外文翻译译文

利用DEA数据包分析法评估集成半导体公司的研发活动

——台湾案例研究

陈智泰1、林明汉2

1副教授，工业工程与管理学系，大华工学院，新竹，307，台湾，中华民国。

电话:+ 886-3-5927700分机2959，传真:+ 886-3-5926848

电子邮件:iectchen@https://www.wendangku.net/doc/e310906419.html,.tw.

2副教授，自动化工程系，大华工学院，新竹，307，台湾，中华民国。

电话:+ 886-3-5927700分机2660，传真:+ 886-3-5921047

电子邮件:aemhlin@https://www.wendangku.net/doc/e310906419.html,.tw.

摘要

研发活动的评估已经成为一个兼学术研究和实际需要的重要问题。本文使用DEA数据包分析方法对52家位于台湾新竹科学工业园区集成半导体公司的研发效率进行了实证研究。结果表明，尽管大多数公司的技术效率较高，它们的研发活动效率却大不相同。除此之外，本文还讨论了提高低研发效率公司研发效率的可能途径。特别地，我们分析发现大多数的低研发效率公司应该增加他们的经济规模来提高其研发效率。

关键词:研发效率、高效、数据包分析法、高科技公司

1、引言

研发活动的评估已经成为一个兼学术研究和实际需要的重要问题。在如今的高度经济全球化时代，一个高效运转的研发部门是公司

竞争优势的主要来源。当前的经济形势正迫使高科技公司通过提高研发效率，同时降低成本来相互竞争。为了评估机会成本和内部研发收入，以及将这些因素纳入成本考虑范围，研发的投入更加强烈地基于经济考虑[9]。针对研发这一话题，已经有许多研究及大量的文献报道。例如:(1)研发的组织[24]；(2)研发项目的评估[38][42]；(3)衡量研发进程的原则或指导思想[20][37][48]；(4) 影响研发结果的因素[9][37]；(5)研发活动的衡量[9][10][20][29][37][48]等。这些研究从微水平视角专注研究了研发活动的某个方面。然而，评估高科技公司研发活动效率的研究却是凤毛麟角。

高科技产业有着高风险和高投资回报的本质。由于研发活动的累积性、不确定性、边缘化及依赖于行业等特点，使得研发效率的评估变得尤为困难。然而，大多数高科技公司的管理者却依靠他们的经验来评估研发活动。特别地，管理者很难确定某个因素对研发活动贡献的多少。因此，管理者不知道应该增强哪个因素来提高公司的研发效率。

本文采用数据包分析(DEA)法对52家位于台湾新竹科学工业园区集成半导体公司的研发效率进行了实证研究。台湾是世界领先的半导体集成供应商之一。集成半导体行业由高灵活性、对行业环境变化反应迅速以便调整产品线来满足客户需求的公司组成。本文的其余部分组成如下。第二部分综述了现有文献对研发活动衡量的研究。第三部分描述了数据包分析法在这项研究中应用。第四部分是数据和影响因素。第五部分讨论了结果和分析方法。第六部分对结果进行了总结。

2、文献综述

为评估研发活动，已经有许多定量和定性的技术和方法被开发出来。例如，诸如利润/收入比率、投资回报、边际回报率和盈利/销售[22][26][46]等财务分析方法;为选则和评估研发效率的研发强度(研发支出/销售)而开发的单比法[7][36][46]等。然而，这些方法的关注点在于一个单一的方面，例如研发支出，实收资本，专利等。为了提高管理水平，管理者需要一种方法来理解研发的整体性能和发展方向。

考虑到评估研发效率的多重属性，一些研究人员依靠经验开发出了一些方法，如采用回归模型来分析研发的生产力。比如，Gilman[26]研究了股票价格和最优研发支出，指出本益比与研发强度呈正相关。Morbey和Reithner[37]使用回归模型研究了研发效果、生产力和盈利能力之间的关系。Zif 等[50]利用多元回归模型分析了高研发投资业务的特点。

此外，研究人员还基于决策科学建立了源于主观的模型，比如为优先权的设置和研发资源的分配而建立的层次分析法(AHP)。例如，Liberatore[33]在研发评估上，为应用层次分析法(AHP)及相关支持方法建立了一个模型框架。Werner和Souder[48-49]综述了研发效能的衡量方法。Rouse和Boff [40]总结了研发/技术管理的研究现状。与此同时，Cordero[18]研究了公司研发性能衡量的总体情况。

然而，对于研发性能的评估来说，输入和输出之间的关系通常是不明显的。数据包分析（DEA）模型[14]不需要像其层次分析法等其他方法一样预先确定输入和输出的相对权重。数据包分析法（DEA）已

在包括中小学和大学、医院、银行等相对效率的衡量中得到有效应用。这些特性刚好符合研发活动的特点。然而，即便如此，很少有应用数据包分析法DEA来评估研发效率的相关研究。

3．方法

数据包分析法(DEA)通过将与之相关联的同组输入转化为输出的方式来计算公司的效率。Charnes等[14]基于Farrel公司的高效率技术理念[23]，率先开发了数据包分析法(DEA)。数据包分析法（DEA）在本质上是一种将多元输入和输出转换成一个效率测量标量的线性程序技术。

这种转换是通过比较各公司提供的输出组合的值和使用的输入值与其他所有公司进行比较而实现的。作为一个高效公司的组合，每个公司都与一个假设的具有相同输出组合的公司进行评估。数据包分析法（DEA）在众多的公司中甄别最高效的公司，且为其他所有公司进行低效的衡量。最有效公司的效率得分为1，而其他较低效企业机构的得分在0和1之间。尽管数据包分析法（DEA）不对最优效率进行衡量，然而它却可以将所有公司中效率最低的几家公司区分开来。因此使用数据包分析法（DEA）来评估机构效率是构建了最佳的实践前沿。

在这项研究中，使用数据包分析法（DEA）来评估研发活动的相对效率，这样能较好理解高科技产业中每家公司的资源利用率。特别地，该分析方法的结果在区分高效和低率公司的同时还能为各个公司效率的提高指明方向。

本研究采用了两个数据包分析法（DEA）模型，即CCR模型[14]和BCC模型[5]。使用CCR模型来分析获得总体效率。利用BCC模型来衡量技术规模和技术规模回报。综合这两种分析模型得出的结论，可以判断低效归因于增加和减少规模回报的程度，除了可能的资源分配差异。以下是CCR模型和BCC模型的详细方程:

A．CCR模型

Charnes等提出的CCR模型[14]被应用于衡量DMUs的效率。如果DMU可以增加输出变量或减少输入变量的值而不增加额外的输入变量或减少输出变量的生成，那么它就是高效的。

对于每个DMUo，CCR模型如下:

B．BBC 模型[5]

对于每个DMUo，BBC模型如下:

4.案例研究

本实证研究包括:(1)输入和输出变量的选择；(2)利用CCR模型对总效率进行评估，通过BCC模型对技术效率和规模效率进行评估；(3)讨论低效率DMUs的改进策略。

4.1输入和输出变量的选择

根据现有的研究([7][12][31][36][43][46-47][50])，影响研发结果的主要因素有：(1)研发支出；(2)研发人员；(3)实收资本；(4)销售；(5)行业分类；

(6)公司规模；(7)公司历史；(8)市场份额；(9)专利数量。在这项研究中，我们选择了四个输入变量和两个输出变量。

四个输入变量如下:

A:公司的历史

P:公司的实收资本

R&D: 公司年度研发支出，包含研发现金支出和资本支出

R: 公司研发或相关部门员工数量

两个输出变量如下:

S:公司的年销售额

T:国内外专利认证机构认证的专利数量

研究人员在研究研发支出和产生的专利数量之间的关系时发现在衡量研发效率时专利起到重要作用[8][21][35][39][47]。因此，专利是在比较高科技企业时甄别哪个领域有技术进步的有用的指示器。另外，结合销售，研发支出，和研发人员数量等数据为评估公司研发效率提供

基础。52家位于台湾新竹科学工业园区集成半导体公司的数据收集于1995年(科技园年度报告，1995)。为了保密，这些公司分别被编码为DMU1到DMU 52。

表1 输入和输出之间的相关性

在构念效度的评估上，我们使用相关性分析来检查验证输入和输出变量的等渗性[6][27 - 28]在输入和中间输出进行验证。

如表1所示，我们发现输入和输出之间存在显著的正相关性。因此，DEA模型的等渗性假设得以验证。另外，如文献[6]所推荐的那样，输入和输出变量的数量必须小于或等于所选择公司数量的三分之一。在本项研究中，我们使用了6个变量来评估这52家公司。所选公司数量及其与之相关的与输入和输出变量的数量应该要足够多以确保效率评估具有意义。

5、结果与讨论

A.总效率

通过计算机编程来实现CCR模型，用其计算结果来评估52家位于台湾新竹科学工业园区集成半导体公司的效率。如表2和表3所示，52家公司的效率值是明显不同的。效率比的平均值为0.563，这意味着约43.7%的公司需要提高其效率。

另一方面，有10家公司的总效率值是1(例如，在效率边界上)。

在所有低效率公司中，DMU31是个例外，其效率值为0.985。有40%的公司(52家中的20家)效率值超过0.5，有22%的公司(42家中的11家) 效率值小于0.1。低效率公司的效率值差异可能由种种不同的原因比如产品技术、研发滞后、公司规模和其他因素等导致。

表2 DMUs的总效率(TOE)，技术效率(TEE)，

规模效率(SCE)规模报酬(RTS)

表3总效率(TOE)，规模效率(SCE)

和技术效率(TEE)的平均值和偏差

B.技术效率和规模效率

技术效率和规模效率可以通过BCC模型得到。总效率的数值(TOE)由两个要素组成:技术效率和规模效率。表2第二列给出了技术

效率值，即所谓的纯技术效率提法[5]。由表2可以看出，技术效率的平均值为0.8。这说明在本研究中大多数公司能很好地利用其技术资源。只有少数公司需要提高其技术资源的利用率。尽管DMU 17、26、28、30、40和41公司的效率低下，但是他们的技术效率却等于1。这意味着他们的总体效率低下是由于公司规模而不是由于他们不能充分利用技术资源而造成的。

规模效率通过TOE对TEE的比率来衡量。它是通过实际生产结构相对于规模效率输出的百分比来计算的，这意味着是由公司规模的预期得到资源的利用率，即它的操作规模。因此(1 - TOE/TEE)即是由于公司规模较小而导致的可能的输出损失。

如表2所示，公司效率低下主要归因于公司规模这一要素，即公司的规模大小的差异。此外，最后一列表示各个公司对于规模的回报类型。表2第四列为其公司权重最优值的总和[16]。如果其RTS值超过、小于或等于1，其数值表现为对规模回报的增加(IRS)，降低(DRS)或保持不变(CRS)。本列的意义在于，如果想要提高公司效率，其为何种资源配置需要重新定向指明了方向。

公司的效率对规模的回报一定是持续的。从表2的最后一列可以看出，只有DMU 3，5，21和33是对规模的回报是递减的。这意味着大多数公司并不在于要扩大其经济规模，而应该扩大其公司的规模。规模效率低下是DMUs效率低下的主要原因。

在所调查的企业中有22个效率低下的企业，其中15家公司的技术效率值比其他公司大(即DMU，6、7、14、17、2326、28 –30、32、

40、41、45、48和52的效率值)。这些公司的资源效率低下可能由于规模因素的影响，而不是因为技术因素。对于那些对规模效应增加的公司来说，关键的问题是尽可能增加投资来扩大规模。DMU 3、5、21和33对规模回报减少，即他们的技术效率值小于规模效率值。因此，他们应该提高他们的技术因素增加研发活动的效率。

6、结论

本文使用数据包分析方法，评估了52家位于台湾新竹科学工业园区集成半导体公司集成半导体公司的研发效率。结论如下: 1．52家集成半导体公司的效率值显著不同。有56.3%的公司总效率值是1。

2．造成公司效率低下的主要因素是规模效应，即资源分配和使用模式的差异。

3．大多数的低效率公司规模回报增加了。这意味着大多数低效率的公司有技术资源，但规模效率应该加强。

4．应该做进一步的研究，来应用数据包分析法（DEA）评估其他高科技公司的研发效率来验证该方法得到的数据的可靠性。

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(文档含英文原文和中文翻译) 中英文翻译原文：

DESIGN and ENVIRONMENT Product design is the principal part and kernel of industrial design. Product design gives uses pleasure. A good design can bring hope and create new lifestyle to human. In spscificity,products are only outcomes of factory such as mechanical and electrical products,costume and so on.In generality,anything,whatever it is tangibile or intangible,that can be provided for a market,can be weighed with value by customers, and can satisfy a need or desire,can be entiled as products. Innovative design has come into human life. It makes product looking brand-new and brings new aesthetic feeling and attraction that are different from traditional products. Enterprose tend to renovate idea of product design because of change of consumer's lifestyle , emphasis on individuation and self-expression,market competition and requirement of individuation of product. Product design includes factors of society ,economy, techology and leterae humaniores. Tasks of product design includes styling, color, face processing and selection of material and optimization of human-machine interface. Design is a kind of thinking of lifestyle.Product and design conception can guide human lifestyle . In reverse , lifestyle also manipulates orientation and development of product from thinking layer.

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外文翻译 原文 Foreign T rade o f China Material Source:W anfang Database Author:Hitomi Iizaka 1.Introduction On December11,2001,China officially joined the World T rade Organization(WTO)and be c a me its143rd member.China’s presence in the worl d economy will continue to grow and deepen.The foreign trade sector plays an important andmultifaceted role in China’s economic development.At the same time, China’s expanded role in the world economy is beneficial t o all its trading partners. Regions that trade with China benefit from cheaper and mor e varieties of imported consumer goods,raw materials and intermediate products.China is also a large and growing export market.While the entry of any major trading nation in the global trading system can create a process of adjustment,the o u t c o me is fundamentally a win-win situation.In this p aper we would like t o provide a survey of the various institutions,laws and characteristics of China’s trade.Among some of the findings, we can highlight thefollowing: ?In2001,total trade to gross domestic pr oduct(GDP)ratio in China is44% ?In2001,47%of Chinese trade is processed trade1 ?In2001,51%of Chinese trade is conduct ed by foreign firms in China2 ?In2001,36%of Chinese exports originate from Gu an gdon g province ?In2001,39%of China’s exports go through Hong Kong to be re-exported elsewhere 2.Evolution of China’s Trade Regime Equally remarkable are the changes in the commodity composition of China’s exports and imports.Table2a shows China’s annu al export volumes of primary goods and manufactured goods over time.In1980,primary goods accounted for 50.3%of China’s exports and manufactured goods accounted for49.7%.Although the share of primary good declines slightly during the first half of1980’s,it remains at50.6%in1985.Since then,exports of manufactured goods have grown at a much