Directory UMM :Data Elmu:jurnal:I:Information and Management:Vol38.Issue1.Oct2000:

Information & Management 37 (2000) 229±239

Research

Organizational adoption of open systems:
a `technology-push, need-pull' perspective
P.Y.K. Chaua,*, K.Y. Tamb
a
School of Business, The University of Hong Kong, Pokfulam, Hong Kong, PR China
Department of Information and Systems Management, School of Business and Management,
Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China
b

Received 23 December 1998; accepted 12 September 1999

Abstract
The growing popularity of open systems in organizational computing has made it important to understand the key
determinants of open-systems adoption. Existing innovation diffusion theories, however, have been criticized for their inability
to provide an adequate explanation for diffusion of complex organizational technology. This study used the `technology-push'
(TP) and `need-pull' (NP) concepts, borrowed from the engineering/R&D management literature to examine the key factors in
the adoption decision. Based on this theory, a research model was developed and tested by collecting data from senior IT

executives in 89 organizations. The results generally offered support for the model and for the usefulness of applying the TPNP theory to explain the adoption decision. Organization size had the largest impact on the decision. Migration costs was the
next greatest in¯uence. We also found that the organization would be less likely to adopt the new technology, unless the
existing systems appeared to be unsatisfactory. # 2000 Elsevier Science B.V. All rights reserved.
Keywords: Technology adoption; Open systems; Technology-push; Need-pull

1. Introduction
Rapid advances in information technology (IT) and
telecommunications systems have created a dilemma
for organizations. On the one hand, they provide
enormous opportunities for skillful managers to
reshape internal operations and their relationships
with their suppliers, customers, and even rivals. On
the other hand, the short life cycle of computer hard-

*

Corresponding author. Tel.: ‡852-2859-1025;
fax: ‡852-2858-5614.
E-mail address: pchau@business.hku.hk (P.Y.K. Chau)

ware platforms and systems software has made it
increasingly dif®cult for MIS directors and corporate
IT-systems designers to keep abreast of the latest
developments. Open systems are advocated as a solution to this dilemma, because they allow those same
people to rely on a stable suite of interfaces, services,
and protocols that function on even the latest platforms. This, in turn, permits application developers to
ensure that their applications continue to be compatible despite changes in the supporting hardware and
basic systems software. The essence of an open-systems strategy is that the adopter bene®ts from a much
simpler method of integrating all the IS by making
technology interoperate more easily and enabling

0378-7206/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 7 2 0 6 ( 9 9 ) 0 0 0 5 0 - 6

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P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

information to be more portable. In simple terms, open
systems promote vendor independence and applications transparency [2].

The decision to adopt open systems has signi®cant
rami®cations on the IT infrastructure and its alignments with the organizational structure. However,
there is little work published on factors that affect
the adoption of open systems in an organization
[5].
Studies on the adoption of IT innovations have been
well documented. Many (see, e.g. [23]) have based
their research models on Rogers' [40] diffusion of
innovations (DOI) theory. Example works include
Hoffer and Alexander [21], Moore and Benbasat
[29] and Ramamurthy and Premkumar [38]. In
DOI, the theory posits that diffusion depends on ®ve
general attributes: relative advantage, compatibility,
complexity, observability, and trialability. Tornatzky
and Klein [44] conducted a meta-analysis of ®ndings
from studies on innovation characteristics and innovation adoption and concluded that compatibility, complexity, and relative advantage are consistently
important during adoption decisions. Nevertheless,
researchers on complex IS have criticized the `de®ciencies' of the DOI theory. For example, Brancheau
and Wetherbe [3] noted that it was clear that DOI
theory did not provide a complete explanation for

technology diffusion. In a review of IT innovation
studies, Fichman [13] argued that classical diffusion
variables by themselves are unlikely to be strong
predictors of complex IT adoption and diffusion,
suggesting that additional factors should be added.
In studies of adoption, Prescott and Conger [37]
concluded that ``DOI factors are not as appropriate
for inter-organizational information technologies as
they are for the others,. . . traditional DOI ®ndings
must be modi®ed. . .''
Zmud [48] suggested using the `technology-push'
(TP) and `need-pull' (NP) concepts borrowed from the
engineering/R&D management literature to explain
behavior in adoption of new technology. In his study,
he developed a model of process innovation to explain
practices in the adoption of software using responses
in a questionnaire from 47 software development
managers. Though the investigation failed to validate
the concepts, the author concluded that ``the general
support observed for the overall research model

should encourage future research . . .''

This study follows Zmud's suggestion by developing an adoption model for open systems. The objective
is twofold:
1. to examine a set of factors that facilitate or inhibit
the adoption of open systems; and
2. to provide an empirical test of the validity of the
concepts applied to technology adoption of open
systems.

2. Background
2.1. The technology-push and need-pull (TP-NP)
concepts
The concepts of technology-push and need-pull
were introduced by Schon [42] as the underlying
motivations and driving forces behind the innovation
of a new technology [6]. Two schools of thought,
namely the TP and the NP, propose and support two
different arguments. The TP school suggests that
innovation is driven by science, and thus drives technology and application: scienti®c discovery triggers

the sequence of events which end in diffusion or
application of the discovery [30]. The TP force stems
from recognition of a new technological means for
enhancing performance. Porter and Millar [35] argued
that, with appropriate structure and strategy, adoption
of new technology could create substantial and sustainable competitive advantages.
From the classical economics' point of view, technology is basically a means of changing the factors of
production. J.A. Schumpeter asserted that the pace and
direction of innovation would be determined by
advances in the underlying scienti®c base. His view
was corroborated by Phillips [34], who argued that the
user needs had a relatively minor role in determining
the pace and direction of innovation.
Gauvin and Sinha [16] suggested two types of
opportunities for adoption of new technology: from
productivity gains achieved with a new technology,
and from expansion of resulting demand or from
replacement of the technological base.
The NP proponents argue that user needs are the
key drivers of adoption. In an early study, Meyers

and Marquis [27] examined innovation within organizations using ex post analyses. They reported

P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

that more than 70% of the innovations could be
classi®ed as need-pull, and suggested that organizations should pay more attention to needs for innovation than in maintaining technical competence.
Langrish [23] examined the issue again and concluded
that both, the TP and NP models existed, but that
the NP model was generally more prevalent. Zmud
also noted that ``need-pull innovations have been
found to be characterized by higher probabilities for
commercial success than have technology-push innovations.''
Some researchers proposed that a successful innovation would occur when a need and the means to
resolve it simultaneously emerge [14]. Munro and
Noori [30], in their study on commitment to new
manufacturing technology, included both, the TP
and the NP factors. Their ®ndings suggested that
the integration of both generally contributed to more
innovativeness. Thus, adoption of a new technology
may be induced by

1. the recognition of a promising new technology,
2. a performance gap, or
3. the motivating forces of both.
2.2. Characteristics of open systems
An open systems environment is
A comprehensive and consistent set of international information technology standards and
functional standards profiles that specify interfaces, services and supporting formats to
accomplish interoperability or portability of
applications, data and people [32].
Each hardware vendor, applications developer and
end-user participating in the development of an open
system speci®cation has his or her interests, and
reconciling various differences can be dif®cult. Thus,
it is often necessary for some to lead the way and
pioneer its adoption. Open systems can be viewed as
an organizational innovation that requires both, technical and administrative innovation [9]. The adoption
of an architecture leads to a radical redesign of the IT
infrastructure of the organization. Thus, it is a radical
technical process innovation [10].
The changes in administrative procedures accompanying the adoption of open systems make such

adoption an administrative innovation [24]. Adoption

231

of open systems requires an organization to revise its
procedures to deal with hardware/software procurements, resources allocation, staff training, and operation and management. An organization must also
possess three characteristics of an administrative innovation, as suggested by Loh and Venkatraman [25].

3. Research model and hypotheses
The research model consists of three sets of variables: TP factors, NP factors, and two other variables.
All these factors are assumed to in¯uence the adoption
decision of open systems. The model is illustrated in
Fig. 1.
3.1. Technology-push factors
The two TP-related factors are the bene®ts obtained
from adopting the technology and the costs associated
with its adoption. The gains should be greater than the
costs. In the context of open systems, numerous
bene®ts, mostly technical, have been mooted. They
include:

providing a flexible environment unconstrained by
proprietary systems;
offering more choices for hardware;
promoting flexibility and integration;
utilizing IT resources more effectively; and
allowing transparent data access.
However, quantifying such benefits is generally difficult. This leads to the following hypothesis:
H1. The extent of perception of benefits to be gained
by adopting open systems will be positively related to
the decision to adopt.
Higher cost for an innovation is negatively associated with its adoption [36]. In open systems, the cost
of adoption may be associated with the technical or
organizational uncertainties involved.
Technical uncertainty may arise from complexity
and/or from the need for knowledge needed to
implement the technology. Adoption is not a single
event, but rather a process of knowledge accumulation. Hage and Aiken [18] reported that knowledge
depth, measured as the extent of professional training
affects innovation adoption. Cohen and Levinthal

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P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

Fig. 1. The research model.

[7] proposed a concept of absorptive capacity,
de®ned as an organization's ability to recognize the
value of new information, assimilate it, and apply
it to productive ends. They argued that it was the
level of skills and knowledge gained over the course
of the adopter's cumulative history of innovative
activities and was a key determinant of an organization's capacity for innovation. Attewell [1] also
emphasized the role of know-how in the adoption
of innovation.
Organizational uncertainty may result from two
sources: the dif®culty of estimating the administrative
and operating costs of adoption and the infeasibility of
replacing the current old technologies, in-house IT
expertise and administrative processes. Open systems
require discontinuous [12,45] and competencedestroying changes [46]. Adoption of such technology
may cause the technologies, applications, expertise
and administrative rules and regulations to become

obsolete. Iivari [22], in his study of adoption of CASE
tools, noted that in addition to learning, adopting new
complex technology might require unlearning of old
practices. It would not be trivial if the underlying or
supporting methodology was very different from the
one currently being used [41].
The second hypothesis is, therefore:
H2. The extent of migration costs associated with
adopting open systems will be negatively related to the
decision for adoption.
3.2. Need-pull factors
There are two NP-related factors proposed in the
research model: performance gap and market uncertainty.
In organizational computing, a performance gap
may result from a low satisfaction level with existing

P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

computer systems, unacceptable price/performance
ratio of the existing systems or inability to serve the
organization's new needs. This argument leads to the
following hypothesis:
H3. The level of satisfaction with the existing computing systems will be negatively related to the decision for adoption.
In addition, the motivation to adopt new technology
may be pressure from the external market (see, e.g.
[39,43]). Mans®eld et al. [26] provided evidence that
intense market competition appeared to stimulate the
rapid diffusion of an innovation. Pfeffer and Leblebici
[33] also argued that it was when the organization
faced a complex and rapidly changing environment
that IT was both, necessary and justi®ed. In a study of
the adoption of telecommunications technologies in
US organizations, Grover and Goslar [17] also found
signi®cant relationships between environmental
uncertainty and use of technology.
Market and environmental factors, such as the
degree of competition, the stability of demand for
products, and the degree of customer loyalty, cannot
be controlled by the management of the organization,
but can affect the way the business is conducted. From
an IT viewpoint, as companies are facing an uncertain
market environment, the competitive atmosphere
demands more responsiveness and ¯exibility in IT
support. This suggests the following hypothesis:

233

H5. IT human-resource availability will be positively
related to the decision for adoption.
The degree of formalization of work procedures
is also expected to in¯uence the adoption decision.
Rogers de®ned formalization as the degree to which
an organization emphasizes rules and procedures
in the performance of its members and argued that
such formalization may inhibit innovation. This suggests a negative relationship between the degree
of formalization and the adoption decision. However,
in studying the diffusion of laptop computers,
Gatignon and Robertson [15] reported that organizational standardization was a prerequisite for improving productivity. Cooper and Zmud [8] also found
that task±technology compatibility was a key factor
associated with the adoption of a production and
inventory control IS. Organizations which currently
have a formal policy on systems±related matters are,
therefore, believed to be better prepared for the adoption of open systems. This suggests the following
hypothesis:
H6. The degree of formalization of systems development and management will be positively related to the
decision for adoption.

4. Methodology
4.1. Informants

H4. The level of market uncertainty will be positively
related to the decision for adoption.
3.3. Additional variables
Two additional variables are IT human-resource
availability and formalization. Many researchers have
suggested, and found, empirical support for the positive association between human-resource availability
and innovation behaviors [19,28]. The basic rationale
is that large organizations have more resources so that
the potential loss due to unsuccessful innovations can
be tolerated more easily. Others studied a closelyrelated concept, organizational slack, and found a
positive relationship between it and the adoption of
IT [4]. Adoption of open systems requires a radical
redesign of the IT infrastructure of an organization.
This lead to the following hypothesis:

Informants for this study were required to be senior
informed respondents within the organizational unit.
An interview list of 300 senior executives responsible
for managing corporate IT functions was compiled
from two sources: a major IT vendor and the Hong
Kong section of the Asian Computer Directory. A
letter stating the purpose of the study was sent and a
follow-up telephone call was made to each of these IT
executives. Eighty-nine respondents (30%) agreed to
participate. The group comprised 11 directors/vicepresidents of IS in their organizations, 64 managers/
section-heads of IS, and 14 executives holding non-IS
titles, such as ®nancial controllers and engineering
managers. The ®rms they represented were involved in
a wide spectrum of industries including manufacturing, utilities, transportation, trading, ®nancial, construction, and retail.

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P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

A preliminary questionnaire was developed and
pilot-tested with ®ve IS managers to assess logical
inconsistencies, ease of understanding, sequence of
questions, and task relevance. Instead of mailing out
the questionnaires, face-to-face interviews were conducted to ensure that respondents clearly understood
all the questions and terms used in the questionnaire.
There were some modi®cations to the original questionnaire to clarify the meaning of particular questions. None of the responses in the pilot test were used
in the analysis reported in this study.
4.2. Construct operationalizations
To operationalize the constructs, direct use of questionnaires employed in other studies of technology
innovation adoption was believed to be inappropriate.
Instead, items were adapted from either instruments
used in other studies or popular IT periodicals and
trade journals.
Bene®ts of adopting open systems were measured
by ®ve items adapted from various IT magazines for
practitioners and pamphlets published by vendors of
open-systems products. Respondents were asked to
give their level of agreement or disagreement with the
following ®ve potential bene®ts of going to an open
system:
1.
2.
3.
4.
5.

no longer constrained by proprietary systems;
more choice for hardware and software;
better utilization of IT resources;
promote flexibility and integration; and
allow transparent data access.

A seven-point Likert-type scale was employed.
Migration costs associated with adopting open
systems was operationalized with three items. Respondents were asked to indicate the extent to which they
agreed with statements relating to the migration costs
of open systems:
1. high cost for migration;
2. existing IS personnel are only familiar with
proprietary systems; and
3. infeasible to dispose of existing proprietary systems.
These items were based on IT adoption studies or were
adapted from various open-systems surveys published
in trade journals. A seven-point Likert-type scale was
used.

The satisfaction level with existing computing systems construct included two items:
1. Does your existing computing system serve the
needs of the company? and
2. Are you satisfied with the price/performance of
your system?
Respondents were asked to respond to these questions
in a seven-point Likert-type scale with anchors from
`to a great extent' to `only a little' and from `very
satisfied' to `very dissatisfied', respectively.
Market uncertainty was operationalized by asking
respondents to describe:
1.
2.
3.
4.
5.

the market for their company's products;
the competition for their company's products;
the demand of their major customers;
the degree of loyalty of their major customers; and
the frequency of price-cutting in their industry.

A seven-point Likert-type scale was used, with
anchors (such as ranging from `extremely stable' to
`extremely unstable') The five items were adapted
from Robertson and Gatignon.
IT human-resource availability was measured by
the number of IT personnel (excluding computer
operators) in the organization. Bretschneider and Wittmer [4] noted that personnel re¯ected resource commitments, more than hardware and software, which
were generally one-time expenses. As suggested by
Zmud, the measure was put in natural logarithm form.
Degree of formalization was operationalized by
counting the number of formal policies or standards
(relating to tasks performed in systems development
and management) being used in the organization, and
then normalizing the result. Tasks included project
control, feasibility study, budget estimation, schedule
estimation, requirements analysis, systems design,
program design, coding, testing, documentation, and
conversion. This measure was similar to those of
Moch and Morse [28] and Ettlie [11] in the innovation
literature. The items were adapted from Zmud [47].
Finally, the dependent variable, open-systems adoption decision, was determined by asking the respondents whether or not their organizations had already
developed a migration plan for open systems. Adopting open systems is not an `all-or-nothing' thing.
Several activities or steps may have been taken before
the adoption decision was made. A task force/com-

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P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

mittee may have been set up to investigate the feasibility of migration and/or some IT people in the
organization may have already talked to certain vendors about open-systems products. While these activities may be considered as tasks leading to the
adoption decision, the adoption decision of open
systems is not considered to be made until a formal
migration plan for open systems has been developed.
The plan must be already endorsed by top management together with a ®nancial budget and a migration
schedule. An organization will not be treated as an
open-systems adopter until it has developed the migration plan such as operationalization was used in previous innovation studies (see, e.g. [1]).
4.3. Construct reliability and validity
Cronbach a was used to assess the reliability or
internal consistency of the constructs. The a values
range from 0.63 to 0.73 (Table 1). `IT human-resource
availability', `degree of formalization' and `open-systems adoption decision' were single-item constructs
and, thus, had no a value. The lower reliability for

`satisfaction level with existing systems' can be partly
attributed to the small number of items in the factor as
the calculation of a can be affected by the length of the
construct. Nunnally [31] suggested that reliability of at
least 0.7 suf®ced for early stages of basic research. As
most of the items of the constructs were adapted from
either previous studies in related areas or popular IT
periodicals and trade magazines, the content validity
of the constructs is deemed acceptable.
In view of its data-driven nature, factor analysis was
not used to identify constructs. Instead, this technique
was used to examine the existence of the constructs
and the groupings of the items. If all items in the
independent variables are factor analyzed and loaded
in accordance with the proposed ones, then construct
validity is further supported. Therefore, principal
components analysis with VARIMAX rotation and a
four-factor solution was performed. Table 2 shows
the results of the factor analysis. Items of the four
factors were loaded as theorized and the four factors
altogether explained 56% of the total variance. Therefore, the construct validity was claimed.
4.4. Data analysis

Table 1
Reliability of constructs
Construct

Cronbach a

Benefits of adopting open systems
B1: no longer constrained by proprietary systems
B2: more choice for hardware and software
B3: better utilization of IT resources
B4: promote flexibility and integration
B5: allow transparent data access

0.729

Migration costs of open systems
U1: high cost for migration
U2: existing IS personnel only familiar with
proprietary systems
U3: infeasible to dispose of existing
proprietary systems
Satisfaction level with existing computing systems
S1: existing computing systems serve the
needs of the organization
S2: satisfied with the price/performance
ratio of the existing system
Market uncertainty
M1: market for the company's major products
M2: competition for the company's major products
M3: demand of major customers
M4: degree of loyalty of major customers
M5: frequency of price-cutting in the industry

0.713

Logistic regression analysis was performed to
examine the signi®cance of the six proposed independent variables on the open-systems adoption decision.
A multivariate statistical technique was chosen over a
Table 2
Results of factor analysis

0.629

0.701

B1
B2
B3
B4
B5
U1
U2
U3
S1
S2
M1
M2
M3
M4
M5
Eigenvalue
Variance (%)

Factor 1

Factor 2

Factor 3

Factor 4

0.574
0.615
0.552
0.683
0.657
0.296
0.201
0.295
0.289
0.305
ÿ0.193
ÿ0.339
ÿ0.269
ÿ0.216
ÿ0.367
2.710
18.1

0.155
0.067
0.279
0.145
0.291
0.161
0.148
0.268
0.067
0.166
0.736
0.465
0.723
0.655
0.451
2.279
15.2

ÿ0.296
ÿ0.181
ÿ0.140
ÿ0.238
ÿ0.0442
0.720
0.802
0.629
ÿ0.129
ÿ0.222
ÿ0.225
0.025
0.089
ÿ0.049
ÿ0.203
1.925
12.8

ÿ0.300
ÿ0.047
0.020
ÿ0.246
ÿ0.246
0.102
ÿ0.066
0.079
0.707
0.753
0.187
0.122
0.074
ÿ0.313
ÿ0.206
1.498
10.0

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P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

multiple regression analysis, because the dependent
variable in the model was a nominal variable. Using a
nominal dependent variable in multiple regression
analysis would violate the assumptions necessary
for hypothesis testing. The signi®cance of the regression coef®cients of the hypothesized independent
variables was examined to determine support for
the hypotheses. Wald statistic was used in the signi®cance test as the coef®cients were all smaller than
one [20]. Contribution of individual constructs to the
model was measured by the R statistic.

6. Discussion
In this study, a research model using the TP-NP
concepts as a basis was developed for examining the
in¯uence of several factors on the decision of opensystems adoption. Speci®cally, six factors were proposed to be important and the results showed
that three of them had signi®cant effects on the
decision.
6.1. Impact of technology-push factors on the
adoption decision

5. Results
Table 3 shows the results of the logistic regression
analysis. Both the ÿ2 log likelihood statistic and the
goodness-of-®t statistic indicated that the model was
not signi®cantly different from a `perfect' model. This
allowed us to proceed with the data analysis as
planned.
The signi®cance of individual constructs was
assessed by the Wald statistic and its corresponding
p-value. The coef®cients of three constructs (migration costs of open systems, satisfaction level with
existing computing systems and IT human-resource
availability) were found to be signi®cantly different
from zero whilst the coef®cients of the other three
constructs (bene®ts of adopting open systems, market
uncertainty and degree of formalization) were not.
Also, based on the R statistic, IT human-resource
availability had the only positive contribution to the
model; both migration costs of open systems and
satisfaction level with the existing computing systems
had a relatively smaller, negative contribution to the
model. Therefore, support was found for hypothesis 2,
3, and 5. Support was not found for the other three
hypotheses.

The research model proposed two TP factors: bene®ts of adopting open systems and uncertainty from
adopting open systems. Organizations might be
attracted or `pushed' to adopt open systems, because
of perceived bene®ts of adopting that technology.
Adopting open systems can provide an organization
with many bene®ts. The study did not support these
claims. Maybe many organizations have had bad
experiences in adopting new IT, especially for organizational innovation.
Uncertainty, and thus costs, might disincline an
organization to adopt a new technology. This `negative' TP factor was found to be signi®cant in the opensystems adoption decisions in this study. The higher
the costs, the lower the chance of adopting open
systems. The novelty of the open-systems technology
may lead to uncertainty, and thus costs, as to the
amount of technical know-how required and the corresponding technological changes needed. Successful
implementation of open systems requires competence
in technologies, such as UNIX and TCP/IP, which are
not yet dominant in corporate computing environments. Expertise in these areas is scarce. The adoption
decision also demands replacing current old technol-

Table 3
Results of the logistic regression analysisa
Factor

Coefficient

Wald statistic

Significance

R statistic

Benefits of adopting open systems
Migration costs of open systems
Satisfaction level with existing computing systems
Market uncertainty
IT human-resource availability
Degree of formalization

0.216
ÿ0.376
ÿ0.509
0.051
0.739
0.754

0.687
3.971
4.628
0.049
7.546
0.748

0.407
0.046
0.032
0.826
0.006
0.387

0.000
ÿ0.126
ÿ0.146
0.000
0.212
0.000

a

ÿ2 Log likelihood: w2 ˆ 92.630 (df ˆ 83); significance ˆ 0.220, Goodness of Fit: w2 ˆ 83.563 (df ˆ 83); significance ˆ 0.462.

P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

ogies, in-house IT expertise and administrative processes.
This suggests that in deciding whether or not to
adopt open systems, organizations seem to pay more
attention to the potential problems than to the potential
bene®ts, that is most organizations are conservative.
6.2. Impact of need-pull factors on the adoption
decision

237

adopt it sooner if the technology was evaluated as
favorable to the organization.
As for the impact of degree of formalization of work
procedures relating to systems development and management on the adoption decision for open systems,
this study did not ®nd any signi®cant relationship between the existence of formal policies on performing
systems tasks and the decision to adopt open systems.
6.4. Overall validity of the research model

In our research model, two NP factors were predicted as having in¯uence on the adoption decision for
open systems. Based on NP concepts, an organization
would not consider adopting a new technology unless
a need, such as a performance gap, was recognized.
Therefore, in the context of adopting open systems,
the satisfaction level with existing computing systems
should be closely related to the need for improvement
and, thus, the adoption decision. This assertion was
supported in our study. Whenever the current systems
satis®ed the needs of the organization, the propensity
to change should be lower. The results also agreed
with the ®ndings of other empirical studies.
In contrast to the ®nding of a signi®cant negative
relationship between the satisfaction level with existing computing systems and the adoption decision, the
pressure coming from the external-market environment was not found to be a signi®cant factor encouraging the organization to adopt open systems.
Thus, consistent with ®ndings concerning the
impact of TP factors on the open-systems adoption
decision, of the two NP factors examined, organizations tended to emphasize the `internal' factor (satisfaction level with existing computing systems) rather
than the in¯uence from the external-market environment (market uncertainty).
6.3. Impact of IT human-resource availability and
degree of formalization on the adoption decision

The overall validity of the research model developed using the TP-NP concepts as a basis was generally supported in the study. Although the study's
hypotheses were not all supported, based on the
statistics measuring the model ®t, the research model
was statistically valid.
The results show that the NP factor had a more
signi®cant in¯uence on the decision model than the TP
factor in terms of partial contribution to the total
variance of the model. However, it should be noted
that the IT human-resource availability factor had a
stronger in¯uence than either TP or NP factors in our
study. This seems to indicate that when an organization has to decide whether or not to adopt open
systems, availability of resources may be the most
important consideration.
Of the three factors found to be signi®cant in
affecting the adoption decision, the satisfaction level
with existing systems factor is basically not in the
Rogers' framework. The migration costs factor can be
considered to `implicitly' include the complexity factor in Rogers' framework. The migration costs factor
in the present model deals with issues more than this
complexity and includes (in)feasibility to dispose of
existing proprietary systems. Lastly, IT human-resource availability may be considered to be similar to
organizational slack and/or size in Rogers' framework.
6.5. Limitations

Consistent with prior technology-adoption studies,
IT human-resource availability was found to be a
signi®cant positive factor in the adoption decision
for open systems. The ®nding supported the `Schumpeter hypothesis'. The results also indirectly supported
the `information capacity hypothesis', postulating that
organizations with greater capacities to obtain and
evaluate information about a new technology should

This study has several limitations. First, because
open systems is a relatively new concept, some IT
executives might have slightly different interpretations of open systems. However, both, the observer
and the respondent bias were kept to a minimum by
employing face-to-face interviews to ensure consistent interpretation of terms and concepts among all

238

P.Y.K. Chau, K.Y. Tam / Information & Management 37 (2000) 229±239

interviewees. Second, open systems is an innovation
quite different from other IT innovations. Although
both reliability and validity were demonstrated, operationalization of the factors in the research model had
not been extensively used and tested in previous
studies.
7. Conclusions
This study sought empirical support for a research
model describing the key factors affecting the decision
for open-systems adoption. The research model was
developed using the TP-NP concepts as a basis plus
two additional variables. The results generally supported the model and the usefulness of applying the
TP-NP concepts to explain the adoption decision. In
making the adoption decision, organizations tended to
worry more about the migration costs associated
with the adoption, would be less likely to adopt unless
the existing computing systems were unsatisfactory,
and had a higher propensity to consider adopting new
technology when they had more IT human resources.
The organizational forces that underlie technology
innovation and adoption are both, complex and varied.
When the innovation context becomes more speci®c,
using `universal' factors, such as perceived bene®ts of
adoption may not be appropriate or suf®cient to
explain the decision. This study adopted the TP-NP
into the context of the open systems adoption decision
and found it to be useful.
Of course, operationalization of key concepts in the
TP-NP concepts is dif®cult. A very important issue
after the adoption decision is the implementation of
the technology. An adoption cannot be considered to
be successful until the technology has been implemented as planned and has assisted the organization to
achieve the results as expected.
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P.Y.K. Chau is Associate Professor of
Information Systems at the University of
Hong Kong. He received his Ph.D. in
business administration from the Richard Ivey School of Business (formerly
Western Business School), The University of Western Ontario, Canada. His
research interests include decision-support systems, information presentation
and model visualization, and issues
related to IS/IT adoption and implementation. He has papers
published in major information systems journals including
Management Information Systems Quarterly, Journal of Management Information Systems, Decision Sciences, Decision Support
Systems, Information and Management, Journal of Organizational
Computing and Electronic Commerce, European Journal of
Information Systems, and INFOR.
K.Y. Tam is currently Professor of
Information Systems and Weilun Senior
Fellow at the Hong Kong University of
Science and Technology. His research
interests include electronic commerce,
adoption of information technology, and
information technology applications. He
has published extensively on these topics
in major management science and information systems journals including Management Science, MIS Quarterly, Information Systems Research,
Decision Support Systems, Journal of Management Information
Systems, and IEEE Transactions on Engineering Management. He is
currently on the editorial board of a number of IS journals. Prof. Tam
has extensive consulting experience with major companies and is
currently leading a team of consultants to develop a HK$ 40 million
digital library for the Open University of Hong Kong. Dr. Tam is a
member of AIS, IEEE, and ACM.