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Exploring causal relationships and critical factors affecting a country’s ICT global competitiveness Abstract Background Methodologies Empirical study Conclusions References

Wei-Wen Wu, Lawrence W. Lan, Yu-Ting Lee 1 2 3 7 11 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 Literature review Methodology Results Discussion Conclusion/Directions for future study References

16 17 19 20 23 24 25

Human capital approach towards enhancing innovation performance in Omani industrial firms: The role of knowledge management Abstract Introduction Background and hypotheses Research methodology Analysis and results Discussion and conclusion Reference

Salim Abdullah Rashid Alshekaili, Ali Boerhannoeddin 27 28 29 31 34 36 37

I


The Current Status of Logistics Performance Drivers in Indonesia: An Emphasis on Potential Contributions of Logistics Service Providers (LSPs)

Abstract Introduction The Challenges of Indonesia Logistics Sector The Current Status of Key Drivers of Indonesia Logistics Performance Potential Contributions and Risks of the LSP Usage Conclusion References

Yeni Sumantri, Sim Kim Lau 40 41 42 43 51 55 56

II


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 1


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 2


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 NaĂŻve 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 3


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 4


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 5


TAN, on the other hand, allows for more complex network structures than the Na誰ve Bayes; and (2) the TAN achieves retention of the basic structure of Na誰ve 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 Na誰ve 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). 6


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 7


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 R 2 value, the E1 (market environment) exhibits the best ability to explain this 8


model (81.6%); and (3) the combination of these 9 pillars has predictive ability of 98% for the Overall_Score.

Figure 2. Relationships among pillars and Overall_Score

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Figure 3. Significant paths among pillars and Overall-Score 3.2 Discussions and implications The results of this empirical study indicate that some hypotheses have been supported by the data analysis. Referring to Figure 3, several interesting patterns from these significant paths can be found. For example, there are five pillars which could positively affect the Overall_Score, including R1 (individual readiness), R2 (business readiness), R3 (government readiness), U1 (individual usage), and U2 (business usage). In contrast, all three environment-related criteria have no significant effects on the Overall_Score. This reveals that readiness-related criteria are the foremost enablers to leverage the overall score of the NRI for a country. It should be noted that, among those five pillars (R1, R2, R3, U1, and U2), U1 and U2 are the most imperative ones to promote the overall score of the NRI. They have positively affected the Overall_Score as well as other pillars since that U1 is the start of the path ―U1→R1→E3→R3→Overall_Score‖ and that U2 is the beginning of the path ―U2→E1→E2→R3→Overall_Score.‖ Furthermore, all these two paths have covered R3, suggesting that R3 is greatly affected by several antecedent criteria. Based on the findings, some managerial implications can be derived. First, the Report emphasized that environment is a crucial enabler of networked readiness and that communication technology readiness facilitates the ICT usage. However, this study had different findings—readiness-related pillars are the foremost enablers and U1 (individual usage) and U2 (business usage) are the two most imperative 10


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

E1 -> E2 E2 -> R3 E3 -> R3 R1 -> E3 R1 -> Overall_Score R2 -> Overall_Score R3 -> Overall_Score U1 -> Overall_Score U1 -> R1 U2 -> E1 U2 -> Overall_Score U3 -> E1

Original Sample (O) 0.89150 0.69620 0.23381 0.77630 0.13803 0.12456 0.31079 0.28387 0.76205 0.72030 0.20907 0.20300

Sample Mean (M) 0.88844 0.69213 0.23924 0.77551 0.14019 0.12118 0.30780 0.28449 0.76142 0.71711 0.21425 0.20368

Standard Deviation (STDEV) 0.02167 0.07803 0.08150 0.02260 0.02670 0.03618 0.03780 0.02308 0.02532 0.06722 0.03294 0.07005

Standard Error (STERR) 0.02167 0.07803 0.08150 0.02260 0.02670 0.03618 0.03780 0.02308 0.02532 0.06722 0.03294 0.07005

T Statistics (|O/STERR|) 41.13414 8.92237 2.86892 34.35084 5.16939 3.44330 8.22187 12.29746 30.09974 10.71558 6.34606 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: 11


super-efficiency DEA method, BN classifiers with TAN algorithm, and PLS path modeling. The empirical study has concluded that (1) readiness-related criteria are the foremost enablers; and (2) rather than all 9 pillars, policymakers may spotlight on U1 (individual usage), U2 (business usage), and R2 (business readiness) because they are the root causes to overall NRI score. Though U3 (government usage) has no direct effect on the overall NRI score, the policymakers should never overlook this pillar because it is also a root cause which indirectly and significantly affects the overall NRI score. The proposed solution framework has successfully established the casual relationships among pillars and NRI score. It can also clearly scrutinize the imperative factors to facilitate the policymakers to arrive at more informed decisions, which is otherwise impossible for only relying on the original NRI scores and rankings from the Report. Consequently, this study contributes to the practical applications of global ICT competitiveness around the world. The proposed approach can help the policymakers focus on the most critical pillars and associated components to effectively advance the ICT competition of a nation. Several directions for future studies can be identified. First, different clustering techniques may produce different results; thus, it calls for further research by comparing with other clustering techniques so as to reach more robust conclusions. Second, since the ICT industry has been changing drastically, it is important to examine the consistency of the significant pillars affecting the overall NRI scores and rankings over time. Future study can employ the proposed approach to conduct similar analyses based on several annual Reports. 5. References Adler, N., Friedman, L., Sinuany-Stern, Z. (2002) Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research 140(2), 249-265. Andersen, P., Petersen, N.C. (1993) A procedure for ranking efficient units in data envelopment analysis. Management Science 39(10), 1261-1264. Anderson, R.D., Vastag, G. (2004) Causal modeling alternatives in operations research: Overview and application. European Journal of Operational Research 156(1), 92-109. Banker, R.D., Charnes, A., Cooper, W.W. (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30(9), 1078-1092. Cerquides, J., R.L. De Mantaras. (2005) TAN Classifiers Based on Decomposable 12


Distributions. Machine Learning 59, 323-354. Chan, A., McNaught, K.R. (2008) Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure. Journal of the Operational Research Society 59(4), 423-430. Charnes, A., Cooper, W.W., Rhodes, E. (1978) Measuring the efficiency of decision-making units, European Journal of Operational Research 2(6), 429-444. Cook, W.D., Seiford, L.M. (2009) Data envelopment analysis (DEA)-Thirty years on. European Journal of Operational Research 192(1), 1-17. Cook, W.D., Seiford, L.M., Zhu, J. (2004) Models for performance benchmarking: measuring the effect of e-business activities on banking performance. Omega 32(4), 313-322. Davis, J., Costa, V.S., Ong, I.M., Page, D., Dutra, I. (2004) Using Bayesian Classifiers to Combine Rules. In 3rd Workshop on Multi-Relational Data Mining, Seattle, USA. Dutta, S., Mia, I. (2010) The Global Information Technology Report 2009-2010. World Economic Forum and INSEAD, SRO-Kundig Geneva, Switzerland. Fornell, C., Bookstein, F. (1982) Two structural equations models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research 19(4), 440-452. Friedman, N., Geiger, D., Goldszmidt, M. (1997) Bayesian Network Classifiers. Machine Learning 29(2-3), 131-163. Golany, B., Roll, Y. (1989) An application procedure for DEA. Omega 17(3), 237-250. Hruschka, E.R. Ebecken, N.F.F. (2007) Towards efficient variables ordering for Bayesian networks classifier. Data & Knowledge Engineering 63(2), 258-269. Hulland, J. (1999) Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal 20(2), 195-204. Jakobowicz, E., Derquennea, C. (2007). A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy. Computational Statistics & Data Analysis 51(8), 3666-3678. Jiang, L., Zhang, H., Cai, Z., Su, J. (2005) Learning Tree Augmented Naive Bayes for Ranking, DASFFA 2005: database systems for advanced applications, Lecture Notes in Computer Science 3453, 688-698, Springer Berlin,. Kao, H.Y., Huang, C.H., Li, H.L. (2005) Supply chain diagnostics with dynamic Bayesian networks. Computers & Industrial Engineering 49(2), 339-347. Klopotek, M.A. (2002) A new Bayesian tree learning method with reduced time and space complexity. Fundamenta Informaticae 49(4), 349-367. Lauria, E.J.M., Duchessi, P.J. (2007) A methodology for developing Bayesian 13


networks: An application to information technology (IT) implementation. European Journal of Operational Research 179(1), 234-252. Lewis, N.D.C. (1999) Continuous process improvement using Bayesian belief networks. Computers & Industrial Engineering 37(1-2), 449-452. Ranganathan, C., Sethi, V. (2002) Rationality in Strategic Information Technology Decisions: The Impact of Shared Domain Knowledge and IT Unit Structure. Decision Sciences 33(1), 59-86. Rhodes, C.J., Keefe, E.M.J. (2007) Social network topology: a Bayesian approach. Journal of the Operational Research Society 58(12), 1605-1611. Temme, D., Kreis, H., Hildebrandt, L. (2006) PLS path modeling: A software review. SFB 649 Discussion Papers SFB649DP2006-084, Humboldt University, Berlin, Germany. Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M., Lauro, C. (2005) PLS path modeling. Computational Statistics and Data Analysis 48(1), 159-205. Wang, T., Touchman, J.W., Xue, G. (2004) Applying two-level simulated annealing on Bayesian structure learning to infer genetic networks. Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE, Publication Date: 16-19, p.647- 648. Wheeler, F.P. (2001) A Bayesian approach to service level performance monitoring in supplier, provider relationships. The Journal of the Operational Research Society 52(4), 383-390. Wixom, B.H., Watson, H.J. (2001) An empirical investigation of the factors affecting data warehousing success. MIS Quarterly 25(1), 17-41. Wu, W.W. (2010) Linking Bayesian networks and PLS path modeling for causal analysis. Expert Systems with Applications 37(1), 134-139. Zhu, H., Beling, P.A., Overstreet, G.A. (2002) A Bayesian framework for the combination of classifier outputs. Journal of the Operational Research Society 53(77), 719-727.

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External Environment Factors Influencing the Technology Adoption-Diffusion Decision in Malaysian Manufacturing Small Medium Enterprises (SMEs) Murzidah Ahmad Murad* Graduate School of Business and Law RMIT University, Melbourne, Australia E-mail: 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 15


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 16


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

17


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 18


(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. 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

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 19


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. 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

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, 20


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 21


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.”

22


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.

23


EXTERNAL FACTORS    

Customer Competitors Economy Malaysian Government regulation

Innovation (Technology) decision process in Malaysian manufacturing companies

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 24


others. Further research will expand upon this study, investigating the related internal and external factors, additional organizations across a range of industry sector categories and use quantitative techniques to validate all factors. 8. References Abdullah, M.A. (2002) An overview of the macroeconomic contribution of SMEs in Malaysia in Harie C & Lee BC eds. The role of SMEs: National economics in East Asia series 2. Cheltenham: Edward Elgar. Akrich, M., Callon M., Latour, B. (2002) The key to success in innovation. Part I: The art of intersessement. International Journal of Innovation Management 6(2), 187-206. Gentili, G. B., Tesi, V., Linari, M., Marsili, M. (2002) A versatile microwave plethysmograph for the monitoring of physiological parameters (Periodical style). IEEE Trans. Biomed. Eng. 49(10), 1204–10. Albors, J., Hervas, J., Hidalgo, A. (2006) Analyzing high technology diffusion and public transference program: the case of the European game program. The Journal of Technology Technology Transfer 31(6), 647-61. Asgari, B., Wong, C.Y. (2007) Decipting the technology and economic development of modern Malaysia. Asian Journal of Technology Innovation 15(1), 167-93. Ayres, R. (1969) Technology forecasting and long-range forecasting. New York: McGraw Hill. Bennet, A., Bennet, D. (2004) Organizational survival in the new world: the intelligent complex adaptive system. Burlington: Butterworth-Heinemann Publication. Burca, S., Fynes, B., Marshall, D. (2005) Strategic technology adoption: extending ERP across the supply chain. Journal of Enterprise Information Management 18(4), 427-40. Chengalur-Smith, I., Duchessi, P. (1999) The initiation and adoption of client-server technology in organizations. Innovation and Management 35, 77-88. Coombs, R., Saviotti, P., Walsh, V. (1987) Economics and technological change. London: MacMillan Education Limited. Damanpour, F. (1991) Organizational innovation: a meta-analysis of effects of determinants and moderators. Academy of Management Journal 34(3), 55-90. Davidoff, L., Kleiner, B. (1991) New developments in innovation diffusion. Work Study 40(6), 6-9. Davis, F. D. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quaterly 13(3), 319-40, 1989. Department of the Finance Ministry of Malaysia (2006) Ninth Malaysian Plan, 25


2006-2010. Kim, C., Galliers, R.D. (2004) Towards a diffusion model for internet systems. Internet Research 14(2), 155-66. Kuan, K.K.Y., Chau, P.Y.K (2001) A Perception-based Model for EDI adoption in small business using a Technology-Organization-Environment Framework. Information and Management 38, 507-21. Okada, Y. (2006) Struggles for survival: institutional and organizational changes in Japan‘s high-tech industries. Japan: Spinger. Premkumar, G., Roberts, M. (1999) Adoption of new information technologies in Rural Small Business. The International Journal of Management Science 27, 467-84. Rogers, E. M. (1962) Diffusion of Innovation, 1sted. New York: The Free Press. Rogers, E. M. (2003) Diffusion of Innovation, 5thed. New York: The Free Press. Rosnah, M., Lo, W., Hashmi (2005) Advanced manufacturing technologies in SMEs. CACCI Journal. Rosnah, M., Megat A., Osman, M. (2004) Barriers to Advance manufacturing technologies implementation in the Small and Medium Scale industries of a developing country. International Journal of Engineering and Technology 1(1), 39-46. Sadowski, B. M., Maitland C., Dongen J. (2002) Strategic use of the Internet by small and medioum sized companies: an exploratory study. Information economics and policy 14, 75-93. Santarelly, E., D‘Altri, S. (2003) The diffusion of e-commerce among SMEs: theoretical implications and empirical evidence. Small Business Economics 21, 273-83. Scupola, A. (2003) The adoption of Internet Commerce by SMEs in the South of Italy: an environment, technological and organizational perspective. Journal of Global Information Technology Management 6(1), 52-71. Torrnatzky, L.G., Fleischer, M. (1990) The processes of technological innovation. Lexington MA: Lexington books. Zaya, P. (2005) Technology adoption SMEs. International Development Research Centre.

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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 Management

Performance,

Human

27

Capital

Approach,

Knowledge


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 28


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 29


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 30


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 31


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 32


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

Cronb. Mean

S.D.

1

2

3

4

5

6

7

8

9 α

variable items 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*

5

Leadership

4.57

1.43

0.22**

0.16*

0.27**

0.42**

6

Commitment

3.76

0.86

0.24**

0.29**

0.38**

0.30**

0.63**

.81 .84 .79

Know. Manag. 7

KM Acq.

4.72

0.62

0.19*

0.17*

0.31**

0.47**

0.32**

0.55**

8

KM Conv.

4.90

0.55

0.18*

0.15

0.23**

0.44**

0.27**

0.60**

0.84**

9

KM App.

4.90

0.58

0.16

0.19*

0.19*

0.52**

0.58**

0.57**

0.72**

0.73**

4.78

0.55

0.20*

0.18*

0.29**

0.61**

0.62**

0.48**

0.64**

0.63**

10

Inn. performance N=201

* Correlation is significant at the .05 level (2-tailed)

.80

** Correlation is significant at the .01 level (2-tailed)

33

.77 .82 0.81**

.86


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. Table 2: Regression results (standardized coefficient) for innovation performance Innovation Performance Variables Step 1

Step2

Org. Age

.06

0.9

Org. Size

.14*

.16*

Control Variables

Response Variables Educational Level

.18*

Experience

.21**

Leadership

.36***

Commitment

.13*

R2 Adjusted

R

2

F

.08

.63

0.07

.52

10.62**

77.49** *

∆ R2 F∆R

2

.05

.47

10.62**

105.76* **

Note: *p < .05.

**p < .01.

***p < .001

Hierarchical regression analysis indicated that 63% of the variance associated with organizational innovation performance is explained by the human capital factors 34


(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 Innovation Performance Variables Step1

Step2

Step3

Org. Age

.06

0.9

.14*

Org. Size

.14*

.16*

.18

Edu. Level

.17*

.12

Experience

.15*

.09

Leadership

.27**

.16

Commitment

.26**

.24*

Control Variables

Response Variables Human Capital

Know. Manage. Know. Acquisition

.28***

Know. Conversion

.34***

Know. Application

.30***

R2 Adjusted

R

2

F ∆R

2

F ∆ R2 Note: *p< .05.

**p< .01.

.09

.39

.51

.08

.38

.48

19.90***

44.11***

62.00***

0.09

0.30

0.12

19.90**

51.50***

104.87***

***p< .001

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 35


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 36


theoretical reasoning, the cross-sectional design may not rule out causality concerning the hypothesized relationships. Future research might address this issue by using longitudinal design in drawing causal inferences. To conclude, human capital approach is a valuable asset for firms desiring to achieve superior innovation and sustainable competitive advantages. The viewpoints of this study highlight the crucial importance of the mediating role of knowledge management when examining the relationship between human capital and innovation performance. 6. References Al-Hajri, S., Tatnall, A. (2007) Inhibitors and Enablers to Internet Banking in Oman - A Comparison with Banks in Australia. International Review of Business Research Papers, 3(5), pp.36-43. Al-Hamadi, A. B., Budhwar, P., Shipton, H. (2007) Managing Human Resources in the Sultanate of Oman. International Journal of Human Resource Management, 18(1) pp.100-113. Alireza, A. Z. (2010) GCC GDP Rises to $983 Billion. [Online]. Available: http://www.silobreaker.com/gcc-gdp-rises-to-983-billion-5_2263791447201284 113 Al-Lamki, S. M. (2000) Omanization: A Three Tier Strategic Framework for Human Resource Management and training in the Sultanate of Oman. Journal of Comparative International Management, 3(1), pp. 55-75. Barry, J., Kevin, D. (2009) Beyond Borders: The Global Innovation 1000. Retrieved from http://www. strategy-business.com/media/file/sb53_08405.pdf Becker, G. S. (1964) Human capital. New York, NY: Columbia University Press. Chen, K., Guan, J. (2010) Mapping the functionality of China's regional innovation systems: A structural approach. China Economic Review, xxx, xxx窶度xx. Cronbach, L. J., Warrington, W. G. (1951) Time-limit tests: Estimating their reliability and degree of speeding. Psychometrika, 16, 167-188. Cui, A. S., Griffith, D. A., Cavusgil, S. T. (2005) The influence of competitive intensity and market dynamism on Knowledge Management capabilities of MNC subsidiaries. Journal of International Marketing, 13(3), 32-53. Damanpour, F. (1991) Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of Management Journal, 34, 555-590. Darroch, J. (2005) Knowledge management, innovation and firm performance. Journal of Knowledge Management, 9(3), 101-115. Hartog, D. N., Van Muijen, J. J., Koopman, P. L. (1997) Transactional versus 37


transformational leadership: an analysis of the MLQ (Multifactor Leadership Questionnaire). Journal of Occupational and Organizational Psychology, 70(1), pp. 19-35. Kesting, P., Parm Ulhøi, J. (2010) Employee-driven innovation: extending the license to foster innovation. Management Decision, 48(1), 65-84. Lin, C. Y., Kuo, T. H. (2007) The mediate effect of learning and knowledge on organizational performance. Industrial Management & Data Systems, 107(7), pp. 1066-1083. Meyer, J. P., Stanley, D. J., Herscovitch, L., Topolnytsky, L. (2002) Affective, continuance and normative commitment to the organization: A meta-analysis of antecedents, correlates and consequences. Journal of Vocational Behaviour, 61, 20-52. Ministry of National Economy â&#x20AC;&#x201C; Oman. (2010) Social Indicators [Online]. Available: http://www.moneoman.gov.om/english/social.htm Mowday, R. T., Porter, L. W., Steer, R. M. (1982) Employee-organization linkages: The psychology of commitment and absenteeism and turnover. New York, NY: Academic Press. Mowday, R. T., Steers R. M. Porter, L. M. (1979) The measurement of organizational commitment. Journal of Vocational Behaviour, 14, 244-247. Onyx, J., Bullen, P. (2000) Measuring social capital in five communities. Journal of Applied Behavioral Science, vol. 36, pp. 23-43. Parker, R. C. (1982) The management of innovation. New York, NY: Wiley. Politis, D. (2005, July) The process of entrepreneurial learning: A conceptual framework. Entreprenuership Theory and Practice, pp. 399-424. Putnam, R. (1993) Making democracy work: Civic traditions in modern Italy, Princeton, NJ: Princeton University Press. Robinson, P., Sexton, E. (1994) The effect of education and experience on self employment success. Journal of Business Venturing, 9, 141-156. Rogers, E. M. (1995) Diffusion of innovations (4th ed.). New York, NY: The Free Press. Ruggles, R. (1998) The state of the notion: Knowledge management in practice. California Management Review, 40(3), 80-89. Scarbrough, H. (2003) Knowledge management, HRM and the innovation process. International Journal of Manpower, 24(5), 501-516. Shafiqur Rahman, M. (2010) Variance Analysis of GDP for GCC Countries, International Review of Business Research Papers, 6(2), 253 -259. Shu-hsien., L., Wu-Chen, F., Chih-Tang, L. (2008) Relationships between knowledge inertia, organizational learning and organization innovation. In: Linton, J. (Editor 38


in Chief). Technovation, 28(4), pp. 183-195. Tabachnick, B. G., Fidell, L. S. (2007) Using Multivariate Statistics, 5th ed. Boston: Allyn and Bacon. Tang, H. K. (1999) An inventory of organizational innovativeness,â&#x20AC;&#x2013; Technovation, vol. 19, pp. 41-51. Van de Ven, A. H. (1986) Central problems in the management of innovation. Management Science, 32, 590-607. Wang, C. L., Ahmed, P. K. (2004) The development and validation of the organizational innovativeness construct using confirmatory factor analysis. European Journal of Innovation Management, 7, pp. 303-313. Wright, P. M., Dunford, B. B., Snell, S. A. (2001) Human resources and the resource-based view of the firm. Journal of Management, 27(6), pp. 701â&#x20AC;&#x201C;21. Zerenler, M., Hasiloglu, S. B., Sezgin, M. (2008) Intellectual Capital and Innovation Performance: Empirical Evidence in the Turkish Automotive Supplier. Journal of Technology Management Innovation, 3(4), pp. 31-40.

39


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

40


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 countryâ&#x20AC;&#x2DC;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 41


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 Indonesiaâ&#x20AC;&#x2DC;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 countryâ&#x20AC;&#x2DC;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 Overall LPI

score

2.76

rank Customs

World score

difference

2.87

-0.11

2.59

-0.16

75

score

2.43

rank

72 42


Infrastructure

score

2.54

rank International shipment

score

2.82

score

2.47

-0.02

2.76

-0.29

2.92

-0.15

3.41

0.06

92

score

2.77

rank Timeliness

2.85

80

rank Tracking & tracing

-0.09

69

rank Logistics competence

2.64

80

score

3.46

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. 43


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)

44


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) 45


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

21.934

21.934

23.469

23.469

772

772

772

772

Urban Road Tol Road

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

Construction

124.67

158.78

181.89

324.60

789.94

-

27.36

114.55

78.46

Average growth (%)

and Rehabilitation Growth (%)

46

40.12


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). 47


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

2000

East Asia & Pacific Region

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

-

Population covered by mobile cellular network (%)

89

90

93

Fixed broadband subscribers (% of total

1.0

9.4

41.9

Quality

48


internet subscribers) Fixed internet bandwidth (bits/second/person)

1

120

470

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

Affordability

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 49


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 companiesâ&#x20AC;&#x2DC; 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 50


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. 51


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

Improving operational level

Item of Potential Contribution

Code of Item of Potential Contribution

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

7

Improving customer relationship

8

Increasing responsiveness to market

9

Accesing latest technology

10

Accesing expertise, skill, and knowledge

11

Accessing material resources

12

Accessing data

13

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 outcome of contract

20

Improving customer service

Accessing resources

Reducing Cost

Improving business

52


performance

Developing business network

Increasing financial strength

21

Decreasing business risk

22

Increasing competitive advantage

23

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 1

2

3

4

5

6

7

8

9

1 0

Papers

1 1

1 2

(Daugherty et al., 1996)

(Sink et al., 1996)

(Sink & Langley, 1997)

(Embleton & Wright, 1998)

√ √

(Boyson et al., 1999)

√ √ √

(Razzaque & Sheng, 1998)

(Fernie, 1999)

(Lankford & Parsa, 1999)

(Fill & Visser, 2000)

(Bolumole, 2001)

√ 53

(Ehie, 2001)


√ √ √

√ √

√ √

(Persson & Virum, 2001)

(Aghazadeh, 2003)

(Elmuti, 2003) (Beaumont & Sohal, 2004)

√ √

(Hong et al., 2004)

(Clegg et al., 2005)

√ √

(Schniederjans & Zuckweiler, 2004) (Wilding & Juriado, 2004)

√ √

√ √

(Kakabadse & Kakabadse, 2005)

(Kremic et al., 2006)

(Harland et al., 2005)

(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 1 3

1 4

1 5

1 6

1 7

1 8

1 9

2 0

2 1

2 2

Papers

2 3

2 4

√ √

(Daugherty et al., 1996) √

(Sink et al., 1996) (Sink & Langley, 1997) √

√ √

√ √

(Fernie, 1999) (Lankford & Parsa, 1999)

(Fill & Visser, 2000) √

(Razzaque & Sheng, 1998) (Boyson et al., 1999)

√ √

(Embleton & Wright, 1998)

(Bolumole, 2001)

(Ehie, 2001) 54


√ √

(Persson & Virum, 2001) (Aghazadeh, 2003)

(Elmuti, 2003) (Beaumont & Sohal, 2004) (Hong et al., 2004)

(Wilding & Juriado, 2004)

(Clegg et al., 2005)

(Harland et al., 2005)

(Kakabadse & Kakabadse, 2005) (Kremic et al., 2006)

√ √

(Schniederjans & Zuckweiler, 2004)

(Lau & Zhang, 2006)

√ √

(Maloni & Carter, 2006)

(Bolumole et al., 2007)

(Selviaridis & Spring, 2007)

(Sahay & Mohan, 2006) (Sohail et al., 2006)

(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.

55


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