Understanding the Impact of ERP System Implementation on Firm … · 2017. 10. 28. ·...
Transcript of Understanding the Impact of ERP System Implementation on Firm … · 2017. 10. 28. ·...
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016), pp. 87-104
http://dx.doi.org/10.14257/ijseia.2016.10.9.09
ISSN: 1738-9984 IJSEIA
Copyright ⓒ 2016 SERSC
Understanding the Impact of ERP System Implementation on
Firm Performance – Focused on Vietnamese SMEs
Minh Duc Le1 and Kyeong Seok Han
2*
1Dept. of Business Administration, Graduate School of Soongsil University, Korea
2Professor of MIS, School of Business Administration, Soongsil University, Korea
Abstract
As Enterprise Resource Planning (ERP) is highly constructive for any augmenting
organization, identifying which determinants of ERP System Implementation impact on
the performance of firms is important. This study aims to examine the dimensions of
successful implementation of ERP systems, and how the ERP implementation affects firm
performance as focusing on Small and Medium-sized Enterprises (SMEs) in Vietnam is a
good case in point. We proposed a modified framework based on the IS Success Model
and developed our hypotheses. The results from an online survey of 402 respondents
revealed that ERP system implementation successfully enhanced the firm performance
indirectly through the effects of organizational capability and competitive advantage, in
which individual impact showed the strongest effect. We further found that, business
sector and firm age were found to have moderating effects in the conceptual framework.
Keywords: ERP implementation, IS Success Model, firm performance, SMEs
1. Introduction
In period of growing global competition, information technology (IT) and information
system (IS) have changed the lives of both individuals and organizations, and play a
crucial role in today’s business world. The wave of new information system adoption has
evolved around the use of Enterprise Resource Planning (ERP) systems. An ERP system
“comprises of a commercial software package that promises the seamless integration of
all the information flowing through the company–financial, human resources, supply
chain and customer information” [4]. ERP systems attempt to integrate all business
processes into one synchronized suite of applications, procedures, and metrics to improve
data homogeneity and integration of modular applications [13]. Once implemented in an
organization, ERP systems remain operational for a long period of time, because they
often serve as a basis to advance an organization’s information infrastructure and allow
future expansion [14]. ERP systems are propitious to accomplish goals concerning
organizational, managerial, strategic, operational, and IT infrastructure. ERP systems are
expected to improve firm performance primarily owing to redesigned business processes,
expanded information capabilities, and integrated managerial functions. As a result, the
need to test the impact of ERP implementation on firm performance represents an
important motivation for this study.
Notwithstanding the fact that ERP solutions have been introduced into the business
environment in Vietnam for approximately 15 years, the success rate of ERP
implementation is still very low and several years behind developed countries. The
number of failed ERP system implementations in Vietnam most prevails over successful
ones. Although an ERP system acts as an effective tool that provides a continuous
* Dr. Kyeong Seok Han is a corresponding author.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
88 Copyright ⓒ 2016 SERSC
competitive advantage and enhances firm performance, numerous studies reported that
the status of applying ERP solutions in Vietnam is still relatively limited.
While few earlier researches examined some practices of the assumed dimensional
structure of ERP implementation, to the best of our knowledge, no study has been found
to have interrogated the ERP system’s successful implementation on the performance of
Vietnamese SMEs, which occupy 97% in total 402,326 active enterprises and play a vital
role for Vietnam’s economic development, and tested the construct’s dimensionality
using structural equation modelling [17]. To gain a better insight into the dimensional
structure of ERP implementation construct, this study intends to figure out which success
factors influence the ERP system implementation, and how the implementation of ERP
systems can impact on the firm performance of SMEs in Vietnam by testing its
dimensionality using higher order factor model, and one factor model on a set of data
collected from 402 Vietnamese SMEs. Answering these issues fills the gap in the
literature on the business value of ERP systems. In theoretical terms, the objective of this
study is (1) to clarify the interplay between ERP implementation and it indicators
drawing from the DeLone and McLean (hereafter D&M) IS Success Model [5], and (2)
to enhance our understanding of the mechanisms by which the successful implementation
of ERP systems contributes to the performance of a firm. Empirically, the aim is to
develop conceptual tools in order to analyze the influence from ERP implementation of
the SMEs in Vietnam, and thus to offer guidelines for both researchers and practitioners.
This paper will proceed as follows: We start by reviewing the related literature in
Section 2. Thereafter, in Section 3 our proposed model and hypotheses will be discussed.
In the section of research methodology, details of the approach and measurement
instruments chosen to conduct the study are explained. Then data analysis and the main
results will be presented in Section 5. Finally, we will put forward the conclusion
condensed from this research, and discuss some of its deficiencies. We will also
designate what we see as feasible routes for further research.
2. Background and Related Works
2.1. Review of ERP Implementation
ERP systems are designed to console both functional and operational processes of the
value chain of a firm, including human resources, accounting and finance, customer and
sales, and supply chain management [4]. Thereupon, the signified benefits of ERP
systems are to streamline the workflow across various business units, ensure a smooth
transition and quicker completion of processes, and enable all inter-departmental
activities to be properly tracked and none of them to be “missed out”.
To date, prior studies have documented mixed evidence on addressing the critical
factors for ERP success. Factors which are unique to ERP implementation consist of
understanding corporate cultural change, reengineering business processes, and using
business analysts on the project team. D&M (1992) determined the factors that may
impact on the success of an ERP system under six main categories namely system
quality, information quality, system usage, user satisfaction, individual effect and
organizational impact [5]. Nevertheless, Ifinedo (2007) asserted that individual impact
and organizational impact are unrelated in the context of ERP systems [10]. Edward
(2008) reformulated the D&M IS Success Model, and examined it in ERP context [6].
Likewise, many empirical studies tested the IS Success Model, and confirmed it to be a
robust and parsimonious model with high explanatory power of the variance in IS success
across a broad variety of contexts including ERP, SCM practices, e-government, e-
commerce, etc.
In considering the evidence donated by experimental researches as well as the
observations reported in the literature, this study examines the determinants of ERP
implementation by revising the dimensions of IS Success Model.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 89
2.2. ERP Implementation and Firm Performance
An ERP system is an integrated system in that it promotes cooperation among groups,
teamwork, process expertise and business knowledge, and helps devolve authority and
responsibility from management to front lines [4]. Therefore, the nature of an ERP
system is to capture the organizational impact that is in line with the use of that system. It
is widely reported that ERP systems influence on the firm performance, for instance,
Hunton et al. (2003) tested the impact of ERP adoption on the performance by making a
comparative analysis of firms which did and did not use ERP systems [9].
In developing countries, ERP systems are typically implemented as part of an
enterprise’s effort to renovate and discriminate itself, not to replace legacy systems. ERP
systems are adopted by enterprises to support an integrated, packaged solution to their
information needs. Aside from the fact that enterprises anticipate significant benefits
from the implementation of their ERP systems, problems within the implementation
process can restrain an enterprise from realizing those predicted benefits, or even
recovering the cost of the implementation effort. To address these issues, numerous
studies have been explored. Poston and Grabski (2001) reported that ERP adoption leads
to an efficiency increase in terms of a decline in the number of employees and in the ratio
of employees to revenue in each year following the ERP implementation [13]. Also, ERP
implementation helps enterprises gain a competitive advantage. Additionally, Wier et al.
(2007) claimed that joint adoption of ERP systems and non-financial performance
incentives (NFPI) will gain better firm performance than either ERP or NFPI alone [15].
However, the literature argues that little attention has been given to research on ERP in
SMEs, as the majority of the ERP studies are based on findings from large enterprises.
2.3. SMEs and ERP Status in Vietnam
SMEs in Vietnam act jointly approximately all sorts of industries, in view of that they
have a variation in their range and significance. SMEs are business establishments that
have registered their business pursuant to law, and are classified into three levels in line
with the sizes of their total capital or the number of employees (under the Decree No.
56/2009/ND-CP by The Vietnamese Government).
Table 1. SME Definition In Terms of Sector in Vietnam
Types of SMEs
Sector
Micro
enterprises
Small-sized
enterprises
Medium-sized
enterprises
Number of
laborers
Capital
(Billion
VND)
Number
of
laborers
Capital
(Billion
VND)
Number
of
laborers
Agriculture, forestry, fishery ≤ 10 ≤ 20 10 - 200 20 - 100 200 - 300
Industry and construction ≤ 10 ≤ 20 10 - 200 20 - 100 200 - 300
Trade and service ≤ 10 ≤ 10 10 - 50 20 - 50 50 - 100 Source: Decree No. 56/2009/ND-CP dated 30
th June, 2009 by The Vietnamese Government
According to data released by the General Statistics Office of Vietnam, the majority of
active businesses are SMEs as defined in the Government’s Decree mentioned above.
The number of SMEs grew steadily from 2009 to 2014 (see Table 2). SMEs accounted
for around 97% of total enterprises, contributed 47% GDP and nearly 40% of the state
budget [17]. SMEs play an important role to support the nation’s economy – strengthen
Vietnam’s industrial base as well as provide the necessary supports to enhance Vietnam’s
development across the economic sectors.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
90 Copyright ⓒ 2016 SERSC
Table 2. Number of Acting Enterprises by Labor Size (annual 31th Dec)
Year Micro
Enterprises
Small
Enterprises
Medium
Enterprises
Large
Enterprises Total
2009 162,785 74,658 5,010 6,389 248,842
2010 187,580 79,085 5,618 7,077 279,360
2011 216,732 93,356 6,853 7,750 324,691
2012 225,037 93,036 6,735 7,864 332,672
2013 252,291 104,499 7,838 8,585 373,213
2014 271,971 112,650 8,449 9,256 402,326 Source: Business performance of enterprises by Vietnamese standard industrial classification, Statistical
Yearbook, 2015, General Statistics Office of Vietnam GSO
In the early 2000s, ERP solutions were first entered in the Vietnamese companies
which were prospecting for ways to help their business more efficient and effective. A
survey carried out by the Vietnam Chamber of Commerce and Industry reported that, by
the middle of 2006, only 1.1% of the Vietnamese enterprises successfully implemented
ERP solutions. Based on a report released by the Ministry of Industry and Trade, the rate
of enterprises using ERP packages was 17% in 2014 [20]. When comparing to many
developed countries, the IT implementation among Vietnamese SMEs is considered to be
at a basic level, and the degree of ERP implementation remains a relatively low rate.
While opting for a business software application in general and an ERP solution in
particular is not quite simple, Vietnam’s SMEs suffer more problems than most in this
regard. A noticeable challenge is that the cost spending for an ERP system is still
extremely high for many Vietnamese SMEs. Issues and lags in ERP implementation
might be a major problem interfering the long-term success of ERP adoption. Thus,
ensuring a quality ERP system after being implemented is markedly important to SMEs
in Vietnam.
3. Research Model and Hypotheses
3.1. Research Model
Figure 1 presents the conceptual framework that serves the purpose of our study. In
this study, stemming from the D&M IS Success Model, the authors assume that ERP
implementation variable is a second-order construct composed of five first-order latent
constructs: System quality, information quality, user fulfillment, individual impact, and
organizational impact. The five arrows leading from the five first-order constructs show
that the ERP implementation variable is comprised of the five latent constructs. Hence,
this variable is totally latent, unobservable, and not directly measurable. Each of the first-
order constructs constitutes a number of measurable characteristics, each of which is also
listed in details in the next section. The research model suggests that ERP implementation
influences on both organizational capability and competitive advantage which in turn
enhance firm performance. Especially, the research model also examines that through
which path type, business sector, age and size of enterprises exert moderating effects on
the relationship between the successful implementation of ERP systems and firm
performance.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 91
3.1.1. Antecedents of ERP Implementation: Previous studies have identified several
determinants for implementation of ERP systems. Our research focuses only on five
frequently factors grounded on the IS Success Model. The D&M’s model is employed as
theoretical foundation to appraise ERP implementation for its integrity and much
empirical support for the conceptual framework [5]. Each antecedent notion is as follows:
Hypothesized Relationship
Moderating Relationship
Figure 1. Conceptual Framework
System quality: System quality can be understood as the technical level involvement
the ERP’s characteristics including data accuracy, system accuracy, system efficiency,
database content and response time.
Information quality: Information quality refers to the extent to which user perception
of measuring the characteristics of the information is produced by the ERP system.
User fulfillment: User fulfillment reflects the degree to which a subjective user
evaluates various consequences after using ERP system.
Individual impact: Individual impact can be defined in broad terms as the user
perception of elevating in personnel performance, task effectiveness, and productivity.
Organizational impact: Organizational impact invokes as the dimension that measures
the effectiveness of the information performance on the organization.
3.1.2. Organizational Capability and Competitive Advantage: Organizational
capability is defined as an organization’s ability to perform a set of tasks using resources
in terms of product variety, information access, process advancement, and financial
flexibility [14]. These identified capabilities are the most harmonious capabilities to the
size-restricted firms, and they reflect a vital perspective of business issues in the ERP-
specific context. Prior studies showed that IT investment and organizational capability
Moderators
Enterprise Type
(Type)
Business Sector
(Sector) Enterprise size
(Size)
Enterprise Age
(Age)
H7
H8
H9
H10
System
Quality
(SQ)
Information
Quality
(IQ)
Competitive
Advantage
(CAD)
User
Fulfillment
(UF)
Firm
Performance
(FPE)
H2
+ H6
H3 +
H4 +
+ Individual
Impact
(IIM)
Organizational
Impact
(OIM)
ERP
Implementation
(ERPI)
Organizational
Capability
(OCA)
+ H1 H5
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
92 Copyright ⓒ 2016 SERSC
are considerable factors, drive differences in firm performance, and a specific system of
organizational capability strengthens the performance effects of IT application.
Regarding competitive advantage, the pursuit itself has been systematically stated and,
to date, remains poorly defined and operationalized. Competitive advantage in this model
is defined as the positional superiority to compete with the rivals based on the
combination of disparity, cost superiority, or through operating [2]. The attainment of a
competitive advantage position can lead to a firm’s significant performance, being
regularly measured in conventional terms such as market-share and profitability.
3.1.3. Firm Performance: Firm performance refers to the degree to which a firm
achieves in comparison with its intended outputs based on financial criteria and market
criteria [7]. Indeed, the issue of heterogeneous firm performance is a key issue in the field
of management, and an effective performance measurement system should be disposed to
capture not only the financial edge of business performance but also the non-financial
aspects so as to assess a more explicit and comprehensive notion of performance.
3.2. Hypotheses
As noted above, ERP systems provide a mechanism by which firms can process,
deliver, and seize a wide array of key performance indicators in (near) real-time, and
through which managers can coordinate and control their decisions across the enterprise.
Implementing an ERP system generally composes a firm’s grandest IS investment in its
development and, in most cases, the greatest corporate project [4]. A successfully
implemented ERP system enables firms to flexibly assemble requisite assets, knowledge,
and business relationships, and allows firms to apprehend environmental changes by
assembling organizational resources. Thus, a successful implementation of ERP systems
magnifies organizational capabilities. Moreover, changes in technology are essential if
they can give prosperity to the competitive advantage. Abdinnour-Helm et al. (2003)
stated that ERP systems do not offer competitive advantages in themselves, but have to
be coupled with social and intellectual capital within the firm [1]. Yet, other researchers
have argued that ERP can be part of making a competitive advantage in some situations
[3]. To provide further empirical evidences, the hypotheses are developed as follows:
Hypothesis H1: ERP implementation has a positive impact on organizational capability.
Hypothesis H2: ERP implementation has a positive impact on competitive advantage.
Earlier learnings have shown that there is a significant relationship between
organizational capability and competitive advantage [2]. Organizational capability plays
an important role in renewing competitive advantage over time. As this area of
competitive advantage has been lacking in empirical research, it would be potentially
contributive to investigate the relationship between organizational capability and
competitive advantage. Based on the above arguments, it is predicted that:
Hypothesis H3: Organizational capability has a positive impact on competitive advantage.
Organizational capability and firm performance are two different constructs having an
apparently complex relationship. Information systems enable firms to yield better
performance with new customized products that have notable features and better quality.
Newbert (2008) suggested that to produce a product with more benefits, a firm must
exploit a set of capabilities greater than that of its rivals. It is hypothesized that no matter
what processes of capabilities are, they affect performance. Additionally, competitive
advantage provides chances to develop their own business performance and ability to
compete with the competitors. Although the investigation of competitive advantage in the
ERP context has limited studies, existing literature provides support that the relationship,
while anecdotic, is logical. It is expected that competitive advantage would have a
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 93
positive effect on performance, and mediate the relationship between organizational
capabilities and firm performance. Thus, following hypotheses are proposed:
Hypothesis H4: Organizational capability has a positive impact on firm performance.
Hypothesis H5: Competitive advantage has positive impact on firm performance.
Recent studies have found evidence that ERP systems affect a firm’s performance [13]
[15]. Poston and Grabski (2001) investigated the effect of ERP implementation on firm
performance during an analysis window of 3 years pre- and post-implementation. The
results pointed no significant improvements in the financial ratios. From another aspect,
the market and the managers perceive value in ERP announcements and ERP
implementations, respectively [9]. Nicolaou and Bhattacharya (2006) indicated that once
an ERP system is implemented in a firm, it will continue to run well in the future, reduce
time lags in production processes, and improve the ability of firms to meet demands
which translate to higher firm performance [12]. From these assertions, in the sense that
in order for a firm to harvest the anticipated benefits, it is critical to start with an
implementation of an ERP system. Relied on these ideas, confirming a positive impact of
ERP implementation on performance is expected:
Hypothesis H6: ERP implementation has a positive impact on firm performance.
Additionally, as of moderating effects, empirical research in the literature of SMEs
context is rare and fragmented. Previous studies have discussed the roles of some
environment variables on ERP implementation such as time and nature of system
transformation, change and knowledge management, organizational culture, etc. [12]. In
light of this background, the current study will empirically dispute the influences of
enterprise profiles to test their moderating effects on the relationship between a firm’s
successful ERP implementation and performance. Among the potential moderators in the
relationships are the enterprise’s features, namely type, age, size and business sector. By
having information on such potential moderating impacts, decisions related to ERP
systems can be guided toward improvements in the firms’ position. Therefore, this study
advances the following hypotheses:
Hypothesis H7~H10: Type, business sector, age and size of enterprises have
moderating effects on firm performance.
4. Research Methodology
4.1. Instrument Design and Refinement
A web-based survey using a four-part questionnaire was designed to obtain responses
from the participants. The first part included 5 topic questions, each had several items
used to operationalize the constructs related to the ERP implementation. The second part
covered a set of questions regarding organizational capability and competitive advantage
whereas the third dealt with the performance outcomes. All scale items were measured
using a 7-point Likert-type scale, ranging from 1 = “strongly disagree” to 7 = “strongly
agree”. The last part involved nominal and/or ordinal scale items used to collect basic
information about demographic characteristics of the enterprises taking part in the survey.
To ensure that an inclusive list of items was covered in the questionnaire, all items
respecting the measurement of factors were adapted from prior scales and rephrased with
appropriate modifications in case of need to make them specifically relevant to the ERP
systems of SMEs context in Vietnam. The pool of items with regards to these constructs’
domain was compiled by the authors due to the lack of information from past findings.
Thence, the further validity of them should be justified by later research study. The final
questionnaire contained 31 items for eight constructs, and 6 questions pertaining to
business entity type, industry, number of employees, the number of years enterprise has
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
94 Copyright ⓒ 2016 SERSC
been engaged in exporting operations, the number of months and years since the ERP
system initiatives, and the type of ERP related package used.
For measuring ERP implementation, our constructs were largely revised from D&M
scale [5]. The measurement included five items for each scale. Some items, however,
were removed as they loaded in two different factors or showed a weak loading. En
masse, 22 items were applied to measure. Organizational capability and competitive
advantage were redrawn based on the instrument developed by Law and Ngai 2007 [11].
Organizational capability was measured by three items and a similar number for
competitive advantage. To capture firm performance, three items were used and reformed
stemmed from the instrument of Ellinger et al. (2008) [7]. Each construct made at least
three items up to assure adequate reliability, as assigned by Nunnally (1978).
In advance of the hypothesized variables, each parity incorporated four moderators
(business type, sector, age and size). The age of enterprise was measured by the number
of years the company had been in operation, and the size was measured by the
enterprise's number of employees currently in employment. The type and business sector
were classified in accordance with criteria cited in the Enterprise Law (2014) of Vietnam.
The constructs have all been used in prior empirical studies, and the four moderating
variables are particularly pertinent to the context of an inspection of ERP implementation
as quoted above. Since all the measurements used in this study were from current
literature, it was judged that running a pilot study would be unnecessary.
4.2. Sample Selection and Data Collection
The data used to test the hypotheses were seized via a self-administered survey that
was essentially relied upon validated apprehension taken from the ERP-related literature.
This research was conducted among enterprises publicly declaring the implementation of
ERP systems for the first time within the period 2008-2014. This is adequate population
referring to ERP implementation, since several studies provide evidence that earnings
accrued from ERP-implementing firms are noticeable only after a lag of time from the
date of initial rollout [12].
Table 3. Summary of Sample Characteristics
Classification Category Frequency Ratio (%)
Business entity
Private companies 9 2.24
Limited liability 196 48.76
Joint-stock 140 34.83
Partnership 38 9.45
Foreign-invested 19 4.73
Industry sector
Agriculture/Fisheries 19 4.73
Manufacturing 99 24.63
Construction 114 28.36
IT/Communication 47 11.69
Wholesale/Retail 41 10.20
Transportation & Storage 28 6.97
Finance/Real estate 34 8.46
Others 20 4.98
Firm Age
1-5 years 115 28.61
5-10 years 152 37.81
> 10 years 135 33.58
Firm Employee < 50 33 8.21
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 95
50-100 108 26.87
100-200 138 34.33
200-300 123 30.60
As our unit of analysis is the “firm”, the original sample included a summed quantity
of 672 enterprises, residing in Ho Chi Minh, Ha Noi and Da Nang City which represent
the largest economic centers in the south, north and middle of Vietnam, respectively. The
enterprises were randomly selected and contacted from the database stemmed from
Vietnam Chamber of Commerce and Industry. At the outset, there were 413 out of 672
enterprises contacted being agreed to feedback the survey. Yet, within the 413 returns, 11
invalid responses were discarded due to large portions of missing and discrepant
information. The remaining 402 questionnaires were retained as the usable sample size
for data analysis, yielding a complete response rate of 59.8%.
The demography of our respondents is summarized in Table 3. Aside from 11
incomplete responses, 402 out of 413 survey responses were analyzed. With regards to
the entity statistics, approximately 50% of the ERP firms came from limited liability
companies - LLCs (196 firms), 35% came from joint-stock companies (140 firms) and
the rest occupied 15%, showed that most of the respondents were LLCs and joint-stock
companies, which are the most popular forms of investment vehicle in Vietnam, in other
words, this rate is entirely appropriate to the Vietnamese enterprise structure. As for the
industry statistics, the sector of industry being made of construction and manufacturing
firms represented the highest proportion of the sample (more than 50%). The distribution
of enterprises’ age and size was nearly equal for all groups except group of less than 50
employees which was more likely to apply separate software packages more than a
complicated software required high-level structure of enterprises.
4.3. Analytical Methods
Data analysis was done in several steps by using three statistical software: SPSS 22,
AMOS 21.0.0 and Microsoft Excel 2013. Initially, we used SPSS to input survey data
and produce a basic description so as to ascertain the level of sample representative. Next,
we ran the reliability analysis, and conducted Exploratory Factor Analysis. Finally, the
proposed framework was tested through Structural Equation Modelling by the maximum
likelihood method using AMOS. The data were analyzed through a two-step approach.
Confirmatory Factor Analysis was primarily processed to identify if the measured
variables reflected the hypothesized latent variables. Then, Structural Equation Modelling
was explored to estimate overall model fit, path coefficients and explanatory power of the
proposed model. During this phase, Excel was performed to calculate the composite
reliability and variance extracted to test the convergent validity.
5. Results
5.1. Evaluation of Measurement Scales
5.1.1. Descriptive Statistics: In case each measured variable in structural equation
modeling does not have a normal distribution, we would get biased estimates and an
inaccurate model. Thus, descriptive statistics analysis using the mean score of each
measurement scale was tested to confirm normality in the multivariate distribution. If the
skewness of variables is less than 3, and kurtosis is below 10, a normal distribution can
be assumed. Descriptive analysis showed that the mean and standard deviations were
within the expected ranges, skewness ranged from -0.43 to -0.11, and kurtosis ranged
from -0.18 to 0.22. The results were well within the robustness thresholds for normality.
As a result, the univariate normality was evidenced in the dataset which may result in the
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
96 Copyright ⓒ 2016 SERSC
fact that we can execute the maximum likelihood estimation in testing the structural
model.
5.1.2. Reliability Assessment: Reliability was estimated by performing the internal
consistency of the scale factors using Cronbach’s Alpha. This study used an Alpha value
of above 0.7 as a benchmark to check whether the scales are reliable [18]. The reliability
for each construct ranged from 0.802 to 0.870, satisfied the recommended threshold, or
that is to say, reflected acceptable reliability levels (see Table 4). Therefore, our items
have a high internal consistency in each scale.
Table 4. Cronbach’s Alpha Reliability Test Result
Construct name Construct
identifier
Initial
number
of items
Number of items
carried forward
to the analysis
Cronbach's
alpha
System quality SQ 5 5 0.850
Information quality IQ 5 5 0.859
User fulfillment UF 5 4 0.862
Individual impact IIM 5 3 0.802
Organizational impact OIM 5 5 0.858
Organizational capability OCA 3 3 0.815
Competitive advantage CAD 3 3 0.870
Firm performance FPE 3 3 0.869
5.1.3. Exploratory Factor Analysis: Exploratory factor analysis (EFA) was performed
to check whether the proposed factor structures are well-consistent with the actual data.
Kaiser’s criterion (Eigenvalue value of 1-point cutoff), percentage of variance (60% of
satisfactory cutoff point), and scree plot (identifying the plot to detect a point at which the
curve shape changes path and turns into horizontal) were used to assist in the decision
regarding the number of factors to maintain [18].
The results revealed that the factor structures evoked by EFA matched the one
designed in the research model. The outcomes of EFA using the principal components
factor extraction approach with Varimax rotation showed an eight-factor solution. This
was supported by the scree plot test. The eight-factor structure was recommended using
the criteria of all Eigenvalue greater than 1, and the extracted factors accounted for
71.26% of the variance. Besides, factor loadings were all above 0.6 on their own factors
as suggested by Hair et al. (2005). These findings suggest that those domains have a good
construct. However, EFA is a poor ending point for testing the scale dimensionality. It is
thence necessary to test the multidimensional nature of the measure to ensure that the
scale is accurate and does not cause defective conclusions. Consequently, Confirmatory
Factor Analysis would be employed to check the dimensional structure of the construct.
5.2. Measurement Model Assessment
CFA was conducted to assess the factorial structure of the entire scale, and to test the
reliability and validity of the measurement model through the convergent and
discriminant validity to ensure unidimensionality of the multiple-item constructs [16].
Convergent validity was assessed by testing Composite Reliability (CR) and Average
Variance Extracted (AVE) from the measures [18]. For a construct to possess good
consistency reliability, whereas a value higher than 0.70 is acceptable for CR, an AVE
value should exceed the threshold of 0.5 in all cases [8]. As shown in Table 5, CR values
were more than 0.7 (0.782 ~ 0.844), and AVE values were above 0.5 (0.506 ~ 0.637). In
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 97
addition, all values of standardized factor loadings exceeded the cutoff point 0.7 (0.701 ~
0.901), which indicated a high degree of a positive relationship among items developed
to measure proposed constructs. The critical ratios associated with the other indicators
were found to be significant at the 0.001 level. Therefore, the convergent validity results
confirmed that the measures in our model are in reality related.
Table 5. Convergent Validity of the Measurement Model
Latent
constructs Items
Std.
Weights S.E. C.R. P CR AVE
Service quality
SQ1
SQ2
SQ3
SQ4
SQ5
0.701
0.746
0.793
0.703
0.705
-
0.083
0.086
0.085
0.084
-
13.401
14.110
12.699
12.733
-
***
***
***
***
0.806 0.517
Information
quality
IQ1
IQ2
IQ3
IQ4
IQ5
0.756
0.770
0.704
0.776
0.713
-
0.063
0.063
0.067
0.069
-
15.333
13.921
15.474
14.122
-
***
***
***
***
0.837 0.506
User fulfillment
UF1
UF2
UF3
UF4
0.791
0.853
0.797
0.702
-
0.060
0.059
0.061
-
18.247
16.907
14.268
-
***
***
***
0.835 0.560
Individual
impact
IIM1
IIM2
IIM3
0.743
0.756
0.777
-
0.074
0.078
-
12.855
12.996
-
***
***
0.782 0.545
Organizational
impact
OIM1
OIM2
OIM3
OIM4
OIM5
0.704
0.703
0.848
0.790
0.722
-
0.088
0.103
0.095
0.094
-
11.688
13.497
12.91
12.063
-
***
***
***
***
0.844 0.523
Organizational
capability
OCA1
OCA2
OCA3
0.723
0.863
0.739
-
0.082
0.081
-
15.093
13.578
-
***
***
0.823 0.609
Competitive
advantage
CAD1
CAD2
CAD3
0.788
0.901
0.812
-
0.059
0.056
-
19.198
17.363
-
***
***
0.840 0.637
Firm
performance
FPE1
FPE2
FPE3
0.826
0.796
0.890
-
0.065
0.056
-
17.918
20.341
-
***
***
0.822 0.606
Furthermore, we verified the discriminant validity of the measurement model by
comparing the AVE value of each latent construct to the squared correlation between this
construct and none of the squared correlations surpassed the AVE. As showed in Table 6,
the square roots of every AVE value associated with each latent construct exceeded the
levels of correlations among any pair of latent constructs. The test indicated that the
discriminant validity is supported for the measurement model.
Table 6. Discriminant Validity Analysis Results
Constructs SQ IQ UF IIM OIM OCA CAD FPE
SQ 0.719
IQ 0.656 0.711
UF 0.576 0.652 0.748
IIM 0.223 0.297 0.205 0.738
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
98 Copyright ⓒ 2016 SERSC
OIM 0.288 0.355 0.244 0.390 0.723
OCA 0.532 0.614 0.601 0.280 0.375 0.780
CAD 0.599 0.644 0.631 0.349 0.419 0.629 0.798
FPE 0.528 0.647 0.604 0.385 0.349 0.664 0.638 0.778 Note: Square roots of AVE are on the diagonal; Correlations between constructs are off-diagonal
5.3. Higher-order Factor Model Assessment
We then tested a higher factor order model, where the five factors were measures of
one single determinant of ERP implementation. As establishing the second-order model,
the index changes according to the direction of the arrow between measurement variables
and factors, called Reflective Indicators. The same number of items was used to measure
each factor as in prior model. The only discrepancy between these models is that the
factors are now correlated to be measures of one higher factor - ERP implementation.
The five latent constructs, lower-order factors, formed the structure model. These factors
were correlated insofar as they measured one higher-order dimension. The second-order
factor model’s adoption is acceptable in case of being found to have a good fit to the data.
Figure 2 presents the analysis results. The goodness-of-fit measures in the acceptable
range showed that this model fit the data, and the measurement items converged on their
relevant variables, in other words, each measure represented a distinct latent variable.
Figure 2. Higher-order Confirmatory Factor Analysis
The analysis output also demonstrated that all parameter estimates were positive and
within the logical expected range of values. More specifically, the path coefficient from
each indicator to the observed variables was found to be significant, and the standardized
regression weights ranged from 0.754 to 0.961, which upheld the validity and reliability
of the items. Consequently, lower-order factors were measured, and items were summed
up to examine the higher-order factor analysis.
5.4. Structural Model Analysis
From literature review and findings in previous sections, the framework was tested
using Structural Equation Modeling (SEM). Bollen (1989) asserted that SEM is a
comprehensive statistical approach to test hypotheses between observed and latent
variables [16]. As our objective is to identify the causal relationship between ERP
implementation and firm performance, SEM would properly be the most appropriate
analysis way. SEM was executed in order to identify which ERP determinants generate
the performance outcomes. Fitness estimates of structural model are summarized in Table
7. Goodness-of-fit statistics indicated that the RMSEA score (0.066) was close fit. The
GFI and AGFI score (0.933 and 0.901, respectively) were close to good fit. The IFI, TLI,
Fit
Indices
Results Level of Acceptance
Absolute
Fit
χ2/df 2.402 1.0 ≤ χ2/df≤2.0~3.0
RMSEA 0.059 ≤ 0.05 ~ 0.08
RMR 0.077 ≤ 0.08
GFI 0.896 ≥ 0.8 ~ 0.9
AGFI 0.871 ≥ 0.8 ~ 0.9
Incremental
Fit
IFI 0.934 ≥ 0.8 ~ 0.9
TLI 0.925 ≥ 0.8 ~ 0.9
CFI 0.933 ≥ 0.8 ~ 0.9
Parsimonious
Fit
PNFI 0.788 ≥ 0.6
PCFI 0.824 ≥ 0.5 ~ 0.6
System
Quality
Information
Quality
User
Fulfillment
+
+
+
Individual
Impact
Organizational
Impact
ERP
Implementation
+
+
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 99
and CFI score (0.959, 0.948 and 0.959, respectively) were all above 0.9, suggesting a
good fit between the structural model and the data. The parsimony-adjusted measures of
the model PNFI and PCFI scores were 0.732 and 0.748, which were significantly above
the suggested value of 0.60, indicating highly acceptable levels of parsimony. The
relative Chi-square (χ2 = 195.353 (df = 71, p = 0.000)) was within the suggested range
with the value of 2.751. All of these fit indices were adequate, supporting that the
structural model exhibits a good fit to the data after taking account into the sample size.
Table 7. Fit Indices of Structural Model
Fit Indices Results Level of
Acceptance
Source
Absolute Fit
χ2/df 2.751 1.0 ≤ χ2/df≤2.0~3.0 Carmines and McIver, 1981
RMSEA 0.066 ≤ 0.05 ~ 0.08 Browne and Cudeck, 1993
RMR 0.048 ≤ 0.08 Hair et al., 2005
GFI 0.933 ≥ 0.8 ~ 0.9 Jöreskog and Sörbom, 1984
AGFI 0.901 ≥ 0.8 ~ 0.9 Hair et al., 2005
Incremental
Fit
IFI 0.959 ≥ 0.8 ~ 0.9 Bentler and Bonett, 1980
TLI 0.948 ≥ 0.8 ~ 0.9 Bentler and Bonett, 1980
CFI 0.959 ≥ 0.8 ~ 0.9 Bentler and Bonett, 1980
Parsimonious
Fit
PNFI 0.732 ≥ 0.6 James et al., 1982
PCFI 0.748 ≥ 0.5 ~ 0.6 James et al., 1982
The Squared Multiple Correlation values, another goodness-of-fit indicator, indicate
the proportion of variance in the endogenous observed variables accounted for by the
exogenous latent variables in structural equations. In terms of explanatory power, this
model explained 31.7% of the variance in organizational capability, 43.3% of the
variance in competitive advantage, and 60.7% of the variance in organizational
performance construct. All hypothetical paths except the one between ERP
implementation and firm performance were positively significant. The results indicated
that there is no direct causal relationships from ERP implementation to firm performance.
The nature of this link appeared to be indirect, the relation might be mediated through
organizational capability and/or competitive advantage. Next, we present the summary of
path analysis results as showed in Table 8, and compare our results to past findings.
Table 8. Path Analysis Results
Hypotheses Path Path
Coefficient C.R. p_value Result R
2
H1 OCA <--- ERPI 0.620 9.840 *** Supported 0.317
H2 CAD <--- ERPI 0.402 5.826 *** Supported 0.433
H3 CAD <--- OCA 0.249 3.762 *** Supported
H4 FPE <--- ERPI 0.104 1.724 0.085 Not Supported
0.607 H5 FPE <--- OCA 0.301 5.047 *** Supported
H6 FPE <--- CAD 0.477 8.086 *** Supported Note: *** p < 0.001, ** p < 0.01, * p < 0.05 level of significance
Hypotheses H1 and H2 were strongly supported (β = 0.620 and 0.402, p < 0.001)
showing that the ERP implementation positively impacts on both organizational
capability and competitive advantage, in which individual impact had a higher effect in
comparison with service quality, information quality, user fulfillment, and organizational
impact. This result can be interpreted to mean that those items of constructs enhance the
organizational capability and competitive advantage of the enterprises surveyed, and
individual impact, regarding the joining in the adoption and implementation process of
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
100 Copyright ⓒ 2016 SERSC
the ERP system by the delegates of the target users, is referred as the most important
determinant in magnifying the capabilities and attaining the competitive position.
In contrast to the proposed hypothesis, hypothesis H4 was found to be non-significant
(β = 0.104; p = 0.085), which indicates that there is no direct relationship between ERP
implementation and firm performance. This contradicts the results of other researchers
which showed that adoption and implementation of IT substantially impact on
performance outcomes [4] [13]. To further examine this relationship, the coefficients of
both total and indirect effects are calculated and discussed later in this section.
Hypotheses H5 and H6 were evidenced through highly significant coefficients at β =
0.301 and 0.477, p < 0.001. The results demonstrate that there are strong direct effects of
both organizational capability and competitive on firm performance. Also, organizational
capability was found to have a positive relationship with competitive advantage (β =
0.249, p < 0.001), as hypothesis H3 was supported. These findings are in line with the
ideas that firms possessing rare resources and capabilities will attain competitive
advantage, which thereby might lead to improved firm performance as they incarnate
dynamic routines to make their product distant, and investments in specific IT system
explain performance differences consistent with their strategic purpose [2].
Insofar as not anticipated, the ERP implementation construct had no direct effect on
the main endogenous variable, in this case – firm performance, the bootstrap test was
then performed to examine whether or not there occurred one or more potential
intervening effects in the baseline model. The significance of the indirect path was
evaluated from 5000 bootstrap samples; a bias-corrected and accelerated confidence
interval (CI) at 95% level was created for significance tests of the indirect path shown in
Table 9.
Table 9. Mediating Testing Results
Path Total effect Direct effect Indirect effect P value Result
CAD <--- ERPI 0.682 0.402 0.280 0.010 Supported
FPE <--- ERPI 0.519 0.104 0.415 0.010 Supported
FPE <--- OCA 0.558 0.301 0.257 0.010 Supported Note: 95% CI from 5000 bootstrapped samples
The results showed that organizational capability partially mediated the relationship
between ERP implementation and competitive advantage (β = 0.280 [0.192; 0.380])
which in turn intervened between organizational capabilities and performance (β = 0.257
[0.173; 0.352]). Interestingly, full mediation occurred between ERP implementation and
performance (β = 0.415 [0.307; 0.538]). The relationships were statistically significant at
the 0.01 level. These findings are momentous to the results of the hypothesis test where
individual impact is considered as the most basic criteria of implementing ERP system
which sequentially intensify the firm performance through indirect effect.
As mentioned above, responses to four sample characteristic questions were used as
moderators. A series of hierarchical hypotheses were tested using the critical ratios for
differences between parameters (DBP) to identify if the parameter estimate differs across
the groups suggested by Bollen (1989) [16]. The business sector was divided into 8
groups, and we reorganized into 2 groups: industry group, comprising manufacturing and
construction enterprises, and non-industry group. The similar reclassification occurred to
other moderators: 402 subjects were categorized into “≤ 10 year-age” group and “> 10
year-age” group. Enterprise type was classed into LLCs and non-LLCs group. For the
size of enterprises, the number of 200 employees was the point to divide groups. Table 10
presents the results of all pairwise parameter comparisons between groups of models
which verifies whether some of the paths reveal significant differences between delimited
groups. There were 4 out of 6 hypotheses being influenced by business sector due to the
absolute value of DBP which were higher than the critical value of 1.96. Thus, business
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 101
sector would be defined as a moderator. The similar finding found in this test suggested
that the enterprise age would moderate the relationships in the model. Nevertheless, the 2
moderator, namely business type and size were found not to affect the relations between
ERP implementation and other endogenous variables as only 2 out of 6 hypotheses were
proved to be affected by these variables so as to be eliminated from the model.
The final research model, illustrated in Figure 3, represents a comprehensive set of
factors that affect the successful implementation of ERP systems which in turn strengthen
firm performance in SMEs in Vietnam based on the authors’ presented findings.
Table 10. Multiple Group Analysis Results
Effects
Enterprise Type Business Sector
Coefficients
DBP Result
Coefficients
DBP Result LLCs
Non-
LLCs Industry
Non-
industry
OCA <--- ERPI 0.309
***
0.466
*** 1.201
No
difference
0.301
***
0.445
*** 2.408 Difference
CAD <--- ERPI 0.385
***
0.179
* -3.600 Difference
0.196
**
0.414
*** 2.006 Difference
CAD <--- OCA 0.694
***
0.755
*** 0.376
No
difference
0.628
***
0.806
*** 1.078
No
difference
FPE <--- ERPI 0.048 0.062 0.101 No
difference
0.124
* 0.048 -1.979 Difference
FPE <--- OCA 0.443
***
0.661
*** 1.231
No
difference
0.410
***
0.761
* 1.906
No
difference
FPE <--- OCA 0.521
***
0.156
* -2.802 Difference
0.487
**
0.177
* -2.362 Difference
Effects
Enterprise Age Enterprise Size
Coefficients DBP Result
Coefficients DBP Result
≤ 10yrs > 10yrs ≤ 200 EEs > 200 EEs
OCA <--- ERPI 0.378
***
0.382
*** 0.028
No
difference
0.321
***
0.515
*** 1.292
No
difference
CAD <--- ERPI 0.365
*** 0.154 -2.280 Difference
0.324
***
0.220
* -0.707
No
difference
CAD <--- OCA 0.723
***
0.783
*** 2.374 Difference
0.831
***
0.587
*** -1.527
No
difference
FPE <--- ERPI 0.137 0.059 -1.996 Difference 0.114
* 0.023 -2.589 Difference
FPE <--- OCA 0.527
***
0.713
*** 0.198
No
difference
0.642
***
0.465
*** -0.994
No
difference
FPE <--- OCA 0.324
***
0.369
*** 2.374 Difference
0.343
***
0.357
** 2.201 Difference
Note: *** p < 0.001, ** p < 0.01, * p < 0.05 level of significance
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
102 Copyright ⓒ 2016 SERSC
Supported
Hypothesized Relationship Not Supported
Moderating Relationship Not Supported
Figure 3. Structural Model Result
6. Conclusions
6.1. Implications
The purpose of this study is to extend the empirical evidence regarding the effect of
ERP system implementation on firm performance across SMEs in Vietnam. We also
sought to identify whether enterprise characteristics moderate associations of these
variables with ERP implementation. The overall explanatory power of our research
model has an R-square of 60.7% for the firm performance, suggesting that the proposed
model is capable of explaining a relatively high proportion of variation of firm
performance. We primarily determined the effect of five constructs reviewed from the IS
Success Model on the implementation of ERP system. It is generally accepted that
individual impact exhibits the strongest association with ERP implementation in this
study. These findings support our conceptual model, and offer a managerial implication
that individual impact plays a critical role to implement ERP systems.
Also, the successful implementation of ERP systems is anticipated to boost firm
performance. We discovered that ERP implementation was not significantly directly
related to firm performance but had positive effects on organizational capability and
competitive advantage which would ultimately enhance the performance outcomes. The
non-significant relationship between the main exogenous and endogenous variable may
be explained by the following: First, given the nature of an ERP investment, the time for
completing an ERP system drives the time-to-benefit concerning the time horizon that an
organization needs to synchronize with the new system and train its users so as to
visualize the expected benefits. Second, organizations may initially have implemented
ERP systems, not because of the motivation to improve outcomes but because of the
pressure from smoothing synchronize business processes and information technologies,
and enhancing integration and coordination between business units. In consequence, firm
performance, after the adoption and on-going stage of an ERP system, would be probably
Moderators
Enterprise Type Business Sector Enterprise size Enterprise Age
System
Quality
Information
Quality
User
Fulfillment
Firm
Performance
0.758 0.477***
0.249*** 0.791
0.104 0.801
0.839 Individual
Impact
Organizational
Impact
ERP
Implementation
Organizational
Capability
0.716
R2 = 0.433
0.620*** 0.301***
R2 = 0.317
R2 = 0.607
0.402***
Competitive
Advantage
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 103
vary from its own performance anterior to the investment of the system. These results
replicate a consistent finding in the literature.
From another aspect, mediating test was performed to advance our understanding of
ERP implementation on its consequences. This study discovered that organizational
capability and competitive advantage play a crucial intervening role in the relationships
in our model. Noticeably, we found that there is a full mediation effect in the relationship
between ERP implementation and firm performance. It can be inferred that firms seeking
to gain a higher competitive advantage need to build collaborative relationships with
capabilities to implement an ERP system, and once enterprises make sure what they do to
implement successfully, they can magnify the firm performance.
Returning to an earlier theme, in multi-group analyses, we found no evidence of
moderation by type and size of enterprises. Yet, there were noteworthy differences
between industry and non-industry groups, and between new and old enterprise groups
regarding the number of ten years in operation. This is an aspect that needs to be
addressed when implementing an ERP solution especially in the context of SMEs in
Vietnam. Thus, providers of ERP packages should recognize the importance of designing
solutions relied on sector-based enterprises for successful implementation.
In conclusion, drawing on the D&M IS Success Model, the results shed light on some
important issues related to firm performance which have not been addressed by previous
studies. This study is one of the few so far that includes ERP implementation as a second-
order construct while determining the relation to firm performance. This study can be
applied in other countries, especially emerging economies where integrated technology
solutions are not really popular, to provide a comprehensive overview of the
implementation of ERP systems residing in a particular country.
6.2. Limitations and Future Research
As with any archival research, our research findings need to be interpreted in light of
the following limitations. First, this study suffers from a major drawback, that is, firms
might report that they have implemented ERP systems, yet the extent of implementation
can vary considerably across firms. Hence, for expanding further, the measurement of
ERP implementation calculated an overall ERP impact score which contains the product
of level of ERP system functionality implemented, and degree of business process
redesign pursued should be to refined to unravel this mysterious paradox.
Second, the sample size in this study is relatively small compared to the total active
enterprises, and sample only focuses on firms in three big cities of Vietnam. This is
mainly due to the small number of firms that disclose the implementation of ERP
systems. Thus, finding a sample of firms that claimed the successful implementation was
quite restrictive. Thereby, the results might reflect only the process from these regions
rather than the whole SMEs. It should be further investigated with respondents from other
location to obtain a far more clear insight picture for the literature.
Third, we only examine organizational capability and competitive advantage whereas,
some other dimensions of socio-technical infrastructures that have been confirmed to
have impact on firm performance were not mentioned in this study. Future study should
attempt to more closely determine the specific features of operational environment to
better examine and understand the apparent synergetic relationship between ERP
implementation and outcomes.
Last but not least, only studying the moderating role of some firm features, naming the
type of entity and industry, firm age and size, this study does not mention other
characteristics of enterprises in current research framework. Therefore, the results may
get the discrepancy which are caused from different demographic groups that influence
the firm performance. An interesting issue for further research is to include some
contextual variables such as culture learning and knowledge, or the time using ERP
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
104 Copyright ⓒ 2016 SERSC
systems in the firms as moderators to measure the degree of integration and configuration
in ERP implementation toward firm performance.
References
[1] Abdinnour-Helm S., Lengnick-Hall M. and Lengnick-Hall C., “Pre-implementation Attitudes and
Organizational Readiness for Implementing an Enterprise Resource Planning System”, European
Journal of Operational Research, vol. 146, no. 2, (2003), pp. 258-273.
[2] Barney J., “Firm Resources and Sustained Competitive Advantage”, Journal of Management, vol. 17,
no. 1, (1991), pp. 99-120.
[3] Beard J. W. and Sumner M., “Seeking Strategic Advantage in the Post-Net Era: Viewing ERP Systems
from the Resource-Based Perspective”, Journal of Strategic Information Systems, vol. 13, no. 2, (2004),
pp. 129-150.
[4] Davenport T. H., “Putting the Enterprise into the Enterprise System”, Harvard Business Review, vol. 76,
no. 4, (1998), pp. 121-131.
[5] DeLone W. H. and McLean E. R., “Information Systems Success: The Quest for the Dependent
Variable”, Information Systems Research, vol. 3, no. 1, (1992), pp. 60-95.
[6] Edward W. N., “IT Governance for Enterprise Resource Planning Supported by the DeLone-McLean
Model of IS Success”, Information and Management, vol. 45, no. 5, (2008), pp. 257-269.
[7] Ellinger A. E., Ketchen D. J. Jr, Hult G. T. M., Elmadag A. B. and Richey R. G. Jr., “Market
Orientation, Employee Development Practices, and Performance in Logistics Service Provider Firms”,
Industrial Marketing Management, vol. 37, no. 4, (2008), pp. 353-366.
[8] Fornell C. and Larcker D. F., “Evaluating Structural Equation Models with Unobservable Variables and
Measurement Error”, Journal of Marketing Research, vol. 18, no. 1, (1981), pp. 39-50.
[9] Hunton J. E, Lippincott B. and Reck J., “Enterprise Resource Planning Systems: Comparing Firm
Performance of Adopters and Non-Adopters”, International Journal of Accounting Information Systems,
vol. 4, (2003), pp. 165–84.
[10] Ifinedo P., “An Empirical Study of ERP Success Evaluations by Business and IT Managers”,
Information Management and Computer Security, vol. 15, no. 4, (2007), pp. 270 – 282.
[11] Law C. C. and Ngai E. W., “An Investigation of the Relationships between Organizational Factors,
Business Process Improvement, and ERP Success”, Benchmarking: An International Journal, vol. 14,
no. 3, (2007), pp. 387-406.
[12] Nicolaou A. I. and Bhattacharya S., “Organizational performance effects of ERP systems usage: The
impact of post-implementation changes”, International Journal of Accounting Information Systems, vol.
7, (2006), pp. 18-35.
[13] Poston R. and Grabski S., “Financial Impact of Enterprise Resource Planning Implementations”,
International Journal of Accounting Information Systems, vol. 2, no. 4, (2001), pp. 271-94.
[14] Shang S. and Seddon P. B., “Assessing and Managing the Benefits of Enterprise Systems: The Business
Manager’s Perspective”, Information Systems Journal, vol. 12, (2002), pp. 271-99.
[15] Wier B., Hunton J. and HassabElnaby H. R., “Enterprise Resource Planning Systems and Non-Financial
Performance Incentives: The Joint Impact on Corporate Performance”, International Journal of
Accounting Information Systems, vol. 8 no. 3, (2007), pp. 165-90.
[16] Bollen K. A., “Structural Equations with Latent Variables”, John Wiley & Sons Inc., New York, (1989).
[17] General Statistics Office of Vietnam, “Statistical Yearbook, 2015”, Statistical Publishing House, Hanoi,
(2015).
[18] Hair J. F., Black W., Babin B., Anderson R. E. and Tatham R. L., “Multivariate Data Analysis”, 5th ed.,
Prentice Hall, New Jersey, (2005).
[19] Kenny David A., “Meta-analysis: Easy to Answer", University Press, University of Connecticut, (2003).
[20] Vietnam Ministry of Industry and Trade, “Vietnam E-commerce Report 2014, Vietnam E-commerce
and Information Technology Agency, Hanoi, (2014).
Authors
Minh Duc Le, She received the B.A. from Posts and
Telecommunications Institute of Technology in 2005 and the MBA
from Danang University of Economics, Vietnam in 2010. She
currently studies for a Ph.D degree in Graduate School of Soongsil
University, Korea. Prior to that, she worked as a lecturer at E-
commerce Department of Vietnam-Korea Friendship Information
Technology College. Her research interests include Business
Consulting, ERP, Behavioral issues in E-commerce.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
Copyright ⓒ 2016 SERSC 105
Kyeong Seok Han, He is a Professor at the School of Business
Administration, Soongsil University, Korea. He received his Ph.D.
degree in MIS from Purdue University, United States in 1989, and
prior to that, he earned B.A. in 1979 and MBA in 1983 from Seoul
National University. He formerly was a Professor of University of
Houston. His major research interests include Digital Economy,
Business Consulting, ERP, e-CRM, Big Data, Agent-based
Simulation.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 9 (2016)
106 Copyright ⓒ 2016 SERSC