外文翻譯--基于dea方法的國家r&d項(xiàng)目多重目標(biāo)績效評(píng)價(jià)比較

1、<p><b>　　中文2287字</b></p><p>　　Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach</p><p>　　Hakyeon Lee a, Yongtae P

2、ark a,*, Hoogon Choi </p><p><b>　　Abstract</b></p><p>　　The strategic importance of performance evaluation of national R&D programs is highlighted as the resource allocation draw

3、s more attention in R&D policy agenda. Due to the heterogeneity of national R&D programs’ objectives, however, it is intractably difficult to relatively evaluate multiple programs and, consequently, few studies h

4、ave been conducted on the performance comparison of the R&D programs. This study measures and compares the performance of national R&D programs using data envelopment</p><p>　　1. Introduction</p&g

5、t;<p>　　As R&D has been considered as a driving force for national competitive advantage, many countries have been raising R&D investments through various national R&D programs (Lee et al., 1996). Sinc

6、e R&D investment is one of the most decisive elements in promoting scientific and technological progress (Wang and Huang, 2007), the effective use of the limited R&D resources can be regarded as a prerequisite fo

7、r benefiting from formulation and implementation of national R&D programs. Thus, performance ev</p><p>　　Although a number of studies have been conducted to measure R&D performance at various levels,

8、 few attempts have been made at the national program-level. This is due to the heterogeneity of R&D programs in terms of policy purpose. Since each R&D program has its own primary objective such as publishing aca

9、demic papers for basic research, issuing patents and developing prototypes for applied research, and providing funds with researchers for R&D human resource development, it is intractably diffic</p><p>　

10、　Two conventional approaches to assessing R&D performance, peer review and bibliometric method do not work well for the relative evaluation of heterogeneous R&D programs. The peer review method, which is based on

11、 perceptions of well-informed experts about various quality dimensions of R&D, is inherently subjective and likely to be biased depending on interests, experience, and knowledge of the evaluators (Nederhof and van Ra

12、an, 1987; Brinnet al., 1996). The bibliometric method is considered relat</p><p>　　The tenet of this paper is data envelopment analysis (DEA) can overcome these limitations. DEA is a linear programming model

13、 for measuring the relative efficiency of decision making units (DMUs) with multiple inputs and outputs (Cooper et al., 2000). Since it can not only handle multiple outputs, but also allow each DMU to choose the optimal

14、weights of inputs and outputs which maximize its efficiency (Cherchye et al., 2007), it is capable of mirroring R&D programs’ unique characteristics by assi</p><p>　　DEA is a non-parametric approach that

15、 does not require any assumptions about the functional form of a production function and a priori information on importance of inputs and outputs. The relative efficiency of a DMU is measured by estimating the ratio of w

16、eighted outputs to weighted inputs and comparing it with other DMUs. DEA allows each DMU to choose the weights of inputs and outputs which maximize its efficiency. The DMUs that achieve 100% efficiency are considered ef

17、ficient while the other</p><p>　　The first DEA model proposed by Charnes et al. (1978) is the CCR model that assumes that production exhibits constant returns to scale. Banker et al. (1984) extended it to th

18、e BCC model for the case of variable returns to scale. When it comes to R&D returns to scale, findings from previous studies are somewhat mixed (Graves and Langowitz, 1996). It was found that R&D activity can exh

19、ibit increasing or decreasing returns to scale as well as constant returns to scale (Bound et al., 1984; Scherer, </p><p>　　2. Conclusions</p><p>　　We measured and compared the performance of th

20、e six national R&D programs with heterogeneous objectives using DEA. Every project in every program was evaluated together, and Kruskal–Wallis test with a post hoc Mann–Whitney U test was then run to compare performa

21、nce of R&D programs. The two alternative approaches to incorporating the importance of variables in reality, the AR model and output integration, were also considered. Due to the heterogeneity of national R&D pro

22、grams’ objectives, few stu</p><p>　　The DEA results are expected to provide practical implications for policy making on national R&D programs. The limited resources can be effectively allocated to severa

23、l R&D programs based on their performance rankings. R&D programs doing well (e.g. Program C and D) deserve more investments; on the other hand, poor programs (e.g. Program A and F) have to be terminated or funds

24、given to them should be cut down unless their performance is improved. Basically, DEA offers the way of improving efficie</p><p>　　To seek the way of enhancing performance, the reasons for poor performance s

25、hould be uncovered by examining the context in which poor programs are formulated and implemented, such as project selection procedure, operational regulation, funding systems, etc. It is obvious that the prerequisite fo

26、r this is to be able to measure and compare the performance of various R&D programs, which is the primary contribution of this study.</p><p>　　Nevertheless, this study is subject to some limitations. Fir

27、stly, since the projects that have not been finished at the time of data collection were not included, program performance was measured without them. Secondly, despite the fact that it takes several years for R&D out

28、puts to be achieved, the outputs produced only for two years after termination of projects were considered. These limitations will be overcome if the analysis is conducted again at some time in future. Thirdly, it may oc

29、cur </p><p>　　基于DEA方法的國家R&D項(xiàng)目多重目標(biāo)績效評(píng)價(jià)比較</p><p><b>　　摘要</b></p><p>　　國家R＆D項(xiàng)目績效評(píng)價(jià)由于它的戰(zhàn)略重要性被高度關(guān)注，其資源分配的施政綱領(lǐng)吸引了更多人的注意。但是由于國家的R＆D項(xiàng)目'的目標(biāo)的多重性，對(duì)多個(gè)項(xiàng)目進(jìn)行評(píng)估是一件相當(dāng)棘手的事，因此，很少有研

30、究對(duì)R&D項(xiàng)目進(jìn)行績效比較。本研究利用數(shù)據(jù)包絡(luò)（DEA）衡量和比較了國家的R＆D項(xiàng)目績效。由于DEA允許每個(gè)決策單元的DEA選擇投入與產(chǎn)出的效率，最大限度地發(fā)揮其最佳的權(quán)重，通過分配高權(quán)重給各個(gè)有優(yōu)勢(shì)的規(guī)劃變量是鏡像研發(fā)項(xiàng)目的獨(dú)特特征。R&D項(xiàng)目中的每一個(gè)計(jì)劃都在DEA模型里被同時(shí)評(píng)價(jià)，然后再在其他的不同的系統(tǒng)中進(jìn)行效率比較。Kruskal–Wallis和Mann- Whitney U的檢驗(yàn)測(cè)試運(yùn)行比較了R&D項(xiàng)

31、目的性能。對(duì)兩個(gè)可選擇的方法納入變量的重要性-----AR模型和輸出一體化-------也進(jìn)行了介紹。該結(jié)果有望對(duì)國家研發(fā)計(jì)劃的有效制定和實(shí)施提供政策支持。</p><p>　　一．DEA方法的介紹</p><p>　　隨著R&D是一國增強(qiáng)競爭優(yōu)勢(shì)的驅(qū)動(dòng)力的觀點(diǎn)的普及和被認(rèn)可，許多國家開始通過各種各樣的R&D項(xiàng)目，對(duì)R&D增加投資。R&D投資是推動(dòng)科技進(jìn)步的

32、決定性要素之一，對(duì)R&D資源的有效使用被認(rèn)為是從國家R&D項(xiàng)目的制定和執(zhí)行中獲益的前提。因此，對(duì)R&D項(xiàng)目進(jìn)行績效評(píng)價(jià)是非常有必要的，它能使有限的R&D資源得到優(yōu)化配置，使項(xiàng)目實(shí)現(xiàn)優(yōu)勝劣汰。</p><p>　　盡管許多研究已對(duì)項(xiàng)目的績效進(jìn)行了不同程度的衡量評(píng)估，但是很少有從國家的項(xiàng)目層次來研究過。這是因?yàn)榛趪艺吣繕?biāo)的R&D項(xiàng)目有其獨(dú)有的復(fù)雜性。</p>

33、<p>　　由于每個(gè)項(xiàng)目都有其自己的首要目標(biāo)，比如基礎(chǔ)研究項(xiàng)目的首要目標(biāo)是發(fā)表學(xué)術(shù)性研究論文，應(yīng)用型研究的目標(biāo)是發(fā)明專利和開發(fā)模型，人類資源發(fā)展研究項(xiàng)目的目標(biāo)則是為研究人員提供資金支持。因此在同樣的時(shí)間同樣的背景下對(duì)不同類型的研發(fā)項(xiàng)目進(jìn)行比較和績效評(píng)價(jià)是相當(dāng)困難的。</p><p>　　同行評(píng)議法和計(jì)量法是評(píng)估R&D項(xiàng)目的兩大傳統(tǒng)方法，但是他們對(duì)復(fù)雜多樣的R&D項(xiàng)目的績效評(píng)估并不能取得

34、很好的效果.同行評(píng)審的方法，基于對(duì)R&D項(xiàng)目各種質(zhì)量方面見多識(shí)廣的專家觀點(diǎn)得出的，本質(zhì)上是主觀的，會(huì)受評(píng)價(jià)者知識(shí)水平，經(jīng)驗(yàn)和利益偏向性的影響，而計(jì)量學(xué)方法被視為是比較客觀的方法，但結(jié)果高度依賴于測(cè)量方法。</p><p>　　本文的宗旨是利用數(shù)據(jù)包絡(luò)分析（DEA）來克服這些限制。DEA是一個(gè)用于處理具有多個(gè)輸入和多個(gè)輸出決策單元的多目標(biāo)決策問題的線性規(guī)劃模型。由于它不僅能處理多個(gè)輸出，也能讓各個(gè)決策單元（

35、DMU）來選擇輸入和輸出的最優(yōu)權(quán)重，多個(gè)輸入（輸出越小越好）和多個(gè)輸出（輸入越大越好）。通過分配高權(quán)重給各個(gè)有優(yōu)勢(shì)的規(guī)劃變量是鏡像研發(fā)項(xiàng)目的獨(dú)特特征，這項(xiàng)研究衡量和比較了六個(gè)國家在韓國的R&D項(xiàng)目上使用的DEA效率性能。</p><p>　　DEA是一種非參數(shù)方法，不需要對(duì)生產(chǎn)函數(shù)的函數(shù)形式和投入與產(chǎn)出的重要性先驗(yàn)信息做出任何假設(shè)。一個(gè)決策單元的相對(duì)效率是通過估算加權(quán)投入產(chǎn)出比來衡量，并與其他決策單元做出

36、比較， DEA允許每個(gè)決策單元的選擇使其投入與產(chǎn)出比率最大化的權(quán)重。決策單元組達(dá)到100％的效率被認(rèn)為是有效的，而低于100％的效率則被認(rèn)為是無效的。</p><p>　　第一個(gè)DEA模型是由Charnes等人于1978年提出的CCR模型，假定生產(chǎn)規(guī)模收益不變。1984年，Banker等人將它延伸到變量返回的規(guī)模下的BBC模型。當(dāng)涉及到研發(fā)規(guī)模收益時(shí)，從以往的研究結(jié)果來看好壞參半（Graves and Lango

37、witz，1996）。結(jié)果發(fā)現(xiàn)，R＆D活動(dòng)可以顯著增加或減少規(guī)模報(bào)酬，以及規(guī)模報(bào)酬不變(Bound et al., 1984; Scherer, 1983).,，因此，本研究便運(yùn)用了BCC模型。著名的DEA模型也是一個(gè)模型的目標(biāo)：最大限度地提高輸出（輸出型）或盡量減少投入（投入導(dǎo)向）。這是含蓄地假定R＆D的目標(biāo)是基于增加產(chǎn)出，而不是減少投入。因此，本研究采用產(chǎn)出導(dǎo)向模式。</p><p><b>　　二

38、．結(jié)論</b></p><p>　　我們利用DEA對(duì)六個(gè)國家的R&D項(xiàng)目的多目標(biāo)績效進(jìn)行了比較。每個(gè)項(xiàng)目中的每個(gè)計(jì)劃進(jìn)行了同時(shí)評(píng)估，Kruskal–Wallis和Mann- Whitney U的檢驗(yàn)測(cè)試運(yùn)行比較了R&D項(xiàng)目的性能。對(duì)兩個(gè)可選擇的方法納入變量的重要性-----AR模型和輸出一體化-------我們也同樣考慮在內(nèi)。由于國家的R＆D項(xiàng)目'的目標(biāo)的多重性，很少有研究對(duì)R

39、&D項(xiàng)目進(jìn)行績效比較。本研究有助于填補(bǔ)該領(lǐng)域運(yùn)用DEA對(duì)國家R&D項(xiàng)目績效評(píng)價(jià)的空白。 DEA方法，特別是不同系統(tǒng)之間的效率比較模型，被證明在多重目標(biāo)的R&D項(xiàng)目的績效比較方面是比較有效的。</p><p>　　在DEA結(jié)果被期望為國家R&D項(xiàng)目決策的制定提供的實(shí)際的應(yīng)用。有限的資源能夠依據(jù)幾個(gè)項(xiàng)目的績效排名來有效地進(jìn)行分配。做得好的R&D項(xiàng)目（例如項(xiàng)目C和D）值得更多的投

40、資，另一方面，績效差的項(xiàng)目（如項(xiàng)目A和F）應(yīng)該被終止或應(yīng)減少給予的資金，除非其績效能夠得到提高?；旧?，DEA方法提供了單位個(gè)體間優(yōu)勝劣汰的方法，盡管它沒有在本文中提出明確處理這個(gè)問題的方式。一系列高效的項(xiàng)目為每個(gè)低效項(xiàng)目提供了一套參考基準(zhǔn)，它又反過來導(dǎo)致各個(gè)項(xiàng)目績效的提升。然而，DEA告訴我們的是提高效率的方法是有多少產(chǎn)出應(yīng)增加以達(dá)到100％的效率，而不是在當(dāng)前設(shè)置增加實(shí)際產(chǎn)出的方法。</p><p>　　為了

41、尋求提高績效方式，表現(xiàn)不佳的項(xiàng)目應(yīng)通過檢查低效方案的制定和實(shí)施，如項(xiàng)目的遴選程序，運(yùn)行規(guī)則，資金系統(tǒng)等發(fā)現(xiàn)不足。各種各樣的R&D項(xiàng)目的績效是通過先決條件來衡量和比較，這是顯而易見的，而且這也算本研究的主要貢獻(xiàn)。</p><p>　　不過，這項(xiàng)研究受到一些限制。首先，由于在數(shù)據(jù)收集時(shí)未完成的項(xiàng)目不包括在內(nèi)，項(xiàng)目績效考核時(shí)并不包括他們。其次，盡管事實(shí)上R＆D的產(chǎn)出需要數(shù)年來實(shí)現(xiàn)，但是產(chǎn)出的程序在只在終止后兩年

42、進(jìn)行審議。假如在未來的某個(gè)時(shí)間這些分析能夠再次進(jìn)行，這些限制將被克服。第三，一個(gè)研發(fā)計(jì)劃被認(rèn)為是高的表演者，即使他們沒有達(dá)到自己的目標(biāo)，但在另一個(gè)領(lǐng)域成就卓越的成果，這種情況也是有可能發(fā)生的。雖然它沒有在這項(xiàng)研究中發(fā)現(xiàn)，但是在這種情況下，判斷可能引起爭議。這些問題應(yīng)在今后的研究中處理。未來研究的另一個(gè)途徑是采用擴(kuò)展的DEA模型來比較不同的結(jié)果。另一種模式將帶領(lǐng)我們尋求一個(gè)評(píng)估和比較多重目標(biāo)的國家R&D項(xiàng)目績效更好的辦法。<