the
globe.  Along with email, Internet allows access to a wide range of information
resources, including government databases, library catalogs and discussion
groups made
up of individuals with common interests.
     Several of these discussion groups specialize in areas of interest to mass
communication faculty and practitioners.  These include Renssalaer Polytechnic
Institute's Comserve lists, the Computer-Assisted Research and Reporting List
(CARR-
L), and Journalism Educators Network.  Members of these electronic mailing lists
discuss
topics related to teaching and research and frequently exchange articles,
syllabi, and
advice on research problems.
     Despite the growing importance of electronic information resources in the
newsroom and throughout a wide array of other institutions, instruction in
computer
communication technology in schools of journalism has been limited.  Johnson
states that
journalism educators have failed to realize the magnitude of change that has
accompanied the spread of new technologies.
     . . . a large majority of journalism students - indeed, the great mass of
all
     students in most universities - are not being adequately prepared to cope
     with the information-retrieval and analysis environment that is used daily
     by government and business, and a steadily increasing number of print and
     broadcast companies.  Our students, therefore, are being defrauded, bilked
     out of the skills vital to their intellectual and professional due.
     A 1991 survey of 258 journalism schools conducted by DeFleur and Davenport
revealed that only one-third offered or planned to offer courses featuring
online database
usage and other computer communication skills.  In contrast, a 1990 study
revealed that
almost 90 percent of the 105 newspapers with a daily circulation of over 100,000
were
found to subscribe to commercial online databases.  Defleur and Davenport's
findings
suggest that journalism schools are suffering from an innovation lag.  They
state that
journalism schools are lagging behind the industry in the adoption of crucial
new
technologies.  Their study, however, looked at adoption by schools as a whole
rather than
investigating adoption behavior by individuals within those institutions.
Research on the Adoption and Diffusion of New Technologies
     Rogers' diffusion of innovation model is regarded as the most widely known
description of the diffusion process.  This model categorizes potential adopters
of an
innovation by innovativeness, the degree to which an individual or group adopts
a new
idea earlier than other members of a social system.  Innovativeness is normally
distributed among adopters, who are labeled innovators, early adopters, early
majority,
late majority, and finally laggards.  This typology, however, fails to address
those who
do not adopt.
     Diffusion research has been criticized because of its pro-innovation bias.
Pro-
innovation bias is the belief that all innovations are beneficial for all
potential adopters
and that the objective of such research should be to increase the rate and
extent of
innovation diffusion.  This viewpoint regards any resistance to innovation as a
merely
irrational reaction or simply something to be ignored as unimportant.
     Although a considerable number of innovations have failed despite great
expectations, unsuccessful innovations have largely been neglected.  Examples of
rejected
communication technologies abound, including educational television in the
1960s,
information services such as Knight-Ridder's attempted mass-market videotext
project,
Viewtron, interactive cable television, electronic banking, picturephones, and
quadraphonic stereo.  However, studies that have sought an explanation for
factors that
led to the failure of innovations such as these have been limited.  The factors
behind
rejection remain to be fully explored.
     The majority of innovation studies published are investigations of
innovations that
have successfully diffused. The phenomenon of resistance to innovation has been
the
subject of few studies and is not well understood.  The reluctance of
researchers to study
resistance to innovation has led to its being called the "less developed
concept" in
diffusion research.  The study of resistance considers qualitative factors of
adoption
present in the innovative-decision process rather than solely charting the rate
of adoption
over time, as is the case with the majority of diffusion studies.
     Some researchers believe that resistance to innovation may be a form of
normal
resistance to change in general.  Such resistance may function as a necessary
stabilizing
force in society.  Klein sees those who resist change as necessary "defenders"
of the social
system who are trying to
     ward off threat, maintain integrity of the system and protect against the
     unwarranted intrusions of others' demands against . . . ill-considered and
     overly precipitous innovations.
 
     Stern, possibly the earliest student of resistance to technological
innovation, stated
that
     Predictions based upon the assumption that if valuable or profitable
     technological inventions are but conceived, they will be incorporated into
     industrial life, ignore the evidence of past experience.
 
Stern has written a detailed history of innovations that have had to overcome
great initial
resistance, including railroads, automobiles, the telegraph, the telephone, the
typewriter,
and even the bathtub.
     According to Stern, such resistance is tied to a need for stability because
of the
difficulty of expending one's energies continuously on making decisions.
Innovation,
however, has a tendency to disrupt this behavioral inertia.
     An innovation, especially one which affect's one's economic status as in
the
     case of technologies, rudely shatters whatever equilibrium a person has
     attained.  It demands not only motor reconditioning but reorganization of
     personality to meet the needs of the new situation.  It is little wonder
that
     an innovation. . . provokes feelings of impropriety, and repelling defense
     attitudes of ridicule and disparagement, or is deliberately ignored. . . .
 
Ram's Model of Resistance to Innovation
     Ram has devised a model that enumerates the factors behind resistance to
innovation.  These factors are categorized into three areas: (1) innovation
characteristics,
(2) consumer characteristics, and (3) characteristics of propagation mechanisms.
Each
of these three areas is made up of several corresponding sub-areas and
components.
     Rogers established five general innovations characteristics: relative
advantage,
compatibility, complexity, trialability, and observability.  Ram has added six
innovation
characteristics to the five proposed by Rogers.  These include perceived risk,
divisibility,
reversibility, realization, amenability to modification, and effect on adoption
of other
innovations.
     Ram has specified nine factors of consumer characteristics.  These are
further
divided into two categories, psychological variables that include perception,
motivation,
personality, attitudes, beliefs, previous innovative experience and demographic
variables
such as education, income and age.
     Ram classifies characteristics of propagation mechanisms such as change
agents
and opinion leaders based on two dimensions: (1) extent of marketer control,
which may
range from high to low, and (2) type of contact with the consumer, which varies
from
personal to impersonal.  The role of these factors varies with the innovation's
life cycle.
Marketer-controlled sources have been found to be greater at the early stages
and
personal contacts more important in later stages.
     Ram suggests that resistance may be found in varying levels throughout each
of
Rogers' five stages of the adoption process.  While obviously a major factor in
the
rejection of innovations, resistance may be present even after an innovation is
adopted.
Ram also believes that resistance to innovation can be tied to broader
resistance to
change theories such as those proposed by Heider and Newcomb.
Method
     The research focused on faculty at schools that have an educational
subscription
to Mead Data Central's Lexis/Nexis full-text database service.  Lexis/Nexis is
an
important information resource widely used in the communications industry.  An
important feature of the educational subscription is its discounted flat-rate,
which makes
the service accessible to individual faculty at no charge to themselves.  This
allowed
control for cost and access factors affecting individuals' use of the
Lexis/Nexis system.
Electronic mail is similarly available without charge to faculty.  In other
words, neither
cost nor access were a problem for those studied, resulting in a
quasi-experimental
situation in which the remaining factors were expected to be more readily
discernable.
     A mail survey of faculty at ten institutions was conducted.  A total of 178
professors made up the sampling frame.  The mail questionnaire consisted of
questions
designed to elicit information in three primary areas: (1) use of computers and
computer
communication technologies, (2) personal information, and (3) resistance to
computer
communications.  Ram's model was used as an organizing framework in the
development
of a scale designed to measure the level of resistance to technological
innovations.  The
survey instrument was pretested using a small convenience sample of university
faculty.
The form was subsequently modified to clarify ambiguous or poorly worded
questions
and make response easier.
     All possible measures were taken to maximize the response rate.  Cover
letters
for each school were devised which clearly identified the educational
institution
sponsoring the research, described the nature of the project and stressed the
importance
of returning a completed form.  A stamped, addressed envelope was included for
the
convenience of the respondents.  The questionnaire, cover letter and envelope
were
placed in a high-quality envelope to make a complete questionnaire packet, which
was
placed in the mailbox of each full-time faculty member by a contact person at
each
school.  This method guaranteed that each faculty member at the selected
institutions
would receive a packet and also served to reduce postage costs.
Findings
     Out of 178 forms that were successfully distributed, a total of 115 were
returned,
yielding an overall return rate of 64.6 percent.  The representativeness of the
survey is
supported by examining the gender of the respondents.  Of the 115 respondents,
83 (72.2
percent) were male and 32 (27.8 percent) were female.  This ratio is similar to
that of
the population surveyed.  A chi square analysis of the response to the survey
reveals
that there is statistical support for the absence of a gender bias ( } = .578 p
= .473).
     Computer Communication Usage.  As Table 1 shows, only a little more than
half
of the respondents reported using Lexis/Nexis.  These individuals were
classified into
four groups according to the frequency of their usage.  Heavy users, categorized
as those
who reported using the service several times per day or about once per day, made
up
about 10 percent of the sample.  Almost a third (29.6 percent) said they used it
several
times per week or about once per week.  These were classified as medium users.
Light
users, those who reported using the service about once per month, were only 12.2
percent of those surveyed.  Almost half, 47.8 percent, indicated using it less
than once
per month or never, and were labeled non-users.
 
                                  Table 1
                    FREQUENCY OF ONLINE DATABASE USAGE
 
                                 Percent     Percent
                          N      of sample   of users
 
          Heavy           12       10.4        20.0
 
          Medium          34       29.6        56.7
 
          Light           14       12.2        20.3
 
          Non-users       55       47.8         0.0
 
 
          Totals         115      100.0       100.0
 
     A similar pattern emerged for email usage.  As Table 2 reveals, only 62 of
the 115
professors, or 53.9 percent, said they use email.  Two usage levels were devised
for the
amount of reported email use.  Respondents who sent or received more than twenty
messages in an average week were categorized as heavy users.  Light users were
identified as those who said they used email, but sent or received twenty
messages or less
per week.
 
 
 
 
 
                                  Table 2
 
                        VOLUME OF ELECTRONIC MAIL
 
                        SENT AND RECEIVED PER WEEK
 
 
 
          Level              Sent              Received
          of usage        N   Percent         N   Percent
 
          Heavy           13    11.3          24    20.9
 
          Light           49    42.6          38    33.0
 
          None            53    46.1          53    46.1
 
 
          Totals         115   100.0         115   100.0
 
 
     Personal Information.  The survey also asked respondents to reveal
information
about their personal characteristics and access to and use of computers.  Cross-
tabulations of frequencies were devised and chi square analyses were performed
to
determine the statistical significance of any possible association between the
use of the
technologies under study and the selected variables.  In all cases, the null
hypothesis
used in each analysis was that there was no association between usage and the
particular
variable in question.  Table 3 summarizes the results of these tests.
 
 
 
 
 
 
 
 
 
 
                                  Table 3
 
              SUMMARY OF THE MAIL SURVEY TESTS OF ASSOCIATION
 
 
          Variable                    Lexis/Nexis Email
 
          Gender                        .261       .175
 
          Age                           .765       .291
 
          Academic rank                 .139       .306
 
          Education level (Ph.D.)       .458       .003
 
          Teaching time                 .037       .801
 
          Research time                 .043       .571
 
          Workplace computer available  .611       .545
 
          Home computer available       .046       .006
 
          School modem                  .001       .039
 
          Home modem                    .002       .001
 
          Amount of Computer usage      .008       .019
 
          Lexis/Nexis usage               -        .013
 
 
     No significant association was found between Lexis/Nexis or email use and
gender.  Similarly, age was not related to whether a professor was or was not a
user of
the technologies.  The relatively high average age of 45.2 years suggests that
one can
"teach an old dog new tricks."  Academic rank was not associated with usage.
Education
level, however, was related to usage of email.  Those having a Ph.D were more
likely to
use email than those without, a finding which did not hold for Lexis/Nexis use.
     The professors were asked about the amount of time they spent teaching.
The
responses yielded an average time of 48.1 percent.  Those who said they spent
more than
the average time teaching were less likely to use Lexis/Nexis.  Similarly, those
who
reported their time devoted to research as more than the average of 24.9 percent
were
more likely to be Lexis/Nexis users.  No such relationship was found for email
use and
allocation of work time.
     Almost all (93.9 percent) of the respondents said they had access to a
personal
computer at their workplace.  No association was found between workplace
computer
access and usage of either Lexis/Nexis or email.  A similarly high number of
respondents
(89.6 percent) reported having a home computer.  A computer in the home,
however,
was found to be related to usage of both Lexis/Nexis and email.  The low number
of
those that said they did not have a computer available either at school or at
home
renders the chi square results suspect.  However, it can be tentatively
concluded that
since personal computers are so widely distributed, any consideration of their
availability
as a factor in computer communication usage may be meaningless.
     A little over half (55.6 percent) said they had a modem or other connection
for
their computer at school.  A larger number (68.7 percent) claimed to have a
modem for
their home computer.  Having a modem in either one's school or home computer was
related to using the technologies.
     Almost all of the professors (96.5 percent) said they used a personal
computer for
some purpose, mostly word processing.  The amount the respondents used their
personal
computer for various tasks was a factor related to usage.  Heavy users, those
who said
they used their computer an average of three hours or more a day, were more
likely to
use both Lexis/Nexis and email than light users, those that said they used a
computer
less than three hours a day.  Finally, users of Lexis/Nexis were more likely to
be users of
email than those who were users of only one of the two technologies.  About a
quarter
of the respondents (27.8 percent) said they used neither.
     Resistance to Innovation.  Respondents were asked to indicate their beliefs
about
online database technologies such as Lexis/Nexis on a Likert-type scale designed
to
measure resistance to innovation.  This scale was based on Ram's 19 resistance
factors.
Four additional items were added following a pretest of the scale, resulting in
a scale
that included a total of 23 items designed to gauge resistance to computer
communication technology.
     Fifteen items concerned beliefs about innovation characteristics.  Eight
items
referred to the respondents' beliefs about themselves with regard to computer
communication technology.  The professors surveyed were given the choice of
replying
"strongly agree," "agree," "don't know," "disagree" or "strongly disagree" to
each statement.
     The results were coded so that higher total scores could be interpreted as
indicating a lower level of resistance to innovation.  The scale was subjected
to an item
analysis using the "known groups technique" as described by Likert.  Statistical
evidence
was found for the validity of all 23 of the belief scale items.  The t
statistics for 28
degrees of freedom (shown in Table 4) resulted in probability levels of less
than .0005,
which meant that each scale item exhibited a significant level of validity.
Table 4
                      RESISTANCE SCALE ITEM ANALYSIS
 
 
                         Item                                                  t
 
     1A.  Relative advantage.  "is more convenient."                  12.115
 
     2A.  Relative advantage.  "can save time."                       11.868
 
     3A.  Relative advantage.  "improves productivity."
11.979
 
     4A.  Compatibility.  "compatible with the way I like to work."
10.151
 
     5A.  Pervasiveness.  "would require me to change work habits."
9.101
 
     6A.  Psychological risk.  "entails little or no risk to me."
5.904
 
     7A.  Social risk.  "can be embarrassing."
4.496
 
     8A.  Complexity.   "is just too complex for me."                  7.064
 
     9A.  Effect on other innovation adoption.  "makes me optimistic."
10.966
 
     10A. Trialability.  "can be easily tried out."                    5.266
 
     11A. Divisibility.  "can be practiced at a comfortable pace."
10.389
 
     12A. Reversibility.  "can be discontinued easily."
6.665
 
     13A. Realization.  "shows its benefits right away."
14.378
 
     14A. Communicability.  "can easily be communicated."             11.887
 
     15A. Amenability to modification.   "can be easily modified."
6.996
 
     1B.  Perceived need.  "I need such services."                    12.355
 
     2B.  Perceived need.  "sufficient without it."                   11.308
 
     3B.  Discontinuity.  "more trouble than they are worth."
13.144
 
     4B.  Self-confidence.  "I'm self-confident in my ability to use."
8.845
 
     6B.  Beliefs about innovation.  "a positive development."
7.845
 
     5B.  Dogmatism.  "I'm too set in my ways to use them."           15.181
 
     7B.  Attitude towards innovation.   "They are a necessary evil."
6.282
 
     8B.  Previous innovation experience.  "unwelcome innovation."
9.986
 
 
          Items 1A-15A address beliefs about innovation characteristics.
          Items 1B-8B concern respondents' beliefs about themselves.
 
     A Spearman rank order correlation coefficient was calculated for
respondents'
total scores and their level of Lexis/Nexis usage.  Higher belief scores and
levels of
general computer usage were found to correlate positively with an r of .34.
Higher belief
scores and levels of usage of Lexis/Nexis were found to correlate positively
with an r of
.49, which yields a coefficient of determination (r}) of .24.
     This high correlation coefficient can be interpreted as showing that the
scale is a
valid predictor of online database usage.  Therefore, it can be interpreted that
a higher
level of resistance exhibited by an individual, as indicated by a low score, is
likely to
accompany a lower level of Lexis/Nexis usage.
Discussion and Conclusions
     The respondents to this survey were professors at institutions that did not
exhibit
"innovation lag"--they all had access to Lexis/Nexis and email at no cost to
themselves.
However, despite the availability of these services, only about half the
professors said
they used them.  This contrasts sharply with the near-universal adoption of
computers for
general purposes such as word processing.
     This low rate of adoption by a group of individuals who ostensibly have
compelling reasons to adopt new information technologies (made even more
attractive
by lack of cost and access barriers) raises important questions for purveyors of
new
communication technologies.  Despite the ready availability of these valuable
resources,
almost half of the faculty failed to adopt these innovations that have obvious
advantages
for individuals whose livelihood depends on teaching and research involving
access to
information.
     This has obvious ramifications for those seeking to pave new "information
highways."  The predictions made by those seeking to invest billions of investor
and
taxpayer dollars in these new technologies may be based on bluer skies than can
realistically be expected.  The professors in this study had a relatively low
rate of
adoption even under circumstances that were ideal for adoption.  The average
person
will be more resistant to new information technologies, particularly since the
ordinary
person will probably have less clearly defined incentives to adopt.
Furthermore, while
costs were controlled for in this study, the average potential adopter will have
to pay for
the new services, which will further increase the level of resistance and lower
the rate of
adoption.  The general result will most likely be that the adoption of these
anticipated
new technologies will occur at a slower rate than expected.
     There are also important implications about the education of future
communication professionals.  If the average person is expected to see new
technologies
steadily make their way into more aspects of their daily lives, future
practitioners will
certainly be required to work in an increasingly computerized information
workplace.
Failure of their professors to adopt and understand these new technologies can
only
result in inadequate preparation for the new workplace environment.  Many
journalists
may find themselves falling behind their counterparts in other sectors of
society for
whom these new technologies are becoming fundamental resources.  The press'
"watchdog" function may suffer if practitioners cannot adequately access
information
about government activities.
     Within communication departments, widening divisions may arise between
professors who have adopted the new technologies and their more resistant
colleagues.
Opposing groups may form which disparage each other either as "techno-nerds" or
"global village idiots," depending on one's experience with new technology.
These
conflicts may become more heated as decisions about the allocation of resources
become
more difficult in an age of financial shortages.
     The findings of this study also suggest that resistance is a concept
valuable to the
development of theory about the adoption process.  A scale based on Ram's Model
of
Innovation Resistance was found to predict usage of Lexis/Nexis.  Those who
scored
higher on the scale, and were therefore judged less resistant to the innovation,
were
more likely to use the service.  Further study of the adoption process that
incorporates
the resistance perspective may prove to be useful in explaining and predicting
why some
innovations are adopted while others are forgotten.
     The present research was limited on several grounds.  The design of the
study,
which took advantage of the quasi-experimental situation of schools that had a
Lexis/Nexis educational discount, meant that the population studied was rather
highly
specialized.  Further study of resistance to innovation needs to be done using
subjects
more representative of a broader population.
     Further investigation is also needed to refine the theoretical perspective
of
innovation resistance.  One potential study could be to isolate the factors of
resistance to
determine the relative importance of each component.  Some factors may be more
important than others under certain conditions, or certain combinations of
factors may
yield different results.  The resistance perspective should also be applied to a
variety of
innovations under different circumstances.
     The study of adoption of innovation will continue to be an important area
of
future research.  As more new communication technologies such as HDTV,
interactive
multimedia, and others become available, consideration of how resistance may
affect the
adoption of innovations will be of growing importance.  The debate so far has
been on
how to get the horse to water without considering whether it will actually
drink.  This
research has revealed that some horses might not be all that thirsty.
                           NOTES AND REFERENCES

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