Directory UMM :Data Elmu:jurnal:I:Information and Management:Vol38.Issue5.Apr2001:
Information & Management 38 (2001) 277±287
Technological innovations: a framework for
communicating diffusion effects
Franklin J. Carter Jr.a,1, Thani Jambulingama, Vipul K. Guptaa,*, Nancy Meloneb
a
b
Erivan K. Haub School of Business, St. Joseph's University, 5600 City Ave, Philadelphia, PA 19131, USA
John F. Donahue Graduate School of Business, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282, USA
Received 21 June 1999; accepted 23 July 2000
Abstract
The paper investigates the impact of the institutional aspects of the innovation±adoption process on the success of its
implementation. More speci®cally, we concentrate on the adoption of ®ve information technologies using a data set from the
aerospace and defense industries. We investigate such factors as advocacy, breadth of support, time of adoption, and intraorganizational communications. Several hypotheses are formulated and empirically tested. We ®nd that advocacy by middle
management does not have a positive effect on the success of implementation. # 2001 Elsevier Science B.V. All rights
reserved.
Keywords: Diffusion of information technologies; Aerospace industry; Software engineering; Technological innovations; Target organization
group; Management advocacy; Communication mechanisms
1. Introduction
The diffusion of an innovation is conceived as the
process by which knowledge of an innovation spreads
throughout a population, eventually to be adopted or
not adopted by a decision-making unit in the organization [29]. The degree of acceptance and the rate at
which this process takes place is contingent upon the
characteristics of the innovation, networks used to
communicate the information about the innovation,
characteristics of those who adopt the innovation, and
the actions and characteristics of the agents of change.
This concept of innovation diffusion has been applied
*
Corresponding author. Tel.: 1-610-660-1622;
fax: 1-610-660-1229.
E-mail addresses: fcarter@sju.edu (F.J. Carter Jr.),
tjambuli@sju.edu (T. Jambulingam), gupta@sju.edu (V.K. Gupta).
1
Tel.: 1-610-660-1463.
to innovations ranging from new ideas to new machine
[3,30,32].
In the last few years, understanding the diffusion of
information technologies (ITs) has been important to
both practitioners and researchers. Nilakanta and Scamell [25], for example, deal with the effects of communications on the diffusion of data base design tools.
Grover et al. [15] addressed the issue of IT diffusion
and organizational productivity as perceived by senior
information systems (IS) executives. A study by Lai
and Guynes [20] investigated the adoption behavior
between IT adopters and nonadopters at the organizational level. The IS research community started focusing on diffusion of innovation research in mid-1980s
and Prescott and Conger [26] summarized this stream
of research from the mid-1980s to the mid-1990s. In
spite of the substantial number of studies and reviews,
the IS innovation literature remains underdeveloped
due to the complex and context-sensitive nature of the
0378-7206/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 7 2 0 6 ( 0 0 ) 0 0 0 6 5 - 3
278
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
phenomenon. It appears that there can be no single all
encompassing theory of IT innovation, as the ever
changing nature of IT will keep `the whole' beyond
our grasp. Few will argue, however, that IT innovation
cannot be understood without careful attention to the
personal, organizational, technological, and environmental context within which it takes place.
This paper focuses on three issues. First, we look at
the properties of ITs that affect adoption. A framework
is presented in which ITs are characterized in terms of
level of abstraction and of target user. We consider
innovations that are either predominately methodological or tool-based. Tool-based innovations are more
concrete and typically require a front-end ®nancial
commitment on the part of the adopting organization.
Methodologies are primarily abstract; while they do
not have to be `purchased,' they often require ®rms to
devote substantial resources to learning how to use the
innovation in order for the adoption to be successful.
In addition, ITs can be described in terms of their
target user in the organization. We studied innovations
that are targeted either to administrative levels in the
organization or to technical staff. We examined both
methodological and tool-based innovations with
respect to their compatibility with innovation advocacy and their effect on the speed and probability of
the adoption.
The second issue is the process by which IT diffusion occurs. Diffusion of innovations has been characterized as a three-stage process involving initiation,
adoption, and implementation. In this study, we concentrate on adoption and implementation, looking at
the factors that affect each stage as well as the connection between stages.
Third, we investigate the effects of various types of
communication on the adoption of IT. Communications are examined with respect to the differential
effectiveness of distinct types of mechanisms, which
are characterized on two levels: organizational
resources required for use and the formalism of their
use. For example, developing training programs
requires relatively high resources. Ad hoc consultation
is an informal mechanism. We also examine commonalties in communication effectiveness across the
adoption process and multiple ITs.
This paper focuses speci®cally on software engineering innovations. Software engineering is the
technological and managerial discipline concerned
with the systematic production and maintenance of
software products developed and modi®ed on
time, according to speci®cation, and within cost estimates [11,27]. Software engineering innovations
may be primarily methodological (e.g. step-wise
re®nement, data hiding) or tool-based (e.g. program
design languages). In some instances, they are a
combination of both. A large portion of an organization's software budget is often devoted to maintaining
and developing of systems containing routines similar
to code developed for other systems. For this reason,
innovations that facilitate reusability and maintenance, or which speed development time or help
control costs, are potentially valuable. Although this
potential value is well known, diffusion of software
engineering tools and methods is often slow and
imperfect [28].
2. Research framework
Adoption of technology proceeds as follows:
1. Initiation: The stage during which the adopting unit
acquires information about the innovation and
goes through an approval process for using the
innovation.
2. Adoption: Developing capabilities for using the
innovation, such as training and/or hiring personnel, or physically acquiring the innovation.
3. Implementation: Using the innovation in production for any complete software development
projects.
The level of abstraction of a particular innovation is
expected to affect the diffusion process. It has been
suggested that intangible innovations, such as new
software development philosophies, because they are
more abstract with less observable outcomes, are
adopted more slowly than more concrete innovations,
such as hardware-based ones. Those in IT also tend to
have large, unobservable, components: Methodologies, in particular.
An important characteristic is the innovation's target organization group (TOG). Some are primarily
targeted at technical staff while others are at administrative staff. The TOG for an IT innovation could be
individual software engineers (SEs): to provide software reusability, facilitate the software production
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
process, and increase the quality and standardization
of the software.
Previous studies of innovation transfer [18,24,35]
have stressed the top management championship as a
precursor to the successful introduction of innovation:
the higher the level of advocacy, the more likely it will
be successfully adopted [10,12,14,17]. Champions
from other organizational levels, however, also have
a role in diffusion [22].
Earlier research often failed to address the possible
impact of `intermediate-level' advocacy on the adoption of innovations. An exception is a study by Daft
[8], who suggests that top-management sets an overall
goal of organizational responsiveness to innovation,
while lower organizational members then champion
innovations consistent with their own area of expertise. Informal communication networks tend to be
used extensively to promote innovations. It is easier
for intermediate-level members to use them.
It is useful to look at the possible effects of `intermediate-level' advocacy by considering whether a
potential advocate may have different effects on adoption depending on the target group. This leads to the
following hypotheses:
H1a: Middle management primary advocacy will
have a signi®cant positive impact on the adoption of
innovations with an administrative TOG.
H1b: Technical staff primary advocacy will have a
signi®cant, positive impact on adoption of innovations
with a software engineering TOG.
Research on opinion leadership provides evidence
that opinion leaders are generally fairly close in outlook and social class to the population they lead.
Howell and Higgins [16] suggest why this principle
may apply to advocacy as well; interaction with
similar people leads to building coalitions of support
for the innovation among peers and others in the
organization.
Other research, however, suggests an alternative
hypothesis. In a study of the adoption of technological
versus administrative innovations by hospitals, characteristics of the chief of medicine and hospital
administrator were analyzed with respect to the adoption of innovations [19]. It was assumed that the chief
of medicine would be more likely to champion technological innovations and the hospital administrator
would be more likely to support administrative innovations. The authors hypothesized, however, that the
279
advocacy of an innovation was associated with
broader involvement in the hospital and would be
positively associated with adoption. This hypothesis
was somewhat supported in the case of chief of
medicine's involvement with administrative activities.
Finally, Daft suggests that technological innovations
supported by administrative personnel and administrative innovations supported by technical staff will
tend to be `out of synchronization with perceived
needs and are less likely to be acceptable'. Based
on the above, we suggest the following hypotheses:
H1c: Advocacy by an inconsistent level will have a
signi®cant negative impact on adoption.
H1d: Top management primary advocacy will have
a signi®cant positive, impact on adoption.
Innovations that require large capital commitments
may have to be adopted in a top-down fashion, with
the championship of top management. Smaller scale
tangible and intangible innovations or those where a
high degree of learning is necessary seem to have
greater potential for a bottom-up adoption in which
there is broad-based support for the innovation, rather
than single primary advocate.
Organizations that experience dif®culty in adopting
an innovation during an early stage of the process may
hesitate to continue. For example, it is dif®cult to
install a tool or train personnel to use a methodology
then the probability of implementation may be
reduced or slowed [21]. Different actions may in¯uence the diffusion process at different stages, in part
because requirements vary [34]. We propose, then, the
following hypotheses:
H2a: The smoothness of the process during the
adoption stage of the diffusion will affect the probability and timing of implementation.
H2b: The earlier that an innovation is adopted, the
earlier will it pass through the implementation stage
and the greater the probability it will be implemented.
Information moves from a source informed about
the innovation, through such channels, such as technical journals, or interpersonal channels such as vendors, consultants or electronic bulletin boards, to an
individual or organization. The importance of using
various communication channels has been studied
[1,4,9,13,30]. Few researchers, however, have explicitly studied the timing of the communication [5].
Research has shown that a successful innovation process is often characterized by extensive
280
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
communication. Transition support mechanisms differ
from communications in various ways. First they tend
to be more proactive. Next, they usually require at
least some commitment of ®nancial resources, which
may be substantial. Also, the intent of providing
transition support is to facilitate adoption, and clearly
this is not always the intent of communication, indeed
negative communication is possible.
We identi®ed several types of representative transitional support mechanisms through interviews with
software engineering experts. Mechanism's can generally be characterized as either low resource level,
requiring relatively little commitment of resources, or
high resource level, personal mechanisms. We, therefore, state the following hypothesis:
H3a: There is a signi®cant difference in the use of
high resource communication mechanisms between
adopters and nonadopters.
Informal mechanisms are unstructured or loose.
Examples include: providing written documentation
about the technology and articles about the technology
from technical or scholarly journals as well as providing pre-packaged technical information. Formal transition mechanisms, on the other hand, may re¯ect a
more organized approach. This leads to an additional
hypothesis as follows:
H3b: There is a signi®cant difference in the use of
formal communication mechanisms between adopters
and nonadopters.
Low commitment, personal, transition mechanisms
include site visits to other organizations using the
technology and sending personnel to seminars or
conferences. Either internal or external personnel,
offsite or onsite, can provide high commitment support. External, high commitment mechanisms are
training by outside personnel and assistance in the
form of expert consultation at the vendor's or developer's facilities. Internal, high commitment mechanisms considered are training prepared by in-house
personnel, providing on-site ad hoc consultation and
on-site regular consultation. Training programs generally represent the most formal mechanisms. Regular
forms of consultation are also relatively formal.
Bayer and Melone [6] have a more complete discussion of an adaptation of the diffusion framework.
3. Empirical study
3.1. ITs as innovations
The IT innovations examined here were selected as
examples of the innovation types. They are software
cost models (SCM), complexity metrics (CM), structured programming (SP), and program design
language (PDL). SCM are estimation tools for development projects. CM are algorithms that can be used
to estimate the complexity of software code. SP is a
methodology used to modularize software code. PDLs
are tools that assist a SE in translating a system design
into executable code.
As shown in Fig. 1, the ®ve innovations were chosen
with different levels of abstraction and TOG. SP and
PDLs are targeted to individual SEs, SCM and CM are
administrative aids, PDL and SCM are tool-based, and
SP and CM are primarily methodologies.
A set of communications mechanisms were chosen
to vary by resource needed (high or low), and structure
type (formal or informal). Low response communications depend on whether they are personal or masscommunication mechanisms. Fig. 2 shows where each
®ts into the framework.
Fig. 1. Characteristics of the innovation.
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
281
Fig. 2. Communications types.
Low response mass-communications mechanisms
selected for study are (1) providing written documentation about the innovation or articles about the innovation from technical or scholarly journals and (2)
providing pre-packaged technical information. These
can be distinguished in terms of degree of formalism;
written documentation and articles are relatively informal mechanisms.
Low response personal-communication mechanisms are (1) going on site visits to organizations where
the innovation is used and (2) sending personnel to
seminars or conferences. Site visits are typically less
formal. Nationally recognized experts are often in¯uential option leaders in the adoption process. Interpersonal interaction with these experts often takes
place at seminars and conferences. High response
communications include (1) training by outside personnel, (2) training by in-house personnel, (3) on-site
regular consultation, and (4) on-site ad hoc consultation. Of these mechanisms, ad hoc consultation is an
informal mechanism. Training programs generally
represent the most formal mechanism.
was whether the ®rm had gone through initiation.
Most diffusion studies contain a pro-innovation bias
which is magni®ed by the grouping together of all
potential adopters.
Participants were informed of the study though a
letter sent by the two principle investigators to
National Security Industrial Association (NSIA)
members, who represent major defense contractors
in the US as well as small consulting ®rms and
developers. In the solicitation letter, the study was
described and individuals were asked to identify people who had knowledge of the adopt, reject or postpone decisions about any of ®ve software engineering
innovations. The initial contacts returned a form indicating who would participate in their unit, and for
which innovations. In some cases, the participant was
the addressee; in most cases, they were other people.
Each business unit was permitted only one participant
for each innovation; thus, a business unit could have a
maximum of ®ve participants. In most cases, a single
participant was knowledgeable about more than one
innovation.
3.2. The population
3.3. The survey instrument
Participants in the research program were individuals responding for major software developers and
consultants for the government. They knew about their
organization's adoption, postponement, or rejection of
the innovations. A screening criterion for including an
organization (or unit) for a particular IT innovation
Data were collected using structured survey instruments administered over the telephone. The 19-page
questionnaires included a broad range of issues related
to the adoption of innovations. Although different
survey instruments were developed for each innovation, they shared a subset of questions.
282
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
3.4. Methodology
A manager trying to allocate resources ef®ciently
might reasonably ask whether resources allocated at
one stage have delayed impacts (e.g. increasing the
probability of successfully executing later stages). For
example, are certain forms of communication more
effective at accelerating adoption if provided at one
stage rather than another? We address questions of this
nature using `event-history' models2 [2].
The empirical evidence of diffusion research
strongly supports the assumption of an S-shaped
curve and there are right-censored data3. That is,
some organizations have not yet passed through the
adoption stage. An analysis which excludes these
observations from the estimated model would be
biased. A commonly used methodology for the analysis of longitudinal data where `censoring' can produce bias and loss of information is event history
analysis.
Typical mathematical diffusion theory models specify to some degree the rate at which innovations are
adopted. The function describing the cumulative rate
of adoption generally is an S-shaped curve. Estimation
of these S-shaped curves depends upon their speci®cation. If the adoption function is speci®ed explicitly,
parametric methods (e.g. maximum likelihood) can be
used for estimation and inference. If not speci®ed,
parametrically weaker statistical methods must be
used. The Cox proportional hazards model [7] is an
example of such a method. The Cox model allows for
right-censoring data. The nonparametric survivor
function permits a wide-range of adoption curves,
including an S-shaped curve. We, therefore, use proportional hazard models to examine the in¯uence of IT
properties and communication mechanisms on timing
of adoption for the two stages. For timing of implementation, we also included adoption history variables.
For each stage, two adoption dependent measures
were considered: movement and timing. Movement
was operationalized by the fact whether the organization began a stage; e.g. for a production project.
Timing was measured as the year that the stage begins.
Using PROBIT models, we examine the effects of
IT properties and extensive use of communication
mechanisms on movement into the stages. To examine
the diffusion process for production, we also included
adoption history variables, such as ease and timing.
2
Event-history analysis, also known by a variety of other names
including survival analysis, lifetime analysis, failure-time analysis,
reliability analysis, or hazard-rate analysis, is a class of models and
methods for dealing with situations in which the dependent variable
is categorical and the data are censored.
3
Statisticians refer to data as `censored' if there is an outcome
but it occurs after the collection of data is discontinued. Use of
regression models rather that the more appropriate event-history
models in these situations can lead to seriously biased estimators.
The survey asked questions requiring closed-form
responses. For example, participants were asked about
the extent to which they had used the transition
mechanisms at a particular stage of adoption. They
responded using a 7-point scale representing a range
from `1' (not at all) to `7' (very much). Similarly,
participants were asked to supply dates when these
activities were started.
It is well-known that phone surveys impose some
problems [23]. In our case, data were collected retrospectively, that is, after the event. For analysis purposes, the data were assumed to approximate
longitudinal data. Causal implications of the research
cannot, however, be strongly asserted.
There were 230 interviews completed: 41 for complexity metrics; 60 for program design languages; 61
for software cost models and 68 for structural programming. The percentage of respondents passing
through each stage, for each innovation, is shown in
Fig. 3.
Primary advocacy for adopting the innovations
came from the following organizational levels: top
management 11.5%; middle management 36.5%;
technical staff 35.5% and broad-based support
16.5%. In approximately 95% of the cases, respondents selected at least one level of primary advocate.
Characteristics of the innovation that have
received empirical support include relative advantage,
compatibility, and perceived complexity of the innovation [33]. Beliefs about advantages and disadvantages of the innovations were used as control variables.
Also, because use of ITs studied here are often
partially controlled be government mandates, we
included questions about the in¯uence of such
mandates.
283
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
Fig. 3. Percentage of organizations that have passed through each stage of diffusion.
3.5. Results
3.5.1. Properties which affect adoption
The probability of reaching the adoption stage, was
found to be a function of several factors: mandates,
perceived technical complexity and problems with the
innovation, and an innovation-speci®c variable (see
Table 1). Organizations that have developed capabilities for using these innovations are more likely to bid
on contracts mandating use of the IT (MANDNT) in
the next year. They also, however, perceive the innovation as being more technically complex than organizations which have not yet developed capabilities
(XCOMPLEX). Adopting organizations are less
likely to have `wait and see' attitude about technical
Table 1
Probit analysis for adoption (develop capabilities)a
Parameter
Non-adopt
(mean for
D 0)
Adopt
(mean for
D 0)
Estimate
S.E.
Constant
MANDNXT
XCOMPLEX
TECHPROB
METHOD
1.00
2.78
3.66
3.50
0.60
1.00
5.62
2.78
2.10
0.32
1.12
0.31
ÿ0.14
±0.29
ÿ0.53
0.41
0.05
0.07
0.07
0.23
a
Log-likelihood ÿ78:06; ÿ2 times log-likelihood ratio (Chisquared 89:12 (4 d.f.).
problems with the IT (TECHPROB). Technical problems are associated with lowering perceptions of
bene®ts of the innovation. In addition, organizations
are less likely to have developed capabilities for using
methods-based ITs, especially complexity metrics
(METHOD).
Table 2 shows the results of estimating Cox proportional hazards models for timing of the adoption stage.
The output is evaluated with the score test of the
standard null hypothesis that all coef®cients are equal
to their zero start values. In all cases shown, the Chisquared statistic has a p-value of
Technological innovations: a framework for
communicating diffusion effects
Franklin J. Carter Jr.a,1, Thani Jambulingama, Vipul K. Guptaa,*, Nancy Meloneb
a
b
Erivan K. Haub School of Business, St. Joseph's University, 5600 City Ave, Philadelphia, PA 19131, USA
John F. Donahue Graduate School of Business, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282, USA
Received 21 June 1999; accepted 23 July 2000
Abstract
The paper investigates the impact of the institutional aspects of the innovation±adoption process on the success of its
implementation. More speci®cally, we concentrate on the adoption of ®ve information technologies using a data set from the
aerospace and defense industries. We investigate such factors as advocacy, breadth of support, time of adoption, and intraorganizational communications. Several hypotheses are formulated and empirically tested. We ®nd that advocacy by middle
management does not have a positive effect on the success of implementation. # 2001 Elsevier Science B.V. All rights
reserved.
Keywords: Diffusion of information technologies; Aerospace industry; Software engineering; Technological innovations; Target organization
group; Management advocacy; Communication mechanisms
1. Introduction
The diffusion of an innovation is conceived as the
process by which knowledge of an innovation spreads
throughout a population, eventually to be adopted or
not adopted by a decision-making unit in the organization [29]. The degree of acceptance and the rate at
which this process takes place is contingent upon the
characteristics of the innovation, networks used to
communicate the information about the innovation,
characteristics of those who adopt the innovation, and
the actions and characteristics of the agents of change.
This concept of innovation diffusion has been applied
*
Corresponding author. Tel.: 1-610-660-1622;
fax: 1-610-660-1229.
E-mail addresses: fcarter@sju.edu (F.J. Carter Jr.),
tjambuli@sju.edu (T. Jambulingam), gupta@sju.edu (V.K. Gupta).
1
Tel.: 1-610-660-1463.
to innovations ranging from new ideas to new machine
[3,30,32].
In the last few years, understanding the diffusion of
information technologies (ITs) has been important to
both practitioners and researchers. Nilakanta and Scamell [25], for example, deal with the effects of communications on the diffusion of data base design tools.
Grover et al. [15] addressed the issue of IT diffusion
and organizational productivity as perceived by senior
information systems (IS) executives. A study by Lai
and Guynes [20] investigated the adoption behavior
between IT adopters and nonadopters at the organizational level. The IS research community started focusing on diffusion of innovation research in mid-1980s
and Prescott and Conger [26] summarized this stream
of research from the mid-1980s to the mid-1990s. In
spite of the substantial number of studies and reviews,
the IS innovation literature remains underdeveloped
due to the complex and context-sensitive nature of the
0378-7206/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 7 2 0 6 ( 0 0 ) 0 0 0 6 5 - 3
278
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
phenomenon. It appears that there can be no single all
encompassing theory of IT innovation, as the ever
changing nature of IT will keep `the whole' beyond
our grasp. Few will argue, however, that IT innovation
cannot be understood without careful attention to the
personal, organizational, technological, and environmental context within which it takes place.
This paper focuses on three issues. First, we look at
the properties of ITs that affect adoption. A framework
is presented in which ITs are characterized in terms of
level of abstraction and of target user. We consider
innovations that are either predominately methodological or tool-based. Tool-based innovations are more
concrete and typically require a front-end ®nancial
commitment on the part of the adopting organization.
Methodologies are primarily abstract; while they do
not have to be `purchased,' they often require ®rms to
devote substantial resources to learning how to use the
innovation in order for the adoption to be successful.
In addition, ITs can be described in terms of their
target user in the organization. We studied innovations
that are targeted either to administrative levels in the
organization or to technical staff. We examined both
methodological and tool-based innovations with
respect to their compatibility with innovation advocacy and their effect on the speed and probability of
the adoption.
The second issue is the process by which IT diffusion occurs. Diffusion of innovations has been characterized as a three-stage process involving initiation,
adoption, and implementation. In this study, we concentrate on adoption and implementation, looking at
the factors that affect each stage as well as the connection between stages.
Third, we investigate the effects of various types of
communication on the adoption of IT. Communications are examined with respect to the differential
effectiveness of distinct types of mechanisms, which
are characterized on two levels: organizational
resources required for use and the formalism of their
use. For example, developing training programs
requires relatively high resources. Ad hoc consultation
is an informal mechanism. We also examine commonalties in communication effectiveness across the
adoption process and multiple ITs.
This paper focuses speci®cally on software engineering innovations. Software engineering is the
technological and managerial discipline concerned
with the systematic production and maintenance of
software products developed and modi®ed on
time, according to speci®cation, and within cost estimates [11,27]. Software engineering innovations
may be primarily methodological (e.g. step-wise
re®nement, data hiding) or tool-based (e.g. program
design languages). In some instances, they are a
combination of both. A large portion of an organization's software budget is often devoted to maintaining
and developing of systems containing routines similar
to code developed for other systems. For this reason,
innovations that facilitate reusability and maintenance, or which speed development time or help
control costs, are potentially valuable. Although this
potential value is well known, diffusion of software
engineering tools and methods is often slow and
imperfect [28].
2. Research framework
Adoption of technology proceeds as follows:
1. Initiation: The stage during which the adopting unit
acquires information about the innovation and
goes through an approval process for using the
innovation.
2. Adoption: Developing capabilities for using the
innovation, such as training and/or hiring personnel, or physically acquiring the innovation.
3. Implementation: Using the innovation in production for any complete software development
projects.
The level of abstraction of a particular innovation is
expected to affect the diffusion process. It has been
suggested that intangible innovations, such as new
software development philosophies, because they are
more abstract with less observable outcomes, are
adopted more slowly than more concrete innovations,
such as hardware-based ones. Those in IT also tend to
have large, unobservable, components: Methodologies, in particular.
An important characteristic is the innovation's target organization group (TOG). Some are primarily
targeted at technical staff while others are at administrative staff. The TOG for an IT innovation could be
individual software engineers (SEs): to provide software reusability, facilitate the software production
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
process, and increase the quality and standardization
of the software.
Previous studies of innovation transfer [18,24,35]
have stressed the top management championship as a
precursor to the successful introduction of innovation:
the higher the level of advocacy, the more likely it will
be successfully adopted [10,12,14,17]. Champions
from other organizational levels, however, also have
a role in diffusion [22].
Earlier research often failed to address the possible
impact of `intermediate-level' advocacy on the adoption of innovations. An exception is a study by Daft
[8], who suggests that top-management sets an overall
goal of organizational responsiveness to innovation,
while lower organizational members then champion
innovations consistent with their own area of expertise. Informal communication networks tend to be
used extensively to promote innovations. It is easier
for intermediate-level members to use them.
It is useful to look at the possible effects of `intermediate-level' advocacy by considering whether a
potential advocate may have different effects on adoption depending on the target group. This leads to the
following hypotheses:
H1a: Middle management primary advocacy will
have a signi®cant positive impact on the adoption of
innovations with an administrative TOG.
H1b: Technical staff primary advocacy will have a
signi®cant, positive impact on adoption of innovations
with a software engineering TOG.
Research on opinion leadership provides evidence
that opinion leaders are generally fairly close in outlook and social class to the population they lead.
Howell and Higgins [16] suggest why this principle
may apply to advocacy as well; interaction with
similar people leads to building coalitions of support
for the innovation among peers and others in the
organization.
Other research, however, suggests an alternative
hypothesis. In a study of the adoption of technological
versus administrative innovations by hospitals, characteristics of the chief of medicine and hospital
administrator were analyzed with respect to the adoption of innovations [19]. It was assumed that the chief
of medicine would be more likely to champion technological innovations and the hospital administrator
would be more likely to support administrative innovations. The authors hypothesized, however, that the
279
advocacy of an innovation was associated with
broader involvement in the hospital and would be
positively associated with adoption. This hypothesis
was somewhat supported in the case of chief of
medicine's involvement with administrative activities.
Finally, Daft suggests that technological innovations
supported by administrative personnel and administrative innovations supported by technical staff will
tend to be `out of synchronization with perceived
needs and are less likely to be acceptable'. Based
on the above, we suggest the following hypotheses:
H1c: Advocacy by an inconsistent level will have a
signi®cant negative impact on adoption.
H1d: Top management primary advocacy will have
a signi®cant positive, impact on adoption.
Innovations that require large capital commitments
may have to be adopted in a top-down fashion, with
the championship of top management. Smaller scale
tangible and intangible innovations or those where a
high degree of learning is necessary seem to have
greater potential for a bottom-up adoption in which
there is broad-based support for the innovation, rather
than single primary advocate.
Organizations that experience dif®culty in adopting
an innovation during an early stage of the process may
hesitate to continue. For example, it is dif®cult to
install a tool or train personnel to use a methodology
then the probability of implementation may be
reduced or slowed [21]. Different actions may in¯uence the diffusion process at different stages, in part
because requirements vary [34]. We propose, then, the
following hypotheses:
H2a: The smoothness of the process during the
adoption stage of the diffusion will affect the probability and timing of implementation.
H2b: The earlier that an innovation is adopted, the
earlier will it pass through the implementation stage
and the greater the probability it will be implemented.
Information moves from a source informed about
the innovation, through such channels, such as technical journals, or interpersonal channels such as vendors, consultants or electronic bulletin boards, to an
individual or organization. The importance of using
various communication channels has been studied
[1,4,9,13,30]. Few researchers, however, have explicitly studied the timing of the communication [5].
Research has shown that a successful innovation process is often characterized by extensive
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communication. Transition support mechanisms differ
from communications in various ways. First they tend
to be more proactive. Next, they usually require at
least some commitment of ®nancial resources, which
may be substantial. Also, the intent of providing
transition support is to facilitate adoption, and clearly
this is not always the intent of communication, indeed
negative communication is possible.
We identi®ed several types of representative transitional support mechanisms through interviews with
software engineering experts. Mechanism's can generally be characterized as either low resource level,
requiring relatively little commitment of resources, or
high resource level, personal mechanisms. We, therefore, state the following hypothesis:
H3a: There is a signi®cant difference in the use of
high resource communication mechanisms between
adopters and nonadopters.
Informal mechanisms are unstructured or loose.
Examples include: providing written documentation
about the technology and articles about the technology
from technical or scholarly journals as well as providing pre-packaged technical information. Formal transition mechanisms, on the other hand, may re¯ect a
more organized approach. This leads to an additional
hypothesis as follows:
H3b: There is a signi®cant difference in the use of
formal communication mechanisms between adopters
and nonadopters.
Low commitment, personal, transition mechanisms
include site visits to other organizations using the
technology and sending personnel to seminars or
conferences. Either internal or external personnel,
offsite or onsite, can provide high commitment support. External, high commitment mechanisms are
training by outside personnel and assistance in the
form of expert consultation at the vendor's or developer's facilities. Internal, high commitment mechanisms considered are training prepared by in-house
personnel, providing on-site ad hoc consultation and
on-site regular consultation. Training programs generally represent the most formal mechanisms. Regular
forms of consultation are also relatively formal.
Bayer and Melone [6] have a more complete discussion of an adaptation of the diffusion framework.
3. Empirical study
3.1. ITs as innovations
The IT innovations examined here were selected as
examples of the innovation types. They are software
cost models (SCM), complexity metrics (CM), structured programming (SP), and program design
language (PDL). SCM are estimation tools for development projects. CM are algorithms that can be used
to estimate the complexity of software code. SP is a
methodology used to modularize software code. PDLs
are tools that assist a SE in translating a system design
into executable code.
As shown in Fig. 1, the ®ve innovations were chosen
with different levels of abstraction and TOG. SP and
PDLs are targeted to individual SEs, SCM and CM are
administrative aids, PDL and SCM are tool-based, and
SP and CM are primarily methodologies.
A set of communications mechanisms were chosen
to vary by resource needed (high or low), and structure
type (formal or informal). Low response communications depend on whether they are personal or masscommunication mechanisms. Fig. 2 shows where each
®ts into the framework.
Fig. 1. Characteristics of the innovation.
F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
281
Fig. 2. Communications types.
Low response mass-communications mechanisms
selected for study are (1) providing written documentation about the innovation or articles about the innovation from technical or scholarly journals and (2)
providing pre-packaged technical information. These
can be distinguished in terms of degree of formalism;
written documentation and articles are relatively informal mechanisms.
Low response personal-communication mechanisms are (1) going on site visits to organizations where
the innovation is used and (2) sending personnel to
seminars or conferences. Site visits are typically less
formal. Nationally recognized experts are often in¯uential option leaders in the adoption process. Interpersonal interaction with these experts often takes
place at seminars and conferences. High response
communications include (1) training by outside personnel, (2) training by in-house personnel, (3) on-site
regular consultation, and (4) on-site ad hoc consultation. Of these mechanisms, ad hoc consultation is an
informal mechanism. Training programs generally
represent the most formal mechanism.
was whether the ®rm had gone through initiation.
Most diffusion studies contain a pro-innovation bias
which is magni®ed by the grouping together of all
potential adopters.
Participants were informed of the study though a
letter sent by the two principle investigators to
National Security Industrial Association (NSIA)
members, who represent major defense contractors
in the US as well as small consulting ®rms and
developers. In the solicitation letter, the study was
described and individuals were asked to identify people who had knowledge of the adopt, reject or postpone decisions about any of ®ve software engineering
innovations. The initial contacts returned a form indicating who would participate in their unit, and for
which innovations. In some cases, the participant was
the addressee; in most cases, they were other people.
Each business unit was permitted only one participant
for each innovation; thus, a business unit could have a
maximum of ®ve participants. In most cases, a single
participant was knowledgeable about more than one
innovation.
3.2. The population
3.3. The survey instrument
Participants in the research program were individuals responding for major software developers and
consultants for the government. They knew about their
organization's adoption, postponement, or rejection of
the innovations. A screening criterion for including an
organization (or unit) for a particular IT innovation
Data were collected using structured survey instruments administered over the telephone. The 19-page
questionnaires included a broad range of issues related
to the adoption of innovations. Although different
survey instruments were developed for each innovation, they shared a subset of questions.
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3.4. Methodology
A manager trying to allocate resources ef®ciently
might reasonably ask whether resources allocated at
one stage have delayed impacts (e.g. increasing the
probability of successfully executing later stages). For
example, are certain forms of communication more
effective at accelerating adoption if provided at one
stage rather than another? We address questions of this
nature using `event-history' models2 [2].
The empirical evidence of diffusion research
strongly supports the assumption of an S-shaped
curve and there are right-censored data3. That is,
some organizations have not yet passed through the
adoption stage. An analysis which excludes these
observations from the estimated model would be
biased. A commonly used methodology for the analysis of longitudinal data where `censoring' can produce bias and loss of information is event history
analysis.
Typical mathematical diffusion theory models specify to some degree the rate at which innovations are
adopted. The function describing the cumulative rate
of adoption generally is an S-shaped curve. Estimation
of these S-shaped curves depends upon their speci®cation. If the adoption function is speci®ed explicitly,
parametric methods (e.g. maximum likelihood) can be
used for estimation and inference. If not speci®ed,
parametrically weaker statistical methods must be
used. The Cox proportional hazards model [7] is an
example of such a method. The Cox model allows for
right-censoring data. The nonparametric survivor
function permits a wide-range of adoption curves,
including an S-shaped curve. We, therefore, use proportional hazard models to examine the in¯uence of IT
properties and communication mechanisms on timing
of adoption for the two stages. For timing of implementation, we also included adoption history variables.
For each stage, two adoption dependent measures
were considered: movement and timing. Movement
was operationalized by the fact whether the organization began a stage; e.g. for a production project.
Timing was measured as the year that the stage begins.
Using PROBIT models, we examine the effects of
IT properties and extensive use of communication
mechanisms on movement into the stages. To examine
the diffusion process for production, we also included
adoption history variables, such as ease and timing.
2
Event-history analysis, also known by a variety of other names
including survival analysis, lifetime analysis, failure-time analysis,
reliability analysis, or hazard-rate analysis, is a class of models and
methods for dealing with situations in which the dependent variable
is categorical and the data are censored.
3
Statisticians refer to data as `censored' if there is an outcome
but it occurs after the collection of data is discontinued. Use of
regression models rather that the more appropriate event-history
models in these situations can lead to seriously biased estimators.
The survey asked questions requiring closed-form
responses. For example, participants were asked about
the extent to which they had used the transition
mechanisms at a particular stage of adoption. They
responded using a 7-point scale representing a range
from `1' (not at all) to `7' (very much). Similarly,
participants were asked to supply dates when these
activities were started.
It is well-known that phone surveys impose some
problems [23]. In our case, data were collected retrospectively, that is, after the event. For analysis purposes, the data were assumed to approximate
longitudinal data. Causal implications of the research
cannot, however, be strongly asserted.
There were 230 interviews completed: 41 for complexity metrics; 60 for program design languages; 61
for software cost models and 68 for structural programming. The percentage of respondents passing
through each stage, for each innovation, is shown in
Fig. 3.
Primary advocacy for adopting the innovations
came from the following organizational levels: top
management 11.5%; middle management 36.5%;
technical staff 35.5% and broad-based support
16.5%. In approximately 95% of the cases, respondents selected at least one level of primary advocate.
Characteristics of the innovation that have
received empirical support include relative advantage,
compatibility, and perceived complexity of the innovation [33]. Beliefs about advantages and disadvantages of the innovations were used as control variables.
Also, because use of ITs studied here are often
partially controlled be government mandates, we
included questions about the in¯uence of such
mandates.
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F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287
Fig. 3. Percentage of organizations that have passed through each stage of diffusion.
3.5. Results
3.5.1. Properties which affect adoption
The probability of reaching the adoption stage, was
found to be a function of several factors: mandates,
perceived technical complexity and problems with the
innovation, and an innovation-speci®c variable (see
Table 1). Organizations that have developed capabilities for using these innovations are more likely to bid
on contracts mandating use of the IT (MANDNT) in
the next year. They also, however, perceive the innovation as being more technically complex than organizations which have not yet developed capabilities
(XCOMPLEX). Adopting organizations are less
likely to have `wait and see' attitude about technical
Table 1
Probit analysis for adoption (develop capabilities)a
Parameter
Non-adopt
(mean for
D 0)
Adopt
(mean for
D 0)
Estimate
S.E.
Constant
MANDNXT
XCOMPLEX
TECHPROB
METHOD
1.00
2.78
3.66
3.50
0.60
1.00
5.62
2.78
2.10
0.32
1.12
0.31
ÿ0.14
±0.29
ÿ0.53
0.41
0.05
0.07
0.07
0.23
a
Log-likelihood ÿ78:06; ÿ2 times log-likelihood ratio (Chisquared 89:12 (4 d.f.).
problems with the IT (TECHPROB). Technical problems are associated with lowering perceptions of
bene®ts of the innovation. In addition, organizations
are less likely to have developed capabilities for using
methods-based ITs, especially complexity metrics
(METHOD).
Table 2 shows the results of estimating Cox proportional hazards models for timing of the adoption stage.
The output is evaluated with the score test of the
standard null hypothesis that all coef®cients are equal
to their zero start values. In all cases shown, the Chisquared statistic has a p-value of