Description
Enterprise resource planning (ERP) systems are costly and complex but vital for companies having to face a rapidly changing business environment and an increasingly competitive marketplace. As the first study to examine the factors within the technology-organization-environment (TOE).
DETERMINANTS OF THE ADOPTION OF
ENTERPRISE RESOURCE PLANNING WITHIN THE
TECHNOLOGY ORGANIZATION ENVIRONMENT
FRAMEWORK: TAIWAN'S COMMUNICATIONS INDUSTRY
MINGJU PAN
Jinwen University of Science and Technology
Taipei, Taiwan
WOAN-YUH JANG
National Taiwan University of Science and
Technology, Taipei, Taiwan
ABSTRACT
Enterprise resource planning (ERP) systems are costly
and complex but vital for companies having to face a rapidly
changing business environment and an increasingly competitive
marketplace. As the first study to examine the factors within the
technology-organization-environment (TOE) framework that
affect the decision to adopt ERP in Taiwan's communications
industry, the empirical tests conducted here are based on personal
interviews with a sample of 99 firms in Taiwan's communications
industry. Eight factors in three broad categories are tested using
logistic regression, and four of these, technology readiness, size,
perceived barriers and production and operations improvements,
are found to be important determinants of the adoption of ERP.
This model correctly classifies 79.8% of the decisions made
with respect to the adoption of ERP. The results substantiate the
usefulness of this model which may be interesting to managers
seeking to be more proactive in planning for their adoption of an
ERP system.
Keywords: Technology-organization-environment frame-
work; Enterprise resource planning; Communications industry;
Adoption.
INTRODUCTION
According to the recent World Economic Forum Reports,
by 2003, Taiwan had established itself as the world's largest
producer of more than a dozen information and communications
technology (ICT) products [25]. To increase production
efficiency and maintain its competitive advantage in the
industry, Taiwan has been committed to the development of
related technologies, including communications technology
that has become a trillion-NT dollar industry. According to the
Yearbook of World Electronics Data [40], in 2005 Taiwan's
communications industry was ranked tenth in the world and
fourth in Asia in terms of production value. Furthermore, as
reported by the Industrial Economics & Knowledge Center of
the Industrial Technology Research Institute, this industry has
been experiencing continued accelerated growth. For instance,
its production value soared from US$9,731 billion in 2003 to
US$12,509 in 2004 and to an overwhelming US$15,413 in 2005,
reflecting an annual growth rate of around 25%. In simple terms,
the communications industry is certainly an important player in
Taiwan's drive to develop the island into Asia's most digitized
country by the year 2008.
Faced with severe market competition and a dramatically
changing business environment, modem, forward-thinking
organizations are still prompted to adopt various state-of-the-art
information technologies to improve their business operations.
To enhance competitive advantage, the enterprise resource
planning (ERP) system is becoming increasingly important not
least because of its ability to integrate such individual functional
systems as manufacturing, finance, procurement and distribution
as well as to support organizational strategies [1, 10, 22, 23, 42,
44,48]. In fact. Yen and Sheu [41 ] advocated that, in the past few
years, ERP has become the most strategic and most valuable tool
with which to develop and improve a firm's competitiveness.
Based on the work of Tomatzky and Fleischer [36], Iacovou
et al. [20], Chau and Tam [7], Kuan and Chau [21], Zhu et al.
[46, 47] and Zhu and Kraemer [45], in this paper we adopt a
technology-organization-environment (TOE) conceptual model
to investigate the adoption of ERP and focus on its relationship
with selected factors that are considered important to Taiwan's
communications firms. With its focus on a single industry, this
study enables us to more thoroughly explore the potential impact
of industry-specific characteristics. By limiting confounding
factors, we are able to concentrate on certain variables of
particular interest. To the best of our knowledge, this is the
first empirical study that examines the factors within the TOE
framework that affect the decision to adopt ERP. With an
increased understanding of and better insight into this model, it is
expected that managers will be more proactive in their planning
for the future adoption of an ERP system.
LITERATURE REVIEW
Characteristics of an ERP system adoption
An ERP system allows companies to replace their existing
information systems, while helping them to standardize the flow
of management information [42]. Gupta [11] concluded that
successfully implemented ERP systems increase competitiveness
by making it possible to increase not only the quality of goods
and services but also customer satisfaction. Zhang et al. [43]
contended that, although the rate of successfully implementing
ERP systems is low, it is still one of the options most widely
sought after by manufacturing companies in acquiring or
maintaining a competitive advantage. Wei et al. [38] found that
ERP in vestment can significantly affect the future competitiveness
and performance of a company.
It is important to study ERP adoption because successful
endeavors with ERP require that the company adopt the right
94 Journal of Computer Information Systems Spring 2008
strategy [9, 22, 37, 41]. Motwani et al. [26] argued that a
cautious, evolutionary and bureaucratic implementation process,
backed by careful change management, network relationships
and cultural readiness, facilitates the successful implementation
of ERP. Many activities that are vital to the overall success of an
ERP project go on, and these include bridging legacy systems,
cleaning up suspect data, training senior management, managing
effective relationships and leading teams in both technical and
non-computer based environments, where they engage in both
manufacturing simulation exercises and transactional training
[42].
On the other hand, the implementation of the ERP system is
highly complex and requires a substantial investment of time,
money and internal resources [1, 18]. It is not surprising that
not all firms succeed in reaping all of its potential benefits.
Davenport [9] gave two reasons for such failure: the technical
complexity of the solutions that ERP provides and the lack of
alignment between people, processes and the new technology.
Other factors, such as being mandated autocratically by top
management without organizational readiness and proper change
management [26], organizational resistance to change [15],
the failure to achieve predetermined corporate goals and poor
management of the implementation process [37, 42], as well as
the selection of an ineffective ERP system [23] also seemingly
contribute to a troubled ERP implementation. It is important to
note, however, that without ERP firms will virtually have no
competitive edge. For successful ERP implementation, firms
must select certain stand-alone or partially integrated functional
ERP software products that are able to support their business
goals and strategies [5, 9, 10, 15, 34, 38].
The TOE framework
Tomatzky and Fleischer [36] are credited with being the
first to develop the TOE framework to study the adoption of
technological innovations. The TOE framework identifies
three aspects of a firm's contexts that influence the adoption
and implementation of a technological innovation: (a) the tech-
nological context — both the internal and external technologies
relevant to the firm; (b) the organizational context — descriptive
measures regarding the organization, such as firm size and
scope, managerial structure and internal resources; and (c) the
environmental context — the arena in which a firm conducts
its business: its industry, competitors and dealings with gov-
ernment.
The TOE framework is consistent with Rogers' [30] theoretical
analysis. The TOE framework has gained considerable empirical
support over the past decade. The adoption of Electronic Data
Interchange (EDI), an interorganizational information systems
(IOS) technology, has been studied extensively. Iacovou et al. [20]
developed a framework that formulates three factors important
to EDI adoption — organizational readiness, external pressures
to adopt and perceived benefits. More recently, Kuan and Chau
[21] proposed a perception-based small business EDI adoption
model incorporating six factors (direct benefits, indirect benefits,
cost, technical competence, industry pressure and government
pressure) and confirm the usefulness of the TOE framework.
Henriksen [13] very recently investigated the adoption of IOS in
the Danish steel and machinery industry within the context ofthe
TOE. His findings show that environmental and organizational
attributes rather than technological attributes are the main
determinants of the adoption of IOS.
Studies on other IS domains also provide empirical support
for the theoretical TOE framework. Chau and Tam [7] adopted
this framework and identified 3 factors that affect the adoption of
open systems, namely, the characteristics of the innovation, the
organizational technology and the external environment. Thong
[35] studied IS adoption and found significant relationships
between IS adoption and technological and organizational
characteristics. Studies on electronic business (e-business) use
also provide consistent empirical support. For example, Zhu et al.
[46] suggested that firms that are relatively more technologically
competent are greater in terms of their scope and those that are
larger in size are more likely to engage in e-business. He also
noted that higher levels of consumer readiness and competitive
pressure are environmental stimulators. On the other hand, a lack
of trading partner readiness hinders the adoption of e-business.
Zhu et al. [47] found that technology readiness (the strongest
factor), financial resources, global scope and the regulatory
environment contribute significantly to e-business value. Zhu
and Kraemer [45] found that technology competence, firm size,
financial commitment, competitive pressure and regulatory
support are important factors for partaking in e-business.
RESEARCH METHODOLOGY
ERP adoption model
The research model used here is illustrated in Figure 1. Since
the primary focus of this study is to determine which factors in
the TOE framework were responsible for the adoption of ERP,
the dependent variable in the conceptual model is a dichotomous
measure which we used to determine whether or not a particular
business had adopted or planned to adopt ERP. Our research
model posits eight adoption predictors within the three contexts
of the TOE framework: technology — IT infrastructure,
technology readiness; organization — size, perceived barriers;
and environment — production and operations improvement,
enhancement of products and services, competitive pressure
and regulatory policy. A brief justification for our selection and
subsequent use of these specific variables followed by some
research questions as well as three research hypotheses are
presented in what follows:
Research hypotheses and measures
Technological context
Kuan and Chau [21] and Zhu et al. [46] attested to the
importance of internal technology resources for successful IS
adoption. Zhu et al. [47] emphasized that given the technology-
driven nature of e-business, firms that make efficient use of
Intemet technologies and exhibit technology readiness are
more apt to create e-business value. Moreover, sufficient
financial resources help companies to acquire the necessary IT
resources and achieve successful e-business implementation [16,
29, 45, 47]. In a way that is consistent with the literature, "IT
infrastructure" and "technology readiness" were adopted in the
present study to capture the technology context.
In this study, "IT infrastructure" was operationalized by
asking respondents to indicate (1) the ratio of the number of
employees on-line to the total number of employees; (2) the
ratio of the number of computers on-line to the total number of
computers; and (3) the ratio of the total number of computers to
Spring 2008 Journal of Computer Information Systems 95
the total number of employees. We coded these three items by
taking a square root. Another three items captured "technology
readiness", namely, 'e-applications', 'business network' and
'IT budget in 2004'. The following items were used to measure
'e-applications': the Intemet, e-business, the company web site,
EDI, e-mail, videoconferencing, e-leaming and Voice over
Intemet Protocol (VoIP). 'Business network' was operationalized
by counting the number of items the sampling unit had been
using in the organization: Intranet, Extranet, Wireless Local
Area Network (WLAN) and virtual private network. 'IT budget
in 2004', was used to measure financial resources. The following
hypotheses are proposed:
HI a: IT infrastructure is positively associated with ERP
adoption.
Hlb: Technology readiness is positively associated with
ERP adoption.
Organizational context
Size is an important organizational factor for technology
adoption [8, 36, 39]. It is often reported that larger organizations
tend to adopt more innovations largely due to their greater
flexibility and ability to absorb more risk [for example, 19,
45, 46]. In noting that the implementation of an ERP system is
a lengthy and costly process, Shehab et al. [31] and Huang
et al. [18] recently confirmed that ERP implementation requires
large amounts of capital and a great number of personnel.
However, other researchers have contested this, stating that
there is no relationship between size and IT adoption [2, 20]. Zhu
et al. [47] recently indicated that firm size even deters e-business
value.
The difficulty in adopting ERP may very well result in
resistance from users, and this could begin to surface during the
course ofthe adoption process. As a consequence, it is essential to
secure the perceived barriers. The greater the top management's
support is, the easier it is for their organization to overcome the
difficulties and complexity encountered in adopting IT [3,19,27,
29,37]. Thong's [35] data analysis showed that the characteristics
of the decision-maker, specifically the CEO's innovativeness
and IS knowledge, have a major effect on the decision to adopt
IS. Cho [8] concluded that firms that perceive less hindrance to
the adoption of a technology will be more likely to adopt the
technology.
In line with the literature, the organization's "size" and
"perceived barriers" should be included in the organizational
dimension. To shed light on the issue of size, in this study,
respondents were asked to fill in the number of employees and the
amount of firm capital. By separately calculating the number of
employees and the amount of capital as "Z" scores, we averaged
the two "Z" scores, and this served as the unit for measuring
organization size. The "perceived barriers" were measured by
counting the number of items to which the respondents answered
"yes" in a question with multiple items. The multi-part question
was: "Did your organization face the following difficulties in
its adoption of ERP: 'insufficient top management support',
'the system workflow is not suitable for current business opera-
tions', 'difficulties in cross-system integration' and 'unfriendly
operating platform or interface'?" This leads us to postulate the
second hypothesis:
H2a: The size of the organization is positively related to
ERP adoption.
H2b: A perceived barrier is negatively related to ERP
adoption.
Environmental context
When an organization faces keen competition and dynamic
changes, creating competitive advantages is a primary objective
EIGURE 1. Research model
Technological context
IT infrastructure
Technology readiness
Organizational context
Size
Perceived barriers
ERP adoption
I
Environmental context
Production and operations improvement
Enhancement of products and services
Competitive pressure
Regulatory policy
96 Journal of Computer Information Systems Spring 2008
in establishing strategic links between the way business is
conducted and IT [7, 19]. Yen and Sheu [41] argued that the
technical aspect of installing ERP systems is undoubtedly
critical, but the strategic aspect of the ERP systems could have
an even greater impact on a firm's competitiveness. Tallon et
al. [33] adopted Porter's [28] business strategy, which refers to
operational effectiveness and strategic positioning as the internal
and external goals for business.
Previous studies [3, 6, 8, 19, 22, 29] have shown that the
internal needs and external strategies of an organization are
important factors that affect the adoption of IT. On the basis
of an extensive review of the extant literature, we classified
internal strategies into two factors: "production and operations
improvement" and "enhancement of products and services".
The former factor included four question items: 'enhance
the capabilities of system planning and integration', 'assist in
reducing costs and in automating production', 'handle the key
components of the product' and 'improve the efficiency and
flexibility of the production process'. The latter factor consisted
of four items: 'develop potential new products', 'improve
product functions', 'improve the applications and services of the
products' and 'ensure that communications products are certified
to meet quality standards'.
Competitive pressure has long been recognized as an
important impetus for adopting innovations [e.g., 20, 29,
45, 46]. Zhu et al. [47] and Zhu and Kraemer [45] stated that
the regulatory environment has been recognized as a critical
environmental factor affecting innovation diffusion. In this study,
we classified external strategies into two factors (a) "competitive
pressure" with four items, 'train domestic information and
communications technology (ICT) talent', 'strengthen research of
crucial technologies', 'recruit overseas ICT talent' and 'develop
related applications/services'; and (b) "regulatory policy" with
four items, 'build an ICT industrial park', 'establish product
demonstrations and an environment conducive to promotion',
'participate in the formulation of international communication
standards' and 'provide preferential measures on tax, rentals,
etc.'.
We measured all of the above question items on a seven-point
Likert scale, where a "seven-point" score meant that the item
was the most important and a "one-point" score meant that it
was the least important. Under the assumption that the strategies
of a firm affect its decision to adopt ERP, we formulate the third
hypothesis as follows:
H3a: The internal strategy — "production and operations
improvement" — of a firm in the communications
industry positively affects its decision to adopt
ERP.
H3b: The internal strategy — "enhancement of products
and services" — of a firm positively affects its
decision to adopt ERP.
H3c: The external strategy — "competitive pressure"
— of a firm positively affects its decision to adopt
ERP.
H3d: The external strategy — "regulatory policy" — of a
firm positively affects its decision to adopt ERP.
DATA COLLECTION
Data were collected in two phases: in a pilot study and
during a final survey questionnaire. Face-to-face interviews
were conducted during both phases to ensure the validity of
the responses. In the pilot study, a panel of experts and leading
officers from business was formed to review each question
and suggest necessary refinements to the written, structured
questionnaire. As the questionnaire was finalized, the survey
was conducted by a professional survey firm. A senior person in
each of the communications businesses selected was contacted
by telephone and asked to participate in the survey. The purpose
of the survey was explained during the initial telephone contact,
and a personal interview was set up so that the most qualified
person could be directly contacted to participate in the face-to-
face interview.
A list of 400 communications businesses was obtained from
the Committee of Communications Industry Development in
the Ministry of Economic Affairs, and the Information Service
Industry Association of the R.O.C. Of these 400 companies, 85
could not be contacted. Of the remaining sample (n = 315), a total
of 99 usable responses completed the surveys, yielding an overall
response rate of 31.4 percent. The detailed breakdown of the
respondents is shown in Table 1. Overall, the sample represents
a wide range of firms, thereby increasing the generalizability of
the results.
DATA ANALYSIS AND RESULTS
Analysis of validity and reliability
First, the constructs were tested for validity and reliability.
A factor analysis of multi-item indicators was performed to
evaluate validity. The rotated factors are presented in Table 2.
As for the analysis performed to test for validity, the principal
components technique with varimax rotation was used to extract
six eigen-values, which were all greater than one. The item-to-
response ratio (22:99) is generally considered to be acceptable
for performing factor analyses [14]. What was particularly
important here was that these six eigen-values could be used to
explain 71.32% of the variation. All the factors have a loading
greater than 0.50 with a majority of them being above 0.70. This
indicates that a well-explained factor structure was employed in
this analysis.
Second, for each composite research variable, reliability
was tested using Cronbach's a coefficient. In general, Cron-
bach's a is reasonable if its value is more than 0.8. A value
of 0.7 or larger, however, is acceptable for an exploratory
study [12]. Table 3 indicates that Cronbach's a values ranged
from 0.725 to 0.891, suggesting that all of the factors are
considered to be satisfactory for the reliability of a multi-item
scale. Thus, the reliability of this study is acceptable although
the relatively low Cronbach's a for "IT infrastructure"
and "technology readiness" can be partly explained by the
smaller number of questions (only three questions used) for
each factor. The proposed measurement instrument exhibits
sufficient reliability.
Hypothesis testing
The logistic regression technique was applied to estimate
the explanatory power and test the hypotheses pertaining to
the decision to adopt ERP. This multivariate statistical tech-
nique was chosen over multiple regression analysis because
the dependent variable (adopters versus non-adopters) was
dichotomous. Similar models have been used in the IS liter-
Spring 2008 Journal of Computer Information Systems 97
Characteristics Frequency
Total number of employees:
Fewer than 20
20-200
200-500
More than 500
Total capital:
Less than US$312,500
US$312,500-US$2,500,000
More than US$2,500,000
Annual sales revenue:
Less than US$312,500
US$312,500-US$ 1,562,500
US$l,562,501-US$3,125,000
US$3,125,001-US$ 15,625,000
US$15,625,001-US$31,250,000
More than US$31,250,000
18
55
14
12
10
30
59
5
24
18
20
10
11
TABLE
Percent
18.2
55.6
14.1
12.1
10.1
30.3
59.6
5.1
24.2
18.2
20.2
10.1
22.2
1 — Characteristics of the sample
Cumulative
Percent
18.2
73.8
87.9
100.0
18.2
73.7
100.0
5.1
29.3
47.5
67.7
77.8
100.0
Characteristics
Information technology budget:
Less than US$3,125
US$3,125-US$15,625
US$15,626-US$31,250
US$31,251-US$156,250
More than US$156,250
Computer-to-employee ratio:
Less than 40%
40%-100%
More than 100%
Frequency
15
20
22
28
14
20
36
43
Percent
15.2
20.2
22.2
28.3
14.1
20.2
36.4
43.4
Cumulative
Percent
15.2
35.4
57.6
85.9
100.0
20.2
56.6
100.0
TABLE 2
Items measured
— Factor and validity analysis
1 2 3
Factor
4 5 6
Production and operations improvement
improve the efficiency and flexibility of the production process
assist in reducing costs and in automating production
handle the key components of the product
enhance the capabilities of system planning and integration
0.82
0.80
0.67
0.56
Competitive pressure
train domestic ICT talent
strengthen research of crucial technologies
recruit overseas ICT talent
develop related applications / services
0.87
0.83
0.80
0.72
Regulatory policy
build ICT industrial park
establish product demonstrations and an environment conducive to promotion
participate in the formulation of international communication standards
provide preferential measures on tax, rentals, etc.
0.80
0.79
0.77
0.54
Enhancement of products and services
improve product functions
improve the applications and services of the products
ensure that the products are certified to meet quality standards
develop potential new products
0.81
0.60
0.57
0.51
Technology readiness
e-applications
business network
IT budget in 2004
0.81
0.80
0.77
IT infrastructure
% of online employees
% of computers on-line
Computers/employees
0.88
0.86
0.60
Eigen value
Percentage of Variance explained
7.12
32.37
2.53
11.48
1.97
8.93
1.62
7.34
1.42
6.47
1.04
4.73
98 Journal of Computer Information Systems Spring 2008
ature to study open system adoption [7], EDI adoption [21],
IT innovation adoption [39], e-business adoption [46] and IT
outsourcing [4].
TABLE 3 — Reliability properties
Factor dimension Mean (S.D.) Cronbach's a
TABLE 5 — Classification Table
IT infrastructure
Technology readiness
Production and operations
improvement
Enhancement of products
and services
Competitive pressure
Regulatory policy
8.541
3.205
5.321
5.672
5.351
5.429
(1.958)
(1.160)
(1.174)
(1.088)
(1.357)
(1.194)
0.737
0.725
0.812
0.839
0.891
0.802
The results of the logistic regression in this study are presented
in Table 4. A likelihood ratio (LR = 93.630) implies a strong
relationship between the dependent variable and the regressors.
The Hosmer and Lemeshow goodness-of-fit test (y} = 4.314,
p-value = 0.828) divided subjects into deciles based on predicted
probabilities, and then computed a chi-square from observed
and expected frequencies. The p-value here indicated that the
proposed model was not significantly different from a per-
fect one that can classify all respondents into their respec-
tive groups correctly. The Nagelkerke R square showed that
about 40.9% of the data variation was explained by the logistic
model.
Wald statistics were used to test the significance of the
regression coefficients of the hypothesized independent vari-
ables. The significantly positive coefficients of "size" and "pro-
duction and operations improvement" confirm their roles as
important adoption factors; the significantly negative coefficient
of "perceived barriers" substantially affects the adoption of ERP.
These results fully support hypotheses H2a, H2b and H3a. As
concerns "technology readiness," the coefficient is significant
at the 10% level, which moderately supports Hlb. Although
the coefficients for "competitive pressure" and "regulatory
policy" have opposite signs, neither of these two factors nor
the remaining two, "IT infrastructure" and "enhancement of
products and services", is significantly different from zero (see
Table 4).
Adopters
Non-adopters
Overall
Observed
Total
64
35
Predicted
Adopters
57
13
Non-adopters
7
22
Percentage
Correct
89.1
62.9
79.8
With respect to the overall discriminating power, the results
shown in Table 5 indicate a prediction accuracy of 79.8% based
on the logistic regression model. As there are 35 non-adopters
and 64 adopters in this study, guessing the adoption by random
choice would result in (35/99)^ + (64/99)^ = 54.29%, which is
much less than in the case of our model. Thus, we conclude that
the logistic regression model has a much higher discriminating
power than the random choice model.
DISCUSSION
The goal of this study is to extend our understanding of
ERP adoption in the communications industry in Taiwan by
identifying factors that distinguish adopters from non-adopters.
In general, the results provide solid support for our proposed TOE
framework model. A total of eight ERP factors were evaluated in
this study. Although not all hypotheses are supported, based on
the tests of reliability and validity, the proposed instrument is a
good measurement tool. In the following subsections, we discuss
each of the factors under the three broad categories.
Technological context
As for the factors related to the technological context, "IT
infrastructure" is found to be insignificant when it comes to
affecting ERP adoption, and therefore, hypothesis H1 a is rejected.
This is fully consistent with Premkumar and Ramamurthy [29]
and Thong [35] and suggests that businesses that adopt IS do
not do so because of their existing "IT infrastructure". When the
mean of the "IT infrastructure" of adopters (8.554) is compared
with that of non-adopters (8.516), there is no difference between
these two groups (p-value = 0.927). One plausible explanation
for this being insignificant is that companies in Taiwan's
communications industry have attained a relatively high level of
TABLE 4 — Results of the logistic regression analysis
-2 Log Likelihood
Nagelkerke R Square
Hosmer and Lemeshow Chi-square Test
Significance
Variable
IT infrastructure
Technology readiness
Size
Perceived barriers
Production and operations improvement
Enhancement of products and services
Competitive pressure
Regulatory policy
93.630
0.409
4.314
0.828
Coefficient (S.D.)
0.015 (0.161)
0.696 (0.384)
1.408 (0.623)
-0.778 (0.309)
0.967 (0.352)
0.192 (0.384)
-0.379 (0.285)
-0.424 (0.267)
Wald
statistic
0.008
3.285
5.109
6.346
7.544
0.250
1.770
2.522
Sig.
0.927
0.070*
0.024"
0.012"
0.006"*
0.617
0.183
0.112
Spring 2008 Journal of Computer Information Systems 99
implementation of IT infrastructure: 50% ofthe total have more
than 0.8 computers per employee; nearly 75% of the total have
70% of their computers on-line; and more than 80% of the total
have 60% of their employees on-line. While Zhu et al. [46, 47]
found "technology readiness" to be an important variable, others
[29, 35] did not find it to be a critical variable. This study finds
only moderate support for Hypothesis Hlb.
Organizational context
The factors in the organizational context are found to be
significant. Since ERP projects are expensive and risky to
undertake, prior research [8, 18,31] has indicated that the size of
a business should play an important role in the decision-making
process. In this study, "size" is a significant discriminator
between ERP adopters and non-adopters. Eurthermore, based on
the positive value of the coefficient in the logistics regression,
businesses that are larger show a greater likelihood of adopting
ERP because they have more resources and may be better able to
undertake more risks. On the other hand, "perceived barriers" to
adopting an ERP system are found to have a negative effect on
ERP adoption. Adopters evidently perceive barriers to be less of
an obstacle than do non-adopters. This supports the view that the
adoption of new technology requires top management support
and a satisfactory level of competence in technology integration
[7, 8, 32, 35]. The results provide support for Hypotheses H2a
and H2b.
Environmental context
In a complex business environment, an organization' s adoption
decision is expected to be affected by the external environment.
ERP systems are complex; hence, strategic reasoning is important
for successful implementation [32, 41]. When viewed within the
environmental context, we regard business strategies as internal
and external strategic positions.
An organization is willing to adopt an innovation if its
business operations have a genuine internal need for it [29].
Table 4 shows that "production and operations improvement" is
significant (H3a), indicating that for the ERP adopter, this factor
is higher than it is for the non-adopter. However, the second
internal need that affects ERP adoption is "enhancement of
products and services" which turns out to be insignificant (H3b)
in our model.
Adoption of an innovation occurs not only as a result of a
rational assessment of the business implications of the new
technology, but also as a response to satisfy external pres-
sures. Although a number of studies have reported that varia-
tions in "competitive pressure" [29, 46] and "regulatory
policy" [41, 47] have an effect on IT adoption decisions,
interestingly, the findings of Thong [35] and Lee [24] have
shown that they do not have any significant direct effect on
the decision to adopt IT. To be sure, the evidence from prior
studies on the significance of the external environment has
been inconclusive. Table 4 shows that hypotheses H3c and H3d
are not significant in this study. This parallels the findings of
Chau and Tam [7], who concluded that the external environ-
mental context has little influence when the decision is made as
to whether or not to adopt ERP.
As shown in Table 4, "production and operations improve-
ment" is the only significant factor that discriminates between
adopters and non-adopters in the environmental context. The
coefficients of "enhancement of products and services", "com-
petitive pressure" and "regulatory policy" are not statistically
significant. However, as we extended our focus to the means
of the latter three factors in the environmental context (Table
3), it is seen that the range is high, varying between 5.351 and
5.672 (on a 7-point Likert scale). Given the high scores for these
three factors, the results indicate that these three factors are
generally perceived to be of importance to ERP practice. One
possible explanation for the insignificant relationship and lack
of any apparent difference between the two groups could be the
limited sample size. Another explanation is that, given the high
risk involved in ERP adoption, many non-adopters might simply
be adopting a wait-and-see attitude.
CONCLUSIONS
The major purpose of an ERP project is to facilitate the
automation of many, if not all, basic processes in order to
integrate all of the information across an enterprise and to
eliminate complex, expensive interfaces among computer
systems. While a large number of businesses have already
jumped on board by adopting ERP, users need to be careful
since this technology may not be the perfect tool for all on
account of its complexity and high cost. The approach using a
logistic regression has allowed us to examine the relationships
between ERP adoption and the individual constructs using the
TOE framework in the communications industry in Taiwan.
The results clearly provide insights as to which aspects of
these constructs are particularly salient in the context of the
adoption function. The coefficients in the logistic regression
indicate that "technology readiness" and "production and
operations improvement" as well as the two factors in the
organizational context, i.e., the "size" and "perceived barriers"
dimensions are important and that they have a marked impact
on the decision to adopt ERP in the communications industry
in Taiwan. However, many factors favoring ERP-adoption must
be carefully evaluated before adopting an ERP system. Addi-
tional research needs to be conducted before more concrete
conclusions can be drawn.
As with all research, our study has some limitations that
may serve as the springboard for further research. Eirst, this
study only focuses on the adoption decision and not on its
implementation. Further research could be conducted to examine
what goes into the successful implementation of ERP. Second,
our cross-sectional data tend to have certain limitations when it
comes to explaining the direction of causality ofthe relationships
among the variables. It is suggested that a longitudinal study be
undertaken to understand the adoption of ERP and strengthen
the direction of causality proposed by the model. Third, we have
explored our model only in the single context of innovation
and in the single communications industry — that of Taiwan.
While it is true that this helps to minimize biases that could be
introduced across industries and countries, the generalizability
of the study could be of some concern, i.e., the majority (87.9%)
of Taiwan's communications companies have less than 500
employees (Table 1), which is defined as a small business in
the US based on the SB A definition [17]. One avenue for future
research would be to examine the robustness of the findings
across industries and countries with the adoption of different
innovations. Last, but not least, other factors that could possibly
affect ERP adoption have not perhaps been considered in this
research. We encourage other researchers investigating the ERP
100 Journal of Computer Information Systems Spring 2008
area to continue this line of research and either confirm or modify
the TOE model that we have proposed.
ACKNOWLEDGEMENTS
Our data source is from a project called "the Investigation of
E-strategies and Application Service in Taiwan's Communica-
tions Industry" by Eocus on Internet News and Data (FIND)
at the Institute for Information Industry (III), which is spon-
sored by the Department of Industrial Technology of the Minis-
try of Economics Affairs, R.O.C. The authors would like to
thank Miss Harriet Y.F. Lin and her team-members of FIND
at the III. Their valuable suggestions on earlier versions of this
paper and assistance with the data collection are very much
appreciated.
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102 Journal of Computer Information Systems Spring 2008
doc_560468336.pdf
Enterprise resource planning (ERP) systems are costly and complex but vital for companies having to face a rapidly changing business environment and an increasingly competitive marketplace. As the first study to examine the factors within the technology-organization-environment (TOE).
DETERMINANTS OF THE ADOPTION OF
ENTERPRISE RESOURCE PLANNING WITHIN THE
TECHNOLOGY ORGANIZATION ENVIRONMENT
FRAMEWORK: TAIWAN'S COMMUNICATIONS INDUSTRY
MINGJU PAN
Jinwen University of Science and Technology
Taipei, Taiwan
WOAN-YUH JANG
National Taiwan University of Science and
Technology, Taipei, Taiwan
ABSTRACT
Enterprise resource planning (ERP) systems are costly
and complex but vital for companies having to face a rapidly
changing business environment and an increasingly competitive
marketplace. As the first study to examine the factors within the
technology-organization-environment (TOE) framework that
affect the decision to adopt ERP in Taiwan's communications
industry, the empirical tests conducted here are based on personal
interviews with a sample of 99 firms in Taiwan's communications
industry. Eight factors in three broad categories are tested using
logistic regression, and four of these, technology readiness, size,
perceived barriers and production and operations improvements,
are found to be important determinants of the adoption of ERP.
This model correctly classifies 79.8% of the decisions made
with respect to the adoption of ERP. The results substantiate the
usefulness of this model which may be interesting to managers
seeking to be more proactive in planning for their adoption of an
ERP system.
Keywords: Technology-organization-environment frame-
work; Enterprise resource planning; Communications industry;
Adoption.
INTRODUCTION
According to the recent World Economic Forum Reports,
by 2003, Taiwan had established itself as the world's largest
producer of more than a dozen information and communications
technology (ICT) products [25]. To increase production
efficiency and maintain its competitive advantage in the
industry, Taiwan has been committed to the development of
related technologies, including communications technology
that has become a trillion-NT dollar industry. According to the
Yearbook of World Electronics Data [40], in 2005 Taiwan's
communications industry was ranked tenth in the world and
fourth in Asia in terms of production value. Furthermore, as
reported by the Industrial Economics & Knowledge Center of
the Industrial Technology Research Institute, this industry has
been experiencing continued accelerated growth. For instance,
its production value soared from US$9,731 billion in 2003 to
US$12,509 in 2004 and to an overwhelming US$15,413 in 2005,
reflecting an annual growth rate of around 25%. In simple terms,
the communications industry is certainly an important player in
Taiwan's drive to develop the island into Asia's most digitized
country by the year 2008.
Faced with severe market competition and a dramatically
changing business environment, modem, forward-thinking
organizations are still prompted to adopt various state-of-the-art
information technologies to improve their business operations.
To enhance competitive advantage, the enterprise resource
planning (ERP) system is becoming increasingly important not
least because of its ability to integrate such individual functional
systems as manufacturing, finance, procurement and distribution
as well as to support organizational strategies [1, 10, 22, 23, 42,
44,48]. In fact. Yen and Sheu [41 ] advocated that, in the past few
years, ERP has become the most strategic and most valuable tool
with which to develop and improve a firm's competitiveness.
Based on the work of Tomatzky and Fleischer [36], Iacovou
et al. [20], Chau and Tam [7], Kuan and Chau [21], Zhu et al.
[46, 47] and Zhu and Kraemer [45], in this paper we adopt a
technology-organization-environment (TOE) conceptual model
to investigate the adoption of ERP and focus on its relationship
with selected factors that are considered important to Taiwan's
communications firms. With its focus on a single industry, this
study enables us to more thoroughly explore the potential impact
of industry-specific characteristics. By limiting confounding
factors, we are able to concentrate on certain variables of
particular interest. To the best of our knowledge, this is the
first empirical study that examines the factors within the TOE
framework that affect the decision to adopt ERP. With an
increased understanding of and better insight into this model, it is
expected that managers will be more proactive in their planning
for the future adoption of an ERP system.
LITERATURE REVIEW
Characteristics of an ERP system adoption
An ERP system allows companies to replace their existing
information systems, while helping them to standardize the flow
of management information [42]. Gupta [11] concluded that
successfully implemented ERP systems increase competitiveness
by making it possible to increase not only the quality of goods
and services but also customer satisfaction. Zhang et al. [43]
contended that, although the rate of successfully implementing
ERP systems is low, it is still one of the options most widely
sought after by manufacturing companies in acquiring or
maintaining a competitive advantage. Wei et al. [38] found that
ERP in vestment can significantly affect the future competitiveness
and performance of a company.
It is important to study ERP adoption because successful
endeavors with ERP require that the company adopt the right
94 Journal of Computer Information Systems Spring 2008
strategy [9, 22, 37, 41]. Motwani et al. [26] argued that a
cautious, evolutionary and bureaucratic implementation process,
backed by careful change management, network relationships
and cultural readiness, facilitates the successful implementation
of ERP. Many activities that are vital to the overall success of an
ERP project go on, and these include bridging legacy systems,
cleaning up suspect data, training senior management, managing
effective relationships and leading teams in both technical and
non-computer based environments, where they engage in both
manufacturing simulation exercises and transactional training
[42].
On the other hand, the implementation of the ERP system is
highly complex and requires a substantial investment of time,
money and internal resources [1, 18]. It is not surprising that
not all firms succeed in reaping all of its potential benefits.
Davenport [9] gave two reasons for such failure: the technical
complexity of the solutions that ERP provides and the lack of
alignment between people, processes and the new technology.
Other factors, such as being mandated autocratically by top
management without organizational readiness and proper change
management [26], organizational resistance to change [15],
the failure to achieve predetermined corporate goals and poor
management of the implementation process [37, 42], as well as
the selection of an ineffective ERP system [23] also seemingly
contribute to a troubled ERP implementation. It is important to
note, however, that without ERP firms will virtually have no
competitive edge. For successful ERP implementation, firms
must select certain stand-alone or partially integrated functional
ERP software products that are able to support their business
goals and strategies [5, 9, 10, 15, 34, 38].
The TOE framework
Tomatzky and Fleischer [36] are credited with being the
first to develop the TOE framework to study the adoption of
technological innovations. The TOE framework identifies
three aspects of a firm's contexts that influence the adoption
and implementation of a technological innovation: (a) the tech-
nological context — both the internal and external technologies
relevant to the firm; (b) the organizational context — descriptive
measures regarding the organization, such as firm size and
scope, managerial structure and internal resources; and (c) the
environmental context — the arena in which a firm conducts
its business: its industry, competitors and dealings with gov-
ernment.
The TOE framework is consistent with Rogers' [30] theoretical
analysis. The TOE framework has gained considerable empirical
support over the past decade. The adoption of Electronic Data
Interchange (EDI), an interorganizational information systems
(IOS) technology, has been studied extensively. Iacovou et al. [20]
developed a framework that formulates three factors important
to EDI adoption — organizational readiness, external pressures
to adopt and perceived benefits. More recently, Kuan and Chau
[21] proposed a perception-based small business EDI adoption
model incorporating six factors (direct benefits, indirect benefits,
cost, technical competence, industry pressure and government
pressure) and confirm the usefulness of the TOE framework.
Henriksen [13] very recently investigated the adoption of IOS in
the Danish steel and machinery industry within the context ofthe
TOE. His findings show that environmental and organizational
attributes rather than technological attributes are the main
determinants of the adoption of IOS.
Studies on other IS domains also provide empirical support
for the theoretical TOE framework. Chau and Tam [7] adopted
this framework and identified 3 factors that affect the adoption of
open systems, namely, the characteristics of the innovation, the
organizational technology and the external environment. Thong
[35] studied IS adoption and found significant relationships
between IS adoption and technological and organizational
characteristics. Studies on electronic business (e-business) use
also provide consistent empirical support. For example, Zhu et al.
[46] suggested that firms that are relatively more technologically
competent are greater in terms of their scope and those that are
larger in size are more likely to engage in e-business. He also
noted that higher levels of consumer readiness and competitive
pressure are environmental stimulators. On the other hand, a lack
of trading partner readiness hinders the adoption of e-business.
Zhu et al. [47] found that technology readiness (the strongest
factor), financial resources, global scope and the regulatory
environment contribute significantly to e-business value. Zhu
and Kraemer [45] found that technology competence, firm size,
financial commitment, competitive pressure and regulatory
support are important factors for partaking in e-business.
RESEARCH METHODOLOGY
ERP adoption model
The research model used here is illustrated in Figure 1. Since
the primary focus of this study is to determine which factors in
the TOE framework were responsible for the adoption of ERP,
the dependent variable in the conceptual model is a dichotomous
measure which we used to determine whether or not a particular
business had adopted or planned to adopt ERP. Our research
model posits eight adoption predictors within the three contexts
of the TOE framework: technology — IT infrastructure,
technology readiness; organization — size, perceived barriers;
and environment — production and operations improvement,
enhancement of products and services, competitive pressure
and regulatory policy. A brief justification for our selection and
subsequent use of these specific variables followed by some
research questions as well as three research hypotheses are
presented in what follows:
Research hypotheses and measures
Technological context
Kuan and Chau [21] and Zhu et al. [46] attested to the
importance of internal technology resources for successful IS
adoption. Zhu et al. [47] emphasized that given the technology-
driven nature of e-business, firms that make efficient use of
Intemet technologies and exhibit technology readiness are
more apt to create e-business value. Moreover, sufficient
financial resources help companies to acquire the necessary IT
resources and achieve successful e-business implementation [16,
29, 45, 47]. In a way that is consistent with the literature, "IT
infrastructure" and "technology readiness" were adopted in the
present study to capture the technology context.
In this study, "IT infrastructure" was operationalized by
asking respondents to indicate (1) the ratio of the number of
employees on-line to the total number of employees; (2) the
ratio of the number of computers on-line to the total number of
computers; and (3) the ratio of the total number of computers to
Spring 2008 Journal of Computer Information Systems 95
the total number of employees. We coded these three items by
taking a square root. Another three items captured "technology
readiness", namely, 'e-applications', 'business network' and
'IT budget in 2004'. The following items were used to measure
'e-applications': the Intemet, e-business, the company web site,
EDI, e-mail, videoconferencing, e-leaming and Voice over
Intemet Protocol (VoIP). 'Business network' was operationalized
by counting the number of items the sampling unit had been
using in the organization: Intranet, Extranet, Wireless Local
Area Network (WLAN) and virtual private network. 'IT budget
in 2004', was used to measure financial resources. The following
hypotheses are proposed:
HI a: IT infrastructure is positively associated with ERP
adoption.
Hlb: Technology readiness is positively associated with
ERP adoption.
Organizational context
Size is an important organizational factor for technology
adoption [8, 36, 39]. It is often reported that larger organizations
tend to adopt more innovations largely due to their greater
flexibility and ability to absorb more risk [for example, 19,
45, 46]. In noting that the implementation of an ERP system is
a lengthy and costly process, Shehab et al. [31] and Huang
et al. [18] recently confirmed that ERP implementation requires
large amounts of capital and a great number of personnel.
However, other researchers have contested this, stating that
there is no relationship between size and IT adoption [2, 20]. Zhu
et al. [47] recently indicated that firm size even deters e-business
value.
The difficulty in adopting ERP may very well result in
resistance from users, and this could begin to surface during the
course ofthe adoption process. As a consequence, it is essential to
secure the perceived barriers. The greater the top management's
support is, the easier it is for their organization to overcome the
difficulties and complexity encountered in adopting IT [3,19,27,
29,37]. Thong's [35] data analysis showed that the characteristics
of the decision-maker, specifically the CEO's innovativeness
and IS knowledge, have a major effect on the decision to adopt
IS. Cho [8] concluded that firms that perceive less hindrance to
the adoption of a technology will be more likely to adopt the
technology.
In line with the literature, the organization's "size" and
"perceived barriers" should be included in the organizational
dimension. To shed light on the issue of size, in this study,
respondents were asked to fill in the number of employees and the
amount of firm capital. By separately calculating the number of
employees and the amount of capital as "Z" scores, we averaged
the two "Z" scores, and this served as the unit for measuring
organization size. The "perceived barriers" were measured by
counting the number of items to which the respondents answered
"yes" in a question with multiple items. The multi-part question
was: "Did your organization face the following difficulties in
its adoption of ERP: 'insufficient top management support',
'the system workflow is not suitable for current business opera-
tions', 'difficulties in cross-system integration' and 'unfriendly
operating platform or interface'?" This leads us to postulate the
second hypothesis:
H2a: The size of the organization is positively related to
ERP adoption.
H2b: A perceived barrier is negatively related to ERP
adoption.
Environmental context
When an organization faces keen competition and dynamic
changes, creating competitive advantages is a primary objective
EIGURE 1. Research model
Technological context
IT infrastructure
Technology readiness
Organizational context
Size
Perceived barriers
ERP adoption
I
Environmental context
Production and operations improvement
Enhancement of products and services
Competitive pressure
Regulatory policy
96 Journal of Computer Information Systems Spring 2008
in establishing strategic links between the way business is
conducted and IT [7, 19]. Yen and Sheu [41] argued that the
technical aspect of installing ERP systems is undoubtedly
critical, but the strategic aspect of the ERP systems could have
an even greater impact on a firm's competitiveness. Tallon et
al. [33] adopted Porter's [28] business strategy, which refers to
operational effectiveness and strategic positioning as the internal
and external goals for business.
Previous studies [3, 6, 8, 19, 22, 29] have shown that the
internal needs and external strategies of an organization are
important factors that affect the adoption of IT. On the basis
of an extensive review of the extant literature, we classified
internal strategies into two factors: "production and operations
improvement" and "enhancement of products and services".
The former factor included four question items: 'enhance
the capabilities of system planning and integration', 'assist in
reducing costs and in automating production', 'handle the key
components of the product' and 'improve the efficiency and
flexibility of the production process'. The latter factor consisted
of four items: 'develop potential new products', 'improve
product functions', 'improve the applications and services of the
products' and 'ensure that communications products are certified
to meet quality standards'.
Competitive pressure has long been recognized as an
important impetus for adopting innovations [e.g., 20, 29,
45, 46]. Zhu et al. [47] and Zhu and Kraemer [45] stated that
the regulatory environment has been recognized as a critical
environmental factor affecting innovation diffusion. In this study,
we classified external strategies into two factors (a) "competitive
pressure" with four items, 'train domestic information and
communications technology (ICT) talent', 'strengthen research of
crucial technologies', 'recruit overseas ICT talent' and 'develop
related applications/services'; and (b) "regulatory policy" with
four items, 'build an ICT industrial park', 'establish product
demonstrations and an environment conducive to promotion',
'participate in the formulation of international communication
standards' and 'provide preferential measures on tax, rentals,
etc.'.
We measured all of the above question items on a seven-point
Likert scale, where a "seven-point" score meant that the item
was the most important and a "one-point" score meant that it
was the least important. Under the assumption that the strategies
of a firm affect its decision to adopt ERP, we formulate the third
hypothesis as follows:
H3a: The internal strategy — "production and operations
improvement" — of a firm in the communications
industry positively affects its decision to adopt
ERP.
H3b: The internal strategy — "enhancement of products
and services" — of a firm positively affects its
decision to adopt ERP.
H3c: The external strategy — "competitive pressure"
— of a firm positively affects its decision to adopt
ERP.
H3d: The external strategy — "regulatory policy" — of a
firm positively affects its decision to adopt ERP.
DATA COLLECTION
Data were collected in two phases: in a pilot study and
during a final survey questionnaire. Face-to-face interviews
were conducted during both phases to ensure the validity of
the responses. In the pilot study, a panel of experts and leading
officers from business was formed to review each question
and suggest necessary refinements to the written, structured
questionnaire. As the questionnaire was finalized, the survey
was conducted by a professional survey firm. A senior person in
each of the communications businesses selected was contacted
by telephone and asked to participate in the survey. The purpose
of the survey was explained during the initial telephone contact,
and a personal interview was set up so that the most qualified
person could be directly contacted to participate in the face-to-
face interview.
A list of 400 communications businesses was obtained from
the Committee of Communications Industry Development in
the Ministry of Economic Affairs, and the Information Service
Industry Association of the R.O.C. Of these 400 companies, 85
could not be contacted. Of the remaining sample (n = 315), a total
of 99 usable responses completed the surveys, yielding an overall
response rate of 31.4 percent. The detailed breakdown of the
respondents is shown in Table 1. Overall, the sample represents
a wide range of firms, thereby increasing the generalizability of
the results.
DATA ANALYSIS AND RESULTS
Analysis of validity and reliability
First, the constructs were tested for validity and reliability.
A factor analysis of multi-item indicators was performed to
evaluate validity. The rotated factors are presented in Table 2.
As for the analysis performed to test for validity, the principal
components technique with varimax rotation was used to extract
six eigen-values, which were all greater than one. The item-to-
response ratio (22:99) is generally considered to be acceptable
for performing factor analyses [14]. What was particularly
important here was that these six eigen-values could be used to
explain 71.32% of the variation. All the factors have a loading
greater than 0.50 with a majority of them being above 0.70. This
indicates that a well-explained factor structure was employed in
this analysis.
Second, for each composite research variable, reliability
was tested using Cronbach's a coefficient. In general, Cron-
bach's a is reasonable if its value is more than 0.8. A value
of 0.7 or larger, however, is acceptable for an exploratory
study [12]. Table 3 indicates that Cronbach's a values ranged
from 0.725 to 0.891, suggesting that all of the factors are
considered to be satisfactory for the reliability of a multi-item
scale. Thus, the reliability of this study is acceptable although
the relatively low Cronbach's a for "IT infrastructure"
and "technology readiness" can be partly explained by the
smaller number of questions (only three questions used) for
each factor. The proposed measurement instrument exhibits
sufficient reliability.
Hypothesis testing
The logistic regression technique was applied to estimate
the explanatory power and test the hypotheses pertaining to
the decision to adopt ERP. This multivariate statistical tech-
nique was chosen over multiple regression analysis because
the dependent variable (adopters versus non-adopters) was
dichotomous. Similar models have been used in the IS liter-
Spring 2008 Journal of Computer Information Systems 97
Characteristics Frequency
Total number of employees:
Fewer than 20
20-200
200-500
More than 500
Total capital:
Less than US$312,500
US$312,500-US$2,500,000
More than US$2,500,000
Annual sales revenue:
Less than US$312,500
US$312,500-US$ 1,562,500
US$l,562,501-US$3,125,000
US$3,125,001-US$ 15,625,000
US$15,625,001-US$31,250,000
More than US$31,250,000
18
55
14
12
10
30
59
5
24
18
20
10
11
TABLE
Percent
18.2
55.6
14.1
12.1
10.1
30.3
59.6
5.1
24.2
18.2
20.2
10.1
22.2
1 — Characteristics of the sample
Cumulative
Percent
18.2
73.8
87.9
100.0
18.2
73.7
100.0
5.1
29.3
47.5
67.7
77.8
100.0
Characteristics
Information technology budget:
Less than US$3,125
US$3,125-US$15,625
US$15,626-US$31,250
US$31,251-US$156,250
More than US$156,250
Computer-to-employee ratio:
Less than 40%
40%-100%
More than 100%
Frequency
15
20
22
28
14
20
36
43
Percent
15.2
20.2
22.2
28.3
14.1
20.2
36.4
43.4
Cumulative
Percent
15.2
35.4
57.6
85.9
100.0
20.2
56.6
100.0
TABLE 2
Items measured
— Factor and validity analysis
1 2 3
Factor
4 5 6
Production and operations improvement
improve the efficiency and flexibility of the production process
assist in reducing costs and in automating production
handle the key components of the product
enhance the capabilities of system planning and integration
0.82
0.80
0.67
0.56
Competitive pressure
train domestic ICT talent
strengthen research of crucial technologies
recruit overseas ICT talent
develop related applications / services
0.87
0.83
0.80
0.72
Regulatory policy
build ICT industrial park
establish product demonstrations and an environment conducive to promotion
participate in the formulation of international communication standards
provide preferential measures on tax, rentals, etc.
0.80
0.79
0.77
0.54
Enhancement of products and services
improve product functions
improve the applications and services of the products
ensure that the products are certified to meet quality standards
develop potential new products
0.81
0.60
0.57
0.51
Technology readiness
e-applications
business network
IT budget in 2004
0.81
0.80
0.77
IT infrastructure
% of online employees
% of computers on-line
Computers/employees
0.88
0.86
0.60
Eigen value
Percentage of Variance explained
7.12
32.37
2.53
11.48
1.97
8.93
1.62
7.34
1.42
6.47
1.04
4.73
98 Journal of Computer Information Systems Spring 2008
ature to study open system adoption [7], EDI adoption [21],
IT innovation adoption [39], e-business adoption [46] and IT
outsourcing [4].
TABLE 3 — Reliability properties
Factor dimension Mean (S.D.) Cronbach's a
TABLE 5 — Classification Table
IT infrastructure
Technology readiness
Production and operations
improvement
Enhancement of products
and services
Competitive pressure
Regulatory policy
8.541
3.205
5.321
5.672
5.351
5.429
(1.958)
(1.160)
(1.174)
(1.088)
(1.357)
(1.194)
0.737
0.725
0.812
0.839
0.891
0.802
The results of the logistic regression in this study are presented
in Table 4. A likelihood ratio (LR = 93.630) implies a strong
relationship between the dependent variable and the regressors.
The Hosmer and Lemeshow goodness-of-fit test (y} = 4.314,
p-value = 0.828) divided subjects into deciles based on predicted
probabilities, and then computed a chi-square from observed
and expected frequencies. The p-value here indicated that the
proposed model was not significantly different from a per-
fect one that can classify all respondents into their respec-
tive groups correctly. The Nagelkerke R square showed that
about 40.9% of the data variation was explained by the logistic
model.
Wald statistics were used to test the significance of the
regression coefficients of the hypothesized independent vari-
ables. The significantly positive coefficients of "size" and "pro-
duction and operations improvement" confirm their roles as
important adoption factors; the significantly negative coefficient
of "perceived barriers" substantially affects the adoption of ERP.
These results fully support hypotheses H2a, H2b and H3a. As
concerns "technology readiness," the coefficient is significant
at the 10% level, which moderately supports Hlb. Although
the coefficients for "competitive pressure" and "regulatory
policy" have opposite signs, neither of these two factors nor
the remaining two, "IT infrastructure" and "enhancement of
products and services", is significantly different from zero (see
Table 4).
Adopters
Non-adopters
Overall
Observed
Total
64
35
Predicted
Adopters
57
13
Non-adopters
7
22
Percentage
Correct
89.1
62.9
79.8
With respect to the overall discriminating power, the results
shown in Table 5 indicate a prediction accuracy of 79.8% based
on the logistic regression model. As there are 35 non-adopters
and 64 adopters in this study, guessing the adoption by random
choice would result in (35/99)^ + (64/99)^ = 54.29%, which is
much less than in the case of our model. Thus, we conclude that
the logistic regression model has a much higher discriminating
power than the random choice model.
DISCUSSION
The goal of this study is to extend our understanding of
ERP adoption in the communications industry in Taiwan by
identifying factors that distinguish adopters from non-adopters.
In general, the results provide solid support for our proposed TOE
framework model. A total of eight ERP factors were evaluated in
this study. Although not all hypotheses are supported, based on
the tests of reliability and validity, the proposed instrument is a
good measurement tool. In the following subsections, we discuss
each of the factors under the three broad categories.
Technological context
As for the factors related to the technological context, "IT
infrastructure" is found to be insignificant when it comes to
affecting ERP adoption, and therefore, hypothesis H1 a is rejected.
This is fully consistent with Premkumar and Ramamurthy [29]
and Thong [35] and suggests that businesses that adopt IS do
not do so because of their existing "IT infrastructure". When the
mean of the "IT infrastructure" of adopters (8.554) is compared
with that of non-adopters (8.516), there is no difference between
these two groups (p-value = 0.927). One plausible explanation
for this being insignificant is that companies in Taiwan's
communications industry have attained a relatively high level of
TABLE 4 — Results of the logistic regression analysis
-2 Log Likelihood
Nagelkerke R Square
Hosmer and Lemeshow Chi-square Test
Significance
Variable
IT infrastructure
Technology readiness
Size
Perceived barriers
Production and operations improvement
Enhancement of products and services
Competitive pressure
Regulatory policy
93.630
0.409
4.314
0.828
Coefficient (S.D.)
0.015 (0.161)
0.696 (0.384)
1.408 (0.623)
-0.778 (0.309)
0.967 (0.352)
0.192 (0.384)
-0.379 (0.285)
-0.424 (0.267)
Wald
statistic
0.008
3.285
5.109
6.346
7.544
0.250
1.770
2.522
Sig.
0.927
0.070*
0.024"
0.012"
0.006"*
0.617
0.183
0.112
Spring 2008 Journal of Computer Information Systems 99
implementation of IT infrastructure: 50% ofthe total have more
than 0.8 computers per employee; nearly 75% of the total have
70% of their computers on-line; and more than 80% of the total
have 60% of their employees on-line. While Zhu et al. [46, 47]
found "technology readiness" to be an important variable, others
[29, 35] did not find it to be a critical variable. This study finds
only moderate support for Hypothesis Hlb.
Organizational context
The factors in the organizational context are found to be
significant. Since ERP projects are expensive and risky to
undertake, prior research [8, 18,31] has indicated that the size of
a business should play an important role in the decision-making
process. In this study, "size" is a significant discriminator
between ERP adopters and non-adopters. Eurthermore, based on
the positive value of the coefficient in the logistics regression,
businesses that are larger show a greater likelihood of adopting
ERP because they have more resources and may be better able to
undertake more risks. On the other hand, "perceived barriers" to
adopting an ERP system are found to have a negative effect on
ERP adoption. Adopters evidently perceive barriers to be less of
an obstacle than do non-adopters. This supports the view that the
adoption of new technology requires top management support
and a satisfactory level of competence in technology integration
[7, 8, 32, 35]. The results provide support for Hypotheses H2a
and H2b.
Environmental context
In a complex business environment, an organization' s adoption
decision is expected to be affected by the external environment.
ERP systems are complex; hence, strategic reasoning is important
for successful implementation [32, 41]. When viewed within the
environmental context, we regard business strategies as internal
and external strategic positions.
An organization is willing to adopt an innovation if its
business operations have a genuine internal need for it [29].
Table 4 shows that "production and operations improvement" is
significant (H3a), indicating that for the ERP adopter, this factor
is higher than it is for the non-adopter. However, the second
internal need that affects ERP adoption is "enhancement of
products and services" which turns out to be insignificant (H3b)
in our model.
Adoption of an innovation occurs not only as a result of a
rational assessment of the business implications of the new
technology, but also as a response to satisfy external pres-
sures. Although a number of studies have reported that varia-
tions in "competitive pressure" [29, 46] and "regulatory
policy" [41, 47] have an effect on IT adoption decisions,
interestingly, the findings of Thong [35] and Lee [24] have
shown that they do not have any significant direct effect on
the decision to adopt IT. To be sure, the evidence from prior
studies on the significance of the external environment has
been inconclusive. Table 4 shows that hypotheses H3c and H3d
are not significant in this study. This parallels the findings of
Chau and Tam [7], who concluded that the external environ-
mental context has little influence when the decision is made as
to whether or not to adopt ERP.
As shown in Table 4, "production and operations improve-
ment" is the only significant factor that discriminates between
adopters and non-adopters in the environmental context. The
coefficients of "enhancement of products and services", "com-
petitive pressure" and "regulatory policy" are not statistically
significant. However, as we extended our focus to the means
of the latter three factors in the environmental context (Table
3), it is seen that the range is high, varying between 5.351 and
5.672 (on a 7-point Likert scale). Given the high scores for these
three factors, the results indicate that these three factors are
generally perceived to be of importance to ERP practice. One
possible explanation for the insignificant relationship and lack
of any apparent difference between the two groups could be the
limited sample size. Another explanation is that, given the high
risk involved in ERP adoption, many non-adopters might simply
be adopting a wait-and-see attitude.
CONCLUSIONS
The major purpose of an ERP project is to facilitate the
automation of many, if not all, basic processes in order to
integrate all of the information across an enterprise and to
eliminate complex, expensive interfaces among computer
systems. While a large number of businesses have already
jumped on board by adopting ERP, users need to be careful
since this technology may not be the perfect tool for all on
account of its complexity and high cost. The approach using a
logistic regression has allowed us to examine the relationships
between ERP adoption and the individual constructs using the
TOE framework in the communications industry in Taiwan.
The results clearly provide insights as to which aspects of
these constructs are particularly salient in the context of the
adoption function. The coefficients in the logistic regression
indicate that "technology readiness" and "production and
operations improvement" as well as the two factors in the
organizational context, i.e., the "size" and "perceived barriers"
dimensions are important and that they have a marked impact
on the decision to adopt ERP in the communications industry
in Taiwan. However, many factors favoring ERP-adoption must
be carefully evaluated before adopting an ERP system. Addi-
tional research needs to be conducted before more concrete
conclusions can be drawn.
As with all research, our study has some limitations that
may serve as the springboard for further research. Eirst, this
study only focuses on the adoption decision and not on its
implementation. Further research could be conducted to examine
what goes into the successful implementation of ERP. Second,
our cross-sectional data tend to have certain limitations when it
comes to explaining the direction of causality ofthe relationships
among the variables. It is suggested that a longitudinal study be
undertaken to understand the adoption of ERP and strengthen
the direction of causality proposed by the model. Third, we have
explored our model only in the single context of innovation
and in the single communications industry — that of Taiwan.
While it is true that this helps to minimize biases that could be
introduced across industries and countries, the generalizability
of the study could be of some concern, i.e., the majority (87.9%)
of Taiwan's communications companies have less than 500
employees (Table 1), which is defined as a small business in
the US based on the SB A definition [17]. One avenue for future
research would be to examine the robustness of the findings
across industries and countries with the adoption of different
innovations. Last, but not least, other factors that could possibly
affect ERP adoption have not perhaps been considered in this
research. We encourage other researchers investigating the ERP
100 Journal of Computer Information Systems Spring 2008
area to continue this line of research and either confirm or modify
the TOE model that we have proposed.
ACKNOWLEDGEMENTS
Our data source is from a project called "the Investigation of
E-strategies and Application Service in Taiwan's Communica-
tions Industry" by Eocus on Internet News and Data (FIND)
at the Institute for Information Industry (III), which is spon-
sored by the Department of Industrial Technology of the Minis-
try of Economics Affairs, R.O.C. The authors would like to
thank Miss Harriet Y.F. Lin and her team-members of FIND
at the III. Their valuable suggestions on earlier versions of this
paper and assistance with the data collection are very much
appreciated.
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