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GEOGRAPHICAL RESEARCH >

2017 , Vol. 36 >Issue 4: 695 - 706

DOI: https://doi.org/10.11821/dlyj201704008

Articles

The impact of multi-dimensional proximities on university-industry cooperative innovation: Case studies of high-tech enterprises in Guangzhou

  • HU Yang , 1 ,
  • LI Xun , 1, 2
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  • 1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
  • 2. Urbanisation Institute, Sun Yat-sen University, Guangzhou 510275, China

Received date: 2016-11-11

  Request revised date: 2017-02-10

  Online published: 2017-04-20

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《地理研究》编辑部
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Abstract

With the growing importance of University-Industry cooperative innovation (U-I cooperative innovation) in regional innovation, there is an increasing concern over the factors influencing U-I cooperative innovation. While U-I cooperative innovation features a process of knowledge transfer, multi-dimensional proximity is an appropriate analytical perspective to study the influencing factors of U-I cooperative innovation. This paper argues that geographical proximity, cognitive proximity and social proximity are essential elements of a conceptual framework for the analysis of U-I cooperative innovation, which contains heterogeneous organization. As such multi-dimensional proximities represent an important factor in the promotion of U-I cooperative innovation, interactive learning has been proved to be the way to realize knowledge transfer under the influence of multi-dimensional proximities for cooperative subjects—since frequent and continuous interaction between U-I cooperative subjects can enhance the level of U-I cooperation. By constructing a theoretical framework of "Multi-dimensional proximities, geographical proximity and related proximities→Interactive learning→Level of cooperation", and based on a multi-case study methodology, this research takes high-tech enterprises in the Guangzhou Development District as an example and explores the influence of multi-dimensional proximities on "point to point" U-I cooperative innovation. The research findings show that: (1) while geographical proximity, cognitive proximity and social proximity all contribute to the level of U-I cooperation, these positive effects vary at different stages of technological innovation; (2) whilst interactive learning has significant moderating effects on multi-dimensional proximities and the level of U-I cooperation, there are noticeable periodic characteristics in its effects in terms of content, way and intensity; (3) geographical proximity, cognitive proximity and social proximity, respectively, have complementary and substitutive effects on the level of U-I cooperative innovation, although the effects may vary in different stages. In the interaction between different types of proximities, the positive influence of complementary effects is usually greater than that of substitutive effects. The conclusion is useful for us to understand the interaction process between the innovation subjects under different circumstances of proximities; in addition, it can also provide evidence for policy-making as regards rational distribution of scientific and technological resources, selection of U-I cooperative partners, as well as appropriate responses to different circumstances of proximities in the process of technological innovation cooperation.

Key words： multi-dimensional proximities; U-I cooperation; interactive learning; technological innovation stage

Cite this article

HU Yang , LI Xun . The impact of multi-dimensional proximities on university-industry cooperative innovation: Case studies of high-tech enterprises in Guangzhou[J]. GEOGRAPHICAL RESEARCH, 2017 , 36(4) : 695 -706 . DOI: 10.11821/dlyj201704008

1 引言

1990年代,在中国工业化加速进程中,不同类型、等级的高新技术园区如雨后春笋般出现。作为政府规划、构建的经济发展的空间形式,其突出优势体现在“利于企业的地理邻近和共享基础设施”[1];进入21世纪,随着中国经济发展由要素驱动向创新驱动转型,通过产学研合作创新,实现创新要素的整合与技术转移成为经济增长的重要推动力。由于产学研合作是创新主体基于相似的技术专长和社会关系,在特定空间范围内展开的合作创新活动,涉及企业、高校、科研机构之间的交流与合作,对创新主体之间邻近性的研究成为创新地理学研究的经典话题,“多维邻近与创新”论题更是西方区域经济学、创新经济学、经济地理学等学科关注的焦点[2]。技术创新合作是一个复杂的空间与非空间要素的综合体[3],多维邻近性是研究产学研合作创新影响因素的恰当的分析视角。本文运用这一分析视角,对项目形式产学研合作中创新主体之间的交互学习及合作程度进行研究,以期有助于理解不同邻近性状态下创新主体之间的相互作用过程。
广州是改革开放的前沿,先后建设了广州高新技术产业园区、科学城、知识城、生物岛等高新技术园区,集聚了中山大学、华南理工大学、广东省科学院等全国著名高校和科研院所,通过产学研合作在技术引进和自主创新等方面形成了较好的成果。以广州高新技术开发区企业的产学研合作为案例进行研究,可为科技资源的空间合理布局、产学研合作伙伴的选择及对技术创新合作中不同邻近性状况的恰当应对提供参考依据。

2 文献综述

自马歇尔提出“产业区”理论以来,地理邻近性一直是研究集聚经济的重要分析工具。20世纪70年代末至80年代初,“新产业区”现象的出现,更使地理邻近性与区域创新的论题受到了空前的关注,但在新的时代条件下,单一的地理邻近性分析视角对组织间交互学习与互动创新机理解释的局限性日益显露。20世纪90年代以来,在法国邻近动力学派和欧盟其他国家相关学者的努力下,邻近性由一维向多维拓展[4-8]。但由于对邻近性概念的理解及研究视角存在差异,对邻近性维度有种种不同划分：地理、认知、组织、制度、社会邻近性[9];地理、组织邻近性[7];地理、技术、组织邻近性[10]。目前,一个界定清晰、规范的多维邻近概念框架尚未建立[9];在相关研究中,“迄今绝大多数是从宽泛意义上探讨多维邻近在企业或产业创新中的作用”[2],初步认识到“产业集群创新更多的是受到多维邻近性的综合作用”[11]。在知识和技术密集型产业,产学研合作是知识溢出的重要渠道[12],就其实质而言,产学研合作创新是一个知识转移的过程,而在表现形式上,则是合作主体之间互动学习的过程。地理、认知、社会邻近性是适宜于对作为异质性组织的产学研合作创新活动进行分析的多维邻近概念框架。
地理邻近可从空间和时间两个维度测量,在实质上是两个合作主体能够面对面交流而没有昂贵成本的程度[10]。在中国产学研合作创新中,市域内层次的合作所占比例最高,地理邻近效应明显[13]。地理邻近可以克服不同类型组织间的制度障碍将合作推向成功[14]。地理邻近对合作创新的影响：① 降低交通费用,节约交易成本,有利于抵抗不确定性风险[15,16]。② 有利于增进行为主体间的互动和信任关系[17]。③ 促进频繁的面对面交流,带动隐性知识的传递,产生知识溢出[18]。④ 就合作创新而言,地理邻近既非充分条件也非必要条件,但可通过构建和增强其他维度邻近促进创新[9]。Torre等提出,合作创新过程中面对面交流,可以通过个体流动等方式,以临时地理邻近来暂时性实现[6]。
认知邻近是一个包含技术邻近在内的内涵更广泛的概念,每一项新的技术都有最小的知识门槛,低于这个门槛,主体间就难以进行交流、理解和成功的互动;新技术里面蕴含着隐性知识,拥有相似的知识基础才能通过交流、学习将其消化吸收[9]。认知邻近是获取外部知识的必要条件。认知邻近对合作创新的影响：① 促进组织合作中的有效沟通,使企业高效低成本地获取并吸收资源和溢出知识[19];② 有利于合作主体间相同知识基础的建立及经验与技术的共享。③ 适度的认知邻近使主体之间的知识具有互补性,可激发创新[20],过度的认知邻近则会降低主体间的异质性并导致技术锁定[21]。
社会邻近是主体间基于信任的社会嵌入关系[9]。“关系空间”能在很大程度上补充或取代实体的“点空间”的作用,推动区域内的创新活动[22,23];实证研究表明,本地与非本地关系都是创新发生的重要因素[24]。社会邻近对合作创新的影响：① 主体间的社会嵌入关系和共同经历可进一步增强双方互信[9],长期可持续的合作关系是促进产学合作创新的主要因素[25],常优于匿名或新增关系的建立[26]。② 基于信任的社会关系有利于隐性知识的交互传递,有效的互动学习需要坚定而持久的社会关系[27];③ 社会邻近可增加主体间的知识流动渠道,为知识交换提供有效的途径[28]。
不同维度的邻近性之间存在交互作用效应。地理邻近能为主体间在认知上的相互影响提供方便,确保隐性知识共享[27];当认知邻近水平不足时,地理邻近对于建立跨学科的研究合作非常重要[29]。认知邻近虽不能直接促进地理邻近性的提高,但可凭借相似的认知水平克服地理邻近的不足[30]。地理邻近与认知邻近结合对创新产生的交互影响效应既可能是互补性的,也可能是替代性的[31]。地理邻近和技术邻近对跨区域研发合作具有促进作用[32]。同一个区域的企业和高校合作有利于促进本地社会资本的增加[33],地理邻近可以通过面对面的交流建立互信从而增强社会邻近,人际社会网络是影响知识流动的重要原因[34];社会邻近可强化地理邻近产生的本地化效应,增进互信,促进交流[29]。社会邻近和地理邻近在共同研发协作过程中表现出相似的特点,可以相互替代[35]。

3 研究方法与数据来源

3.1 理论分析框架

多维邻近是产学研合作创新的重要促进性因素,互动学习是合作主体在多维邻近的作用下获取知识溢出、实现知识转移的方式,产学研主体之间频繁、持续的互动交流能促进合作程度加深。基于此,提出“多维邻近→互动学习→合作程度”的理论分析框架（图1）。
Fig. 1 Theoretical analysis framework

图1 理论分析框架

多维邻近——产学研合作创新活动受内外部多种因素的影响,多维邻近是密切关联的影响因素体系。地理邻近、认知邻近、社会邻近不仅可在合作创新中独立地发挥影响,而且由于地理邻近对其他维度邻近具有构建和增强作用,可形成不同的组合,对合作创新产生交互效应,主体之间的交互作用和共同协调是邻近关系的核心[5]。
互动学习——在合作的视角下,技术创新是企业、大学、研究机构等拥有不同类型知识和能力的主体通过互动学习实现知识转移的过程。网络关系的密切互动是创新火花产生的源泉[36],创新的程度往往取决于多维邻近下互动学习的程度和类型,因此,互动学习是创新主体之间的邻近性发挥作用的介质,将影响产学研合作的效果。
合作程度——技术创新过程可分为技术开发和技术成果转化两个阶段,前者是通过科学研究与实验产生新技术知识和新发现,后者是对技术开发形成的应用技术成果所进行的后续试验、开发、中试、生产,直至产生具有实际效用的产品[37]。在产学研项目合作中,通常是学研机构承担上游的技术开发,企业进行下游的技术成果转化,合作主体对自己不主导的一段的参与状况反映合作程度。

3.2 数据来源

本文主要从邻近性视角考察产学研合作创新主体之间的互动学习及合作程度。Eisenhardt认为,多案例研究方法适合于过程和机理类问题的研究;多案例研究一般以4~10个案例为宜[38]。案例选择原则：其一,所选样本应为与不同空间距离学研机构有合作关系的企业,所选案例应为案例企业不同空间距离的产学研合作项目;其二,所选案例企业应有一定的行业分散度,便于就邻近性对不同行业企业的产学研合作影响的共性方面进行探讨;其三,所选案例企业的外部环境应具有相似性,以避免环境变异对研究结果造成影响。
据此,在广州高新技术开发区选择了4家与市域内和省域外学研机构有合作关系、不同行业的高新技术企业为案例企业。首先,多途径搜集、查阅有关二手资料,对企业概况、产学研合作情况作初步了解。其次,设计半结构式访谈提纲,提前发送给企业相关人员。第三,进行面对面的访谈。对象为企业技术部门负责人或研发主管;地点为企业会议室或访谈对象办公室;时间一般在1个半小时以上。第四,现场访谈结束后,就需要进一步了解的问题进行半结构式电话访谈。第五,通过电子邮件与企业相关人员交流,对不齐全或遗漏的信息资料予以补充。
通过访谈,分别从4家案例企业获得区域内、跨区域合作案例各1个,其合作伙伴均为大学。分别用行业名称第一个字的声母表示4个案例企业：电子信息——D,装备制造——Z,生物制药——S,新材料——X;用N表示与案例企业合作的区域内大学,用W表示与案例企业合作的区域外大学,各案例企业的合作伙伴分别表示为：DN、DW、ZN、ZW、SN、SW、XN、XW。

4 案例分析

4.1 赋值依据

针对各案例企业与两个不同空间距离大学的产学研项目合作关系,根据访谈记录、二手资料等事实证据,对各影响因素进行客观描述,并根据具体情况,以高、中、低三个层级对地理邻近、认知邻近、社会邻近、互动学习赋值（表1）。
Tab. 1 The basis of value assignment of the effect factors on the level of U-I cooperation

表1 产学研合作程度影响因素赋值依据

高 中 低
地理邻近 合作双方同在广州市域内 合作伙伴在市域外、省域内 合作伙伴在广东省域外
认知邻近 双方技术领域基本相似 双方技术领域部分相似,能互补 双方技术领域不相似,但能匹配
社会邻近 双方涉及信任的社会关系很多,有较长的合作历史 双方涉及信任的社会关系较多,有过合作经历 双方涉及信任的社会关系较少,初次合作
互动学习 双方互动频繁;企业获得了所需的技术成果,并学习了相关技术经验;大学学习了有关市场需求、技术成果转化应用等方面的知识 双方有一定程度互动;企业获得了所需的技术成果,大学从企业的经验中有所收获 双方互动很少;企业获得了所需的技术成果
产学研合作程度是多维邻近和互动学习共同作用的结果,在技术创新的两个阶段,企业较多参与前一段,学研机构较多参与后一段,为“高”;企业较多参与前一段而学研机构不参与后一段,或企业不参与前一段而学研机构较多参与后一段,为“较高”;双方各管一段,接力式地完成整个技术创新过程,为“一般”。

4.2 案例比较分析

4个案例企业与区域内外大学产学研合作的情况分析如表2~表5所示。
Tab. 2 Case description and analysis: Enterprise case D

表2 个案描述与分析：案例企业D

D-DN产学研合作 D-DW产学研合作
合作内容 集成电路柔性封装基板研发 喷墨打印机墨盒柔性电路材料研发
地理邻近 同处一地,交流方式更灵活、更及时,不是一个电话打来打去。在中试过程中,双方一起进行现场检测,现场讨论;接下来也隔三岔五地进行讨论 （高） 双方正式见面的时候不多,一年就三四次。开始要联系得多一些。出差时会顺道与对方有交往的人员见见面。在DW共建了研发中心,但没有派驻人员 （低）
认知邻近 2011年以来,D有一个团队进行“高密度柔性IC封装基板”研究,有一定技术积累;DN在检测技术与自动化装置研究、开发、设计等方面有很强实力,并成功开发了用于IC封装的全自动上芯机 （中） D是全国最大的柔性印制电路板产品制造商,是FPC/PCB的技术引领者。DW长期关注全印制电子技术的开发与发展,其材料系、微电子研究院在喷墨打印制作导电图形领域有深入的研究 （中）
社会邻近 已有十几年合作,与DN的研发人员太熟悉了,有的就是朋友,进行沟通根本不需借助其他关系 （高） 在全国行业会议上,通过技术交流认识了DW研发人员,建立起了联系,已有近六年的合作历史 （中）
互动学习 由于往来密切,都学到了东西。DN知道了要解决的关键问题是什么,还可以用来干什么,技术如何转化应用;我们跟着学到了做科研的方法和经验 （高） 双方互动交流的时候不多。通过合作,我们获得了加成法工艺制造的有关技术成果;公司的FPC/PCB技术也有DW可借鉴的地方 （低）
合作程度 DN根据D的要求进行前期研究,公司派人跟着学习和了解研究进展;后期DN仍密切参与公司的后续实验,遇到问题几乎是把实验室搬到D的测试现场（高） D提出技术指标后,前期完全由DW做,公司不参与。中试和产业化由D自己完成 （一般）
Tab. 3 Case description and analysis: Enterprise case Z

表3 个案描述与分析：案例企业Z

Z-ZN产学研合作 Z-ZW产学研合作
合作内容 工业机器人伺服电机驱动技术研发 数控伺服系统研发
地理邻近 都在市内方便很多,有时候一个星期技术人员就会来回走动两三次;不是遇到问题才交流,也有很多个人往来。不过,前期见面的时候更多一些 （高） 项目初期见面要多一些,后来确有需要的时候,会派人到对方单位去。技术人员约三个月见一次面。在ZW共建了研究中心,但公司没有人员驻点（低）
认知邻近 Z掌握用于数控机床的伺服电机及伺服驱动技术,进入工业机器人领域之后,对部分关键技术有突破。ZN是国内机器人研究的领军者,曾在中国机器人比赛中取得第二名,有比较成熟的伺服驱动技术 （高） 公司在数控伺服系统方面有一定的技术积累,ZW在这一领域的研究在国内是一流的。公司先是从ZW引进一些小的技术进行学习了解,然后就一些具体的技术的研发与ZW合作 （中）
社会邻近 在十多年合作中,研发人员建立了良好的私人关系;公司有很多ZN毕业生,起到了合作纽带作用 （高） 政府平台、行业协会、数控圈子里的各种社会资源对合作起到了促进作用;有近十年的合作（中）
互动学习 在参与研究中,Z不仅学习了ZN的思路和经验,还学到了焊缝跟踪技术;后期ZN经常来人解答技术问题,加深了我们对技术原理的理解;ZN了解到了用户的技术需求,从企业学到了实际操作知识和经验 （高） ZW会应邀来公司进行技术交流,对有关技术问题进行研讨,后期根据成果转化需要,对公司有关人员进行培训。ZW从Z这里了解到了高档数控领域要解决的技术问题 （中）
合作程度 前期主要由ZN做技术攻关,但Z技术人员与ZN研发人员一起做实验,做研究,有时会长达三四个月在一起。后期ZN参与的不多 （较高） 技术研发阶段企业参与的不多,主要是ZW做;产业化阶段ZW基本上不参与 （一般）
Tab. 4 Case description and analysis: Enterprise case S

表4 个案描述与分析：案例企业S

S-SN产学研合作 S-SW产学研合作
合作内容 米索前列醇阴道片研发 手性药物合成研究
地理邻近 S不定期组织会议商讨合作事宜,平时面对面的交流也很多;经常去对方实验室,使用对方的设备 （高） 双方见面很少,毕竟不方便,一般是通过网络或电话交流,确有必要的时候,会到对方单位去一下 （低）
认知邻近 S专门从事新药研发和生产,建有设备先进的研发中心,在研新药项目达40项;SN在药学、制药领域有较强实力,建有功能齐全、设备良好的药物研究平台 （中） S建有手性药物合成技术平台,手性药物新产品研发是公司重要的技术创新方向;在国内,SW在手性制备技术和手性药物开发方面有很强的实力 （高）
社会邻近 已有数度合作。S研发人员与SN研发人员很多是同学关系,私下交流很多,进行一些技术层面的讨论,比如技术的流程怎么做,技术的尺度如何把握等 （高） SW在广东有一个校友会,S前任领导曾担任会长,SW的校友对公司研发发挥了重要作用,主要通过校友与SW进行联系和合作。已有近十年的合作 （高）
互动学习 一是在研究中交流学习,二是私下相互交流,三是不定期召开技术研讨会,四是SN提供有关技术资料。S学到了药物研发理念、药剂研制的有关方法和技术,SN对技术转化过程和产业化生产有了了解 （高） 合作中双方都有收获。S很方便地了解到了手性药物研究的最新进展、有关药政动态,但技术层面的东西不多;SW对企业的产业化方式有了一定的了解 （中）
合作程度 S一位技术员以读在职研究生的形式全程参与了SN的前期研究,其他人员也有参与;产业化阶段SN也有比较多的参与,随时帮助解决有关技术问题。总的来说,这个产品是大家一起做的 （高） 在前期研究中,S去了一个技术员,主要是跟着学习,了解情况,药物合成研究由SW独立完成;成果回来后,由S进行中试和生产放大 （一般）
Tab. 5 Case description and analysis: Enterprise case X

表5 个案描述与分析：案例企业X

X-XN产学研合作 X-XW产学研合作
合作内容 木塑复合材料研发 碳纤维复合材料研发
地理邻近 X与XN是长期的合作关系,双方人员交流很多,项目后期要稍微少一些。在XN共建了高分材料研发中心,但没有人员驻点 （高） 平时主要通过电话、电邮联系,但在XW共建有工程中心,并依托中心搞了一个碳纤维的小试线,公司有一个团队在那里驻点 （低）
认知邻近 X专注于木塑复合材料和生态木型材的研发、生产;XN通过对废旧塑料和生物质纤维的选择和改性,研究出了多种环保高性能木塑复合材料制备技术 （高） X看好碳纤维行业未来发展空间,积极进行碳纤维及其复合材料的技术积累和研发;XW材料学院在先进材料成型理论与技术方面研究实力强,成果丰富 （高）
社会邻近 都在一个行业,一个地方,双方都很熟;X技术人员中有XN的毕业生,与过去的老师一直保持联系。有近二十年的合作关系,双方一直相处得很好 （高） X负责人是XW毕业的,在北京人脉很广,不仅有母校的资源,还有十几个专家组成的顾问团队。X与XW有近二十年愉快的合作经历 （高）
互动学习 有双方技术骨干之间的交流,XN到公司举办技术研讨会,组织技术培训,专家到公司就技术问题交流,等等。X学到了研究思路和方法,接触到了新的技术;XN了解了市场需要,明确了今后的研究方向 （高） 在共建的工程中心,双方人员有机会经常沟通交流;双方不定期组织技术研究讨会,后期XW在公司组织技术培训。X人员对碳纤维复合技术与方法有了一定了解,XW也了解到碳纤维生产技术与工艺 （高）
合作程度 平时,XN会找企业检验有关技术的应用性,X也找XN解决有关技术问题。在本项目合作中,X较多地参与了XN木塑材料表面处理研究;后一阶段,XN也会过来进行技术指导,但总的来说参与得不多 （较高） 技术攻关主要是XW做,由于X在共建的中心有条小试线,参与了小试的工作;后期的技术应用、开发主要是企业自己做,XW很少参与 （较高）
4.2.1 地理邻近的影响 案例显示,不同的空间距离对产学研合作程度有明显不同的影响。4对区域内合作,由于“同处一地,交流的方式更灵活、更及时”,双方“不是遇到问题才交流,也有很多个人往来”,“有时候一个星期技术人员就会来回走动两三次”;双方共享研究资源,“经常去对方实验室,使用对方的设备”;不仅如此,“在中试过程中,双方一起进行现场检测,现场讨论”。正是在频繁而深入的互动中,相关信息、知识、经验在不知不觉中发生转移。4对跨区域合作面对面交流普遍很少,除了有特别需要派人去对方单位,主要通过电话、电邮、视频进行联系。案例分析结果显示,4对区域内合作的合作程度为2对“较高”、2对“高”,4对跨区域合作仅1对为“较高”,这说明地理邻近对产学研合作程度具有积极影响。
案例表明,双方前一阶段面对面交流更多,后一阶段明显减少。在技术开发阶段,企业一方面要就各项技术指标及其具体含义与大学研究人员深入沟通,另一方面,为了便于后期成果转化,会主动参与新技术研究过程;大学为了缩短实验室研究与技术实现的距离,要向企业了解产品生产的有关技术、工艺和设备等问题。当技术开发阶段结束,企业得到了符合自己技术要求的成果,就会尽量利用自身的技术力量消化吸收。这表明,地理邻近对产学研合作创新的影响具有阶段性,在新技术形成阶段,由于知识的隐性程度很高,双方需要经常面对面交流,地理邻近可以提供种种方便;当进入技术成果转化阶段,大学提供的主要是清晰而明确的显性知识,企业不再需要频繁的面对面交流,地理邻近的重要性随之降低。
4.2.2 认知邻近的影响 8个案例的合作内容都处于企业有关技术领域和大学有关学科知识领域的交叉点或连接点,双方具有程度不同的知识、技术基础或技术积累,其认知邻近水平为4“高”、4“中”。4个案例企业都与省域外的大学有项目合作,而认知邻近有一半为中等水平。这主要是由于与本地学研机构在技术匹配上有困难,因而选择了技术相似度不高但可以互补的外地大学。这说明技术匹配性是企业选择合作伙伴的主要依据;同时也表明,大学的研究水平越高,远距离合作的可能性越大[39]。从8个合作案例的分析结果来看,认知邻近水平与合作程度总体呈正相关,认知邻近水平高的,其合作程度多为“高”或“较高”。
值得注意的是,在区域内合作中,Z-ZN、X-XN的认知邻近水平为“高”,而合作程度仅为“较高”;D-DN、S-SN的认知邻近水平为“中”,而合作程度却为“高”。这显示出,双方在某一领域有很相似的技术专长,企业一旦通过合作得到了所需的技术成果,就会更多地依靠自身的技术与经验进行转化;而在两对认知邻近水平相对低一些的合作关系中,由于企业对新技术的消化吸收能力相对较弱,仍然有较强的互动需求,从而促使大学的合作创新活动向后一阶段延伸。“几乎是把实验室搬到D的测试现场”所描述的就是D-DN后一阶段的合作情况;S也指出：“总的来说,这个产品是大家一起做的”。
4.2.3 社会邻近的影响 无论是区域内合作还是跨区域合作,社会邻近水平为“高”的,其合作程度多为“较高”、“高”,二者有明显的对应关系。合作主体之间基于信任的社会关系,一是有利于隐性知识的相互传递,双方研发人员不仅在工作中有良好的沟通,而且有较多的私人往来,有关技术分析、设想、技能、经验在不经意间发生交互。二是有利于协调合作,从协议签订、资源配置,到研发活动开展,都能顺畅地进行。三是有利于提高合作效率,以往的合作经历使新的工作程序和互动模式得以顺利产生。
案例显示,在区域内合作中,社会邻近对合作程度提升的影响呈逐渐增大或由逐渐增大转向逐渐减小趋势。由于位居邻近,合作关系建立、研发资源配置等勿需多借助社会关系,当进入技术开发过程,人际关系在知识交流中越来越多地发挥作用。在技术成果转化阶段,如果双方互动学习意愿强,在共处一地的有利条件下,社会邻近的积极影响将得以持续,例如D-DN、S-SN;反之,将逐渐减小,例如Z-ZN和X-XN。在跨区域合作中,社会邻近对合作程度提升的影响主要表现在前一阶段,总体呈逐渐减弱趋势。由于相距遥远,新一轮合作关系的建立,各种事宜的沟通协调,技术开发中的交流互动,良好的人际关系可以发挥重要作用;进入后一阶段,面对面交流的需求减少,加上空间距离的阻隔,社会邻近的重要性相应降低。
4.2.4 互动学习的影响 产学研合作创新是一个通过互动学习实现主体间知识流动的过程,但知识不可能无成本无障碍地获取,多维邻近则为合作主体的互动学习提供了有利条件。案例表明,4对区域内合作的地理、认知、社会邻近水平总体较高,其互动学习水平均为“高”;X-XW这对跨区域合作的永久地理邻近虽为“低”,但X在XW有一个团队驻点,建立了稳定的短期地理邻近,同时,其认知、社会邻近水平均为“高”,因而其互动学习水平为“高”。
案例显示,在多维邻近条件下,积极有效的互动学习是提升产学研合作程度的关键因素。在8对合作关系中有5对合作的互动学习水平为“高”,双方互动频繁,通过在研究过程中交流学习、私下沟通交流、召开技术研讨会、技术问题解答、实验现场观测探讨等途径,既向对方学习了显性知识,也获得了隐性知识,其互动交流还向自己不主导的另一段延伸,在多维邻近与合作程度之间发挥了积极的调节作用,合作程度均为“较高”或“高”。
4个跨区域合作案例显示,互动学习意愿对合作程度有重要影响。X为了加强互动学习,不仅在XW共建了工程中心,还派了一个团队驻点,使“双方人员有机会经常沟通交流”,并参与了XW的小试,这表明X有很强的互动学习意愿;D和Z也分别在DW、ZW共建了研究中心,但主要是作为研究条件而存在的,企业没有通过派驻人员形成密切参与和互动,其互动学习水平和合作程度明显不如X-XW,这不能不说是缺乏互动学习意愿而导致的结果。
案例同时还显示,在产学研合作创新的不同阶段,互动学习存在明显差异。在学习内容上,在技术开发阶段新技术尚未形成,因而以难以明确表达的技能、技巧、经验等隐性知识为主,技术成果转化阶段则以经过编码的显性知识为主;在学习方式上,由于隐性知识具有难以模仿性,前期以面对面交流为主,后期则以文字资料、远程通信技术、短期访问等多种方式进行;在学习强度上,技术开发阶段互动频率更高,技术成果转化阶段则明显减少。
4.2.5 多维邻近的交互影响 地理邻近与认知邻近结合可以产生互动学习,是合作创新最基本的条件,二者的交互影响既可以是互补的,也可以是替代的。在区域内合作中,2对认知邻近水平为“中”的合作,其互动学习和合作程度均为“高”,这表明,创新主体之间知识、技术上的适度差异更有利于紧密合作,双方借助地利之便,对技术创新的前后两段都互有参与,地理邻近与认知邻近在整个创新过程中表现为互补关系;在2对认知邻近水平为“高”的合作中,后一阶段企业主要依靠自身力量进行成果转化,尽管双方位居邻近,但互动减少,认知邻近对地理邻近形成替代关系。在跨区域合作中,从认知邻近水平与互动学习、合作程度的对应关系来看,在总体上认知邻近对地理邻近表现为替代关系。
地理邻近与社会邻近的组合有类似的交互作用。在区域内合作中,2对合作程度为“高”的合作,地理邻近与社会邻近的交互作用在前后两个阶段均表现为互补关系;2对合作程度为“较高”的合作,前期地理邻近与社会邻近为互补关系,但随着后期企业自主进行成果转化,地理邻近的重要性减弱,社会邻近转而对其形成替代关系。在4对跨区域合作中,只有X-XW的合作程度为“较高”,这是因为技术开发阶段X在XW派驻了一个研发团队,建立了稳定的短期地理邻近,与社会邻近形成了互补关系。另外3对跨区域合作的社会邻近水平为1“高”、2“中”,在相距遥远的情况下,对协调双方关系、增进信任、促进互动发挥了积极作用,社会邻近对地理邻近表现为替代关系。
地理邻近与认知邻近结合可以产生互动学习,但需要一个相互适应的过程;社会邻近不仅可以协调合作,促进互信,而且以往合作的惯例和程序能够使双方的互动更加顺畅。因此,地理邻近与认知、社会邻近的交互影响对合作创新的促进作用最明显。在区域内合作中,2对认知邻近为“中”,地理、社会邻近为“高”的合作,其合作程度均为“高”,三个维度的邻近在整个创新过程中的交互影响表现为互补效应;地理、认知、社会邻近水平均为“高”的2对合作,其合作程度却为“较高”,这是由于企业有能力自主对新技术成果进行转化,并有各种社会关系助力,后期双方互动减少,认知、社会邻近对地理邻近形成替代关系。在跨区域合作中,X-XW不仅认知、社会邻近为“高”,而且在技术开发阶段建立了稳定的短期地理邻近,三者形成互补关系;后一阶段随着研发团队撤离XW,认知、社会邻近对地理邻近形成替代关系。另外3对合作的地理邻近水平均为“低”,认知、社会邻近达到中上水平,后者与地理邻近的交互影响表现为替代效应。

5 结论与讨论

5.1 结论

（1）地理、认知、社会邻近对产学研合作程度的提升均有积极影响,但在不同阶段存在差异。
三个维度的邻近在技术开发阶段的正向影响均高于技术成果转化阶段的正向影响,总体呈下降趋势。频繁的面对面交流有利于技术开发过程中隐性知识的传递,但随着新技术成果的产生,知识变得清晰而明确,企业面对面交流的需求减少,地理邻近的正向影响减弱;技术领域或技术水平接近,可为双方参与技术创新全过程奠定认知基础,但也使企业有能力自主进行技术成果转化,后期与大学的互动减少,认知邻近的正向影响减弱;已有的社会关系和合作经历,使双方互信进一步增强,只要有意愿,均可参与到对方主导的另一段中去,但在技术成果转化阶段,双方互动学习意愿降低,社会邻近的的正向影响减弱。
（2）互动学习对多维邻近与产学研合作程度具有显著的调节作用,在内容、方式、强度上有明显的阶段性特征。
多维邻近只是有利于产学研合作程度提升的促进性因素,其促进作用的充分发挥有赖于合作主体之间积极的互动交流、相互学习。如果合作主体能根据合作创新的需要,对各种有利因素善加利用,主动、持续地开展互动学习,产学研合作程度就能得到不同程度的提升,反之,互动学习的正向调节效应则不明显,多维邻近对合作程度的积极影响受到局限。同时,互动学习具有明显的阶段性特征,前后两阶段在学习内容、方式、强度上存在差异。
（3）地理、认知、社会邻近对产学研合作程度的交互影响呈互补或替代效应,在特定情况下存在阶段性差异。

5.2 讨论

邻近性之间表现为何种交互作用效应,很大程度上取决于作为互动学习必要条件的认知邻近而不是地理邻近,同时,合作主体之间邻近性状况的差异和创新活动的进程也有重要影响。创新主体之间知识、技术上的适度差异更有利于双方紧密合作,不同维度邻近的交互影响全程呈互补效应;主体之间知识、技术相似度高,致使企业有能力自主进行技术成果转化,地理邻近的重要性减弱,后一阶段认知邻近及社会邻近对其形成替代关系。在不同邻近性的交互作用中,互补效应的积极影响通常优于替代效应。
本文研究存在一定局限性,只是就多维邻近性对不同行业企业产学研合作创新的影响从一般意义上作了探讨,而有文献表明,不同知识背景、不同技术特点、不同技术更新周期的行业,对学术知识的依赖程度存在差异,这很可能会导致多维邻近性对企业产学研合作创新影响的差异。在今后的研究中,将致力于对不同行业产学研合作创新中多维邻近的影响及效应进行探讨,并将注重定性分析与定量分析的结合。

The authors have declared that no competing interests exist.

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[26]
Broekel T, Boschma R.Knowledge networks in the Dutch aviation industry: The proximity paradox. Journal of Economic Geography, 2012, 12(2): 409-433.ABSTRACT The importance of geographical proximity for interaction and knowledge sharing has been discussed extensively in economic geography in recent years. There is increasing consensus that it is just one out of many types of proximities that might be relevant. We argue that proximity may be a crucial driver for agents to connect and exchange knowledge, but too much proximity between these agents on any of the dimensions might harm their innovative performance at the same time. In a study on knowledge networks in the Dutch aviation industry, we test this so-called proximity paradox empirically. We find evidence that the proximity paradox holds to some degree. Our study clearly shows that cognitive, social and geographical proximity are crucial for explaining the knowledge network of the Dutch aviation industry. But while it takes cognitive, social and geographical proximity to exchange knowledge, we found evidence that proximity lowers firms's innovative performance, but only in the cognitive dimension.

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[27]
Maskell P, Malmberg A.Localised learning and industrial competitiveness. Cambridge Journal of economics, 1999, 23(2): 167-185.ABSTRACT Changes in the international economy have gradually shifted the basis of industrial competitiveness from static price competition towards dynamic improvement, benefiting firms that are able to create knowledge faster than their competitors. This paper argues that proximity between firms plays an important role in interactive learning processes and that knowledge creation is supported by the institutional embodiment of tacit knowledge useful for particular classes of activity. Sustainable competitiveness requires the ongoing replacement of decrepit resources, the rebuilding of obsolete structures, and the renewal of economically important national or regional institutions, when imitation gradually turns localized capabilities into global ubiquities. Copyright 1999 by Oxford University Press.

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[28]
Oerlemans L, Meeus M.Do organizational and spatial proximity impact on firm performance?. Regional Studies, 2005, 39(1): 89-104.Oerlemans L. A. G. and Meeus M. T. H. (2005) Do organizational and spatial proximity impact on firm performance?, Regional Studies39, 89-104. Recent theoretical developments in organization science, economic geography and regional economics have emphasized the importance of organizational and geographical proximity for the performance of firms. Empirical evidence on these relationships is scarce, though. The paper asks to what extent firm-specific resources, network activity, proximity and industry factors influence innovative and economic outcomes. We used a theoretical synthesis of regional and organizational science, and economic geography to build a research model that enabled us to derive several hypotheses on the influence of different forms of proximity on outcomes, taking other relevant predictors for performance into account. The empirical findings specify the importance of proximity especially for innovative outcomes. We found that in particular intra- and interregional relations with buyers and suppliers are conducive for firm performance. Moreover, innovation strategy (dis)similarity has interesting effects on relative firm performance. Finally, sectoral research and development spillovers influence outcomes in a positive way. Oerlemans L. A. G. et Meeus M. T. H. (2005) La proximite organisationnelle et geographique, importe-t-elle pour la performance des entreprises?, Regional Studies39, 89-104. De recentes avances theoriques dans la science organisationnelle, la geographie economique, et l'economie regionale ont souligne l'importance de la proximite organisationnelle et geographique pour la performance des entreprises. Cependant, rares sont les preuves empiriques sur ces rapports. Cet article cherche a determiner dans quelle mesure les ressources specifiques a l'entreprise, la constitution de reseaux, la proximite, et les facteurs lies a l'industrie influencent les resultats innovateurs et economiques. A partir d'un synthese theorique de la science regionale et organisationnelle, et de la geographie economique, on construit un modele de recherche qui permet d'obtenir plusieurs hypotheses sur l'influence de diverses formes de proximite sur les resultats, tout en tenant compte des autres moyens d'estimer la performance. Les resultats empiriques precisent l'importance de la proximite, surtout pour ce qui est des resultats innovateurs. Il s'avere en particulier que des rapports intra et interregionaux avec les acheteurs et les vendeurs sont propices a la performance des entreprises. En outre, la(dis)similitude entre les strategies en faveur de l'innovation a des effets interessants sur la performance relative des entreprises. Pour finir, les retombees de R et D sectorielles influencent les resultats de facon positive. Oerlemans L. A. G. und Meeus M. T. H. (2005) Die Auswirkung organisatorischer und raumlicher Nahe auf Firmenleistung, Regional Studies39, 89-104. Die neuesten theoretischen Entwicklungen auf den Gebieten der Organisationswissenschaft, Wirtschaftsgeographie und Regionalwirtschaft haben die Bedeutung organisatorischer und geographischer Nahe fur Firmenbildung betont. Es gibt jedoch nur wenig empirische Beweise fur diese Beziehungen. In diesem Aufsatz wird die Frage aufgeworfen, in welchem Ausmass firmenspezifische Ressourcen, Netzwerkunternehungen, Nahe und Industriefaktoren innovative und wirtschaftliche Ergebnisse beeinflussen. Die Autoren benutzen eine theoretische Synthese regionaler und organisatorischer Wissenschaften sowie der Wirtschaftsgeographie, um ein Forschungsmodell zu konstruieren, das sie in die Lage versetzt, verschiedene Hypothesen uber den Einfluss unterschiedlicher Formen der Nahe auf Ergebnisse anzugeben, wobei andere relevante Voraussagefaktoren fur Leistung in Rechnung gestellt werden. Die empirischen Befunde heben die Bedeutung der Nahe besonders fur innovative Ergebnisse hervor. Es ergibt sich, dass der Firmenleistung vorallem intra-und interregionale Beziehungen zu Kaufern und Lieferanten dienlich sind. Daruberhinaus uben (Un)ahnlichkeiten der Innovationsstrategien interessante Wirkungen auf relative Firmenleistung aus. Letztlich hat auch die Verbreitung von Forschung und Entwicklung einen positiven Einfluss auf die Ergebnisse. Oerlemans L. A. G. y Meeus M. T. H. (2005) Como afecta al rendimiento de las empresas la proximidad espacial y organizacional?, Regional Studies39, 89-104. Los desarrollos teoricos recientes en torno a la ciencia organizacional, la geografia economica y la economia regional han resaltado la importancia de la proximidad geografica y organizacional para el rendimiento de las empresas. No obstante, la evidencia empirica en torno a dichas relaciones es escasa. En este articulo, nos planteamos hasta que punto los recursos especificos de las empresas, actividades de networking, la proximidad y los factores industriales influyen los resultados economicos y de innovacion. Utilizamos una sintesis teorica de la ciencia regional y organizacional, asi como de la geografia economica, para elaborar un modelo de investigacion que nos permitiera derivar una serie de hipotesis sobre la influencia de los diferentes tipos de proximidad en los resultados, teniendo en cuenta otros indicadores relevantes para el rendimiento. Nuestros resultados empiricos realzan la importancia de la proximidad especialmente para los resultados de innovacion. Los resultados obtenidos demuestran que, en particular, las relaciones intra- e inter-regionales con compradores y proveedores conducen al rendimiento de las empresas. Ademas, la desemejanza entre las estrategias de innovacion tiene efectos interesantes en el rendimiento relativo de las empresas. Finalmente, los efectos de arrastre de I&D sectoriales influyen en los resultados de forma positiva.

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[29]
Singh J.Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 2010, 51(5): 756-770.

[30]
Freel M S.Sectoral patterns of small firm innovation, networking and proximity. Research Policy, 2003, 32(5): 751-770.Drawing upon a sample of 597 small and medium-sized manufacturing firms, this article investigates the extent to which cooperation for innovation is associated with firm-level product and process 'innovativeness' and, where collaborative relationships are reported, the factors which influence their spatial distribution. With respect to the former issue, the data suggests considerable variety of association across Pavitt's [Research Policy 13 (1994) 343] sectoral taxonomy and innovation type. However, the data also indicates the need for caution when developing network strategies or policies: the evidence presented here is unequivocal in noting that innovation is neither a necessary nor less a sufficient condition for innovation. Moreover, internal resources often act as complements to, or indeed appears to negate the need for, external resources. With regards to the spatial distribution of firm linkages, it appears that increasing firms size and export propensity are positively associated with external linkages at a higher spatial level. Moreover, the spatial reach of innovation-related linkages is also likely to be greater for firms reporting the introduction of relatively novel innovations (i.e. products or processes which are new to the industry). In contrast, smaller firms and firms engaged in incremental product innovations appear more likely to be locally embedded.

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[31]
李琳, 雒道政. 多维邻近性与创新: 西方研究回顾与展望. 经济地理, 2013, 33(6): 1-7.近年来,多维邻近性与创新的论题引起了西方学术界的热切关注.相关研究虽已取得重大进展,但整体上尚处于探索阶段.在梳理相关研究成果的基础上,试图从法国邻近动力学派和其他邻近学派两个视角探析邻近性对创新的影响机理,以期通过厘清主要学派的研究脉络、主要观点及未来研究方向,为推进学术界对于邻近性与创新论题的深入探究做些努力.

[Li Lin, Luo Daozheng.Multi-proximity and innovation: The retrospect and prospect on Western researches. Economic Geography, 2013, 33(6): 1-7.]

[32]
Scberngell T, Hu Y.Collaborative knowledge production in China: Regional evidence from a gravity model approach. Regional Studies, 2011, 45(6): 755-772.Scherngell T. and Hu Y. Collaborative knowledge production in China: regional evidence from a gravity model approach, . This study investigates collaborative knowledge production in China from a regional perspective. The objective is to illustrate spatial patterns of research collaborations between thirty-one Chinese regions, and to estimate the impact of geographical, technological, and economic factors on the variation of cross-region collaboration activities within a negative binomial gravity model framework. Data are used on Chinese scientific publications from 2007 with multiple author addresses coming from the China National Knowledge Infrastructure (CNKI) database. The results provide evidence that geographical space impedes cross-region research collaborations in China. Technological proximity matters more than geography, while economic effects only play a minor role.

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[33]
Laursen K, Masciarelli F, Prencipe A.Regions matter: How localized social capital affects innovation and external knowledge acquisition. Organization Science, 2012, 23(1): 177-193.Abstract To introduce new products, firms often use knowledge from other organizations. Drawing on social capital theory and the relational view of the firm, we argue that geographically localized social capital affects a firm's ability to innovate through various external channels. Combining data on social capital at the regional level, with a large-scale data set of the innovative activities of a representative sample of 2,413 Italian manufacturing firms from 21 regions, and controlling for a large set of firm and regional characteristics, we find that being located in a region characterized by a high level of social capital leads to a higher propensity to innovate. We find also that being located in an area characterized by a high degree of localized social capital is complementary to firms' investments in internal research and development (R&D) and that such a location positively moderates the effectiveness of externally acquired R&D on the propensity to innovate.

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[34]
Breschi S, Lissoni F.Mobility of skilled workers and co-invention networks: An anatomy of localized knowledge flows. Journal of Economic Geography, 2009, 9(4): 439-468.

[35]
Cassi L, Plunket A.Research collaboration in co-inventor networks: Combining closure, bridging and proximities. Regional Studies, 2015, 49(6): 936-954.ABSTRACT This paper investigates the determinants of co-inventor tie formation using micro-data on genomic patents from 1990 to 2006 in France. In a single analysis, we consider the relational and proximity perspectives that are usually treated separately. In order to do so, we analyse various forms of proximity as alternative driving forces behind network ties that occur within existing components (i.e. closure ties) as well as those between two distinct components (i.e. bridging ties). In doing so, we contrast network and proximity determinants of network formation and we investigate to what extent social networks allow economic actors to cross over geographical, technological and organizational boundaries.

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[36]
Ozer M, Zhang W.The effects of geographic and network ties on exploitative and exploratory product innovation. Strategic Management Journal, 2015, 36(7): 1105-1114.ABSTRACT Addressing the inconsistent findings in the literature, we first distinguish the type of innovation and study the relationship of industrial clusters with exploitative and exploratory product innovation. Furthermore, we study how focal cluster firms’ network ties with their suppliers and buyers in their clusters might moderate these relationships. Our empirical study showed that while cluster membership enhanced firms’ exploitative product innovation, it hindered their exploratory product innovation. Moreover, the results showed that focal cluster firms’ network ties with their suppliers and buyers in their clusters strengthened the effects of cluster membership on exploitative product innovation. They also showed that focal cluster firms’ network ties with their buyers but not suppliers in their clusters reduced the negative effects of cluster membership on exploratory product innovation.

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[37]
余泳泽. 我国高技术产业技术创新效率及其影响因素研究: 基于价值链视角下的两阶段分析. 经济科学, 2009, (4): 62-74.本文基于价值链的视角，将高技术产业的技术创新过程分为技术开发和技术成果转化两个阶段，并利用松弛变量的DEA模型分别对各阶段的效率及其影响因素进行了实证研究。研究的基本结论有：两个阶段中技术创新的平均效率都较低，且有持续恶化趋势，这主要源于纯技术无效率；技术创新两阶段生产力的提高均主要来自于技术进步；从价值链视角看，技术开发效率和成果转化效率都有进一步改善的空间；市场化程度、企业规模、政府政策支持和企业自身的经营绩效对各地区高技术产业技术创新效率均有正的影响。

[Yu Yongze.Rsearch on the technological innovation efficiency and influencing factors of China's high-tech industries: Based on the two-stage analysis from the perspective of value chain. Economic Science, 2009, (4): 62-74.]

[38]
Eisenhardt K M.Building theories from case study research. Academy of Management Review, 1989, 14(4): 532-550.

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[39]
Musoio A.University-industry linkages: What are the determinants of distance in collaborations?. Papers in Regional Science, 2013, 92(4): 715-739.No abstract is available for this item.

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