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Mar 31, 2016
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Table of Content
Title / Chapter Author(s) / Page
Exploring causal relationships and critical factors affecting a
country’s ICT global competitiveness
Wei-Wen Wu,
Lawrence W. Lan,
Yu-Ting Lee
Abstract 1
Background 2
Methodologies 3
Empirical study 7
Conclusions 11
References 12
External Environment Factors Influencing the Technology
Adoption-Diffusion
Decision in Malaysian Manufacturing Small Medium Enterprises (SMEs)
Murzidah Ahmad Murad,
John Douglas Thomson
Abstract 15
Introduction 16
Literature review 17
Methodology 19
Results 20
Discussion 23
Conclusion/Directions for future study 24
References 25
Human capital approach towards enhancing innovation
performance in Omani industrial firms: The role of
knowledge management
Salim Abdullah Rashid Alshekaili,
Ali Boerhannoeddin
Abstract 27
Introduction 28
Background and hypotheses 29
Research methodology 31
Analysis and results 34
Discussion and conclusion 36
Reference 37
II
The Current Status of Logistics Performance Drivers in Indonesia:
An Emphasis on Potential Contributions of Logistics Service Providers (LSPs)
Yeni Sumantri,
Sim Kim Lau
Abstract 40
Introduction 41
The Challenges of Indonesia Logistics Sector 42
The Current Status of Key Drivers of Indonesia Logistics Performance 43
Potential Contributions and Risks of the LSP Usage 51
Conclusion 55
References 56
1
Exploring causal relationships and critical factors affecting a
country’s ICT global competitiveness
Wei-Wen Wu*
Department of International Trade
Ta-Hua Institiute of Technology, Taiwan
E-mail: [email protected]
Lawrence W. Lan
Department of Marketing and Logistics Management
Ta-Hua Institiute of Technology, Taiwan
Yu-Ting Lee
Department of International Trade
Ta-Hua Institiute of Technology, Taiwan
Abstract
The Global Information Technology Report published by World Economic
Forum used Networked Readiness Index (NRI) to measure the global competitiveness
of a country‘s information and communication technologies (ICT). The NRI covers
three subindexes with nine pillars, which are treated with equal weights. It does not
explore the causal relationships. In order to provide more information to the
policymakers for better decisions making, this paper proposes a solution framework to
create the causal relationships among the pillars and overall NRI scores, and
furthermore, to identify the critical factors affecting the overall NRI scores. Three
techniques are employed in the solution framework: super-efficiency data
envelopment analysis, Bayesian network classifiers, and partial least squares path
modeling. An empirical study is carried out. Policy implications to advance a
country‘s ICT competiveness are discussed according to the empirical results.
Keywords: causal relationship, information and communication technologies, World
Economic Forum
* Corresponding author
2
Exploring causal relationships and critical factors affecting a
country’s ICT global competitiveness
1. Background
Over the past decade, the World Economic Forum (WEF) has published a series
of annual reports in various areas such as financial development, trade, travel and
tourism, gender gap, information technology, among others. Some of which are on the
country basis (e.g., Africa Competitiveness Report 2009, Country Studies: Mexico
2007-2008); some others are on the global basis (e.g., Global Competitiveness Report
2009-2010; Global Information Technology Report 2008-2009; Global Gender Gap
Report 2008).
It is interesting to note that in the Global Competitiveness Report 2009-2010, for
instance, the WEF used Global Competitiveness Index (GCI) to measure the global
competitiveness of each country. The GCI is a weighted score from twelve pillars of
competitiveness under three main subindexes—basic requirements, efficiency
enhancers, innovation and sophisticated factors. All the countries were divided into
five groups, according to their income thresholds (i.e., GDP per capita) for
establishing stage development, and different weights were used for the three main
subindexes at each stage of development. In general, the results are viewed quite fair.
Unlike the Global Competitiveness Report, however, in the Global Information
Technology Report 2009-2010, the WEF used Networked Readiness Index (NRI) to
measure the global competitiveness of a country‘s information and communication
technologies (ICT). The NRI covers three subindexes (environment, readiness, usage)
with nine pillars, which are respectively denoted as E1 (market environment), E2
(political and regulatory environment), and E3 (infrastructure environment) under the
environment subindex; R1 (individual readiness), R2 (business readiness), and R3
(government readiness) under the readiness subindex; U1 (individual usage), U2
(business usage), and U3 (government usage) under the usage subindex. Some
sixty-eight components are further utilized to elucidate the nine pillars. Details of the
68 components, 9 pillars, and 3 subindexes under the NRI are summarized in
Appendix 1 (Dutta and Mia, 2010).
In this Report, the final NRI score for each country is a simple average of the
three composing subindex scores; wherein the score for each subindex is also a simple
average of its composing pillars. In other words, all of the nine pillars have been
strongly assumed with equal contributions to a country‘s networked readiness, which
is of course not true. Treating the nine pillars with identical weights (equal importance)
is neither sound nor useful. In theory, it would be more reasonable if one could have
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introduced an appropriate method that can objectively reflect the relative importance
of a set of criteria (pillars) rather than subjectively assign identical weights to them. In
practice, the final NRI scores and rankings in this Report have revealed no
information about the causal relationships amongst these pillars. And this can
deteriorate the quality of decision-making in determining the most critical items to
enhance a country‘s competiveness of ICT.
It is essential for the policymakers to understand the causal relationships
amongst pillars within the NRI so as to advance the decision-making quality and
thereby facilitate the process of transforming strategic objectives into effective actions
(Wu, 2010). With causal relationships, the policymakers can concentrate on the
critical pillars and the corresponding components which bring in the greatest
economic benefits. However, establishment and identification of the causal
relationships amongst the nine pillars within the NRI can be a complicated and
challenging issue. To perform causal analyses, the causal directions between pillars
must be explored first. Once the causal directions are confirmed, the hypotheses can
then be effectively developed. Finally, by testing the hypotheses one can easily
scrutinize the most critical pillars affecting the overall ICT competiveness of a
country.
Based on this, the present paper aims to propose a solution framework to (1)
create the causal relationships amongst the nine pillars within the NRI, (2) utilize the
causal directions to develop hypotheses, and (3) test the hypotheses to find out the
most crucial pillars. The proposed framework will incorporate with three specific
techniques: super-efficiency data envelopment analysis (DEA), Bayesian network
(BN) classifiers with tree augmented Naïve Bayes (TAN), and partial least squares
(PLS) path modeling. An empirical study is carried out to demonstrate the
applicability of the proposed approach. The NRI scores used in the empirical study
are directly drawn from the Global Information Technology Report 2009-2010.
The remainder of this paper is organized as follows. Section 2 briefly explains
the methodologies including super-efficiency DEA, BN classifiers, PLS path
modeling and the proposed approach. Section 3 conducts an empirical study and
discusses the managerial implications based on the findings. Finally, the conclusions
and recommendations for future research are presented.
2. Methodologies
2.1 Data envelopment analysis
The data envelopment analysis (DEA) is a useful non-parametric technique to
assess the relative efficiency of decision making units (DMUs). It employs linear
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programming to determine the relative efficiencies of a set of homogeneous and
comparable units. The relative efficiency can be defined as the ratio of total weighted
output to total weighted input. Since the DEA method has some advantages (e.g., one
can handle multi-output multi-input production technologies without the need of
specifying the functional form in prior (Cook et al., 2004); especially, DEA method
allows each candidate to choose its own weights in order to maximize the overall
ratings subject to certain conditions), a variety of DEA models have been developed
and widely applied in different areas for performance measurement and benchmarking
over the past three decades.
According to Golany and Roll (1989), Adler et al. (2002), as well as Cook and
Seiford (2009), the most popular DEA models include the CCR model (Charnes et al.,
1978), the BCC model (Banker et al., 1984), and the super-efficiency model
(Andersen and Petersen, 1993). The CCR model measures the overall efficiency for
each unit, which assumes a constant returns-to-scale relationship between inputs and
outputs. Moreover, the CCR model does not place any restrictions on the weights in
the model, but it is possible for units to be rated as efficient through a very uneven
distribution of weights. Unlike the CCR model with assumption of constant
returns-to-scale, the BCC model adds an additional constant variable in order to allow
variable returns-to-scale. Thus, the BCC model permits an increase in inputs without
generating a proportional change in outputs. The overall efficiency of a CCR model
divided by the technical efficiency of a BCC model will define the scale efficiency.
Generally, CCR or BCC models produce an efficiency score (between zero and
one) for each DMU. All DMUs with score 100% are regarded as relatively efficient,
while those units with score less than 100% are viewed as relatively inefficient. A
CCR or BCC model evaluates the relative efficiency of DMUs, but does not allow for
a ranking of the efficient units themselves (Golany and Roll, 1989). For the purpose
of ranking, Andersen and Petersen (1993) first developed the super-efficiency DEA
model which can not only measure the relative efficiency of DMUs but also rank the
efficient units. This is because the super-efficiency model enables an extreme efficient
unit to achieve an efficiency score greater than 100%. The proposed approach will
employ the super-efficiency DEA method to divide the DMUs into two classes—the
efficient DMUs (with score equal to or greater than 100%) and the inefficient DMUs
(with score less than 100%). To save space, details of the super-efficiency DEA
model can be referred to (Adler et al., 2002).
2.2. Bayesian network classifier
The Bayesian network (BN) has been successfully applied in various fields over
the past decade. For instance, Lewis (1999) addressed the issues surrounding
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Bayesian Belief Network software process modeling. Wheeler (2001) presented a
Bayesian approach to service level performance monitoring. Zhu et al. (2002)
explored a Bayesian framework for constructing combinations of classifier outputs.
Kao et al. (2005) performed the supply chain diagnostics with dynamic BNs.
Rhodes and Keefe (2007) employed a Bayesian approach to study the social network
topology. Chan and McNaught (2008) applied BNs to improve fault diagnosis.
The BN is a graphical representation of probabilistic relationships between
multiple attributes/variables (Lewis, 1999; Klopotek, 2002; Kao et al., 2005). It is
more robust for inferring structure than other methods because it is better resistant to
noise in data (Wang et al., 2004). Moreover, the BN incorporates probabilistic
inference engines that support reasoning under uncertainty (Hruschka and Ebecken,
2007). It is an outcome of a machine-learning process that finds a given network‘s
structure and its associated parameters, and it can provide diagnostic reasoning,
predictive reasoning, and inter-causal reasoning (Lauria and Duchessi, 2007). A BN is
a directed acyclic graph (DAG) that consists of a set of nodes/vertices linked by arcs,
in which the nodes represent the attributes and the arcs stand for relationships among
the connected attributes (Hruschka and Ebecken, 2007). In a DAG, the arcs designate
the existence of direct causal relations between the linked variables, and the strengths
of these relationships are expressed in terms of conditional probabilities.
Inferring Bayesian structure from expression data can be viewed as a search
problem in the network space (Wang et al., 2004). Thus, to heuristically search the
BN space, it is necessary to employ a variety of search methods, such as simulated
annealing algorithm, genetic algorithm, and tree augmented Naïve Bayes (TAN). For
structure learning through BNs, the software WEKA offers various algorithms
including hill climbing, K2, simulated annealing, genetic, tabu, TAN, and so on.
Among these algorithms, the TAN can produce a causal-effect graph (not just a
tree-like graph), in which the class attribute treated as the only and greatest parent
node of all other nodes is located at the top in the DAG (Friedman et al., 1997). The
causal-effect graph of the TAN is formed by calculating the maximum weight
spanning tree using (Chow and Liu, 1968).
The TAN is an extension of the Naïve Bayes—it removes the Naïve Bayes
assumption that all the attributes are independent. Moreover, the TAN finds
correlations among the attributes and connects them in the network structure learning
process. According to Friedman et al. (1997), the TAN provides for additional edges
between attributes that capture correlations among them, and it approximates the
interactions between attributes by using a tree structure imposed on the Naïve Bayes
structure. Davis et al. (2004) pointed out that (1) although the Naïve Bayes is more
straightforward to understand as well as easy and fast to impart through training, the
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TAN, on the other hand, allows for more complex network structures than the Naïve
Bayes; and (2) the TAN achieves retention of the basic structure of Naïve Bayes,
permitting each attribute to have at most one other parent, and allowing the model to
capture dependencies between attributes.
The BN classifiers incorporated in WEKA, such as the BN with the TAN search
algorithm, have exhibited excellent performance in data mining (Cerquides and De
Mantaras, 2005). In fact, the conditional independence assumption of Naïve Bayes is
not real, and the TAN is developed to offset this disadvantage. It does achieve a
significant improvement in terms of classification accuracy, efficiency and model
simplicity (Jiang et al., 2005). Although the TAN may not always perform the best
with regard to classification accuracy, the proposed approach will adopt the TAN
because it can create a causal-effect graph in which the class attribute treated as the
supreme parent node is located at the top in the DAG. To save space, details of BN
classifier with TAN algorithm can be referred to Friedman et al. (1997).
2.3 Partial least squares path modeling
It is well known that linear structural relations (LISREL) and partial least squares
(PLS) path modeling are two main SEM approaches to establishing the relationships
between latent variables (Tenenhaus et al., 2005; Temme et al., 2006). LISREL
focuses on maximizing the explained covariation among the various constructs; it
highlights theory confirmation. In contrast, PLS path modeling maximizes the
explained variation among the various constructs; it stresses causal explanation
(Lauria and Duchessi, 2007). Unlike LISREL, with its assumption of homogeneity in
the observed population, PLS path modeling is more suitable for real world
applications. It is particularly more advantageous to employ PLS path modeling when
models are complex (Fornell and Bookstein, 1982). Moreover, a major merit of using
PLS path modeling is that its required minimum sample size is mere 30 (Anderson
and Vastag, 2004).
Anderson and Vastag (2004) argued that SEM is likely the preferred method if
the objective is only a description of theoretical constructs with no interest in
inference to observable variables; however, BN should be used if the objectives
include prediction and diagnostics of observed variables. PLS path modeling is more
suitable for analyzing exploratory models with no rigorous theory grounding; it
requires minimal assumptions about the statistical distributions of data sets; more
importantly, it can work with smaller sample sizes (Ranganathan and Sethi, 2004).
Therefore, the proposed approach also incorporate with the PLS path modeling. For
brevity, details of the PLS path modeling can be referred to Jakobowicz and
Derquennea (2007).
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2.4 The proposed solution framework
The proposed solution framework mainly contains the following three steps:
Step 1: Cluster all of the DMUs into two classes with the super-efficiency DEA model.
The scores of nine pillars are used as the input variables, while the overall
score of NRI is used as the output variable.
Step 2: Explore the causal directions amongst the pillars and overall score by the BN
classifier with the TAN search algorithm. The resulted causal relationship
diagram is then used to develop the hypotheses.
Step 3: Test the hypotheses by the PLS path modeling.
3. Empirical study
To demonstrate the applicability of the proposed approach, an empirical study
based on the NRI rankings in the Global Information Technology Report 2009-2010
is conducted. As mentioned above, a total of 9 pillars/criteria are identified within the
NRI; namely, E1 (market environment), E2 (political and regulatory environment),
and E3 (infrastructure environment) under the environment subindex; R1 (individual
readiness), R2 (business readiness), and R3 (government readiness) under the
readiness subindex; U1 (individual usage), U2 (business usage), and U3 (government
usage) under the usage subindex. The following will present the detailed results step
by step and then discuss the managerial implications accordingly.
3.1 Results
To perform the super-efficiency DEA to divide the DMUs into two classes, it
requires identifying the input and output variables. The nine pillars are used as the
input variables, while the overall score is treated as the output variable. The data
analysis is implemented by the software called EMS (Efficiency Measurement
System). The detailed results are presented in Appendix 2, wherein the overall score,
rank, and scores of nine pillars are directly extracted from the Global Information
Technology Report; whereas the DEA_Score and class are the results from the
super-efficiency DEA.
To establish the causal directions, BN classifier with the TAN search algorithm
is performed with nine pillars and DEA_Score as the inputs. It is implemented with
the software WEKA, using a test mode of 10-fold cross-validation. Figure 1 displays
the causal relationship diagram, from which it visibly shows the causal directions
between pillars and DEA_Score.
The hypotheses can therefore be developed according to Figure 1. Note that the
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causal directions acquired by using the BN classifier with the TAN search algorithm
is required to make them reverse when using PLS path modeling[12](Wu, 2010).
Thus, all the hypotheses can be developed according to Figure 2, which has reverse
directions of Figure 1. From Figure 2, a total of 17 hypotheses can be identified. The
Overall_Score is directly affected by nine pillars: U1, R1, E3, R3, R2, U3, E2, U2,
and E1. However, U1 will affect R1, which in turn affects E3; R3 is affected by both
E3 and E2. E1 affects E2 but is affected by R2, U3 and U2.Taking U1 as an example,
one hypothesis is that individual usage (U1) will positively affect not only individual
readiness (R1) but also Overall_Score. However, U1 is not affected by other pillars;
thus, U1 may be a potentially important root cause.
Figure 1. The causal relationship diagram
Finally, the aforementioned 17 hypotheses are tested by the PLS path modeling
method, which is implemented with the software SmartPLS. Figure 3 displays the
significant paths among pillars and Overall_Score, after removing the non-significant
ones. Table 1 also presents the detailed information about the significant path
coefficients. From Figure 3, it is apparent that (1) the highest path coefficient (0.891)
is the E1 (market environment) → E2 (political and regulatory environment); (2) as
for the 2R value, the E1 (market environment) exhibits the best ability to explain this
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model (81.6%); and (3) the combination of these 9 pillars has predictive ability of
98% for the Overall_Score.
Figure 2. Relationships among pillars and Overall_Score
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Figure 3. Significant paths among pillars and Overall-Score
3.2 Discussions and implications
The results of this empirical study indicate that some hypotheses have been
supported by the data analysis. Referring to Figure 3, several interesting patterns from
these significant paths can be found. For example, there are five pillars which could
positively affect the Overall_Score, including R1 (individual readiness), R2 (business
readiness), R3 (government readiness), U1 (individual usage), and U2 (business
usage). In contrast, all three environment-related criteria have no significant effects on
the Overall_Score. This reveals that readiness-related criteria are the foremost
enablers to leverage the overall score of the NRI for a country.
It should be noted that, among those five pillars (R1, R2, R3, U1, and U2), U1 and U2
are the most imperative ones to promote the overall score of the NRI. They have
positively affected the Overall_Score as well as other pillars since that U1 is the start
of the path ―U1→R1→E3→R3→Overall_Score‖ and that U2 is the beginning of the
path ―U2→E1→E2→R3→Overall_Score.‖ Furthermore, all these two paths have
covered R3, suggesting that R3 is greatly affected by several antecedent criteria.
Based on the findings, some managerial implications can be derived. First, the
Report emphasized that environment is a crucial enabler of networked readiness and
that communication technology readiness facilitates the ICT usage. However, this
study had different findings—readiness-related pillars are the foremost enablers and
U1 (individual usage) and U2 (business usage) are the two most imperative
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facilitators. These findings did not mean that environment-related pillars are not
important. Perhaps it would be safer to conclude that environment-related factors are
indispensable, yet they cannot significantly bring out grand performance for the
overall score of the NRI of a country. Second, R3 (government readiness) is the
central component of the NRI, based on the findings, yet it is influenced by a series of
antecedent criteria. In this regard, one should advance U1 and U2 because they are the
root causes. From the causal analysis, it is sensible to focus on three specific pillars
(U1, U2, and R2) rather than all 9 criteria.
Table 1. The coefficients of significant paths
Original
Sample (O)
Sample
Mean (M)
Standard Deviation
(STDEV)
Standard Error
(STERR)
T Statistics
(|O/STERR|)
E1 -> E2 0.89150 0.88844 0.02167 0.02167 41.13414
E2 -> R3 0.69620 0.69213 0.07803 0.07803 8.92237
E3 -> R3 0.23381 0.23924 0.08150 0.08150 2.86892
R1 -> E3 0.77630 0.77551 0.02260 0.02260 34.35084
R1 -> Overall_Score 0.13803 0.14019 0.02670 0.02670 5.16939
R2 -> Overall_Score 0.12456 0.12118 0.03618 0.03618 3.44330
R3 -> Overall_Score 0.31079 0.30780 0.03780 0.03780 8.22187
U1 -> Overall_Score 0.28387 0.28449 0.02308 0.02308 12.29746
U1 -> R1 0.76205 0.76142 0.02532 0.02532 30.09974
U2 -> E1 0.72030 0.71711 0.06722 0.06722 10.71558
U2 -> Overall_Score 0.20907 0.21425 0.03294 0.03294 6.34606
U3 -> E1 0.20300 0.20368 0.07005 0.07005 2.89786
4. Conclusions
As emphasized by Klaus Schwab, Executive Chairman of WEF, ICT nowadays
has empowered individuals with unprecedented access to information and knowledge,
with important consequences in terms of providing education and access to markets,
of doing business, and of social interactions, among others. By increasing productivity
and therefore economic growth in developing countries, ICT can play a formidable
role in reducing poverty and improving living conditions and opportunities for the
poor all over the world. The extraordinary capacity of ICT to drive growth and
innovation should not be overlooked, since it can play a critical role not only in
facilitating countries‘ recovery but also in sustaining national competitiveness in the
medium to long term.
In order to increase the credibility and utility of the NRI score rankings from the
Global Information Technology Report, this paper has proposed a novel approach to
properly create the causal relationships among nine pillars and overall score of NRI,
to develop and test the hypotheses so that the most critical ones can be scrutinized.
The proposed approach employed three techniques in its operational procedure:
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super-efficiency DEA method, BN classifiers with TAN algorithm, and PLS path
modeling. The empirical study has concluded that (1) readiness-related criteria are the
foremost enablers; and (2) rather than all 9 pillars, policymakers may spotlight on U1
(individual usage), U2 (business usage), and R2 (business readiness) because they are
the root causes to overall NRI score. Though U3 (government usage) has no direct
effect on the overall NRI score, the policymakers should never overlook this pillar
because it is also a root cause which indirectly and significantly affects the overall
NRI score.
The proposed solution framework has successfully established the casual
relationships among pillars and NRI score. It can also clearly scrutinize the imperative
factors to facilitate the policymakers to arrive at more informed decisions, which is
otherwise impossible for only relying on the original NRI scores and rankings from
the Report. Consequently, this study contributes to the practical applications of global
ICT competitiveness around the world. The proposed approach can help the
policymakers focus on the most critical pillars and associated components to
effectively advance the ICT competition of a nation.
Several directions for future studies can be identified. First, different clustering
techniques may produce different results; thus, it calls for further research by
comparing with other clustering techniques so as to reach more robust conclusions.
Second, since the ICT industry has been changing drastically, it is important to
examine the consistency of the significant pillars affecting the overall NRI scores and
rankings over time. Future study can employ the proposed approach to conduct
similar analyses based on several annual Reports.
5. References
Adler, N., Friedman, L., Sinuany-Stern, Z. (2002) Review of ranking methods in the
data envelopment analysis context. European Journal of Operational Research
140(2), 249-265.
Andersen, P., Petersen, N.C. (1993) A procedure for ranking efficient units in data
envelopment analysis. Management Science 39(10), 1261-1264.
Anderson, R.D., Vastag, G. (2004) Causal modeling alternatives in operations
research: Overview and application. European Journal of Operational Research
156(1), 92-109.
Banker, R.D., Charnes, A., Cooper, W.W. (1984) Some models for estimating
technical and scale inefficiencies in data envelopment analysis. Management
Science 30(9), 1078-1092.
Cerquides, J., R.L. De Mantaras. (2005) TAN Classifiers Based on Decomposable
13
Distributions. Machine Learning 59, 323-354.
Chan, A., McNaught, K.R. (2008) Using Bayesian networks to improve fault
diagnosis during manufacturing tests of mobile telephone infrastructure. Journal
of the Operational Research Society 59(4), 423-430.
Charnes, A., Cooper, W.W., Rhodes, E. (1978) Measuring the efficiency of
decision-making units, European Journal of Operational Research 2(6), 429-444.
Cook, W.D., Seiford, L.M. (2009) Data envelopment analysis (DEA)-Thirty years on.
European Journal of Operational Research 192(1), 1-17.
Cook, W.D., Seiford, L.M., Zhu, J. (2004) Models for performance benchmarking:
measuring the effect of e-business activities on banking performance. Omega
32(4), 313-322.
Davis, J., Costa, V.S., Ong, I.M., Page, D., Dutra, I. (2004) Using Bayesian
Classifiers to Combine Rules. In 3rd Workshop on Multi-Relational Data Mining,
Seattle, USA.
Dutta, S., Mia, I. (2010) The Global Information Technology Report 2009-2010.
World Economic Forum and INSEAD, SRO-Kundig Geneva, Switzerland.
Fornell, C., Bookstein, F. (1982) Two structural equations models: LISREL and PLS
applied to consumer exit-voice theory. Journal of Marketing Research 19(4),
440-452.
Friedman, N., Geiger, D., Goldszmidt, M. (1997) Bayesian Network Classifiers.
Machine Learning 29(2-3), 131-163.
Golany, B., Roll, Y. (1989) An application procedure for DEA. Omega 17(3),
237-250.
Hruschka, E.R. Ebecken, N.F.F. (2007) Towards efficient variables ordering for
Bayesian networks classifier. Data & Knowledge Engineering 63(2), 258-269.
Hulland, J. (1999) Use of partial least squares (PLS) in strategic management research:
A review of four recent studies. Strategic Management Journal 20(2), 195-204.
Jakobowicz, E., Derquennea, C. (2007). A modified PLS path modeling algorithm
handling reflective categorical variables and a new model building strategy.
Computational Statistics & Data Analysis 51(8), 3666-3678.
Jiang, L., Zhang, H., Cai, Z., Su, J. (2005) Learning Tree Augmented Naive Bayes for
Ranking, DASFFA 2005: database systems for advanced applications, Lecture
Notes in Computer Science 3453, 688-698, Springer Berlin,.
Kao, H.Y., Huang, C.H., Li, H.L. (2005) Supply chain diagnostics with dynamic
Bayesian networks. Computers & Industrial Engineering 49(2), 339-347.
Klopotek, M.A. (2002) A new Bayesian tree learning method with reduced time and
space complexity. Fundamenta Informaticae 49(4), 349-367.
Lauria, E.J.M., Duchessi, P.J. (2007) A methodology for developing Bayesian
14
networks: An application to information technology (IT) implementation.
European Journal of Operational Research 179(1), 234-252.
Lewis, N.D.C. (1999) Continuous process improvement using Bayesian belief
networks. Computers & Industrial Engineering 37(1-2), 449-452.
Ranganathan, C., Sethi, V. (2002) Rationality in Strategic Information Technology
Decisions: The Impact of Shared Domain Knowledge and IT Unit Structure.
Decision Sciences 33(1), 59-86.
Rhodes, C.J., Keefe, E.M.J. (2007) Social network topology: a Bayesian approach.
Journal of the Operational Research Society 58(12), 1605-1611.
Temme, D., Kreis, H., Hildebrandt, L. (2006) PLS path modeling: A software review.
SFB 649 Discussion Papers SFB649DP2006-084, Humboldt University, Berlin,
Germany.
Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M., Lauro, C. (2005) PLS path modeling.
Computational Statistics and Data Analysis 48(1), 159-205.
Wang, T., Touchman, J.W., Xue, G. (2004) Applying two-level simulated annealing
on Bayesian structure learning to infer genetic networks. Computational Systems
Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE,
Publication Date: 16-19, p.647- 648.
Wheeler, F.P. (2001) A Bayesian approach to service level performance monitoring in
supplier, provider relationships. The Journal of the Operational Research Society
52(4), 383-390.
Wixom, B.H., Watson, H.J. (2001) An empirical investigation of the factors affecting
data warehousing success. MIS Quarterly 25(1), 17-41.
Wu, W.W. (2010) Linking Bayesian networks and PLS path modeling for causal
analysis. Expert Systems with Applications 37(1), 134-139.
Zhu, H., Beling, P.A., Overstreet, G.A. (2002) A Bayesian framework for the
combination of classifier outputs. Journal of the Operational Research Society
53(77), 719-727.
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External Environment Factors Influencing the Technology
Adoption-Diffusion
Decision in Malaysian Manufacturing Small Medium Enterprises
(SMEs)
Murzidah Ahmad Murad*
Graduate School of Business and Law
RMIT University, Melbourne, Australia
E-mail: [email protected]
John Douglas Thomson
Graduate School of Business and Law
RMIT University, Melbourne, Australia
Abstract
This paper is based upon an initial study that researches the external
environment factors that may influence technology adoption decision processes in
Malaysian manufacturing Small and Medium Enterprises(SMEs). The preliminary
semi structured interviews were conducted with four managers of Malaysian
manufacturing companies to obtain their insights of topic. Their experiences and
opinions of the external environment factors that influence their decisions to adopt
new technology into their business operations have been gained for further research
purposes.
Keywords: technology adoption, Malaysian manufacturing Small and Medium
Enterprises (SMEs), external environment factors
* Corresponding author
16
External Environment Factors Influencing the Technology
Adoption-Diffusion
Decision in Malaysian Manufacturing Small Medium Enterprises
(SMEs)
1. Introduction
The epistemology of technology diffusion and adoption is survival (Okada 2006;
Bennet & Bennet, 2004). Competition and adaptation have been issues for any
business entity to survive in the business world. To understand the competitive
environment of technology adoption decisions by a business entity, it is necessary to
look into the external factors that may influence the technology adoption decision.
Abdullah (2002) stated that one of the important issues in Malaysia‘s economic
growth is technology adoption among Malaysian Small and Medium Enterprises
(SMEs) to enable them to be more competitive and survive in the global business
environment. Kuan & Chau (2001) agreed that on SMEs‘ abilities to utilize
technology can render it competitive and sustainable. Realizing the importance of
technology diffusion, the Malaysian Government has attempted to ensure the adoption
of technologies which will contribute efficiently and effectively towards the
development of competitive Malaysian industries (The Ninth Malaysian Plan, 2006).
However, Malaysian Government technology policy continues to focus mainly
on encouraging innovation and not on the diffusion of technology. Such policy leads
to too little adoption of technology (Rosnah, Lo & Hashmi, 2005). Malaysian
manufacturing SMEs are aware of the potential benefits of manufacturing
technologies. Unfortunately, these manufacturing companies lack of understanding of
specific ways in which technology can help their businesses (Rosnah, Megat &
Osman, 2004).
Moreover, Zaya (2005) found that although manufacturing companies are aware
of a wide range of technologies, they only make use of a few of them. The argument
is strengthened by Asgari & Wong (2007) who identified that one of the barriers to
industrialization is the lack of technology adoption by industry.
This research is concerned with industrial manufacturing technology used by
Malaysian manufacturing companies. In particular, industrial manufacturing
technologies which includes machinery and equipment in production operations.
Industrial manufacturing technology can be the catalyst for Malaysia to become a
high-tech nation (The Ninth Malaysian Plan, 2006).
This research aims to provide an initial understanding of factors that may
influence Malaysian manufacturing companies‘ technology decision process. For this
17
paper‘s purposes, the researcher is examining the organization‘s external environment
factors that influence technology adoption decisions in four Malaysian manufacturing
companies. Further research will be necessary to obtain thorough data coverage of the
issue.
2. Literature review
2.1 The innovation (technology)-decision process
According to Rogers (2003), the technology-decision process is the process
through which an individual (or other decision-making unit) passes from first
knowledge of a technology, to forming an attitude toward the technology, to a
decision to adopt or reject or to implement the new idea, and to confirm this decision.
Rogers (2003) diffusion of innovation theory consists of five stages in the
innovation-decision process (Figure 1):
Figure 1. Model of stages in the innovation-decision process (Rogers, 2003;
Damounpor, 1991)
From Figure 1, it can be seen that (Rogers, 2003, pp. 169):
1. ‗Knowledge occurs when an individual (or other decision-making unit) is
exposed to the innovation‘s existence and gains some understanding of
how it functions;
2. Persuasion (attitude formation) occurs when an individual (or other
decision-making unit) forms a favorable or unfavorable attitude toward the
innovation;
3. Decision occurs when an individual (or other decision-making unit) engages
in activities that lead to a choice to adopt or reject the innovation;
4. Implementation occurs when an individual (or other decision-making unit)
puts an innovation to use; and
5. Confirmation occurs when an individual (or other decision-making unit)
seeks reinforcement of an innovation-decision already made, but he or she
18
may reverse this previous decision if exposed to conflicting messages about
the innovation.‘
These stages were summarized into two phases by Damanpour (1991):
1. Initiation; and
2. Implementation.
In the first phase, initiation, the firm considers the need to introduce the
innovation, it researches for information, training is carried out, resources are
proposed, the process is evaluated and finally the decision to adopt the innovation is
made. In the second phase, implementation, first use of the innovation is made, and
subsequently organizational routines are modified appropriately.
Premkumar and Roberts (1999) consider five phases in the adoption process,
which are similar to Roger‘s technology-decision process. There consist of:
1. Awareness;
2. Persuasion;
3. Decision;
4. Implementation; and
5. Confirmation.
Coombs, Saviotti & Walsh (1987) suggest that the term ‗diffusion‘ relates to the
level of adoption of innovation. Adoption has also been considered as part of the
diffusion process and a measure of its success (Albors, Hervas & Hidalgo, 2006).
According to Ayres (1969), diffusion of a new technology is the evolutionary
process of replacement of an old technology by a newer one. Organizations that do not
accept new technologies and do not alter themselves to accept the new technologies
will fall behind (Davidoff & Kleiner, 1991).
Rogers‘ (1962) diffusion of innovation theory provides the initial foundation for
this research.
2.2 External environment factors
The fundamental approach to study the adoption and diffusion of new
technologies is the diffusion of innovations theory (Rogers, 2003). The literature on
adoption and diffusion of innovations has mostly focused on the factors affecting
adoption and diffusion. One of the factors that affect technology adoption and diffusion
includes the environment context (Scupola, 2003; Tonartzky and Fleischer, 1990). The
environment context includes the external actors and factors that affect a company‘s
decision to adopt a technology, either directly or indirectly. These may include
customers, competitors, market, government or economy. The external environment
comprises the industry (suppliers and customers), the competitors, and dealing with
regulatory bodies such as the government (Tonartzky and Fleischer, 1990). Scupola
19
(2003) stressed that the competitors, the suppliers and the customers can exert direct or
indirect pressures on SMEs to adopt new technology.
A summary of the external factors mentioned in the literature that affect
technology adoption in companies is shown in Table 1.
Among the external factors relating to technology adoption, the researcher has
found the following are common:
customer demand;
competitors;
supplier perspective;
dynamic market;
government support; and
Government regulation.
3. Methodology
The data for this study was collected through semi-structured interviews to
facilitate participants‘ ability to express their viewpoints more openly than may be the
case with more structured interview situations (Flick, 1998).
The participants were first approached by email to get their permission to
interview them and set the interview date. The participants who agreed to participate in
the interview were contacted via telephone to confirm their participation. The
Table 1. External factors affect technology adoption
External factors
Bu‘rca, Fyner and Marshall (2005) Customer demand
Supplier perspective
Kim and Galliers (2004)
Santarelly and D‘altri (2003)
Business environment
Global markets
Dynamic market
Scupola (2003) Competitors
Suppliers
Customers
Sadowski, Maitland, Van Dongen (2002) Competitive pressure
External support
Incentives
Chengalur-Smith, Duchessi (1999) Market condition
Competitors
20
researcher visited the selected companies in Malaysia and interviewed the decision
maker of each company to get an initial idea and data for further research. The
interviews were conducted face to face and digitally recorded. Prior to the interview
session, the study was outlined more formally, confidentially, anonymity confirmed
and gave participants freedom to choose not to answer any question. The participants
then signed a consent form and gave permission for the interview to be digitally
recorded. Each interview lasted approximately 40 minutes.
From the interview data, the researcher transcribed the digitally recorded
interviews. In order to facilitate a data analysis, the researcher used the following
process: reading through the transcription and examining all data (review data);
coding the data; looking for themes and sub-themes (search and extraction);
interrelating themes and description; and interpreting the meaning of the themes and
descriptions (summarization).
4. Results
4.1 Interviewee position and role on technology decision
The interviewees were asked about their position in the company (Table 2). They
also were asked about their role regarding making technology decisions in their
company. It is important to ensure their knowledge of technology and their authority
in technology decision making.
4.2 Companies profile
Company one (C1) is a medium sized electronics based manufacturing company.
C1 is a well established supplier of security and convenience products to some of the
world‘s major retail and wholesale companies. C1 offers specialized design,
Table 2. The role of the interviewee in the company regarding technology decision making
People Position Responsibility regarding technology
Mr. A Project Manager decides on certain company project and
technology to use for the project
Mr. B Operations Director decides what technology to be adopt for
company‘s operations
Mrs. C Managing Director makes decisions on technology after
discussions with the Executive Vice
President of the company
Mr. D Manager decides what technology or equipment is to
be used in the company
21
manufacturing, marketing, logistics and customer service.
Company two (C2) is a Malaysian-based medium sized electronic
manufacturing company. C2 operations include grinding, slicing, lapping and
polishing processes. C2 also offers value added contract manufacturing and
engineering services to clients across multiple industries.
Company three (C3) is a small sized oil and gas equipment manufacturing
company. C3 specializes in alternative technology solutions for its clients, leveraging
on their network of business alliances to achieve maximum exposure to a technology
and integrating the available products, services and resources to optimize the solution
to its client‘s requirements.
Company four (C4) is small sized food based manufacturing company. C4
manufactures ice products (ice block and ice cube) for both business and household
purposes. C4 prides itself in its technological competence in manufacturing ice
products.
4.3 External factors that influence technology adoption and diffusion
A number of themes emerged consistently. The data has been organized into
these themes. The themes are discussed in an order suggested by the intensity with
which participants explored them.
4.3.1 Customer
All the participants in the interview perceived that competitors influence their
decision when adopting technology into their company. Demand from customers
influenced them to look into new product development and operations which
influenced them to adopt a new technology into their operations. One of the participants
(C4) stated that, ―I always look into the pattern of our customer. If the customer needs a
new product from us, I will consider investing into new operations and new
technology.”
Other participants (C1 and C3) agreed that customers influenced their technology
decisions, ―We have to consider the demand of the customer as well. If customer
demand is less, then there’s no point in adopting new technology into our
operations…..We have to consider customer expectations and customer demand.”
Demand from the customer gives effect for company (C3) to make a decision to
develop a new product and eventually to adopt a new technology into their operations,
“So, I would say the requirement has to be there, the demand has got to be there.
Creating the demand has to be there too.”
4.3.2 Competitors
22
Malaysian manufacturing SMEs would like to be both different and competitive
in the global marketplace. In order to be successful in their marketplace, Malaysian
manufacturing SMEs should give some attention to their competitors. C2 mentioned
that “There is also the concern of the competitors. We have concern of competitors
especially the Chinese manufacturers.” One of the ways to be different is to strengthen
operations and ‗catch up‘ with new technology. ―We always make sure that we are
competitive in the market by making sure our technology produces products that
competitive in the market,‖ C4. Companies always strive hard to raise their competitive
advantages by adopting new technology.
4.3.3 Malaysian Government regulation
All four companies agree that Malaysian Government regulation does not affect
their decision to adopt a new technology into their operations. “Malaysian government
regulation on technology does not give much impact on our company.”
C1 mentioned that, “So far we don’t face any problems with regulation because
we don’t have a direct relation with the Malaysian Government since we are a private
institution. We are 100% privately owned. So, there is no direct link to the government
fund.” This is agreed by C3 who pointed out that “Malaysian regulation regarding
technology is actually no hamper to any technology transfer or adopting decision.”
4.3.4 Economy
From the findings, there are similar perspectives from the participants about the
influence of the economy on their technology decision adoption. One of the
participants said:
C1: “Economy, yes it will affect our production as well. From this Global
Financial Crisis downturn over the last one or two years, our production is down. So,
we definitely don’t want to spend on adopting new technology into our operation
during that period.”
This is also agreed by C2, “So, I guess external factors - for sure economy would
be one thing”. C4 confirmed that “Economy crisis does impact our operation.” This
shows that Malaysian manufacturing SMEs see that the ups and downs in national
economy will bring pressure onto their technology adoption decision processes.
However, only one participant mentioned that the economy did not really affect
their business operation and did not influence their decision to adopt new technology
into their company. He said that:
C3: “The recent economic crisis, we are not badly affected. Our operation is still
operating as usual.”
23
5. Discussion
Malaysian manufacturing SMEs always strive hard to be competitive and survive
in the business world. In order to survive in the business world, Malaysian
manufacturing SMEs have to adapt to the rapid changes in the business environment
including adopting new technology to improve their operations. Previous study
suggests external environment factors could influence the technology adoption
decision process (Bu‘rca, Fyner and Marshall, 2005; Sadowski, Maitland, Van Dongen,
2002; Scupola, 2003; Tonartzky and Fleischer, 1990).
The initial interviews with four Malaysian manufacturing SMEs attempted to
find the external factors that may influence adoption of industrial manufacturing
technology in Malaysian manufacturing companies. The information obtained from
this research found that external environment factors influence Malaysian
manufacturing SMEs technology adoption and diffusion.
The results of this study show that Malaysian manufacturing SMEs find there are
four principal external environment factors that may influence their decisions to adopt a
new technology into their business operations. The four external environment factors
relating to technology adoption are:
customers;
competitors,
Malaysian Government regulations; and
economy.
The results of this research indicated that all factors in the external environment
factors are important to take into account. These factors have a noticeable impact on
the decision to adopt new technology in the manufacturing SMEs in Malaysia. They
also show that external environment factors are important and may influence
Malaysian manufacturing SMEs decisions to adopt new technology into their
companies.
From this analysis and based on the literature study, the conceptual framework
of external environment factors that may influence the technology adoption process in
Malaysian manufacturing technologies is shown in Figure 2. The initial findings of
these factors are expected to assist the researchers in the next phase.
24
Figure 2. Conceptual framework (Authors, 2010)
Consequently, the conceptual framework in this paper provides one of the
elements for the model of industrial manufacturing technology adoption-diffusion in
Malaysian manufacturing SMEs. It is expected to facilitate Malaysian manufacturing
decision makers to consider and plan potential adoption of industrial manufacturing
technologies. This research is anticipated to provide further support for the innovation
decision process model developed by Rogers (2003).
6. Conclusion
In conclusion, the research found that while diffusion of innovation research is
supported in Malaysia, external factors should be included as principal determinants
of technology adoption. Malaysian manufacturing companies should comprehensively
understand external environment factors before making decisions on technology
adoption. Furthermore, the Malaysian Government should consider these factors
when giving assistance to Malaysian manufacturing companies regarding technology
adoption.
7. Directions for future study
Future research and discussion will be conducted to explore thoroughly the
factors that facilitate or hinder technology adoption and diffusion. The researcher may
also look into other innovation diffusion and adoption models such as Technology
Adoption Model (Davis, 1989), ―Interessement‖ (Akrich, Callon& Latour, 2002) and
EXTERNAL
FACTORS
Customer
Competitors
Economy
Malaysian
Government
regulation
Innovation (Technology)
decision process in
Malaysian manufacturing
companies
25
others. Further research will expand upon this study, investigating the related internal
and external factors, additional organizations across a range of industry sector
categories and use quantitative techniques to validate all factors.
8. References
Abdullah, M.A. (2002) An overview of the macroeconomic contribution of SMEs in
Malaysia in Harie C & Lee BC eds. The role of SMEs: National economics in
East Asia series 2. Cheltenham: Edward Elgar.
Akrich, M., Callon M., Latour, B. (2002) The key to success in innovation. Part I: The
art of intersessement. International Journal of Innovation Management 6(2),
187-206.
Gentili, G. B., Tesi, V., Linari, M., Marsili, M. (2002) A versatile microwave
plethysmograph for the monitoring of physiological parameters (Periodical style).
IEEE Trans. Biomed. Eng. 49(10), 1204–10.
Albors, J., Hervas, J., Hidalgo, A. (2006) Analyzing high technology diffusion and
public transference program: the case of the European game program. The Journal
of Technology Technology Transfer 31(6), 647-61.
Asgari, B., Wong, C.Y. (2007) Decipting the technology and economic development
of modern Malaysia. Asian Journal of Technology Innovation 15(1), 167-93.
Ayres, R. (1969) Technology forecasting and long-range forecasting. New York:
McGraw Hill.
Bennet, A., Bennet, D. (2004) Organizational survival in the new world: the
intelligent complex adaptive system. Burlington: Butterworth-Heinemann
Publication.
Burca, S., Fynes, B., Marshall, D. (2005) Strategic technology adoption: extending
ERP across the supply chain. Journal of Enterprise Information Management
18(4), 427-40.
Chengalur-Smith, I., Duchessi, P. (1999) The initiation and adoption of client-server
technology in organizations. Innovation and Management 35, 77-88.
Coombs, R., Saviotti, P., Walsh, V. (1987) Economics and technological change.
London: MacMillan Education Limited.
Damanpour, F. (1991) Organizational innovation: a meta-analysis of effects of
determinants and moderators. Academy of Management Journal 34(3), 55-90.
Davidoff, L., Kleiner, B. (1991) New developments in innovation diffusion. Work
Study 40(6), 6-9.
Davis, F. D. (1989) Perceived usefulness, perceived ease of use, and user acceptance
of information technology. MIS Quaterly 13(3), 319-40, 1989.
Department of the Finance Ministry of Malaysia (2006) Ninth Malaysian Plan,
26
2006-2010.
Kim, C., Galliers, R.D. (2004) Towards a diffusion model for internet systems.
Internet Research 14(2), 155-66.
Kuan, K.K.Y., Chau, P.Y.K (2001) A Perception-based Model for EDI adoption in
small business using a Technology-Organization-Environment Framework.
Information and Management 38, 507-21.
Okada, Y. (2006) Struggles for survival: institutional and organizational changes in
Japan‘s high-tech industries. Japan: Spinger.
Premkumar, G., Roberts, M. (1999) Adoption of new information technologies in
Rural Small Business. The International Journal of Management Science 27,
467-84.
Rogers, E. M. (1962) Diffusion of Innovation, 1sted. New York: The Free Press.
Rogers, E. M. (2003) Diffusion of Innovation, 5th
ed. New York: The Free Press.
Rosnah, M., Lo, W., Hashmi (2005) Advanced manufacturing technologies in SMEs.
CACCI Journal.
Rosnah, M., Megat A., Osman, M. (2004) Barriers to Advance manufacturing
technologies implementation in the Small and Medium Scale industries of a
developing country. International Journal of Engineering and Technology 1(1),
39-46.
Sadowski, B. M., Maitland C., Dongen J. (2002) Strategic use of the Internet by small
and medioum sized companies: an exploratory study. Information economics and
policy 14, 75-93.
Santarelly, E., D‘Altri, S. (2003) The diffusion of e-commerce among SMEs:
theoretical implications and empirical evidence. Small Business Economics 21,
273-83.
Scupola, A. (2003) The adoption of Internet Commerce by SMEs in the South of Italy:
an environment, technological and organizational perspective. Journal of Global
Information Technology Management 6(1), 52-71.
Torrnatzky, L.G., Fleischer, M. (1990) The processes of technological innovation.
Lexington MA: Lexington books.
Zaya, P. (2005) Technology adoption SMEs. International Development Research
Centre.
27
Human capital approach towards enhancing innovation performance
in Omani industrial firms: The role of knowledge management
Salim Abdullah Rashid Alshekaili*
Faculty of Economics and Administration
University of Malaya, Malaysia
Ali Boerhannoeddin
Faculty of Economics and Administration
University of Malaya, Malaysia
Abstract
In today‘s competitive landscape, innovation is perceived as an essential target.
Superior innovation provides organizations with opportunities to grow faster, better
and smarter than their competitors. Because of the various environmental changes
affecting industrial organizations around the world in the last years, most of them
attempted to achieve innovation performance. Several researchers indicated that the
Omani firms faced many challenges to achieve innovation performance. However,
there are many approaches can stimulate organizations to achieve innovation
performance; one of the most applicable approaches is human capital approach. On the
other hand, innovation performance is most likely to occur when there are suitable
knowledge management practices. Therefore, understanding the role of knowledge
management is crucial to accelerate the impact of human capital on innovation
performance. This paper aims to study the influence of human capital approach on
innovation performance in Omani industrial firms. Additionally, it examines the
mediating role of knowledge management in this relationship. The findings support the
proposed hypotheses. The study contributes to the theoretical and practical
development of the conceptual model.
Keywords: Innovation Performance, Human Capital Approach, Knowledge
Management
28
Human capital approach towards enhancing innovation performance
in Omani industrial firms: The role of knowledge management
1. Introduction
A continuous flow of industrial innovation is the key to sustained dynamic growth
by any country. Innovation in industries has been of central interest in recent years
because it is vital for organizational adaptation and renewal as well as for competitive
advantage. All firms are interested in knowing what influences the results they achieve,
how and why they succeed or fail. Although innovation is widely recognized as
essential for the organizational survival and growth, understanding the factors
influencing an organization‘s ability to innovate successful new products, services,
practices and ideas is a key strategic concern for firms competing in dynamic
high-technology markets. The concept of organizational innovation has been defined as
a new idea or behavior by individual to the organization, such as new product, service,
technology, or practice (Damanpour, 1991; Rogers, 1995).
In the last few years, the Gulf Cooperation Council (GCC) governments (Oman,
Saudi Arabia, Qatar, Bahrain, UAE and Kuwait) have taken various proactive steps to
support the innovation performance. The GCC countries are focusing on innovation for
growth opportunities. They are taking a long-term sustainable approach to achieve
innovation performance (Shafiqur Rahman, 2010). Many of the GCC countries have
already started making progress toward that goal.
In spite of these countries are oil and gas producer, the gross domestic product
(GDP) is very high (rose by 4.4 percent in 2010 to $983 billion, compared in 2009)
(Alireza, 2010) and the continues efforts exerted by the governments and industrial
sectors to accomplish of innovation, many researchers in the field of innovation and
economists believe that the GCC states failed to catch up with the developed countries
(Barry and Kevin 2009; Shafiqur Rahman, 2010). This because of the nature of the
challenges the GCC countries are facing. In fact, GCC countries face genuine obstacles
to innovation and this is precisely why they remain undeveloped. These obstacles
derive from a) inappropriate business and governance climates, b) weaknesses of
educational level of human capital of those working in the industrial sector, c)
insufficient efforts exerted for human capital learning and knowledge technology
programs and d) low budget spent on research and development (R&D) (Al-Lamki,
2000). Thus, in order to achieve innovation performance, the GCC countries should
cope with these difficult situations.
Sultanate of Oman is a middle-income economy that is heavily dependent on oil
resources. Oil declining reserves, global competition and the continuous changing
29
nature of innovation are critical factors forcing Omani government and industries to
search for the appropriate approach that can achieve high level of innovation
performance (Ministry of National Economy – Oman, 2010).
Several studies indicated that many approaches can stimulate organizations to
achieve innovation performance such as: contingency approach, technological
approach and evolutionary approach (Damanpour, 1991; Kesting and Parm Ulhøi,
2010). In economic terms, the impact of human capital in innovation performance is
considerably more dramatic. They can transform existing products, services and ideas
to create new ones and make enormous economic contributions (Al-Hamadi,
Budhwar and Shipton, 2007). This suggests that human capital approach is one of the
accessible approaches which can achieve innovation performance in the industrial
firms. Numerous studies have confirmed that firms can achieve innovation
performance through the human capital approach. For instance, Onyx and Bullen
(2000) in their empirical study indicated the significance of the quality of human
capital in promoting innovation. Putnam (1993) also concluded that managers should
enhance the effectiveness of human capital factors to stimulate innovation
performance in the organization.
The researchers suggested factors such as; leadership behavior and employee
commitment as the most essential factors related to human capital approach in
affecting innovation performance (Lin, and Kuo, 2007). On the other hand, high level
of human capital is a necessary but insufficient factor for achieving innovation
performance (Kesting, and Parm Ulhøi, 2010). Today, when the world living the
transition to the knowledge society, the economy of developed countries is solidly
based on science, technology, innovation and advanced education. The studies
suggested that innovation is most likely to occur when there are appropriate
knowledge management practices (Ministry of National Economy – Oman, 2010;
Shu-hsien., Wu-Chen, and Chih-Tang, 2008). However, limited attention has been
paid to elucidation of issues pertaining to human capital factors and knowledge
management and its contributions to innovation performance in Omani industrial
firms.
Therefore, innovation in these key areas will help ensure a prosperous long-term
future for Oman‘s industrial sector. Thus, this research bridges the gaps in the current
literature by linking human capital approach (education, experience, leadership and
commitment) with innovation performance in the Omani industrial firms. In addition,
the research studies the role of knowledge management in this relationship.
2. Background and hypotheses
The effects of human capital approach on innovation performance in industrial
30
firms depend on the presence of previous capabilities by which firms synthesize and
acquire knowledge resources and generate human capital as well as new applications
from those resources (Zerenler, Hasiloglu, and Sezgin, 2008). In this section, the
researcher examines two hypotheses about how human capital approach affects
innovation performance depending on knowledge management.
2.1 Innovation Performance
Today, firms are facing a competitive and continuously changing situation. In
this context the performance, and even the survival, of firms depend more than ever
on their ability to achieve a solid and competitive position and on their flexibility,
adaptability and responsiveness. Therefore, it is hardly surprising that there is
growing interest in innovation as a strategy that allows the firm to improve its
flexibility, competitive position and performance (Van de Ven, 1986). Organizational
innovation performance is defined as the propensity of a firm to actively support new
ideas, novelty, experimentation, and creative solutions (Wang, and Ahmed, 2004).
Scores of studies have highlighted how innovation enables organizations to
renew themselves, adapt to changing environments and ensure their long term growth
and survival (Chen, and Guan, 2010; Damanpour, 1991; Van de Ven, 1986).
Innovation provides an important foundation for an organization‘s dynamic
capabilities, and is indeed a cornerstone for its competitiveness (Zerenler, Hasiloglu,
and Sezgin, 2008). Thus, innovation performance is often an important aspect of
worker performance.
2.2 Human Capital Approach and Innovation Performance
Human capital is just one of an organization‘s intangible assets. It is basically all
of the competencies and abilities of the people within an organization, i.e. their skills,
experience, experience, behaviors, commitments and capacities (Al-Hamadi, Budhwar,
and Shipton, 2007). A recent study (Chen, and Guan, 2010) concluded that human
capital, with knowledge, expertise and skills, is a valuable resource of firms.
Therefore, organizations that effectively manage and leverage the knowledge and
expertise embedded in the individuals‘ minds will be able to create more value and
achieve superior competitive advantages (Ruggles, 1998; Scarbrough, 2003).
Furthermore, human capital theory emphasizes emotions, values, and the
importance of investment in people for economic benefits for individuals as a whole
to encouraging innovation and performance in organizations (Wright, Dunford, and
Snell, 2001). Human capital factors reflect a large part of the stock of knowledge
within an organization. Robinson and Sexton (1994) report a strong positive
relationship between levels of education and experience and innovation of individuals.
Additionally, the transformational leadership theory demonstrates the role of
31
leadership behavior in achieving organizational innovation (Parker, 1982). Moreover,
an organization can exhibit commitment to its employees to achieve innovation
performance (Mowday, Porter, and Steer, 1982). Consequently, the higher the level of
education, experience, leadership behavior and commitment, the more receptive an
individual has been found to be to innovation. Thus, this study offers the following
hypothesis;
H1. There is a positive relationship between Human capital approach and
innovation performance.
2.3 The Mediating Role of Knowledge Management
Knowledge management is ―a systematic and integrative process of coordinating
organization-wide in pursuit of major organizational goals‖ (Ruggles, 1998).
Knowledge management serves not only as an antecedent to organizational innovation,
but also a medium between individual factors and organizational innovation.
Knowledge management could serve as one of the intervening mechanisms through
which human factors influence innovation performance. Identifying how individuals
interact with knowledge management to increase organizational innovation
performance is the first rationale of this research. Knowledge management
researchers have emphasized the pivotal role of knowledge management, particularly
in creating an internal working environment that supports creativity and fosters
innovation (Darroch, 2005). The knowledge-based Theory concerns knowledge as a
valuable resource of firms (Al-Hajri, and Tatnall, 2007). Knowledge embedded in
human capital enables firms to enhance distinctive competencies and discover
innovation opportunities (Robinson, Sexton, 1994).
Moreover, Politis (2005) provided an important empirical evidence to support
the role of knowledge management within firms to operational and overall
organizational performance through leadership behaviors. In addition, Meyer et al.
(2002 ) contended that organizations that create mechanisms and environments
favorable to learning and development will increase employees‘ knowledge
engagement and subsequently, this knowledge experience will increase their
commitment to achieve innovation performance. Thus, knowledge management could
serve as one of the intervening mechanisms through which human capital factors
influence innovation performance. Hence, this study proposed the following
hypothesis:
H2: Knowledge management mediates positively the relationship between
human capital approach and innovation performance.
3. Research methodology
32
This section presents the methods used to carry out the study and test the
research hypotheses. It discusses the sample selection, followed by the process of
developing the questionnaire and collecting data.
3.1 Data Collection and Sample
This study uses a questionnaire to collect data from a sample of general
managers, functional managers and HRM managers working in the Omani industrial
organizations. In this study the research sample was chosen from various Omani
industry sectors and they included manufacturing, financial services and banking,
healthcare services, higher education and hospitality. Variables in the questionnaire
include firms‘ background information, human capital factors (educational level,
experience, leadership and commitment), knowledge management, and innovation
performance. The questionnaire was sent by fax and e-mail as well as delivered by
hand. A total of 201 usable questionnaires were returned.
3.2 Variable Definition and Measurement
a) Human Capital Approach:
Becker (1964) defined human capital as the knowledge, skills, behaviors and
commitment of employees in a firm‘s workforce. Formal education was measured by
asking respondents to specify their degree levels of post-high school education
attained. Work experience was measured by asking participants how many years work
experience they had in their previous industry and company. The scale used in this
study measured the leadership impacts in innovation performance adapted from two
validated scales; (1) the Multifactor Leadership Questionnaire (MLQ) (Hartog, Van
Muijen and Koopman, 1997), which measured the organizational leadership.
Organizational commitment was measured using the standard measure Organizational
Commitment Questionnaire (OCQ) Mowday, Steers and Porter, 1979).
b) Knowledge Management:
Knowledge management represents the mediator variable in the study. The scale
for knowledge management was developed based on the key elements of knowledge
management dimensions. These dimensions are: knowledge acquisition, conversion
and application (Cui, Griffith, and Cavusgil, 2005). In particular, the fifteen elements
of the knowledge management scale were derived from selected items in the
Inventory of Organizational Innovativeness (IOI) model (Tang, 1999).
c) Innovation Performance:
Innovation performance represented the dependent variable in this study. Since
organizational innovation in this study refers to a type of atmosphere at the
33
organizational level rather than frequencies, rates, or numbers of innovations adoption
by the focal organizations, questions of this type contained in the original scales were
excluded from the newly-composed scale. A fourteen-item scale based on previous
research (Damanpour, 1991; Wang, and Ahmed, 2004) reflects the extent of firm‘s
support and encouragement of development and implementation of innovation
performance.
d) Control Variables:
Firm size and age may influence innovation performance because firms of
different size and age may exhibit different organizational characteristics and resource
deployment. Firm size is measured by the number of employees and firm age is taken
as the number of years from the founding date.
3.3 Reliability
Composite reliability assesses the inter-item consistency, which was
operationalzed using the internal consistency method estimated with Cronbach‘s alpha.
Typically, reliability coefficients of .70 or higher are considered adequate (Cronbach,
and Warrington, 1951). Although the constructs developed in this study were
measured primarily with previously validated measurement items and strongly
grounded in the literature, they are adapted to the Omani context. As can be seen from
Table 1, Cronbach‘s alpha values of all factors were well above .70.
Table 1: Descriptive statistics and correlation matrix
Factor name and
variable items
Mean S.D. 1 2 3 4 5 6 7 8 9
Cronb.
α
Control variables
1 Org. age 3.70 1.68 .80
2 Org. size 2.45 1.11 -0.03 .83
Human capital
3 Educational level 3.06 0.90 -0.18* 0.28** .85
4 Experience 2.63 0.78 0.40** 0.18* 0.24* .81
5 Leadership 4.57 1.43 0.22** 0.16* 0.27** 0.42** .84
6 Commitment 3.76 0.86 0.24** 0.29** 0.38** 0.30** 0.63** .79
Know. Manag.
7 KM Acq. 4.72 0.62 0.19* 0.17* 0.31** 0.47** 0.32** 0.55** .80
8 KM Conv. 4.90 0.55 0.18* 0.15 0.23** 0.44** 0.27** 0.60** 0.84** .77
9 KM App. 4.90 0.58 0.16 0.19* 0.19* 0.52** 0.58** 0.57** 0.72** 0.73** .82
10 Inn. performance 4.78 0.55 0.20* 0.18* 0.29** 0.61** 0.62** 0.48** 0.64** 0.63** 0.81** .86
N=201 * Correlation is significant at the .05 level (2-tailed) ** Correlation is significant at the .01 level (2-tailed)
34
4. Analysis and results
This study employed Structural Equation Model (SEM). In SEM, all
independent variables were entered simultaneously into the model and their influence
on the dependent variables, were calculated. Since this was an exploratory study, this
method was appropriate as one was trying to "simply assess relationships among
variables and answer the basic question of multiple correlations" (Tabachnick, and
Fidell, 2007).
4.1 Main Effects of Human Capital Approach on Innovation
Hypothesis 1 proposed a relationship between human capital approach and
innovation performance. A hierarchical regression model was developed to test the
relationship between human capital factors and innovation performance. Table 2 shows
that the control variable (size of organization) was a significant predictor of innovation
performance as shown in Step I. Step 2 in Table 2 revealed that educational level
(β= .18, p < .001), experience (β= .21, p < .001), leadership (β= .36, p < .01) and
commitment (β= .13, p < .01) were found to be significant predictors of innovation
performance.
Hierarchical regression analysis indicated that 63% of the variance associated
with organizational innovation performance is explained by the human capital factors
Table 2: Regression results (standardized coefficient) for innovation performance
Variables Innovation Performance
Step 1 Step2
Control Variables
Org. Age .06 0.9
Org. Size .14* .16*
Response Variables
Educational Level .18*
Experience .21**
Leadership .36***
Commitment .13*
R2 .08 .63
Adjusted R2 0.07 .52
F 10.62** 77.49**
*
∆ R2 .05 .47
F ∆ R2
10.62** 105.76*
**
Note: *p < .05. **p < .01. ***p < .001
35
(R2adj= 0.52, p < .001). As predicted, Table 2 shows a direct, positive and significant
relationship between human capital approach and innovation performance. Thus, the
results support hypothesis 1.
4.2 Testing for Mediating Effects
In this study, Hypothesis 2 proposed a mediating effect of knowledge
management on the relationships between human capital factors and innovation
performance. A stepwise multiple regression process was used to examine the
hypothesis mediation effects. Step 1in Table 3 shows that the control variable (size of
organization) was a significant predictor of innovation performance.
Table 3: Regression results (standardized coefficient) for innovation performance as a dependent
variable
Whereas, Step 2 revealed that human capital variables including educational level
(β=.17, p < .05), experience (β=.15, p < .05), leadership (β=.27, p < .01) and
commitment (β=.26, p < .01) were found to be significant predictors of innovation
performance. This relationship accounted for 38% of the variance in the dependent
variable when human capital variables were inc1uded in the sample. The inclusion of
Variables Innovation Performance
Step1 Step2 Step3
Control Variables
Org. Age .06 0.9 .14*
Org. Size .14* .16* .18
Response Variables
Human Capital
Edu. Level .17* .12
Experience .15* .09
Leadership .27** .16
Commitment .26** .24*
Know. Manage.
Know. Acquisition .28***
Know. Conversion .34***
Know. Application .30***
R2 .09 .39 .51
Adjusted R2 .08 .38 .48
F 19.90*** 44.11*** 62.00***
∆ R2 0.09 0.30 0.12
F ∆ R2 19.90** 51.50*** 104.87***
Note: *p< .05. **p< .01. ***p< .001
36
knowledge management factors in Step 3 of the process reveals that knowledge
management factors including: acquisition (β= .28, p< .001), conversion (β= .34,
p< .001) and application (β= .30, p< .001) are mediating variables for the human capital
approach and innovation performance relationship. Thus, the results support H2.
5. Discussion and conclusion
This study examines the role of knowledge management in the relationship
between human capital approach and innovation performance. The findings support: a)
the influence of human capital factors in innovation performance, and b) the mediating
effect of knowledge management on the relationship between human capital and
innovation performance. Human capital works their beneficial effects on innovation
performance through the capacity in knowledge acquisition, conversion, and
application. These findings highlight the critical roles of human capital and knowledge
management in enhancing innovation performance, a research result consistent with
previous findings (Meyer, Stanley, Herscovitch, and Topolnytsky, 2002; Parker, 1982;
Shu-hsien., Wu-Chen, and Chih-Tang, 2008).
This study contributes to the literature by examining the relationships among
human capital, knowledge management, and innovation performance. The findings of
this study fill the gap in the literature that is lack of examining the mediating role of
knowledge management in the relationships between human capital and innovation
performance. Policy makers and organizational leaders can use the results of this study
to create evidence-based plans and decisions in the human capital development and
innovation achievement. To facilitate the link of human capital factors and favorable
innovation performance, managers first need to recognize the importance of knowledge
management. Then they should utilize human capital factors to cultivate a better level
of knowledge management which in turn will result in favorable innovation outcomes.
However, this study has some limitations. Firstly, limitation is the fact that a
single respondent was used to report information from each firm. It may be, especially
for such indicators as internal sharing, that multiple respondents would give a different,
more accurate picture of the situation in each firm. Secondly, as with all studies, there
are other possible variables that were not examined that may have exogenous effects on
the relationships studied. In particular, both organizational culture and social capital
have been cited as key factors for building new knowledge within organizations.
Finally, this study uses self-report data which may have the possibility of common
method variance.
Future studies should be based on a larger sample and might well explicitly
integrate the influences of external factors. Although the results are consistent with
37
theoretical reasoning, the cross-sectional design may not rule out causality concerning
the hypothesized relationships. Future research might address this issue by using
longitudinal design in drawing causal inferences.
To conclude, human capital approach is a valuable asset for firms desiring to
achieve superior innovation and sustainable competitive advantages. The viewpoints
of this study highlight the crucial importance of the mediating role of knowledge
management when examining the relationship between human capital and innovation
performance.
6. References
Al-Hajri, S., Tatnall, A. (2007) Inhibitors and Enablers to Internet Banking in Oman - A
Comparison with Banks in Australia. International Review of Business Research
Papers, 3(5), pp.36-43.
Al-Hamadi, A. B., Budhwar, P., Shipton, H. (2007) Managing Human Resources in the
Sultanate of Oman. International Journal of Human Resource Management, 18(1)
pp.100-113.
Alireza, A. Z. (2010) GCC GDP Rises to $983 Billion. [Online]. Available:
http://www.silobreaker.com/gcc-gdp-rises-to-983-billion-5_2263791447201284
113
Al-Lamki, S. M. (2000) Omanization: A Three Tier Strategic Framework for Human
Resource Management and training in the Sultanate of Oman. Journal of
Comparative International Management, 3(1), pp. 55-75.
Barry, J., Kevin, D. (2009) Beyond Borders: The Global Innovation 1000. Retrieved
from http://www. strategy-business.com/media/file/sb53_08405.pdf
Becker, G. S. (1964) Human capital. New York, NY: Columbia University Press.
Chen, K., Guan, J. (2010) Mapping the functionality of China's regional innovation
systems: A structural approach. China Economic Review, xxx, xxx–xxx.
Cronbach, L. J., Warrington, W. G. (1951) Time-limit tests: Estimating their reliability
and degree of speeding. Psychometrika, 16, 167-188.
Cui, A. S., Griffith, D. A., Cavusgil, S. T. (2005) The influence of competitive intensity
and market dynamism on Knowledge Management capabilities of MNC
subsidiaries. Journal of International Marketing, 13(3), 32-53.
Damanpour, F. (1991) Organizational innovation: A meta-analysis of effects of
determinants and moderators. Academy of Management Journal, 34, 555-590.
Darroch, J. (2005) Knowledge management, innovation and firm performance. Journal
of Knowledge Management, 9(3), 101-115.
Hartog, D. N., Van Muijen, J. J., Koopman, P. L. (1997) Transactional versus
38
transformational leadership: an analysis of the MLQ (Multifactor Leadership
Questionnaire). Journal of Occupational and Organizational Psychology, 70(1),
pp. 19-35.
Kesting, P., Parm Ulhøi, J. (2010) Employee-driven innovation: extending the license
to foster innovation. Management Decision, 48(1), 65-84.
Lin, C. Y., Kuo, T. H. (2007) The mediate effect of learning and knowledge on
organizational performance. Industrial Management & Data Systems, 107(7), pp.
1066-1083.
Meyer, J. P., Stanley, D. J., Herscovitch, L., Topolnytsky, L. (2002) Affective,
continuance and normative commitment to the organization: A meta-analysis of
antecedents, correlates and consequences. Journal of Vocational Behaviour, 61,
20-52.
Ministry of National Economy – Oman. (2010) Social Indicators [Online]. Available:
http://www.moneoman.gov.om/english/social.htm
Mowday, R. T., Porter, L. W., Steer, R. M. (1982) Employee-organization linkages:
The psychology of commitment and absenteeism and turnover. New York, NY:
Academic Press.
Mowday, R. T., Steers R. M. Porter, L. M. (1979) The measurement of organizational
commitment. Journal of Vocational Behaviour, 14, 244-247.
Onyx, J., Bullen, P. (2000) Measuring social capital in five communities. Journal of
Applied Behavioral Science, vol. 36, pp. 23-43.
Parker, R. C. (1982) The management of innovation. New York, NY: Wiley.
Politis, D. (2005, July) The process of entrepreneurial learning: A conceptual
framework. Entreprenuership Theory and Practice, pp. 399-424.
Putnam, R. (1993) Making democracy work: Civic traditions in modern Italy,
Princeton, NJ: Princeton University Press.
Robinson, P., Sexton, E. (1994) The effect of education and experience on self
employment success. Journal of Business Venturing, 9, 141-156.
Rogers, E. M. (1995) Diffusion of innovations (4th ed.). New York, NY: The Free
Press.
Ruggles, R. (1998) The state of the notion: Knowledge management in practice.
California Management Review, 40(3), 80-89.
Scarbrough, H. (2003) Knowledge management, HRM and the innovation process.
International Journal of Manpower, 24(5), 501-516.
Shafiqur Rahman, M. (2010) Variance Analysis of GDP for GCC Countries,
International Review of Business Research Papers, 6(2), 253 -259.
Shu-hsien., L., Wu-Chen, F., Chih-Tang, L. (2008) Relationships between knowledge
inertia, organizational learning and organization innovation. In: Linton, J. (Editor
39
in Chief). Technovation, 28(4), pp. 183-195.
Tabachnick, B. G., Fidell, L. S. (2007) Using Multivariate Statistics, 5th ed. Boston:
Allyn and Bacon.
Tang, H. K. (1999) An inventory of organizational innovativeness,‖ Technovation, vol.
19, pp. 41-51.
Van de Ven, A. H. (1986) Central problems in the management of innovation.
Management Science, 32, 590-607.
Wang, C. L., Ahmed, P. K. (2004) The development and validation of the
organizational innovativeness construct using confirmatory factor analysis.
European Journal of Innovation Management, 7, pp. 303-313.
Wright, P. M., Dunford, B. B., Snell, S. A. (2001) Human resources and the
resource-based view of the firm. Journal of Management, 27(6), pp. 701–21.
Zerenler, M., Hasiloglu, S. B., Sezgin, M. (2008) Intellectual Capital and Innovation
Performance: Empirical Evidence in the Turkish Automotive Supplier. Journal
of Technology Management Innovation, 3(4), pp. 31-40.
40
The Current Status of Logistics Performance Drivers in Indonesia:
An Emphasis on Potential Contributions of Logistics Service
Providers (LSPs)
Yeni Sumantri#1,2
#1
School of Information Systems and Technology
University of Wollongong, Australia #2
Department of Industrial Engineering
University of Brawijaya, Indonesia
E-mail: [email protected]
Sim Kim Lau
School of Information Systems and Technology
University of Wollongong, Australia
E-mail: [email protected]
Abstract
Logistics performance can impact on economic performance of a country. High
logistics performance can contribute to increase operational efficiency, improve
accessibility to international network and increase trade volume. Six major drivers of
logistics performance have been identified in the blue print of logistics in Indonesia.
These drivers are human resource management, law and regulation, infrastructure,
information and communication technology, key commodities for export and
domestic markets, and logistics service providers. This paper reports on mapping of
these drivers to the current state of logistics performance in Indonesia. In particular
we focus our investigation on logistics service providers as one of the main drivers
that contributes to logistics performance in Indonesia. We analyse its role in term of
potential contribution to logistics performance as perceived by their customers. These
contributions can be classified into eight categories based on ultimate improved areas
which include improving operational level, improving customer service, accessing
resources, reducing cost, focusing on core business, increasing market share,
improving business performance, and developing business network.
Keywords: Indonesia; logistics performance driver; LSP
41
The Current Status of Logistics Performance Drivers in Indonesia:
An Emphasis on Potential Contributions of Logistics Service
Providers (LSPs)
1. Introduction
Logistics has a complex role in managing the flow of goods, services and related
information. Currently, the role of logistics expands not only to move products and
materials but also to create competitive advantage by providing services which meet
customer demand (Chapman et al., 2002). Logistics influences market demand
effectively by creating customer satisfaction, sales and market share. Stack et al.
(2003) found logistics performance significantly influences customer satisfaction and
in return customer satisfaction generates repurchase intention positively and
significantly (Anderson et al., 1994). It has been shown that repurchase intention
increases volume and variety of purchasing (Reichheld et al., 2000). When logistics
effectively integrates upstream operational function and downstream marketing
function in the supply chain, the overall business performance also significantly
improves which encourages the sustainability of an existing market and the spread of
a new market (Sezen, 2005).
At the macro level logistics performance of industries in a country has a major
impact on economic performance of the country. The logistics performance of all
sectors influences on the economic growth and prosperity of a country (Hannigan &
Mangan, 2001). The more efficient the logistics management, the smaller margin
logistics costs in the goods or services purchased by consumers. The quality of
logistics performance will reduce margins costs in the product or service, improve
operational efficiency, improve a country‘s access to international markets and
increase the trade volume. When all sectors within a country have a superior logistics
performance, the competitiveness of a country will increase which improves their
bargaining power in regional and international levels. In a competitive supply chain
world, effectiveness and efficiency of domestic logistics systems and their
connectedness to global logistics is a key to the success of a country.
The importance of logistics sector for a country has encouraged Indonesia to
identify key drivers of Indonesia logistics performance. In order to support the
development of Indonesia logistics performance, this paper aims to map current state
of drivers of logistics performance in Indonesia. In particular this paper focuses on
logistics service providers (LSP) as one of the main drivers that contributes to
logistics performance in Indonesia. We have conducted investigation to analyze its
role in term of its potential contribution to customer performance as perceived by
42
their customers. The rest of the paper is organized as follows. Section 2 discusses
challenges of Indonesia logistics sector. Section 3 identifies current states of key
drivers of Indonesia logistics performance. Section 4 identifies potential contributions
and risks of the LSP usage and section 5 concludes the paper.
2. The Challenges of Indonesia Logistics Sector
Indonesia‘s efforts to achieve an effective and efficient logistics system is
influenced by the state of Indonesia which has 17,504 islands, 225 million population
and abundant natural resources such as oil, gas, coal and palm oil. The circumstances
indicate that Indonesia is a promising market as well as wealth resources. The
geographical condition that it only has 22% of the land means the supply and demand
distribution has become a crucial issue and requires reliable distribution systems.
Logistics sector also faces challenges internationally. Free trade agreement in the
ASEAN region leads to more competitive market. Customer expectations of offered
goods and services have increased. Similarly customers demand lower costs. To
respond to this situation, Indonesia needs an outperformed logistics performance.
To observe how far the performance of Indonesian logistics sector is, a national
logistics performance measurement is needed. The performance of a country‘s
logistics sector compared to logistics sector in other countries in the world can be
identified using the Logistics Performance Index (LPI). The LPI in 2010 shows that
the Indonesian logistics sector needs to be improved (see Table 1). LPI is the weighted
average of the country scores on six key dimensions which consist of efficiency of the
clearance process; quality of trade and transport related infrastructure; ease of
arranging shipments; competence and quality of logistics services; ability to track and
trace consignments; and timeliness of shipments in reaching destination within the
scheduled or expected delivery time. The scorecards demonstrate comparative
performance using a scale from 1 to 5 in which 1 being the worst performance for the
given dimension.
Table 1. The 2010 Logistics Performance Index of Indonesia Compare to World
Average Score
Indonesia World
score difference
Overall LPI score 2.76 2.87 -0.11
rank 75
Customs score 2.43 2.59 -0.16
rank 72
43
Infrastructure score 2.54 2.64 -0.09
rank 69
International shipment score 2.82 2.85 -0.02
rank 80
Logistics competence score 2.47 2.76 -0.29
rank 92
Tracking & tracing score 2.77 2.92 -0.15
rank 80
Timeliness score 3.46 3.41 0.06
rank 69
Source: World Bank
3. The Current Status of Key Drivers of Indonesia Logistics Performance
Support of government for the development of logistics sector has been
published in the blueprint of the Indonesian logistics sector which includes a vision
and a national logistics strategy. The goal of the Indonesian government is to have a
strong network among urban region and industrial area by 2025. Future goals are
embodied in the vision headlines of 2025, that is ―Locally Integrated, Globally
Connected‖ and the vision statement states that ―by year 2025, Indonesia logistics that
domestically integrated across archipelago and internationally connected to the major
global economies, effectively and efficiently, would improve national competitiveness
to succeed in the world era of supply chain competition ― (Kementrian Koordinator
Bidang Perekonomian Republik Indonesia, 2008).
To achieve the goal, the government establishes a national logistics strategy that
encourages low-cost economy. Indonesian logistics strategy prioritizes strategies for
the six major determinants of national logistics which consists of key commodities;
laws and regulations; infrastructure; human resources and management; information
and communication technology; and logistics service providers. The Indonesian
logistics strategy can be summarized in a statement, that is ―Through improvement
and enforcement of laws and regulations; optimal investment and utilization of
infrastructure; advancement of logistics information and communication technology,
the government would provide a platform for professional logistics human resource
management and world class logistics service provider to develop the strategic key
commodities so that the country‘s competitiveness can be achieved‖ (Kementrian
Koordinator Bidang Perekonomian Republik Indonesia, 2008). In order to understand
the challenge of each driver, an overview of the current states of each driver of
Indonesia logistics systems is needed.
44
A. Laws and Regulations
The development of Indonesia logistics sector requires a strong regulatory
protection. Currently, synchronization among regulations and laws is low.
Regulations and laws should be prepared in the logistics perspective so that they
do not overlap and can provide a clear direction for the future development. In
preparing for the regulations and laws, benchmarking with regulations and laws
of other countries regulation is necessary. For regulations and laws realization,
the enforcement is needed so that laws and regulations can be implemented
effectively (Kementrian Koordinator Bidang Perekonomian Republik Indonesia,
2008).
B. Infrastructure
The logistics sector depends on the condition of transportation infrastructure,
roads, ports, and airports. Factually, Indonesian logistics system needs a cheaper
infrastructure to achieve efficient distribution (Kementrian Koordinator Bidang
Perekonomian Republik Indonesia, 2008). The increased of trading volume should be
supported by the infrastructure capacity. Investment of infrastructure is very
expensive and long term return on investment should be maximized to ensure full
utilization of existing facilities. The comparison between the growth of trading
volume and the infrastructure capacity can be seen from table 2 to table 12. The data
show that increasing of trading volume has not been balanced by the development of
infrastructure capacity.
Table 2. The Number of Cargo of Railways Transportation, 2006-2009 (000 Tons)
Year (000 Tons)
2006 17.275
2007 17.078
2008 19.444
2009 18.924
Source: BPS (recompiled)
45
Table 3. The Number of Domestic Cargo of Air Transportation at Main Airports in Indonesia,
2006-2009 (Tons)
Year Polonia
(Tons)
Sukarno
Hatta (Tons)
Juanda
(Tons)
Ngurah Rai
(Tons)
Hasanudin
(Tons)
2006 10.404 121.196 23.195 4.191 24.575
2007 10.809 133.663 23.441 5.144 27.375
2008 11.385 152.303 22.425 6.362 22.522
2009 12.096 146.134 27.276 6.433 21.815
Source: BPS (recompiled)
Table 4. The Number of International Cargo of Air Transportation at Main Airports in
Indonesia, 2006-2009 (Tons)
Year Polonia (Tons) Sukarno Hatta
(Tons)
Juanda (Tons) Ngurah Rai
(Tons)
2006 2.188 100.748 6.597 24.674
2007 1.888 106.132 7.455 26.784
2008 3.353 118.379 7.790 27.195
2009 2.308 110.467 8.150 28.839
Source: BPS (recompiled)
Table 5. Total of Loading Domestic Cargo at Main Ports in Indonesia, 2006-2009 (Tons)
Year Belawan
(Tons)
Tanjung
Priok (Tons)
Tanjung
Perak (Tons)
Balikpapan
(Tons)
Makassar
(Tons)
2006 538.602 5.948.414 10.486.872 10.123.854 2.552.865
2007 974.286 6.824.602 13.610.296 13.394.413 2.707.219
2008 1.186.819 7.351.121 9.463.008 11.642.516 3.294.072
2009 1.216.190 8.341.275 8.829.194 8.218.005 3.711.557
Source: BPS (recompiled)
Table 6. Total of Unloading Domestic Cargo at 5 Main Ports in Indonesia, 2006-2009 (Tons)
Year Belawan
(Tons)
Tanjung
Priok (Tons)
Tanjung
Perak (Tons)
Balikpapan
(Tons)
Makassar
(Tons)
2006 6.959.975 14.020.612 10.658.357 8.593.227 3.183.440
2007 7.242.572 15.808.737 11.803.339 8.783.094 3.461.109
2008 8.269.358 16.860.782 8.446.983 8.557.097 4.992.781
2009 7.527.212 15.152.551 7.765.622 7.601.787 6.673.336
Source: BPS (recompiled)
46
Table 7. International Cargo Loading and Unloading Indonesia, 2005-2008 (Tons)
Year Loading (000 Tons) Unloading (000 Tons)
2005 160.743 50.385
2006 145.891 45.173
2007 240.767 55.357
2008 145.120 44.925
Source: BPS (recompliled)
Table 8. The Condition of Road Assets, 2009 (%)
Condition National Road Province Road Regional Road
Major damage 3.44 32.9 21.87
Minor damage 13.34 28.21 31.14
Fair 33.56 34.88 24.53
Good 49.67 5.85 22.46
Source: ―Perhubungan Darat dalam Angka 2009‖, Ministry of Transportation
Republic of Indonesia, Directorate General of Land Transportation http:
www.hubdat.web.id
Tabel 9. The growth of Road in Indonesia, 2005-2008 (km)
2005 2006 2007 2008
National Road 34.318 34.318 36.318 36.318
Province Road 46.771 46.771 50.044 50.044
Regional Road 229.208 229.208 245.253 245.253
Urban Road 21.934 21.934 23.469 23.469
Tol Road 772 772 772 772
Source: ―Profil Data Perhubungan Darat Tahun 2009‖, Ministry of Transportation
Republic of Indonesia, Directorate General of Land Transportation http:
www.hubdat.web.id
Tabel 10. The Number of Construction and Rehabilitation of Railway, 2004-2007 (km)
Tahun 2004 2005 2006 2007 Total Average
growth
(%)
Construction
and
Rehabilitation
124.67 158.78 181.89 324.60 789.94
Growth (%) - 27.36 114.55 78.46 40.12
47
Source: ―Informasi Transportasi‖, Ministry of Transportation Republic of Indonesia,
Secretariate General of Data and Information, 2007
Tabel 11. The Development of Airport Facility, 2003 - 2007
Year Rehabilitation
of Airport
(m2)
Construction
of Airport
(m2)
Rehabilitation
and
Construction
(m2)
Growth (%)
2003 4.450 6.634 11.084 -
2004 1.726 1.811 3.537 -68.09
2005 4.014 37.450 41.491 1073.06
2006 1.755 58.062 59.817 1591.18
2007 7.473 2.253 9.726 -83.74
Total 19.418 106.210 125.628
Source: ―Informasi Transportasi‖, Ministry of Transportation Republic of Indonesia,
Secretariate General of Data and Information, 2007
Table 12. The Development of Port Facility, 2004-2007
Year Construction (m) Growth (%)
2004 1.703 -
2005 2.602 52.79
2006 1.748 -32.82
2007 1.550 -11.33
Source: ―Informasi Transportasi‖, Ministry of Transportation Republic of Indonesia,
Secretariate General of Data and Information, 2007
C. Human Resource Management
Efficient and integrated logistics systems need the availability of human
resources. In fact, the growth of Indonesia logistics business is not supported by the
growth of professional human resources. There is a gap between the availability of
education and training with demands in the logistics sector and the level of
competency and human resource development have not been well planned. In general,
only 6.5% of labor has tertiary education (Table 13). The main challenge of the
national logistics sector is the need to improve the quality and quantity of human
resources in this sector (Kementrian Koordinator Bidang Perekonomian Republik
Indonesia, 2008).
48
Table 13. The 2007 Indonesia Education: at a Glance
Indicator Percentage
Primary Gross Enrolment Ratio (%) (6 years) 117
Lower Secondary (%) (3 years) 91
Upper Secondary (%) (3 years) 57
Vocational and Technical (% of secondary enrolment) 12.8
Tertiary Gross Enrolment Ratio (%) 17.5
Labor Force with Secondary Education (% of labor
force)
20.6
Labor Force with Tertiary Education (% of labor force) 6.5
Source: World Bank
D. Information and Communication Technology
Information and communication technology (ICT) supports delivery of
information and improves logistics pipeline visibility. For instance, Transportation
Management System (TMS) can provide information about location, direction of
travel and speed of transportation in real time whilst Warehouse Management System
(WMS) can manage information about goods in the warehouse. Condition of ICT in
Indonesia greatly influences the performance of logistics sector. In general, the
development of Indonesia ICT has shown a good progress (Table 14).
Table 14. The ICT Indonesia: at a Glance
ICT Performance Indonesia East Asia
& Pacific
Region
2000 2008 2008
Access
Telephone lines (per 100 people) 3.2 13.4 21.7
Mobile cellular subscriptions (per 100 people) 1.8 61.8 52.9
Fixed internet subscribers (per 100 people) 0.2 1.4 9.0
Personal computers (per 100 people) 1.0 2.0 5.6
Households with a television set (%) 62 65 -
Quality
Population covered by mobile cellular
network (%)
89 90 93
Fixed broadband subscribers (% of total 1.0 9.4 41.9
49
internet subscribers)
Fixed internet bandwidth (bits/second/person) 1 120 470
Affordability
Residential fixed line tariff (US$/month) - 4.5 4.5
Mobile cellular prepaid tariff (US$/month) - 5.3 5.0
Fixed broadband internet access tariff
(US$/month)
- 21.7 21.7
Source: World Bank
E. Key Commodities
The development of logistics sector should take into consideration the main
commodities for international and domestics market. Each commodity has different
production, marketing and material handling requirements. For the export market,
Indonesia has priority commodities consisting of fuel, gas, crude palm oil (CPO), coal,
agricultural product, forest products and containerized commodities such as textiles,
pharmaceuticals, electronics, furniture, handicraft, processes food and office
equipment. For domestics market, the main commodities involve fuel and gas,
agricultural products, cement, fertilizer and liquid commodities such as cooking oil
and milk (Kementrian Koordinator Bidang Perekonomian Republik Indonesia, 2008).
Through understanding these priority commodities, national logistics systems can
focus on the need of the commodities. The production and marketing areas of the
commodities should be mapped into the logistic strategy in order to understand the
priority development area.
F. Logistics Service Provider (LSP)
Time-based competition has become increasingly important for companies. New
manufacturing methods such as just in time and flexible manufacturing system
encourage companies to improve their logistics performance. Time-based
competitiveness needs the flow of information, manufacturing and delivery of product
on time to respond to the change of customer demand. Logistics has emerged as a key
frontier of competition in the future (Sohail et al., 2006). Companies compete to offer
excellent service performance through optimizing logistics supply chain inventory,
lead times and economies of scale. In pursuing these efforts, companies have
encountered several problems, such as lack of knowledge about customer, tax
regulation and infrastructure of destination countries. These conditions prompt the
50
company to use LSP to plan, implement and control forward and reverse flow and
storage of goods, services and related information.
In the blueprint of Indonesian logistics sector, the government has supported the
development of the Indonesian LSP industry. The role of the LSP is to improve
customer service of the companies. High competitive market in the era of
globalization has forced companies to develop a logistics strategy which not only
maintains the existing market but also expands the market at a global level. Generally,
the Indonesian LSPs have provided some form of basic services. Large scale and
comprehensive services from upstream to downstream are mostly dominated by
multinational LSPs. The LSPs in Indonesia are associated within different
associations depending on the service type provided and are fostered within different
departments or ministry. For instance, LSPs which provide transportation service are
fostered within Department or Ministry of Transportation whilst LSPs which provide
warehouse service are fostered within Department or Ministry of Trade. In this
condition, developing LSPs industry need the coordination inter department or
ministry.
The main goal of Indonesia LSPs is to provide excellent service at low cost with a
competitive spirit, commercial culture and capital access. Competitive spirit focuses
on customer service, reliable management and information technology investment to
monitor and regulate the operation whilst commercial culture focuses on providing
attractive incentives for management (Kementrian Koordinator Bidang Perekonomian
Republik Indonesia, 2008).
The Indonesia domestic and ASEAN regional environment influence on the
growth of LSP in Indonesia. The improved infrastructure, the growing of plantation,
oil, gas, mining, telecommunication and retail industry have encouraged the
development of Indonesia LSP industry. The LSP growth is also influenced by the
growth of trading among ASEAN countries. In a roadmap for the integration of the
ASEAN logistics sector, ASEAN member countries are recommended to support the
ASEAN logistics service providers through providing common standard services (The
Nathan Associates Inc., 2007). The dynamic environment in the Asia Pacific region,
such as the increasing of companies‘ demand on LSP, the development of transport
services and improvement of ICT service have enhanced the LSP industry
development (Lieb, 2008). The logistics service sector has become a promising
business sector in Indonesia and ASEAN region.
However, the free trade agreement in regional and international areas does not
only create new market opportunity but also triggers competitive businesses among
LSPs. In a competitive market, customers require a high service level with efficient
cost. In this state the price is more competitive which results in shrinking profit
51
margin. The other problems are hiring of qualified staff, retraining them and
minimizing turnover; lacking regulatory issues information about local market and
running of transport operations. In order to optimize potential contribution of LSPs to
their customer, information on potential contribution and risk of the LSP usage is
needed.
4. Potential Contributions and Risks of the LSP Usage
Increasing competition, changing customer service expectation, lack of
deregulations information in destination countries and increasing new technology
implementation contribute to the growth of LSP industry (Sheffi, 1990; Razzaque &
Sheng, 1998). Benefits from the LSP usage have also accelerated their growth.
Organizations decide to use LSPs when they can acquire a lot of benefits from the
usage of LSPs (Maltz & Ellram, 1997). By using LSPs, companies expect to improve
their service level (Fernie, 1999; Lau & Zhang, 2006; Razzaque & Sheng, 1998;
Selviaridis & Spring, 2007), such as delivery and reliability level (Elmuti, 2003).
Through increasing service level, LSPs fulfil expectation of customers of companies
(Qureshi et al., 2008) and enhance satisfaction of customers (Embleton & Wright,
1998; Selviaridis & Spring, 2007; Qureshi et al., 2008). LSPs efficiently manage
demand of customers (Razzaque & Sheng, 1998), increase repeat purchase of
customer and ultimately increase market share and revenue of companies (Elmuti,
2003). In summary, the long-term goal of using LSPs is to create excellent business
performance of the companies which use their service.
In order to enhance customer service level, companies should respond to the
needs of customers quickly (Harland et al., 2005) as well as offer minimum cost
(Selviaridis & Spring, 2007; Cho et al., 2008; Bolumole et al., 2007). To be
responsive, companies should improve their system operations (e.g. improving
delivery time) (Elmuti, 2003) and recovers availability of resources (e.g. raw material)
(Persson & Virum, 2001; Schniederjans & Zuckweiler, 2004). Companies also need to
upgrade customers data (Razzaque & Sheng, 1998), advanced equipments,
information and communication systems (Razzaque & Sheng, 1998; Cho et al., 2008),
and adopt latest technology (Kremic et al., 2006; Schniederjans & Zuckweiler, 2004).
Furthermore, companies need to enhance expertise, skill (Bolumole, 2001; Kakabadse
& Kakabadse, 2005), and innovative knowledge (Fill & Visser, 2000). By using LSPs,
the companies can improve their responsiveness without incurring significant cost and
they can focus on their core business (Sheehan, 1989). By concentrating on core
business, companies can deliver competitive advantage to their customers (Qureshi et
al., 2008) through creating superior and unique qualities of products or services.
52
LSPs also contribute to minimizing the cost of the companies through improving
service on operational level, such as improving flexibility in delivery (Daugherty et
al., 1996; Selviaridis & Spring, 2007; Maloni & Carter, 2006), improving operational
efficiency (Aghazadeh, 2003; Bolumole, 2001), and the supply chain process
(Razzaque & Sheng, 1998; Aghazadeh, 2003). Additionally, LSPs supports in
developing supply chain partners, accessing international distribution network, and
sharing risk. Finally, the long-term outcome of the cooperation between LSP and the
companies can be seen on financial performance of the companies. To sum up, the
expectation of companies in using LSP can be classified into improving operational
level, improving customer service, accessing resources, reducing cost, focusing on
core business, increasing market share, improving business performance, and
developing business network (Table 15 & 16).
Table 15. The Potential Contributions of the LSP Usage
Potential Contribution Item of Potential Contribution Code of Item
of Potential
Contribution
Improving operational level Improving productivity 1
Improving flexibility of operation 2
Improving speedy of operation 3
Improving efficiency of operation 4
Improving quality of operation 5
Improving reliability of operation 6
Improving customer service Improving customer service 7
Improving customer relationship 8
Increasing responsiveness to market 9
Accessing resources Accesing latest technology 10
Accesing expertise, skill, and
knowledge
11
Accessing material resources 12
Accessing data 13
Reducing Cost Reducing cost 14
Reducing asset 15
Reducing inventory level 16
Focusing on core business Focusing on core business 17
Increasing market share Increasing customer demand 18
Spreading market 19
Improving business Improving outcome of contract 20
53
performance
Increasing financial strength 21
Decreasing business risk 22
Increasing competitive advantage 23
Developing business network Developing business network 24
Besides benefits, the LSP usage has several disadvantages. These are increasing
inventory risk, lacking market information, leaking of secured information (Svensson,
2001; Hong et al., 2004). In some cases, the LSP usage also increase cost and time
effort, crave on provider expertise (Vissak, 2008), lose capability, disrupt inbound
flows, and loss of customer feedback (Selviaridis & Spring, 2007). In addition, the
LSP usage can lead to attitudes of lacking great effort to fight, dealing with complex
relationship, losing control in operation (Dwyer et al., 1987), losing professional
knowledge (Sink et al., 1996), and sometimes increasing customer complaints (Sink
& Langley, 1997).
Although companies are aware of these disadvantages of the LSP usage, LSPs
have continually to grow. This is motivated by the benefits arise from the LSP usage
compared to disadvantages which result in the trend of its usage (Aktas & Ulengin,
2005). The increasing demand of service of LSP has undoubtedly expanded the
growth of logistics service provider industry (Bolumole, 2001). Through
understanding the potential contributions and risks of using LSPs, improvement of
customer logistics performance can be investigated.
Table 16a. The Papers Supporting Item of Potential Contributions
Code of Item of Potential Contribution Papers
1 2 3 4 5 6 7 8 9 1
0
1
1
1
2
√ (Daugherty et al., 1996)
√ √ √ √ (Sink et al., 1996)
√ √ √ √ (Sink & Langley, 1997)
√ √ √ √ √ √ √ (Embleton & Wright, 1998)
√ √ √ √ √ √ √ √ √ √ √ (Razzaque & Sheng, 1998)
√ (Boyson et al., 1999)
√ √ √ (Fernie, 1999)
√ √ √ √ √ (Lankford & Parsa, 1999)
√ √ √ (Fill & Visser, 2000)
√ √ √ √ (Bolumole, 2001)
√ √ √ √ √ √ √ √ (Ehie, 2001)
54
√ √ √ √ (Persson & Virum, 2001)
√ √ √ √ (Aghazadeh, 2003)
√ √ √ √ √ √ √ √ √ √ (Elmuti, 2003)
(Beaumont & Sohal, 2004)
√ √ √ (Hong et al., 2004)
√ √ √ √ √ √ (Schniederjans & Zuckweiler, 2004)
√ √ √ (Wilding & Juriado, 2004)
√ √ √ √ √ (Clegg et al., 2005)
√ √ √ √ √ (Harland et al., 2005)
√ √ √ (Kakabadse & Kakabadse, 2005)
√ √ √ √ √ (Kremic et al., 2006)
√ √ √ (Lau & Zhang, 2006)
√ (Maloni & Carter, 2006)
√ √ √ √ (Sahay & Mohan, 2006)
√ √ √ √ (Sohail et al., 2006)
√ √ √ (Bolumole et al., 2007)
√ √ √ (Selviaridis & Spring, 2007)
√ √ √ √ √ √ √ (Ghodeswar & Vaidyanathan, 2008)
√ √ √ (Cho et al., 2008)
√ √ √ (Qureshi et al., 2008)
√ √ (Fabbe-Costes et al., 2009)
Table 16b. The Papers Supporting Item of Potential Contributions (Continued)
Code of Item of Potential Contribution Papers
1
3
1
4
1
5
1
6
1
7
1
8
1
9
2
0
2
1
2
2
2
3
2
4
√ (Daugherty et al., 1996)
√ √ √ √ (Sink et al., 1996)
√ √ (Sink & Langley, 1997)
√ √ √ (Embleton & Wright, 1998)
√ √ √ √ √ √ √ √ √ √ √ (Razzaque & Sheng, 1998)
√ √ √ (Boyson et al., 1999)
√ √ √ √ √ (Fernie, 1999)
√ √ √ √ (Lankford & Parsa, 1999)
√ √ √ √ (Fill & Visser, 2000)
√ √ √ (Bolumole, 2001)
√ √ √ √ √ √ (Ehie, 2001)
55
√ √ √ √ √ (Persson & Virum, 2001)
√ √ √ √ (Aghazadeh, 2003)
√ √ √ √ √ √ √ (Elmuti, 2003)
√ √ (Beaumont & Sohal, 2004)
(Hong et al., 2004)
√ √ √ √ (Schniederjans & Zuckweiler, 2004)
√ √ √ √ (Wilding & Juriado, 2004)
√ √ √ (Clegg et al., 2005)
√ √ (Harland et al., 2005)
√ √ (Kakabadse & Kakabadse, 2005)
√ √ √ (Kremic et al., 2006)
√ √ √ √ (Lau & Zhang, 2006)
√ √ (Maloni & Carter, 2006)
√ √ √ √ √ (Sahay & Mohan, 2006)
√ √ √ (Sohail et al., 2006)
√ √ √ (Bolumole et al., 2007)
√ √ √ √ √ (Selviaridis & Spring, 2007)
√ √ √ √ √ (Ghodeswar & Vaidyanathan, 2008)
√ √ (Cho et al., 2008)
(Qureshi et al., 2008)
√ √ (Fabbe-Costes et al., 2009)
5. Conclusion
Focusing on six key drivers of Indonesia logistics performance is an appropriate
first step to improve Indonesia logistics performance. The mapping result of the six
key drivers of Indonesia logistics performance show that each driver needs to be
improved continuously. There are four ways to improve the six key drivers, these are
improvement of policy (for laws and regulations); optimization and utilization of
investment (for infrastructure and information and communication technology);
development, training and business opportunity (for human resource management and
LSP) and development of production and marketing (for key commodities). In regards
to the role of LSP as one of the key drivers in Indonesia logistics performance, their
role has demonstrated a significant contribution to customer logistics performance.
Information about customer perceived risks and contributions is important to
contribute to improvement of Indonesia logistics performance.
56
Acknowledgment
This study was funded by The Ministry of National Education of the Republic of
Indonesia.
6. References
Aghazadeh, S.-M. (2003) How to choose an effective third party logistics provider.
Management Research News 26(7), 50.
Aktas, E. & Ulengin, F. (2005) Outsourcing logistics activities in Turkey. Journal of
Enterprise Information Management 18(3), 316
Anderson, E.W., Fornell, C. & Lehmann, D.R. (1994) Customer satisfaction, market
share, and profitability: Findings from Sweden. Journal of Marketing 58(3),
53.
Beaumont, N. & Sohal, A. (2004) Outsourcing in Australia. International Journal of
Operations & Production Management 24(7), 688 - 700.
Bolumole, Y.A. (2001) The supply chain role of third-party logistics providers.
International Journal of Logistics Management 12(2), 87.
Bolumole, Y.A., Frankel, R. & Naslund, D. (2007) Developing a Theoretical
Framework for Logistics Outsourcing. Transportation Journal 46(2), 35.
Boyson, S., Corsi, T., Dresner, M. & Rabinovich, E. (1999) Managing effective third
party logistics relationships: What does it take? Journal of Business Logistics
20(1), 73.
Chapman, R.L., Soosay, C. & Kandampully, J. (2002) Innovation in logistic services
and the new business model: A conceptual framework. Managing Service
Quality 12(6), 358.
Cho, J.J.-K., Ozment, J. & Sink, H. (2008) Logistics capability, logistics outsourcing
and firm performance in an e-commerce market. International Journal of
Physical Distribution & Logistics Management 38(5), 336 - 359.
Clegg, S.R., Burdon, S. & Nikolova, N. (2005) The Outsourcing Debate: Theories and
Findings. Journal of the Australian and New Zealand Academy of
Management 11(2), 37.
Daugherty, P.J., Stank, T.P. & Rogers, D.S. (1996) Third-party logistics service
providers: Purchasers' perceptions. International Journal of Purchasing and
Materials Management 32(2), 23.
Dwyer, F.R., Schurr, P.H. & Oh, S. (1987) Developing Buyer-Seller Relationships.
Journal of Marketing 51(2), 11.
Ehie, I.C. (2001) Determinants of success in manufacturing outsourcing decisions: A
survey study. Production and Inventory Management Journal 42(1), 31.
57
Elmuti, D. (2003) The perceived impact of outsourcing on organizational performance.
Mid - American Journal of Business 18(2), 33.
Embleton, P.R. & Wright, P.C. (1998) A practical guide to successful outsourcing.
Empowerment in Organizations 6(3), 94.
Fabbe-Costes, N., Jahre, M. & Roussat, C. (2009) Supply chain integration: the role
of logistics service providers. International Journal of Productivity and
Performance Management 58(1), 71 - 91.
Fernie, J. (1999) Outsourcing distribution in U.K. retailing. Journal of Business
Logistics 20(2), 83.
Fill, C. & Visser, E. (2000) The outsourcing dilemma: a composite approach to the
make or buy decision. Management Decision 38(1), 43 - 50.
Ghodeswar, B. & Vaidyanathan, J. (2008) Business process outsourcing: an approach
to gain access to world-class capabilities. Business Process Management
Journal 14(1), 23 - 38.
Hannigan, K. & Mangan, J. (2001) The role of logistics and supply chain
management in determining the competitiveness of a peripheral economy.
Irish Marketing Review 14(1), 35.
Harland, C., Knight, L., Lamming, R. & Walker, H. (2005) Outsourcing: assessing the
risks and benefits for organisations, sectors and nations. International Journal
of Operations & Production Management 25(9), 831 - 850.
Hong, J., Chin, A.T.H. & Liu, B. (2004) Logistics Outsourcing by Manufacturers in
China: A Survey of the Industry. Transportation Journal 43(1), 17.
Kakabadse, A. & Kakabadse, N. (2005) Outsourcing: Current and future trends.
Thunderbird International Business Review 47(2), 183.
Kementrian Koordinator Bidang Perekonomian Republik Indonesia (2008) Penataan
dan Pengembangan Sektor Logistik Indonesia.
Kremic, T., Tukel, O.I. & Rom, W.O. (2006) Outsourcing decision support: a survey
of benefits, risks, and decision factors. Supply Chain Management: An
International Journal 11(6), 467 - 482.
Lankford, W.M. & Parsa, F. (1999) Outsourcing: a primer. Management Decision
37(4), 310.
Lau, K.H. & Zhang, J. (2006) Drivers and obstacles of outsourcing practices in China.
International Journal of Physical Distribution & Logistics Management 36(10),
776 - 792.
Lieb, R. (2008) The year 2007 survey. International Journal of Physical Distribution
& Logistics Management 38(6), 495.
Maloni, M.J. & Carter, C.R. (2006) Opportunities for Research in Third-Party
Logistics. Transportation Journal 45(2), 23.
58
Maltz, A.B. & Ellram, L.M. (1997) Total cost of relationship: An analytical
framework for the logistics outsourcing decision. Journal of Business
Logistics 18(1), 45.
Persson, G. & Virum, H. (2001) Growth strategies for logistics service providers: A
case study. International Journal of Logistics Management 12(1), 53.
Qureshi, M.N., Kumar, D. & Kumar, P. (2008) An integrated model to identify and
classify the key criteria and their role in the assessment of 3PL services
providers. Asia Pacific Journal of Marketing and Logistics 20(2), 227 - 249.
Razzaque, M.A. & Sheng, C.C. (1998) Outsourcing of logistics functions: a literature
survey. International Journal of Physical Distribution & Logistics
Management 28(2), 89.
Reichheld, F.F., Markey, R.G., Jr. & Hopton, C. (2000) The loyalty effect - the
relationship between loyalty and profits. European Business Journal 12(3),
134.
Sahay, B.S. & Mohan, R. (2006) 3PL practices: an Indian perspective. International
Journal of Physical Distribution & Logistics Management 36(9), 666.
Schniederjans, M.J. & Zuckweiler, K.M. (2004) A quantitative approach to the
outsourcing-insourcing decision in an international context. Management
Decision 42(8), 974 - 986.
Selviaridis, K. & Spring, M. (2007) Third party logistics: a literature review and
research agenda. The International Journal of Logistics Management 18(1),
125 - 150.
Sezen, B. (2005) The role of logistics in linking operations and marketing and
influences on business performance. Journal of Enterprise Information
Management 18(3), 350.
Sheehan, W.G. (1989) Contract Warehousing: The Evolution Of An Industry. Journal
of Business Logistics 10(1), 31.
Sheffi, Y. (1990) Third Party Logistics: Present and Future Prospects. Journal of
Business Logistics 11(2), 27.
Sink, H.L. & Langley, C.J., Jr. (1997) A managerial framework for the acquisition of
third-party logistics services. Journal of Business Logistics 18(2), 163.
Sink, H.L., Langley Jr, C.J. & Gibson, B.J. (1996) Buyer observations of the US
third-party logistics market. International Journal of Physical Distribution &
Logistics Management 26(3), 38.
Sohail, M.S., Bhatnagar, R. & Sohal, A.S. (2006) A comparative study on the use of
third party logistics services by Singaporean and Malaysian firms.
International Journal of Physical Distribution & Logistics Management 36(9),
690 - 701.
59
Stank, T.P., Goldsby, T.J., Vickery, S.K. & Savitskie, K. (2003) Logistics service
performance: Estimating its influence on market share. Journal of Business
Logistics 24(1), 27.
Svensson, G. (2001) The impact of outsourcing on inbound logistics flows.
International Journal of Logistics Management 12(1), 21.
The Nathan Associates Inc. (2007) Toward a Roadmap for Integration of the ASEAN
Logistics Sector: Rapid Assessment & Concept Paper
Vissak, T. (2008) Achieving Success in Logistics Services Outsourcing: Some
Recommendations. Organizacijo Vadyba: Sisteminiai Tyrimai (46), 149.
Wilding, R. & Juriado, R. (2004) Customer perceptions on logistics outsourcing in the
European consumer goods industry. International Journal of Physical
Distribution & Logistics Management 34(8), 628 - 644.
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