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Building the measurement framework of technology efciency with technologydevelopment and management capability Evidence from the ASEAN countries

Shih-Chien Chien a , , Tai-Yue Wang b , Su-Li Lin aa Department of International Business and Trade, Shu-Te University, Kaohsiung, Taiwanb Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan

a r t i c l e i n f o

Keywords:Technology efciencyTechnology developmentManagement capabilityDEA

a b s t r a c t

In this paper, we present a research framework with data envelopment analysis (DEA) approach to eval-uate a nations technology efciency and effectiveness in ASEAN countries. The study proposes two out-puts, patents and licenses (PL) and technology exports (TE) along with three inputs, information andcommunication technology (ICT), R&D (RD), and governance capability (GC) in the model. Building onevidence from our research, we found that country has better outcome in PL can be derived from betterapplication in ICT which in terms of TE to RD and GC as well. Additional ndings also revealed the var-iable of ICT is mainly advantageous to technology efciency in ASEAN countries. Further, from the view-point of country, our results indicate both Singapore and the Philippines are the most efcient countriesamongthe variables in technology efciency, scale efciency, andwindow analysis as well. Moreover, ourndings suggested some other countries may explore the suitable strategy to enhance their technologyefciency with benchmarking countries.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

The organization and structure of business processes in techno-logical activities can be regarded as strategic tools to sustain thecore competence of a nations economy. As Raymond (1997) notedthat technology plays a critical ingredient in the economic devel-opment of countries. Thus, continual investment in technology tobuild dynamic capabilities is one of the three sources of successfulcompetition in the 21st century ( Zahra, 1999 ). Wang (1999) alsopointed out that the application of information and communica-tion technology (ICT) has revolutionized the structure of both man-agement and competition in the emerging global economy.Findings of his research indicated the effect of technology applica-tion (i.e. high technology efciency) on economic growth can beachieved merely through strong national information infrastruc-ture (i.e. technology development and management capability).

Additionally, prior works dened the core competence as bun-dles of skills and technologies ( Prahalad & Hamel, 1990 ), or poolsof experience, knowledge, and systems that can be considered tocreate and accumulate new strategic assets ( Markides & William-son, 1994 ). These strategic assets, which are imperfectly imitable,constitute a nations competitive advantage. Therefore, followingthe above research, we have condence that effective technologydevelopment and management capability are the determinant

factors to sustain core competence for nations. In addition, to sus-tain national competitiveness and create value in the networkeconomy, a rich body of prior research shows that technologicalcompetence of a country is not only to be given precedence butneeds to be developed network competence to link with other alli-ances ( Ritter & Gemunden, 2004 ). While the former capabilities arebased on the integration of technology; the latter should be man-aged under a system of global integration to allocate resourceswithin strategic alliance. Consequently, the strategic applicationof this principle must emphasize both dynamic and capabilitywithin management system to achieve technology efciency(Schulz, 2001; Teece, 1998 ).

According to Ulrich and Lake (1991) argue that competingfrom the inside out requires a continuous effort in building appro-priate technological skills with sufcient management resources.In this respect, how to advance technology efciency by meansof better technology development (e.g. improved information andcommunication technology, higher R&D expenditures, and morescientists and engineers in R&D) as well as management capabili-ties (e.g. higher education levels and a better socio-economicenvironment) are critical issues for researchers and authority of anation.

Moreover, a variety of factors may be included in the categoryof technology development, management development and tech-nology efciency as well. With regard to the determinant factorsof national competitiveness ( Hmlinen, 2003 ), enterprise com-petitiveness with the diamond model ( Porter, 1990 ), the double

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Corresponding author. Fax: +886 7 615 8000x3300.E-mail addresses: [email protected] , [email protected] (S.-C. Chien).

Expert Systems with Applications 38 (2011) 68566865

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diamond model ( Moon, Rugman, & Verbeke, 1995 ), the nationalinnovation system ( Dosi, Freeman, nelson, Silverberg, & Soete,1988; Freeman, 1982; Lundvall, 1992; Nelson, 1993 ), technologycompetitiveness ( Roessner, Porter, Newman, & Caufel, 1996 ),macro-economic competitiveness ( Ulengin, Ulengin, & Onsel,2002 ), and the competitive advantage factors of an enterprise ( Li& Deng, 1999 ), there are several notable research institutions andresearchers have proposed evaluation models and frameworks tomeasure competitiveness for specic issues, such as world compet-itiveness yearbook the International Institute for ManagementDevelopment (IMD); the global competitiveness report WorldEconomic Forum (WEF).

According to the prior research models and frameworks, variousfactors are explored that related to the interrelationships amongtechnology development, management capability, and technologyefciency in developed countries, respectively. However, few stud-ies focus on these issues with regard to less-developed countries(Dahlman & Frischtak, 1993; Katz & Bercovice, 1993 ). Recently,National Cheng Kung University (NCKU) joined with the researchin ASEAN countries and proposed a national competitiveness mod-el in both developing and less-developed countries. Yet, it was stilllack of ofcial published reports both in developed and developingcountries and limited their analysis in this region ( Wang, Chien, &Kao, 2007 ).

Based on the past literature, technology development and man-agement capability are the two factors that nations need to putmore efforts in order to specialize in their technology efciencyand stimulate economic growth. Linn, Zhang, and Li (2000) empha-sized that technology should not only fulll the managementneeds of a specic set of technologies within a domain and inter-domain relationship, but develop a better management capability.In other words, how to implement strategies with available socialresources (education, health, and welfare), current technologies,future markets, and socio-economic environment to improve tech-nology efciency is crucial for most nations. In addition, the syn-ergy of technology development and management capability maysustain a nations economic growth, and consequently, it is not sur-prising that few developed countries have previously engaged intechnology investment under effective management capability toachieve their economic growth.

Unfortunately, there fails clear evaluation for technology ef-ciency in relation to national resources (e.g. education, technologyinvestment, R&D, nance, health, and welfare). One denitioncould be made of resources in the attainment of technologicalgoals, taking technological and managerial factors into account.Still, another problem in evaluating the technology efciency of nations is lack of a good estimate of the production function (i.e.the functional relation between inputs and outputs) ( Rousseau &Rousseau, 1997 ). To solve this problem, the relative technologyefciency of units was introduced ( Farrell, 1957 ). However, Link

(1996) argued the production function approach has a number of signicant limitations which render its usefulness to an evaluationof government-sponsored research projects questionable ( Farrell,1957 ). Consequently, the measurement of technology efciencywill be undertaken in this study by using the technique of dataenvelopment analysis (DEA) approach.

DEA is an operations research-based method to measure theperformance efciency of decision units that are characterized bymultiple inputs and outputs. In addition, DEA converts multiple in-puts and outputs of a decision unit into a single measure of perfor-mance, generally referred to as relative efciency. The research of Charnes, Cooper, and Rhodes (1978) was the rst to propose theDEA method as an evaluation tool for decision units. Since then,DEA has been applied successfully as a performance evaluation

tool in many research elds, e.g. in studies of scientic wealth of European nations ( Rousseau & Rousseau, 1997 ), hospital adminis-

tration, the organization of the US Navy Recruitment Command(Norman & Stoker, 1991 ), school districts, secondary education(Sherman, 1984 ), and universities ( Degraeve, Lambrechts, & VanPuyenbroeck, 1996 ), respectively.

In DEA the performance of each unit under study is comparedwith that of every other one. Units which perform best use their in-puts more optimally than the others and the most optimal unitsform a frontier, called the efciency frontier. Less performingunits need more input to produce the same amount of outputand are therefore situated at some distance of the frontier. Unitssituated on the efciency frontier will have a relative performancerate of 1 (they are a 100% efcient), and the frontier is said to en-velop all decision-making units (DMUs). In this study, we speci-cally designed the DMU be the ASEAN countries.

The organization of the study is organized as follows: in the sec-tion two, we present the research framework and data structure.Next, we describe the data sources and DEA. In the following sec-tions, we provide the empirical studies of ASEAN countries, andthe conclusions as well as suggestions are discussed in the nalsection.

2. The research framework and data structure

This section explains the construct of research framework anddata structure. The categories of input and output include technol-ogy development, management capability and technology ef-ciency as well. From the logic of this research design, theresearch framework and data structure are will be explained asfollows.

2.1. Research framework

From the theoretical viewpoint, DEA requires several input/out-put variables to measure the efciency of the closed system. Theframework is derived from Wangs model ( Wang, 1999 ) and com-bines a management capability category so as to examine the inu-ences of education investment and socio-economic support. Theresearch framework includes three parts: technology developmentand management capability, technology efciency outcome, and

DEA Domain

Output Domain

Input Domain

Technology

Development

Management

Capability

Technology

Efficiency

Efficiency

Measurement

Feedback

Contribution

Evaluation

Fig. 1. The research framework of technology efciency measurement.

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DEA measurement as well. Fig. 1 shows the basic conceptual struc-ture and is explained as follows:

1. The input domain two categories of technology developmentand management capability are included in this domain. Eachcategory is then divided into several factors. We contend thebetter technology development and management capability,the better technology efciency for ASEAN countries.

2. The output domain the achievement of technology is derivedfrom the investment in technology development and manage-ment capability, and these results represent the efciencies of technology for ASEAN countries.

3. DEA domain the DEA is calculated by the optimization of theinput and output weights for each DMU (i.e. each ASEAN coun-try). The efcient and inefcient units are then distinguished bytheir relative input and output variables. In addition, the contri-bution of individual input and output variables can also bemeasured.

4. The evaluation information will feed back and rene thestrategies of the input domain by examining the gap betweeninefcient and efcient units, we gure out the contribution of each sub-category of input and output. This evaluation informa-tion will then feedbackto the original strategy, as well as offer aclear path using the niche variables of the input domain to pro-mote technology efciency in related technological investment.

2.2. The data structure of technology development, management capability and technology efciency

The source for the data used in this paper is mainly derivedfrom prior studies and institutional research databases. Weattempt to retrieve data from Asia Development Bank (ADB), UnionNational Development Program (UNDP), Central IntelligenceAgency (CIA), and World Bank (WB) databases before dening

the items for each category. The rst input category of technologydevelopment deals with two sub-categories (namely informationand communication technology, R&D), which are measured bythe following indicators: (1) telephone mainlines (per 1000 peo-ple), (2) cellular subscribers (per 1000 people), (3) internet users(per 1000 people), (4) R&D expenditures (as % of GDP), and (5)scientists and engineers engaged in R&D (per million people). Next,the input category of management development deals with gover-nance capability which is measured by three factors: (1) an educa-tion index, (2) a life expectancy index, and (3) a human povertyindex. Whereas, the output category of technology efciencyincludes two sub-categories (namely patents and licenses andtechnology exports), which are measured by four factors: (1)patents granted to residents (per million people), (2) receipts of royalties and license fees (US$ per person), (3) registrations inthe US Patent and Trademark Ofce, and (4) high technologyexports (as % of manufactured exports). The detail denitionsand references from research databases or literature for each itemare listed in Table 1 .

3. Data sources and DEA basics

This section discusses the source of data, the scaling process,and the DEA basics.

3.1. Data sources

In this paper, information and communication technology (ICT),R&D (RD), and governance capability (GC) are adopted as inputsub-categories, while patents and licenses (PL) and technology ex-ports (TE) are used as output sub-categories in this study. Theseindices are combined with several items. More specically, partialindices are combined into composite indices that represent thecomplex managerial achievements related to a specic issue (e.g.

Table 1

The list of items for each category.

Category Sub-category Items References Sources

Technologydevelopment

Information andcommunicationtechnology (ICT)

Telephone mainlines (per1000 people)

Wang (1999), Archibugi and Coco (2004), Chin(2005), WEF (2005), UNDP (2005) and WB(2005)

Central Intelligence Agency(CIA), Asian Development Bank(ADB), Union NationDevelopment Program (UNDP)(Human Development Report,HDR), World Bank (WB)

Cellular subscribers (per1000 people)

Wang (1999), Chin (2005), Archibugi and Coco(2004), UNDP (2005) and WB (2005)

CIA, ADB, UNDP (HDR), WB

Internet users (per 1000people)

Chin (2005), WEF (2005) , Archibugi and Coco(2005) and UNDP (2005)


Research anddevelopment (RD)

R&D expenditures (as % of GDP)

Wang (1999), Wanger, Brahmakulam, Brian, Jackson, and Wong (2001) and UNDP (2005),WB (2005)


Scientist and engineers inR&D (per million people)

Wang (1999), Wanger et al. (2001), UNDP(2005) and WB (2005)


Managementdevelopment

Governance capability(GC)

Education index a Wang (1999) , Archibugi and Coco (2001) andUNDP (2005)

UNDP (HDR)

Life expectancy index b UNDP (2005) UNDP (HDR)Human poverty index c UNDP (2005) UNDP (HDR)

Technology efciency Patents and licenses(PL)

Patents granted to residents(per million people)

UNDP (2005) UNDP (HDR)

Receipts of royalties andlicense fees (US$ perperson)

Desai et al. (2002) and UNDP (2005) UNDP (HDR)

Registered in US Patent andTrademark Ofce

WEF(2005), Archibugi and Coco (2004) andLalland Albaladejo (2001)

US Patent and Trademark Ofce(UPTO)

Technology exports(TE)

High technology exports (as% of manufactured exports)

Lall and Albaladejo (2001), Desai et al. (2002)and UNDP (2005)

UNDP (HDR)

a It is based on the adult literacy rate and the combined gross enrolment ratio for primary, secondary and tertiary schools.b It measures the relative achievement of a country in life expectancy at birth.c It is based on a long and healthy life, education and a decent standard of living.

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the education, life expectancy index, and human poverty indices).The description of each composite index is described in Table 1 .Each sub-category item is collected from a related database (e.g.the HDR from the UNDP (2003, 2004, 2005) , the World Bank(2005) and the prior studies are described in Table 1 ).

Due to the different ranges of items, the value of each item isstandardized by the following equation before analysis:

Standardized value the value is from 1 to 10

data sample minimum =sample maximum

sample minimum 9 1 1

The scaling data are standardized in Table 2 . Each data is repre-sented from the year 2001 to 2003 for 10 ASEAN countries.

3.2. DEA basics

DEA is a linear programming based technique for measuring therelative performance of Decision Making Units (DMUs) where the

presence of multiple inputs and outputs makes comparisons dif-cult. DEA provides a means of calculating apparent efciency levelswithin a group of DMUs. The efciency of a DMU is calculated rel-ative to the groups observed best practice. In this respect, we haveto optimize the performance of each DMU compared with the bestperforming units of the group. In doing so, we need to minimizethe ratio of weighted inputs to weighted outputs subject to thecondition that similar ratios for every DMU more than or equalto unity. The decision variables of this model are the weightingcoefcients: one set for each unit. They are thus constructed thatthey yield the most protable result for the unit concerned. In con-trast, regression estimates just one set of weights for all DMUs andproduces one functional form relating inputs and outputs of alloutlets under consideration. Admittedly, we may contend DEA is

a reasonable method to evaluate the technology efciency and ndthe most protable variables to each ASEAN country.

The output-based efciency of any DMU is computed as theminimum of a ratio of weighted inputs ( X ik; i = 1, . . ., m ; k = 1, . . .,n) to weighted outputs ( Y rk ; r = 1, . . ., s; k = 1, . . ., n) and the weightsof all DMUs under consideration are less than or equal to 1. Thus,the minimum efciency 1 g k for DMU k (k = 1,

. . ., n) is:

Min 1

g k

P mi1 v i X ikP sr 1 ur Y rk

2

subject toP mr 1 v i X ijP sr 1 ur Y rj P 1 for all j 1; . . .; n 3ur ; v i P e > 0 r 1; . . .; s; i 1; . . .; m 4

The weighting coefcient v i is regarded as the weight of the ithinput variableand u r is regarded as theweight of the r th output var-iable. In addition, the above formulation has s output variables, minput variables, and n DMUs. e represents a very small positive va-lue. In practice, weights will be low when inputs and outputs arerelatively low, while weights will be high when the relative of in-puts and outputs are high. Theoretically, Eq. (4) corresponds withthe requirement that the weights are positive. In addition, recentadvances in DEA allow for the estimated weights to be constrainedso that none input or output variable will dominate the efciencyestimation. It is also possible to set minimum limits for the esti-

mated weights so that all inputs and outputs are forced to play arole in efciency computation. Mathematically speaking, these justamount to additional constraints in the above optimization.

4. Empirical studies

In this section, we discuss the stages of technological measure-ment of ASEAN countries, which includes grouping, correlating,comparing, and nding the most signicant variables of technologyinvestment for them. Also, the inuences of size (scale efciency)and time series (window analysis) are discussed in this section.

4.1. Grouping the ASEAN countries by technology development andmanagement capability

We rst employ the hierarchical cluster analysis method togroup countries with similar technology development and man-agement capability. The rst group includes Singapore, Malaysia,Thailand, Indonesia, and the Philippines (dened as group 1), whileLaos, Brunei, Cambodia, Myanmar, and Vietnam are included insecond group (dened as group 2). Additionally, a t statistic is usedto examine the means between the two groups for all sub-catego-ries, and the results show that the means of all indices are signi-cantly different under p < 0.05 (listed in Table 3 ).

4.2. Correlating the input and output variables

To evaluate technology efciency, the input and output vari-

ables should have close relationships before we analyze the data.Through the correlation analysis, ICT and RD have a high

Table 2

The scaling input/output data of SEA countries for the year 20012003.

Country (year) ICT PL RD TE GC

Bru-2001 5.04 1.03 0.01 a 0.01 9.5Cam-2001 1.05 1 0.01 0.01 1.56Ind-2001 1.46 1.15 1.12 1 6.51Lao-2001 1.06 1 0.01 0.01 1.19Mal-2001 5.53 2.65 1.19 7.95 7.93

Mya-2001 1.04 1 2.5 0.01 3.78Sin-2001 10 10 10 8.42 9.67Tha-2001 2.53 2.39 5.5 3.84 6.6Vie-2001 1.36 1.03 1.22 0.01 6.98Phi-2001 2.05 1.44 1.18 10 8.25Bru-2002 4.77 1 0.01 1 8.99Cam-2002 1.11 1 0.01 0.01 1.61Ind-2002 1.65 1.32 1.13 2.77 5.14Lao-2002 1.1 1 0.01 0.01 1.19Mal-2002 3.66 2.33 1.65 8.97 7.74Mya-2002 1.03 1 0.01 0.01 3.81Sin-2002 10 10 10 9.26 10Tha-2002 3.1 1.65 1 4.98 7.81Vie-2002 1.48 1.09 1.45 0.01 6.71Phi-2002 2.23 1.41 1.19 10 8.06Bru-2003 2.1 1 0.01 0.01 8.76Cam-2003 1.19 1 0.01 0.01 1.59

Ind-2003 1.74 1.15 0.01 2.89 6.39Lao-2003 1.11 1 0.01 0.01 1.27Mal-2003 5.77 2.23 2.13 9.84 8.14Mya-2003 1 1 0.01 0.01 4.61Sin-2003 10 10 10 10 10Tha-2003 3.7 1.92 1 5.42 7.79Vie-2003 1.68 1.01 0.01 1 6.67Phi-2003 2.17 1.24 0.01 0.01 8.18

a Because the process of DEA cant have blank data, missing data is replaced witha small value.

Table 3

The t statistics for two groups of SEA countries with different variables.

Index of sub-category

Group 1 Group 2 t Statistic p Value

Mean Standarddeviation

Mean Standarddeviation

ICT 4.373 3.184 1.741 1.32 2.957 0.006252 a

PL 3.392 3.453 1.011 0.024 2.671 0.0125 a

RD 3.141 3.764 0.353 0.755 2.813 0.009 a

TE 6.357 3.555 0.142 0.348 6.738 0 a

GC 7.881 1.357 4.548 3.138 3.775 0.0008 a

a p Value < 0.05.

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correlation with PL, while ICT, RD and TE, GC and TE have a med-ium correlation which except GC has a low correlation with PL,even though its coefcient value is close to 0.5. Admittedly, the in-put and output variables have close relationships. Additionally,Singapore has the highest positive correlation between the inputand output variables in this study. More detailed information is gi-ven in Table 4 , and the scatter plots of pair variables ( Figs. A1A6 )are presented in Appendix A.

4.3. Comparing the technology efciency of ASEAN countries

Table 5 shows all the analysis results for the ASEAN countriesvia three inputs and two outputs that the linear program was for-mulated and solved using the FRONTIER software with the CCR model and output-based efciency. The technology efciency andthe reference set of each country are considered in three year timeperiods. Also, the rankings in the three years are derived from theirefciency calculations. All 100% ( n) efcient countries receive rankone, with the next country then receiving rank n + 1.

In 2001, Singapore, Malaysia, and the Philippines (in group 1) aswell as Brunei, Laos, and Cambodia (in group 2) are fully efcient.Thailand also scores high efciency, while Indonesia and Vietnamseem to be less efcient. In 2002, Singapore, Malaysia, and the Phil-ippines (group 1) as well as Brunei, Laos, Cambodia, and Myanmar(group 2) are fully efcient. The following next year, in 2003, Sin-gapore, Malaysia, the Philippines, and Indonesia (group 1) as wellas Laos, and Myanmar (group 2) in 2003. In sum, the ndings re-

veal that Singapore, the Philippines (group 1), and Laos (group 2)have full technology efciency during the years of 20012003.

Further, Table 5 indicates the information about the unit perfor-mance in comparison with its reference sets or peers. Referencesets are the 100% efcient units, against which each inefcient unit

is compared, and each inefcient unit will have one or more peersin its reference set. Taking group 1 countries for example, althoughit is efcient, Malaysia seldom appears in the reference sets, as ithas a lower rank with regard to efcient units. In contrast, Singa-pore appears in every set for the three years, since it has a higherrank with regard to efcient units. In other hand, among the group2 countries, Laos and Cambodia seldom appear in the referencesets, while Singapore, Indonesia and the Philippines (group 1),are frequently do for all three years. Overall, it is obvious that Sin-gapore has more technology efciency than those of other ASEANcountries.

Furthermore, we discuss the relative efciency among theASEAN countries for the three years. The research results revealthat the rank correlation is 0.3950.875 for group 1 countriesand 00.79 for group 2 countries, respectively. Further detailedanalysis indicates the years 2001 and 2002 as well as 2002 and2003 have similar positive correlations for group 1 and 2 countries.In contrast, the years 2001 and 2003 has less correlation for bothgroups of countries.

4.4. Finding the contribution of input/output variables for technologyefciency in ASEAN countries

Also, in this section, we further discuss the contribution of input/output variables to technology efciency (listed in Table 6 ).In other words, these results can identify the most protable vari-ables for each country under the DEA analysis, which governmentsshould put more efforts in order to leverage their resources so as todevelop and maintain their technology efciency.

In group 1 countries, Singapore, Thailand, and Indonesia (exceptyear 2003) benet from ICT and have better outcome of PL. How-ever, RD and GC are apparently not comparable with ICT, andmay lead to worse outcome of TE. By contrast, Malaysia benetsfrom GC and has better in TE. The phenomenon indicates that thesecountries may have different strategies and resources both in tech-

nology investment and government policy. In addition, Thailandand Indonesia have little change in 2003, and the outcome of PL beneted from ICT to RD. The Philippines mainly beneted fromGC and has better TE in 2001 and 2002, somewhat there is a lackof ofcial data for TE in 2003, and thus we believe that the

Table 4

The correlation between input and output variables.

Correlation PL TE

ICT 0.91 0.68RD 0.95 0.6GC 0.49 0.61

Table 5

The relative efciency of technology for ASEAN countries for the years 20012003.

Country 2001efciency

Reference set Rank 2002efciency

Reference set Rank 2003efciency

Reference set Rank

Singapore 100 1 100 1 100 1Malaysia 100 1 100 1 100 1Thailand 99.48 Singapore, The

Philippines2 68.67 Singapore, The

Philippines, Myanmar,

Laos

4 94.91 Singapore, Malaysia,Indonesia, Laos

3

Indonesia 80.7 Singapore, ThePhilippines, Cambodia

4 90.63 Singapore, ThePhilippines, Myanmar

2 100 1

The Philippines 100 1 100 1 100 1Brunei 100 1 100 1 81.59 Indonesia, Singapore,

The Philippines5

Vietnam 76.11 Singapore, Cambodia 5 73.69 Singapore, Myanmar 3 88.69 Indonesia, Myanmar,The Philippines

4

Laos 100 1 100 1 100 1Cambodia 100 1 100 Laos 1 98.9 Indonesia, Laos, The

Philippines2

Myanmar 96.15 Singapore 3 100 1 100 1

Remark Group 1 nations 2001 vs. 2002 Rank correlation 0.875 p value 0.0522002 vs. 2003 Rank correlation 0.79 p value 0.1112001 vs. 2003 Rank correlation 0.395 p value 0.51

Group 2 nations 2001 vs. 2002 Rank correlation 0.79 p value 0.1112002 vs. 2003 Rank correlation 0.363 p value 0.5482001 vs. 2003 Rank correlation 0.0 p value 1



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contribution of input/output variables may exist some bias in thatyear. However, in general, the Philippines outperforms than othergroup 1 countries for the outcome of TE.

Together, the group 2 countries, Myanmar and Vietnam bene-

ted from ICT as much as Brunei beneted from RD, and all three

nations had better outcome of PL. However, the performance of the input and output variables of the group 2 countries are worsethan group 1. Notably and unfortunately, Cambodia lacks pub-lished data for the RD and PL sub-categories. In this regard, thecontributions of input and output have kind of bias existed. Thelast group 2 county Laos, which beneted from different inputsduring these three years and has better in PL. Nevertheless, somegroup 2 nations have full relative efciency under DEA analysiswhich theirphysical achievement with regard to technology signif-icantly lags the group 1 countries. Building on ndings from ourobservation, we found that countries have better application inICT may foster better outcome in PL in terms of better RD andGC to better outcome in TE.

4.5. Regression analysis of DEA efciencies

The regression analysis was carried out to verify whether theefciency scores are inuenced by the ICT, RD, and GC. An ordinaryleast-squares (OLS) regression was performed for ICT, RD, and GC,as shown in Table 7 , to determine their inuences on the technol-ogy efciency scores. Table 7 presents that the linear regression

R-squared and adjusted R-squared are 0.267 and 0.182, respec-tively. The technology efciency is thus signicantly dependenton ICT and GC, even though RD is not a signicant variable forASEAN countries. Also, we found that the ICT variable has the high-est and most positive coefcient, and which can strongly inuencetechnology efciency. Further, from the research results, we be-lieve that high technology efciency can lead to economic growth.Additionally, our results are also supported by prior studies whichfocused on the developed countries ( Colecchia, 2002; Wang, 1999 ).Similarly, Colecchia (2002) pointed out the diffusion and usage of ICT play key roles in economic growth, and depend on the rightframework conditions, instead of the existence of a large ICT-producing sector.

4.6. The measurement of scale efciency and window analysis for ASEAN countries

In this study, the ASEAN countries are considered efcient, interms of both CRS (constant returns to scale) and VRS (variable re-turns to scale). CRS is often regarded as productive efciency, whileVRS is regarded as technical efciency. The scale efciency is theratio of productive efciency to technical efciency. It has beenshown that DEA efciency scores computed with the CRS assump-tion are less than or equal to the corresponding VRS efciencyscores ( Banker, Charnes, & Cooper, 1984; Charnes, Cooper, & Lewin,1994 ), and thus, the scale efciency of a DMU operating in its mostproductive size is 1.

In Table 8 , we regard the three years data as an independent

DMU as well as evaluate the technology efciency with CRS and

Table 6

The contribution of input and output for SEA countries.

Country (year) Input Output

ICT RD GC PL TE

Cambodia-2001 1 99 100Cambodia-2002 100 100Cambodia-2003 95 5 100Brunei-2001 4 96 100Brunei-2002 6 94 18 82Brunei-2003 35 65 100Laos-2001 100 100Laos-2002 97 3 100Laos-2003 96 4 100Myanmar-2001 100 100Myanmar-2002 91 9 100Myanmar-2003 87 13 100Vietnam-2001 96 4 100Vietnam-2002 97 3 100Vietnam-2003 30 70 100Malysia-2001 11 89 35 65Malysia-2002 15 85 30 70Malysia-2003 100 100Philippines-2001 100 100Philippines-2002 100 100Philippines-2003 36 64 100

Singapore-2001 100 100Singapore-2002 88 10 2 100Singapore-2003 87 10 3 100Thailand-2001 100 89 11Thailand-2002 88 6 6 77 23Thailand-2003 53 12 35 43 57Indonesia-2001 92 8 95 5Indonesia-2002 93 7 84 16Indonesia-2003 81 19 98 2

Table 7

The results of multiple regressions.

Variable Independent variable

ICT RD GCCoefcient 3.19669 1.04611 2.03258t -statistic 2.53085 1.09598 2.72543 p value 0.017769

*

0.283136 0.011336*

Intercept 99.67R-squared 0.267Adjusted R-squared 0.182

*

Indicates the variable is signicant with technology efciency ( p value < 0.05).

Table 8

The scale efciency of ASEAN countries.

Nation (year) 2001 2002 2003

CRS efciency VRS efciency Scale efciency CRS efciency VRS efciency Scale efciency CRS efciency VRS efciency Scale efciency

Singapore 1 1 1 1 1 1 1 1 1Malaysia 0.9985 1 0.999 1 1 1 0.9967 0.9995 0.998Thailand 0.9855 1 0.986 0.6569 0.8346 0.787 0.7247 0.895 0.81Indonesia 0.793 0.8143 0.974 0.8747 0.8949 0.977 1 1 1The Philippines 1 1 1 1 1 1 1 1 1Brunei 0.8307 0.8307 1 0.8314 0.8314 1 0.8159 0.8159 1Vietnam 0.7574 0.7574 1 0.7365 0.7365 1 0.8869 0.8869 1Laos 1 1 1 1 100 1 0.9973 0.9973 1Cambodia 1 1 1 0.9888 0.9888 1 0.9865 0.9865 1

Myanmar 0.9615 0.9745 0.987 0.9967 0.9967 1 1 1 1



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VRS. Also, the scale efciency is then represented as the potentialgrowth in output-based efciency. As a result, Singapore and thePhilippines are the most efcient countries, which sustain theirefciency from 2001 to 2003. In contrast, Thailand has decreasingefciency in 2001 and 2003 and Indonesia and Vietnam have posi-tive growth in 2001 and 2003.

Our research ndings provide evidence that the CRS efcienciesare lower than the corresponding VRS efciencies.Forexample, theCRS and VRS efciencies in 2002 for Thailand are 65.69 and 83.46,respectively. It suggests that Thailand does not operate at the bestpossible scale size. Further, in Table 8 , Thailands scale efciency is0.787 (


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application of ICT as well as TE to RD and GC. Second, the variableof ICT is the mainly protable factor for technology efciency to

ASEAN countries. In particular, from the standpoint of countryview, both Singapore and the Philippines are the most efciencycountries among technology efciency, scale efciency as well aswindow analysis. However, although it is lack of ofcial publisheddata in some countries, this study, as expected, makes great dealnumbers of valuable contribution to the eld of technology ef-ciency research to ASEAN countries. Third, our ndings also indi-cated there are some less-developed countries which have fulltechnology efciency, and merely attain low ranking in compari-son with the group 1 countries.

In sum, in this paper we introduced and examined a model thatuse DEA analysis in technological evaluation studies along withsuitable input and output variables that reect the physical tech-nology efciency in ASEAN countries. Furthermore, our results

present different measurements to evaluate the related indices aswell as make comparisons with the countries studied. Finally and

not surprisingly, we acknowledge our study has potential limita-tions that must be kept in mind when evaluating these results.

Firstly, it is a lack of ofcial published data for some less-developedcountries. Secondly, we employ the dominant factors of technologydevelopment and management capability for the countries exam-ined, instead of considering the factors of technology investmentand evaluating the technology efciency. The main reason is thatsuch less-developed countries should track the path of developedcountries on the synergy of technology development and manage-ment capability so as to enhance their technology efciency. Theseinputs will offer more information to analysis and provide a robustframework measuring the technology efciency of ASEAN coun-tries. Finally, there is still much to learn, and it is hoped that fur-ther research will explore some of the above issues in more detail.

Appendix A

See Figs. A1A6 .

Fig. A6. The correlation between GC and TE.

Fig. A5. The correlation between GC and PL.



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References

Archibugi, D., & Coco, A. (2001). Thetechnological performance of Europein a globalsetting. Industry & Innovation, 8 , 245266.

Archibugi, D., & Coco, A. (2004). A new indicator of technological capabilities fordeveloped and developing countries (ArCo). World Development, 32 (4),629654.

Archibugi, D., & Coco, A. (2005). Measuring technological capabilities at the countrylevel: A survey and a menu for choice. Research Policy, 34 , 175194.

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimatingtechnical and scale efciencies in data envelopment analysis. Management Science, 30 (9), 10781092.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring theefciency of decisionmaking units. European Journal of Operational Research, 2 , 429444.

Charnes, A., Cooper, W. W., & Lewin, A. Y. (1994). Data envelopment analysis: Theory,methodology and applications . Boston: Kluwer.

Charnes, A., Cooper, W. W., Lewin, A. Y., Morey, R. C., & Rousseau, J. (1985).Sensitivity and stability analysis in DEA. Annals of Operations Research, 2 ,139156.

Chin, S. Y. (2005). Diverging information societies of the Asia Pacic. Telematics andInformatics, 22 , 291308.

Colecchia, A. (2002). ICT investment and economic growth in the 1990s: Is theUnited States a unique case? Review of Economic Dynamics, 5 , 408442.

Dahlman, C., & Frischtak, C. (1993). National systems supporting technical advancein industry: The Brazilian experience. In R. Nelson (Ed.), National innovationsystems . Oxford: Oxford University Press.

Degraeve, Z., Lambrechts, M., & Van Puyenbroeck, V. (1996). Een vergelijkendeprestatiestuidie van de departementen van de Katholieke Universiteit Leuven.

Tijdschrift voor Economie en Management, 41 , 165193.Desai, M., Fukuda, S., Johansson, C., & Sagasti, F. (2002). Measuring the technology

achievement of nations and the capacity to participate in the network age. Journal of Human Development, 3 (1), 95122.

Dosi, G., Freeman, C., Nelson, R., Silverberg, G., & Soete, L. (1988). Technical changeand economic theory . London: Francis Pinter Publishers.

Farrell, M. J. (1957). The measurement of productive efciency. Journal of the RoyalStatistical Society, 30 (120), 253281.

Freeman, C. (1982). The economics of industrial innovation . London: PinterPublishers.

Hmlinen, T. J. (2003). In E. Elgar (Ed.), National competitiveness and economic growth: The changing determinants of economic performance in the worldeconomy, Cheltenham, UK .

Katz, J., & Bercovice, N. (1993). National systems of innovation supporting technicaladvance in industry: The case of Argentina. In R. Nelson (Ed.), Nationalinnovation systems . Oxford: Oxford University Press.

Lall, S., & Albaladejo, M. (2001). Indicators of relative importance of IPRs indeveloping countries. Background paper for ICTSD/UNCTAD capacity building project on trips and development .

Li, Y., & Deng, S. (1999). A methodology for competitive advantage analysis andstrategy formulation: An example in a transitional economy. European Journal of Operational Research, 118 , 259270.

Link, A. N. (1996). Economic performance measures for evaluating government-sponsored research. Scientometrics, 36 (3), 325342.

Linn, R. J., Zhang, W., & Li, Z. Y. (2000). An intelligent management systemfor technology management. Computers and Industrial Engineering, 38 ,397412.

Lundvall, B. A. (1992). National systems of innovation: Towards a theory of innovationand interactive learning . London: Pinter Publishers.

Markides, C. C., & Williamson, P. J. (1994). Related diversication, core competencesand corporate performance. Management Journal, 15 , 149165.

Moon, H. C., Rugman, A. M., & Verbeke, A. (1995). The generalized double diamondapproach to international competitiveness. In A. Rugman, J. Van Den Broeck, &A. Verbeke (Eds.). Research in global strategic management. Beyond the diamond(Vol. 5, pp. 97114). Greenwich, CT: JAI Press.

Nelson, R. (Ed.). (1993). National systems of innovation: A comparative study . Oxford:Oxford University Press.

Norman, M., & Stoker, B. (1991). Data envelopment analysis: The assessment of performance . New York: John Wiley & Sons.

Porter, M. E. (1990). The competitive advantage of nations . New York: Free Press.Prahalad, C. K., & Hamel, G. (1990). Thecore competence of thecorporation. Harvard

Business Review, 68 (3), 7991.Raymond, S. U. (Ed.). (1997). Science-based economic development. Annals of the New

York academy of sciences (Vol. 798). New York: The New York Academy of Sciences.

Ritter, T., & Gemunden, H. G. (2004). Theimpact of a companys business strategy onits technological competence, network competence and innovation success. Journal of Business Research, 57 (5), 548556.

Roessner, J. D., Porter, A. L., Newman, N., & Caufel, D. (1996). Anticipating thefuture high-tech competitiveness of nations: indicators for twenty-eightcountries. Technological Forecasting and Social Change, 51 (2), 133149.

Rousseau, S., & Rousseau, R. (1997). Data envelopment analysis as a tool forconstructing scientometric indicators. Scientometrics, 40 (1), 4556.

Schulz, M. (2001). The uncertain relevance of newness: Organizational learning andknowledge ows. Academy of Management Journal, 44 (4), 661681.

Sherman, H. D. (1984). Data envelopment analysis as a new managerial auditmethodology Test, evaluation and auditing. Journal of Practice and Theory, 4 ,3553.

Teece, D. J. (1998). Capturing value from knowledge assets: The new economy,markets for know-how, and intangible assets. California Management Review, 40 ,5579.

Ulengin, F., Ulengin, B., & Onsel, S. (2002). A power-based measurement approach tospecify macroeconomic competitiveness of countries. Socio-Economic Planning Sciences, 36 , 203226.

Ulrich, D., & Lake, D. (1991). Organizational capabilities: Creating competitiveadvantage. Academy of Management Executive, 5 (1), 7792.

Union Nation Development Program (UNDP) (2003). Human development report .Retrieved from < http://www.hdr.undp.org/statistics/data/2006.01.20 >.



Wang, E. H. H. (1999). ICT and economic development in Taiwan: Analysis of theevidence. Telecommunications Policy, 23 , 235243.

Wang, T. Y., Chien, S. C., & Kao, C. (2007). The role of technology development innational competitiveness Evidence from Southeast Asian countries.

Technological Forecasting and Social Change, 27 , 471488.Wanger, C. S., Brahmakulam, I. T., Brian, A., Jackson, A., & Wong, T. Y. (2001). Science

and technology collaboration: Building capacity in developing countries .Washington, DC: RAND Corporation. Document No. MR-1357.0-WB.

World Bank (2005). World development indicators, Washington, DC . .

World Economic Forum (WEF) (2005). The global competitiveness report . New York:Oxford University Press.

Zahra, S. A. (1999). The changing rules of globalcompetitiveness in the21st century. Academy of Management Executive, 13 (1), 3642.

http://www.hdr.undp.org/statistics/data/2006.01.20http://www.hdr.undp.org/statistics/data/2006.01.20http://www.hdr.undp.org/statistics/data/2006.01.20http://www.worldbank.org/http://www.worldbank.org/http://www.worldbank.org/http://www.worldbank.org/http://www.worldbank.org/http://www.hdr.undp.org/statistics/data/2006.01.20http://www.hdr.undp.org/statistics/data/2006.01.20http://www.hdr.undp.org/statistics/data/2006.01.20

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