ساخت چارچوب اندازه گیری بهره وری فن آوری با توسعه فن آوری و قابلیت مدیریت - شواهد از کشورهای ASEAN

In this paper, we present a research framework with data envelopment analysis (DEA) approach to evaluate a nation’s technology efficiency and effectiveness in ASEAN countries. The study proposes two outputs, patents and licenses (PL) and technology exports (TE) along with three inputs, information and communication technology (ICT), R&D (RD), and governance capability (GC) in the model. Building on evidence from our research, we found that country has better outcome in PL can be derived from better application in ICT which in terms of TE to RD and GC as well. Additional findings also revealed the variable of ICT is mainly advantageous to technology efficiency in ASEAN countries. Further, from the viewpoint of country, our results indicate both Singapore and the Philippines are the most efficient countries among the variables in technology efficiency, scale efficiency, and window analysis as well. Moreover, our findings suggested some other countries may explore the suitable strategy to enhance their technology efficiency with benchmarking countries.

The organization and structure of business processes in technological activities can be regarded as strategic tools to sustain the core competence of a nation’s economy. As Raymond (1997) noted that technology plays a critical ingredient in the economic development of countries. Thus, continual investment in technology to build dynamic capabilities is one of the three sources of successful competition in the 21st century (Zahra, 1999). Wang (1999) also pointed out that the application of information and communication technology (ICT) has revolutionized the structure of both management and competition in the emerging global economy. Findings of his research indicated the effect of technology application (i.e. high technology efficiency) on economic growth can be achieved merely through strong national information infrastructure (i.e. technology development and management capability). Additionally, prior works defined the core competence as bundles of skills and technologies (Prahalad & Hamel, 1990), or pools of experience, knowledge, and systems that can be considered to create and accumulate new strategic assets (Markides & Williamson, 1994). These strategic assets, which are imperfectly imitable, constitute a nation’s competitive advantage. Therefore, following the above research, we have confidence that effective technology development and management capability are the determinant factors to sustain core competence for nations. In addition, to sustain national competitiveness and create value in the network economy, a rich body of prior research shows that technological competence of a country is not only to be given precedence but needs to be developed network competence to link with other alliances (Ritter & Gemunden, 2004). While the former capabilities are based on the integration of technology; the latter should be managed under a system of global integration to allocate resources within strategic alliance. Consequently, the strategic application of this principle must emphasize both “dynamic” and “capability” within management system to achieve technology efficiency (Schulz, 2001 and Teece, 1998). According to Ulrich and Lake (1991) argue that “competing from the inside out” requires a continuous effort in building appropriate technological skills with sufficient management resources. In this respect, how to advance technology efficiency by means of better technology development (e.g. improved information and communication technology, higher R&D expenditures, and more scientists and engineers in R&D) as well as management capabilities (e.g. higher education levels and a better socio-economic environment) are critical issues for researchers and authority of a nation. Moreover, a variety of factors may be included in the category of technology development, management development and technology efficiency as well. With regard to the determinant factors of national competitiveness (Hämäläinen, 2003), enterprise competitiveness with the diamond model (Porter, 1990), the double diamond model (Moon, Rugman, & Verbeke, 1995), the national innovation system (Dosi et al., 1988, Freeman, 1982, Lundvall, 1992 and Nelson, 1993), technology competitiveness (Roessner, Porter, Newman, & Cauffiel, 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 and researchers have proposed evaluation models and frameworks to measure competitiveness for specific issues, such as world competitiveness yearbook – the International Institute for Management Development (IMD); the global competitiveness report – World Economic Forum (WEF). According to the prior research models and frameworks, various factors are explored that related to the interrelationships among technology development, management capability, and technology efficiency in developed countries, respectively. However, few studies focus on these issues with regard to less-developed countries (Dahlman and Frischtak, 1993 and Katz and Bercovice, 1993). Recently, National Cheng Kung University (NCKU) joined with the research in ASEAN countries and proposed a national competitiveness model in both developing and less-developed countries. Yet, it was still lack of official published reports both in developed and developing countries and limited their analysis in this region (Wang, Chien, & Kao, 2007). Based on the past literature, technology development and management capability are the two factors that nations need to put more efforts in order to specialize in their technology efficiency and stimulate economic growth. Linn, Zhang, and Li (2000) emphasized that technology should not only fulfill the management needs of a specific 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 social resources (education, health, and welfare), current technologies, future markets, and socio-economic environment to improve technology efficiency is crucial for most nations. In addition, the synergy of technology development and management capability may sustain a nation’s economic growth, and consequently, it is not surprising that few developed countries have previously engaged in technology investment under effective management capability to achieve their economic growth. Unfortunately, there fails clear evaluation for technology efficiency in relation to national resources (e.g. education, technology investment, R&D, finance, health, and welfare). One definition could be made of resources in the attainment of technological goals, taking technological and managerial factors into account. Still, another problem in evaluating the technology efficiency 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 technology efficiency” of units was introduced (Farrell, 1957). However, Link (1996) argued the production function approach has a number of significant limitations which render its usefulness to an evaluation of government-sponsored research projects questionable (Farrell, 1957). Consequently, the measurement of technology efficiency will be undertaken in this study by using the technique of data envelopment analysis (DEA) approach. DEA is an operations research-based method to measure the performance efficiency of decision units that are characterized by multiple inputs and outputs. In addition, DEA converts multiple inputs and outputs of a decision unit into a single measure of performance, generally referred to as relative efficiency. The research of Charnes, Cooper, and Rhodes (1978) was the first to propose the DEA method as an evaluation tool for decision units. Since then, DEA has been applied successfully as a performance evaluation tool in many research fields, e.g. in studies of scientific wealth of European nations (Rousseau & Rousseau, 1997), hospital administration, the organization of the US Navy Recruitment Command (Norman & Stoker, 1991), school districts, secondary education (Sherman, 1984), and universities (Degraeve, Lambrechts, & Van Puyenbroeck, 1996), respectively. In DEA the performance of each unit under study is compared with that of every other one. Units which perform best use their inputs more optimally than the others and the most optimal units form a frontier, called the “efficiency frontier”. Less performing units need more input to produce the same amount of output and are therefore situated at some distance of the frontier. Units situated on the efficiency frontier will have a relative performance rate of 1 (they are a 100% efficient), and the frontier is said to envelop all decision-making units (DMUs). In this study, we specifically designed the DMU be the ASEAN countries. The organization of the study is organized as follows: in the section two, we present the research framework and data structure. Next, we describe the data sources and DEA. In the following sections, we provide the empirical studies of ASEAN countries, and the conclusions as well as suggestions are discussed in the final section.

In this study, we aim to employ DEA analysis to assess and examine the efficiency and effectiveness of a nation’s technology efficiency with the ASEAN countries. Notably, our research reveals there exist high correlations between inputs and outputs, and two groups of nations divided by input achievement with significantly different mean under p < 0.05, compared with their relative efficiency. Additionally, we make several advances in understanding that technology efficiency construction in our research findings. First, the better outcome of PL can be derived from better application of ICT as well as TE to RD and GC. Second, the variable of ICT is the mainly profitable factor for technology efficiency to ASEAN countries. In particular, from the standpoint of country view, both Singapore and the Philippines are the most efficiency countries among technology efficiency, scale efficiency as well as window analysis. However, although it is lack of official published data in some countries, this study, as expected, makes great deal numbers of valuable contribution to the field of technology efficiency research to ASEAN countries. Third, our findings also indicated there are some less-developed countries which have full technology efficiency, and merely attain low ranking in comparison with the group 1 countries. In sum, in this paper we introduced and examined a model that use DEA analysis in technological evaluation studies along with suitable input and output variables that reflect the physical technology efficiency in ASEAN countries. Furthermore, our results present different measurements to evaluate the related indices as well as make comparisons with the countries studied. Finally and not surprisingly, we acknowledge our study has potential limitations that must be kept in mind when evaluating these results. Firstly, it is a lack of official published data for some less-developed countries. Secondly, we employ the dominant factors of technology development and management capability for the countries examined, instead of considering the factors of technology investment and evaluating the technology efficiency. The main reason is that such less-developed countries should track the path of developed countries on the synergy of technology development and management capability so as to enhance their technology efficiency. These inputs will offer more information to analysis and provide a robust framework measuring the technology efficiency of ASEAN countries. Finally, there is still much to learn, and it is hoped that further research will explore some of the above issues in more detail.