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I

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: itmike@thit.edu.tw

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

3

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

5

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

6

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).

7

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

8

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

9

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

10

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

11

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:

12

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.

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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: murzidah.ahmadmurad@rmit.edu.au

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

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plethysmograph for the monitoring of physiological parameters (Periodical style).

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public transference program: the case of the European game program. The Journal

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467-84.

Rogers, E. M. (1962) Diffusion of Innovation, 1sted. New York: The Free Press.

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Rosnah, M., Lo, W., Hashmi (2005) Advanced manufacturing technologies in SMEs.

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Zaya, P. (2005) Technology adoption SMEs. International Development Research

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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

sshukili@hotmail.com

Ali Boerhannoeddin

Faculty of Economics and Administration

University of Malaya, Malaysia

alifeaum@yahoo.com

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.

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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: ys487@uowmail.edu.au

Sim Kim Lau

School of Information Systems and Technology

University of Wollongong, Australia

E-mail: sim_lau@uow.edu.au

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.

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