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Article(id=1315693752350520090, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315693724970103290, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1753459200000, receivedDateStr=2025-07-26, revisedDate=1780848000000, revisedDateStr=2026-06-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1791615434967, onlineDateStr=2026-10-10, pubDate=1791993600000, pubDateStr=2026-10-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791615434967, onlineIssueDateStr=2026-10-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791615434967, creator=13701087609, updateTime=1791615434967, updator=13701087609, issue=Issue{id=1315693724970103290, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='10', pageStart='2017', pageEnd='2240', issueExtLink='null', onlineDate='null', pubDate='1791993600000', pubDateStr='2026-10-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791615428440, creator='13701087609', updateTime=1791615454688, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1315693835188031766, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315693724970103290, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1315693835188031767, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1315693724970103290, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2144, endPage=2155, ext={EN=ArticleExt(id=1315693752669287195, articleId=1315693752350520090, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=Collaborative R&D of agribusiness and the realization of innovative entrepreneurial opportunities, columnId=1309226334803550422, journalTitle=Studies in Science of Science, columnName=Innovation Explorations, runingTitle=null, highlight=null, articleAbstract=

Collaborative R&D by agribusinesses to trigger knowledge recombination creation, thereby enhancing innovative entrepre- neurial opportunities, is a complex systems management issue. Accordingly, this study constructs a “motivation-capability-trigger” element framework for collaborative R&D in agribusinesses based on Fogg’s Behavior Model. Furthermore, drawing on absorptive capacity theory and incorporating a configurational perspective and a complex mediation model, it analyzes how different elements of collaborative R&D jointly configure to drive knowledge recombination creation within agribusiness. This study finds that high knowledge recombination creation can be achieved through three types of collaborative R&D configurations: orientation-driven, market-driven, and institution-driven. These configurations not only directly facilitate the realization of innovative entrepreneurial opportunities but also indirectly promote such realization through knowledge recombination creation; that is, knowledge recombination creation plays a complex mediating role between the three configurations and the realization of innovative entrepreneurial opportunities. The findings have important theoretical and practical implications for how agribusinesses can promote the realization of innovative entrepreneurial opportunities through collaborative R&D strategies.

, authors=Yong-zheng WANG, Li CAI, Zi-ying CHEN, authorsList=Yong-zheng WANG, Li CAI, Zi-ying CHEN, authorCompany=null, correspAuthors=Zi-ying CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1315693753751417633, articleId=1315693752350520090, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=涉农企业合作研发与创新型创业机会实现, columnId=1309226334996488408, journalTitle=科学学研究, columnName=创新探索, runingTitle=null, highlight=null, articleAbstract=

涉农企业通过合作研发以触发知识重组创造,进而促进创新型创业机会实现是一个复杂系统管理问题。基于此,本研究通过Fogg行为理论提炼了涉农企业合作研发的“动机—能力—触发器”要素框架。在此基础上,借鉴吸收能力理论并引入组态视角和复杂中介模型来分析合作研发不同要素驱动涉农企业知识重组创造的组态。研究发现:高知识重组创造包括导向驱动型、市场驱动型、制度驱动型3种合作研发组态,其不仅能直接促进创新型创业机会实现,也可通过知识重组创造间接促进创新型创业机会实现,即知识重组创造在3种组态和创新型创业机会实现间发挥复杂中介作用。本研究结论对于涉农企业如何通过合作研发策略促进创新型创业机会实现具有重要的理论与实践启示。

, authors=王永正, 蔡莉, 陈姿颖, authorsList=王永正, 蔡莉, 陈姿颖, authorCompany=null, correspAuthors=陈姿颖, authorNote=

王永正(1994-),男,博士研究生。

蔡莉(1960-),女,教授、博士生导师。

, correspAuthorsNote=
陈姿颖(1997-),女,博士研究生,E-mail:。
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王永正(1994-),男,博士研究生。

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王永正(1994-),男,博士研究生。

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蔡莉(1960-),女,教授、博士生导师。

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IEEE Transactions on Engineering Management, 2021, 68(6): 1734-1744., articleTitle=Capabilities for collaborative innovation of technological alliance: A knowledge-based view, refAbstract=null)], funds=[Fund(id=1315693757664703316, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, awardId=72572072, language=CN, fundingSource=国家自然科学基金资助项目(72572072), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1315693754137293602, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, xref=null, ext=[AuthorCompanyExt(id=1315693754149876515, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, companyId=1315693754137293602, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Business and Management, Jilin University, Changchun 130012, China), AuthorCompanyExt(id=1315693754158265124, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, companyId=1315693754137293602, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=吉林大学商学与管理学院, 吉林长春 130012)])], figs=[ArticleFig(id=1315693756066673472, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Figure 1, caption=Complex mediation model, figureFileSmall=yXm/Un3YcCzfeD0PNEXjhw==, figureFileBig=b6rBZVdUcS//1wL4/h6kBg==, tableContent=null), ArticleFig(id=1315693756150559553, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=图1, caption=复杂中介模型, figureFileSmall=yXm/Un3YcCzfeD0PNEXjhw==, figureFileBig=b6rBZVdUcS//1wL4/h6kBg==, tableContent=null), ArticleFig(id=1315693756322526018, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Fig.2, caption=Integration of antecedent grouping states for knowledge recombination creation in agribusinesses, figureFileSmall=e7MqFC7Xk25zu28TCTrUfQ==, figureFileBig=W0XKln60kZJqPm/lw0aHAQ==, tableContent=null), ArticleFig(id=1315693756377051971, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=图2, caption=涉农企业知识重组创造的前因组态整合, figureFileSmall=e7MqFC7Xk25zu28TCTrUfQ==, figureFileBig=W0XKln60kZJqPm/lw0aHAQ==, tableContent=null), ArticleFig(id=1315693756448355140, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 1, caption=

Calibration anchors for each variable

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 校准锚点
完全隶属点 交叉点 完全不隶属点
CIO 5.889 4.505 3.746
CIA 7.369 1.841 0.250
IPP 84.548 74.140 55.753
ME 15.80 12.337 6.772
KRC 3.621 1.322 0.405
), ArticleFig(id=1315693756511269701, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表1, caption=

各变量校准锚点

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 校准锚点
完全隶属点 交叉点 完全不隶属点
CIO 5.889 4.505 3.746
CIA 7.369 1.841 0.250
IPP 84.548 74.140 55.753
ME 15.80 12.337 6.772
KRC 3.621 1.322 0.405
), ArticleFig(id=1315693756582572870, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 2, caption=

Analysis of necessary conditions for the creation of knowledge recombination creation

, figureFileSmall=null, figureFileBig=null, tableContent=
条件变量 High KRC Non-high KRC
一致性 覆盖率 一致性 覆盖率
CIO 0.664 0.638 0.625 0.660
~CIO 0.645 0.609 0.657 0.683
CIA 0.587 0.623 0.605 0.706
~CIA 0.723 0.624 0.677 0.643
ME 0.633 0.574 0.720 0.718
~ME 0.688 0.691 0.572 0.632
IPP 0.601 0.565 0.672 0.695
~IPP 0.675 0.652 0.579 0.615
), ArticleFig(id=1315693756658070343, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表2, caption=

知识重组创造的必要条件分析

, figureFileSmall=null, figureFileBig=null, tableContent=
条件变量 High KRC Non-high KRC
一致性 覆盖率 一致性 覆盖率
CIO 0.664 0.638 0.625 0.660
~CIO 0.645 0.609 0.657 0.683
CIA 0.587 0.623 0.605 0.706
~CIA 0.723 0.624 0.677 0.643
ME 0.633 0.574 0.720 0.718
~ME 0.688 0.691 0.572 0.632
IPP 0.601 0.565 0.672 0.695
~IPP 0.675 0.652 0.579 0.615
), ArticleFig(id=1315693756733567816, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 3, caption=

Analysis of groupings leading to the creation of high/non-high knowledge recombination creation

, figureFileSmall=null, figureFileBig=null, tableContent=
因素类别 前因变量 High KRC Non-high KRC
S1 S2 S3 M1
动机 CIO ● ● ● $\bigotimes$
能力 CIA $\bigotimes$ ● ● $\bigotimes$
触发器 ME $\bigotimes$ ● $\bigotimes$ ●
IPP $\bigotimes$ $\bigotimes$ ● ●
一致性 0.373 0.275 0.286 0.393
原始覆盖率 0.110 0.023 0.013 0.393
唯一覆盖率 0.837 0.889 0.884 0.841
总体解一致性 0.426 0.393
总体解覆盖度 0.822 0.841
), ArticleFig(id=1315693756821648201, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表3, caption=

导致高/非高知识重组创造的组态分析

, figureFileSmall=null, figureFileBig=null, tableContent=
因素类别 前因变量 High KRC Non-high KRC
S1 S2 S3 M1
动机 CIO ● ● ● $\bigotimes$
能力 CIA $\bigotimes$ ● ● $\bigotimes$
触发器 ME $\bigotimes$ ● $\bigotimes$ ●
IPP $\bigotimes$ $\bigotimes$ ● ●
一致性 0.373 0.275 0.286 0.393
原始覆盖率 0.110 0.023 0.013 0.393
唯一覆盖率 0.837 0.889 0.884 0.841
总体解一致性 0.426 0.393
总体解覆盖度 0.822 0.841
), ArticleFig(id=1315693756884562762, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 4, caption=

Robustness test

, figureFileSmall=null, figureFileBig=null, tableContent=
因素类别 前因变量 High KRC
(案例频数=4)		 High KRC
(PRI=0.65)		 High KRC
(原始一致性阈值=0.86)
S4 S5 S6
动机 CIO ● ● ●
能力 CIA $\bigotimes$ ● ●
触发器 ME ⊗ $\bigotimes$ ●
IPP $\bigotimes$ ● $\bigotimes$
一致性 0.373 0.285 0.275
原始覆盖率 0.373 0.041 0.031
唯一覆盖率 0.837 0.884 0.889
总体解一致性 0.373 0.317
总体解覆盖度 0.837 0.873
), ArticleFig(id=1315693756968448843, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表4, caption=

稳健性检验

, figureFileSmall=null, figureFileBig=null, tableContent=
因素类别 前因变量 High KRC
(案例频数=4)		 High KRC
(PRI=0.65)		 High KRC
(原始一致性阈值=0.86)
S4 S5 S6
动机 CIO ● ● ●
能力 CIA $\bigotimes$ ● ●
触发器 ME ⊗ $\bigotimes$ ●
IPP $\bigotimes$ ● $\bigotimes$
一致性 0.373 0.285 0.275
原始覆盖率 0.373 0.041 0.031
唯一覆盖率 0.837 0.884 0.889
总体解一致性 0.373 0.317
总体解覆盖度 0.837 0.873
), ArticleFig(id=1315693757035557708, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 5, caption=

Descriptive statistics

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类型 变量名 平均值 标准差 最小值 最大值
因变量 IEO 0.206 0.107 -0.001 0.581
自变量 组态S1 0.212 0.187 0 0.740
组态S2 0.147 0.122 0 0.560
组态S3 0.154 0.126 0 0.530
中介变量 KRC 1.626 1.115 0.223 5.307
控制变量 IP 0.977 1.280 0 4.916
UP 0.549 1.030 0 4.245
GR 0.133 0.143 -0.153 0.694
FC -3.854 0.495 -4.541 -0.927
RDI 0.072 0.125 0 0.603
), ArticleFig(id=1315693757111055181, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表5, caption=

描述性统计

, figureFileSmall=null, figureFileBig=null, tableContent=
类型 变量名 平均值 标准差 最小值 最大值
因变量 IEO 0.206 0.107 -0.001 0.581
自变量 组态S1 0.212 0.187 0 0.740
组态S2 0.147 0.122 0 0.560
组态S3 0.154 0.126 0 0.530
中介变量 KRC 1.626 1.115 0.223 5.307
控制变量 IP 0.977 1.280 0 4.916
UP 0.549 1.030 0 4.245
GR 0.133 0.143 -0.153 0.694
FC -3.854 0.495 -4.541 -0.927
RDI 0.072 0.125 0 0.603
), ArticleFig(id=1315693757190746958, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 6, caption=

Regression analysis of collaborative R&D grouping patterns on the realization of innovative entrepreneurial opportunities

, figureFileSmall=null, figureFileBig=null, tableContent=
因变量:IEO
模型1 模型2 模型3
组态S1 0.142***
(2.790)
组态S2 0.228***
(3.060)
组态S3 0.225***
(3.050)
IP 0.041***
(5.260)		 0.038***
(4.880)		 0.039***
(4.990)
UP -0.023***
(-2.880)		 -0.017**
(-2.220)		 -0.016**
(-2.090)
GR 0.202***
(3.170)		 0.178***
(2.800)		 0.167***
(2.600)
FC -0.024**
(-2.510)		 -0.019**
(-2.020)		 -0.021**
(-2.280)
RDI -0.239***
(-3.140)		 -0.206***
(-2.810)		 -0.228***
(-3.060)
常数项 0.056
(1.370)		 0.070*
(1.770)		 0.061
(1.540)
观测值 94 94 94
调整后拟合优度 0.342 0.353 0.352
), ArticleFig(id=1315693757245272911, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表6, caption=

合作研发组态对创新型创业机会实现的回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
因变量:IEO
模型1 模型2 模型3
组态S1 0.142***
(2.790)
组态S2 0.228***
(3.060)
组态S3 0.225***
(3.050)
IP 0.041***
(5.260)		 0.038***
(4.880)		 0.039***
(4.990)
UP -0.023***
(-2.880)		 -0.017**
(-2.220)		 -0.016**
(-2.090)
GR 0.202***
(3.170)		 0.178***
(2.800)		 0.167***
(2.600)
FC -0.024**
(-2.510)		 -0.019**
(-2.020)		 -0.021**
(-2.280)
RDI -0.239***
(-3.140)		 -0.206***
(-2.810)		 -0.228***
(-3.060)
常数项 0.056
(1.370)		 0.070*
(1.770)		 0.061
(1.540)
观测值 94 94 94
调整后拟合优度 0.342 0.353 0.352
), ArticleFig(id=1315693757308187472, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 7, caption=

Robustness test

, figureFileSmall=null, figureFileBig=null, tableContent=
因变量:IEO(除去极端值) 因变量:IEO(用赫芬达尔指数来测量市场竞争优势)
模型4 模型5 模型6 模型7 模型8 模型9
组态S1 0.142***
(2.790)		 1.668**
(2.060)
组态S2 0.228***
(3.060)		 2.735**
(2.300)
组态S3 0.225***
(3.050)		 3.034***
(2.600)
IP 0.041***
(5.260)		 0.038***
(4.880)		 0.039***
(4.990)		 0.269**
(2.150)		 0.228*
(1.840)		 0.237*
(1.920)
UP -0.023***
(-2.880)		 -0.017**
(-2.220)		 -0.016**
(-2.090)		 -0.166
(-1.320)		 -0.101
(-0.820)		 -0.085
(-0.690)
GR 0.202***
(3.170)		 0.178***
(2.800)		 0.167***
(2.600)		 -0.865
(-0.850)		 -1.155
(-1.130)		 -1.336
(-1.310)
FC -0.024**
(-2.510)		 -0.019**
(-2.020)		 -0.021**
(-2.280)		 -0.246
(-1.640)		 -0.190
(-1.270)		 -0.217
(-1.470)
RDI -0.239***
(-3.140)		 -0.206***
(-2.810)		 -0.228***
(-3.060)		 -0.116
(-0.110)		 0.266
(0.230)		 -0.049
(-0.040)
常数项 0.056
(1.370)		 0.070*
(1.770)		 0.061
(1.540)		 0.612
(0.940)		 0.766
(1.220)		 0.623
(0.990)
观测值 94 94 94 94 94 94
调整后拟合优度 0.342 0.353 0.352 0.048 0.059 0.074
), ArticleFig(id=1315693757379490641, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表7, caption=

稳健性检验

, figureFileSmall=null, figureFileBig=null, tableContent=
因变量:IEO(除去极端值) 因变量:IEO(用赫芬达尔指数来测量市场竞争优势)
模型4 模型5 模型6 模型7 模型8 模型9
组态S1 0.142***
(2.790)		 1.668**
(2.060)
组态S2 0.228***
(3.060)		 2.735**
(2.300)
组态S3 0.225***
(3.050)		 3.034***
(2.600)
IP 0.041***
(5.260)		 0.038***
(4.880)		 0.039***
(4.990)		 0.269**
(2.150)		 0.228*
(1.840)		 0.237*
(1.920)
UP -0.023***
(-2.880)		 -0.017**
(-2.220)		 -0.016**
(-2.090)		 -0.166
(-1.320)		 -0.101
(-0.820)		 -0.085
(-0.690)
GR 0.202***
(3.170)		 0.178***
(2.800)		 0.167***
(2.600)		 -0.865
(-0.850)		 -1.155
(-1.130)		 -1.336
(-1.310)
FC -0.024**
(-2.510)		 -0.019**
(-2.020)		 -0.021**
(-2.280)		 -0.246
(-1.640)		 -0.190
(-1.270)		 -0.217
(-1.470)
RDI -0.239***
(-3.140)		 -0.206***
(-2.810)		 -0.228***
(-3.060)		 -0.116
(-0.110)		 0.266
(0.230)		 -0.049
(-0.040)
常数项 0.056
(1.370)		 0.070*
(1.770)		 0.061
(1.540)		 0.612
(0.940)		 0.766
(1.220)		 0.623
(0.990)
观测值 94 94 94 94 94 94
调整后拟合优度 0.342 0.353 0.352 0.048 0.059 0.074
), ArticleFig(id=1315693757475959634, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=EN, label=Table 8, caption=

The effect of collaborative R&D grouping and knowledge recombination creation on the realization of innovative entrepreneurial opportunities

, figureFileSmall=null, figureFileBig=null, tableContent=
因变量:IEO
模型10 模型11 模型12 模型13
组态S1 0.096*
(1.830)
组态S2 0.193***
(2.660)
组态S3 0.181**
(2.480)
KRC 0.028***
(3.350)		 0.023**
(2.570)		 0.024***
(2.980)		 0.024***
(2.830)
IP 0.032***
(3.970)		 0.035***
(4.300)		 0.032***
(4.080)		 0.033***
(4.170)
UP -0.017**
(-2.180)		 -0.020**
(-2.530)		 -0.015**
(-2.060)		 -0.015*
(-1.960)
GR 0.178***
(2.820)		 0.183***
(2.940)		 0.161***
(2.630)		 0.154**
(2.490)
FC -0.018*
(-1.930)		 -0.020**
(-2.180)		 -0.016*
(-1.810)		 -0.018**
(-2.030)
RDI -0.177**
(-2.440)		 -0.215***
(-2.890)		 -0.196***
(-2.780)		 -0.212***
(-2.950)
常数项 0.065*
(1.660)		 0.047
(1.180)		 0.051
(1.340)		 0.046
(1.190)
观测值 94 94 94 94
调整后拟合优度 0.365 0.382 0.407 0.401
), ArticleFig(id=1315693757555651411, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1315693752350520090, language=CN, label=表8, caption=

合作研发组态、知识重组创造对创新型创业机会实现的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
因变量:IEO
模型10 模型11 模型12 模型13
组态S1 0.096*
(1.830)
组态S2 0.193***
(2.660)
组态S3 0.181**
(2.480)
KRC 0.028***
(3.350)		 0.023**
(2.570)		 0.024***
(2.980)		 0.024***
(2.830)
IP 0.032***
(3.970)		 0.035***
(4.300)		 0.032***
(4.080)		 0.033***
(4.170)
UP -0.017**
(-2.180)		 -0.020**
(-2.530)		 -0.015**
(-2.060)		 -0.015*
(-1.960)
GR 0.178***
(2.820)		 0.183***
(2.940)		 0.161***
(2.630)		 0.154**
(2.490)
FC -0.018*
(-1.930)		 -0.020**
(-2.180)		 -0.016*
(-1.810)		 -0.018**
(-2.030)
RDI -0.177**
(-2.440)		 -0.215***
(-2.890)		 -0.196***
(-2.780)		 -0.212***
(-2.950)
常数项 0.065*
(1.660)		 0.047
(1.180)		 0.051
(1.340)		 0.046
(1.190)
观测值 94 94 94 94
调整后拟合优度 0.365 0.382 0.407 0.401
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涉农企业合作研发与创新型创业机会实现
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王永正 , 蔡莉 , 陈姿颖
科学学研究 | 创新探索 2026,44(10): 2144-2155
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涉农企业合作研发与创新型创业机会实现
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王永正, 蔡莉, 陈姿颖
作者信息
  • 吉林大学商学与管理学院, 吉林长春 130012
通讯作者:
陈姿颖(1997-),女,博士研究生,E-mail:。
作者简介:

王永正(1994-),男,博士研究生。

蔡莉(1960-),女,教授、博士生导师。

Collaborative R&D of agribusiness and the realization of innovative entrepreneurial opportunities
Yong-zheng WANG, Li CAI, Zi-ying CHEN
Affiliations
  • School of Business and Management, Jilin University, Changchun 130012, China
出版时间: 2026-10-15
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摘要
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涉农企业通过合作研发以触发知识重组创造,进而促进创新型创业机会实现是一个复杂系统管理问题。基于此,本研究通过Fogg行为理论提炼了涉农企业合作研发的“动机—能力—触发器”要素框架。在此基础上,借鉴吸收能力理论并引入组态视角和复杂中介模型来分析合作研发不同要素驱动涉农企业知识重组创造的组态。研究发现:高知识重组创造包括导向驱动型、市场驱动型、制度驱动型3种合作研发组态,其不仅能直接促进创新型创业机会实现,也可通过知识重组创造间接促进创新型创业机会实现,即知识重组创造在3种组态和创新型创业机会实现间发挥复杂中介作用。本研究结论对于涉农企业如何通过合作研发策略促进创新型创业机会实现具有重要的理论与实践启示。

涉农企业  /  合作研发  /  创新型创业机会实现  /  复杂中介模型  /  知识重组创造  /  吸收能力

Collaborative R&D by agribusinesses to trigger knowledge recombination creation, thereby enhancing innovative entrepre- neurial opportunities, is a complex systems management issue. Accordingly, this study constructs a “motivation-capability-trigger” element framework for collaborative R&D in agribusinesses based on Fogg’s Behavior Model. Furthermore, drawing on absorptive capacity theory and incorporating a configurational perspective and a complex mediation model, it analyzes how different elements of collaborative R&D jointly configure to drive knowledge recombination creation within agribusiness. This study finds that high knowledge recombination creation can be achieved through three types of collaborative R&D configurations: orientation-driven, market-driven, and institution-driven. These configurations not only directly facilitate the realization of innovative entrepreneurial opportunities but also indirectly promote such realization through knowledge recombination creation; that is, knowledge recombination creation plays a complex mediating role between the three configurations and the realization of innovative entrepreneurial opportunities. The findings have important theoretical and practical implications for how agribusinesses can promote the realization of innovative entrepreneurial opportunities through collaborative R&D strategies.

agribusiness  /  collaborative R&D  /  innovative entrepreneurial opportunity realization  /  complex mediation model  /  knowledge recombination creation  /  absorptive capacity
王永正, 蔡莉, 陈姿颖. 涉农企业合作研发与创新型创业机会实现. 科学学研究, 2026 , 44 (10) : 2144 -2155 .
Yong-zheng WANG, Li CAI, Zi-ying CHEN. Collaborative R&D of agribusiness and the realization of innovative entrepreneurial opportunities[J]. Studies in Science of Science, 2026 , 44 (10) : 2144 -2155 .
正文
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基于熊彼特提出的创造性破坏思想,创新型创业成为了推动国家经济可持续发展的催化剂[1]。同时,农业强国是我国社会主义现代化强国的根基,作为第一、第二产业融合,制造业反哺农业重要载体的涉农企业,在其创业活跃度提升的同时仍旧面临着创业机会质量低、产品创新性不足的窘境,业界和学界需加强对涉农企业创新型创业机会实现的关注,创新型创业机会实现指企业通过开发新颖性的新产品来获得竞争优势[2]。进一步地,研发水平会影响创新型创业机会实现,而涉农企业具有抗风险能力差、投资回报率低、受自然因素制约等特征,导致其研发经费、人才短缺,需要进行合作研发。基于此,明晰涉农企业合作研发与创新型创业机会实现间的关系成为了亟待探讨的关键问题。
纵观现有涉农企业合作研发与创业机会实现研究,大多关注其合作研发对创业机会实现数量的直接影响[3],并未就其与创新型创业机会实现间的关系进行探讨。与此同时,复杂的社会问题需要借助跨边界知识重组来创造新的解决方案,故涉农企业合作研发、知识重组创造与创新型创业机会实现的关系极具复杂性:一方面,合作研发是与环境互动的社会技术系统[4],要素间相互依赖形成了多条驱动高知识重组创造的组态路径,然而现有实证研究多基于还原论,忽略了合作研发的系统属性,难以完全解释合作研发与知识重组创造存在的复杂依存关系;另一方面,知识重组创造能够通过知识互补、知识成本效应影响创新型创业机会实现[5]。这意味着涉农企业合作研发、知识重组创造和创新型创业机会实现间可能存在复杂的中介效应。
针对上述复杂系统管理问题,本文借鉴已有研究[6]:首先,厘清如何形成合作研发,即从Fogg行为理论的“动机—能力—触发器”框架来提炼涉农企业合作研发的构成要素,并分析其与高/非高知识重组创造的组态;其次,明晰多元合作研发的等效驱动路径如何直接、间接影响创新型创业机会实现。基于上述问题分析,对于践行创新驱动发展战略指引涉农企业通过合作研发实现创业机会,以推动我国农业现代化的实现具有重要意义。
1 文献述评与理论基础
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1.1 涉农企业合作研发
既有涉农企业合作研发相关研究主要聚焦在要素构成、作用机制和所得结果三方面。
关于要素构成。基于Fogg行为理论,合作研发由动机、能力和触发器三要素构成[7]:动机是合作研发的原动力,能力是实施合作研发所需的技能与资源,触发器是促使合作研发意愿转化为行动的关键环节。动机可体现为合作研发导向,即涉农企业在多大程度上将他人视为合作研发伙伴[8]。具备合作研发能力的涉农企业通过与合作主体的互动进行资源配置[9]。同时,知识产权保护[10]、市场环境不确定性[11]等合作研发触发器会影响涉农企业科技成果转化速度,并触发其合作研发动机与能力。
关于作用机制。涉农企业通过合作研发中的知识共享、整合可促进机会实现[12],同时也会因为逆向选择导致主体间信息不对称,造成技术知识的不完善,并抑制专利质量[13]。
关于所得结果。涉农企业合作研发被视为一种创新驱动机制,能够通过资源互补、分摊成本、分散风险来提高研发成功率[14]。
综上所述,已有研究指出了合作研发要素构成包括合作研发导向、合作研发能力、合作研发触发器(知识产权保护和市场环境),同时提及知识的重要性,但并未基于组态思维探讨合作研发要素与知识重组创造间存在何种影响关系。基于此,本研究将通过组态思维来明晰两者的复杂关系。
1.2 涉农企业创业机会实现
机会作为创业研究的核心,创业机会实现是机会感知与管理实践结合的产物[15]。已有涉农企业创业机会实现研究主要聚焦机会实现前因、机会实现过程、机会实现结果三方面。
关于机会实现前因。外因方面:完善的制度环境[16]、市场环境(市场成熟度、市场进入壁垒等)[17]均可促进涉农企业机会实现。内因方面:涉农企业内部新知识、新技术等可驱动其在非均衡市场中实现创业机会[9]。少部分研究基于社会互动视角指出机会可通过社会互动、资源流动来实现[17]。
关于机会实现过程。现有研究指出涉农企业可通过社会资本[17]、知识[9]等资源的开发过程,或通过当地的空间资源拼凑过程来促进机会实现[18]。
关于机会实现结果。涉农企业创业机会实现不仅能够提升国家、区域层面的创业活动[3];同时也能够促进企业的生态创新和可持续发展[13],并且能够赋权女性群体克服创业技能等挑战[19]。
综上所述,已有涉农企业创业机会实现研究指出了制度环境、市场环境、知识等因素对其的影响,虽提及新知识能够促进涉农企业创业机会实现,但并未明晰涉农企业合作研发、知识重组创造与创新型创业机会实现间的关系,故本文将对此做深入分析。
1.3 研究述评
已有研究为本文提供了重要的思路,但仍存在两方面局限:第一,既有涉农企业合作研发相关研究虽分别指出合作研发导向[8]、合作研发能力[9]、合作研发触发器(知识产权保护[10]、市场环境[11])对涉农企业发展的影响,同时提及知识的重要性[12,13],但并未基于组态思维探讨合作研发要素与知识重组创造存在何种作用关系,其复杂因果关系未被揭示。
第二,既有涉农企业创业机会实现相关研究虽提及知识有助于机会数量增加[3],但并未探讨驱动知识重组创造的不同合作研发组态与创新型创业机会实现间的关系,以及知识重组创造在此关系中的复杂中介机制。
基于此,本研究借鉴QCA与回归分析的混合方法,以明晰不同合作研发组态、知识重组创造与创新型创业机会实现间的复杂作用关系。
2 研究模型构建
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2.1 合作研发要素与知识重组创造的复杂作用机制
根据Fogg行为理论,涉农企业合作研发由动机(合作研发导向)、能力(合作研发能力)和触发器(知识产权保护与市场环境)共同作用所致,三者可促进涉农企业与合作主体的知识重组创造。
合作研发导向体现了涉农企业将他人视为合作伙伴以实现共同研发目标的动机,其通过弥补涉农企业知识缺口并增强其对市场与技术变化的警觉性,促进对外部知识的内化,从而推动知识重组创造。合作研发能力体现了涉农企业与合作主体间的资源配置水平,其通过知识的互补、替代效应拓展知识重组创造的广度、深度。合作研发触发器包括知识产权保护和市场环境:首先,知识产权保护通过规范市场秩序、降低交易成本、激励创新投入推动涉农企业知识的有效吸收,并降低知识重组创造的风险;其次,市场环境通过需求信号传递、竞争压力施加以及资源可得性改善引导抗风险能力较弱的涉农企业知识内化,从而推动知识重组创造。
综上所述,虽然上述合作研发要素均能影响知识重组创造,然而现有研究尚未阐明要素间如何组合来影响知识重组创造。基于此,本文首先探讨合作研发要素组合驱动高知识重组创造的组态效应,为后续复杂中介模型的构建奠定基础。
2.2 复杂中介模型构建
(1)组态视角下合作研发要素对创新型创业机会实现的影响
基于Fogg行为理论,合作研发导向、合作研发能力和合作研发触发器通过不同组合影响涉农企业创新型创业机会实现主要受收益函数和成本函数的相互制约:一方面,要素组合通过共同愿景提升涉农企业的预期收益,促进其获取、内化互补性知识,克服知识锁定效应[20],并使其通过精准对接技术供给与市场需求,从而提高创新型创业机会实现的技术可行性和市场适配性;另一方面,合作研发的深入要求不同背景主体为实现共同目标,投入大量时间与协调成本来转译彼此知识,从而阻碍创新型创业机会实现。
(2)组态视角下的复杂中介模型构建
合作研发要素也可通过知识重组创造间接影响涉农企业创新型创业机会实现,且具有两方面的复杂性:
其一,组态视角下的合作研发三要素对知识重组创造存在多重等效路径。合作研发导向通过协调利益相关者期望,以明确共同愿景并促进知识重组创造;合作研发能力通过组织间知识的互补、替代效应推动知识重组创造;合作研发触发器通过法规或行业标准为知识重组创造提供政策保障和资源支持。即合作研发三要素组合促进涉农企业通过不同的认知图式构思问题解决方案,而偏离自身技术范围的解决方案往往会促进知识重组创造。
其二,涉农企业合作研发要素组合通过知识重组创造间接影响创新型创业机会实现。涉农企业在研发资源约束下,可通过合作研发要素组合设定清晰目标、明确产权归属,以增强合作主体间的信任,引导资源分配和行动协调来克服信息不对称等障碍。同时合作主体可通过挖掘知识的互补、替代效应来实现知识的合理配置,并激发知识重组创造的新构想,而在合作研发中考虑市场需求能促进两者的契合,有助于降低重复研发成本,使本就有限的研发资源聚焦于更具潜力的技术方向,从而促进创新型创业机会实现。
基于上述分析得到研究模型,如图1所示。吸收能力理论作为本文理论基础,在如下两方面契合本文议题:其一,资源互补视角。涉农企业创新资源匮乏,通过合作研发获取异质性知识并提升潜在吸收能力,可印证创新型创业机会实现的可取性。其二,风险协同视角。涉农企业抗风险能力差,通过合作研发实现风险共担并强化现实吸收能力,可提高创新型创业机会实现的可行性。
3 研究设计
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3.1 研究方法
本研究采用QCA与回归分析的混合方法。具体来说:
首先,运用QCA方法主要出于如下两点考虑:其一,有别于传统统计方法的“净效应”,涉农企业知识重组创造受多个前因条件的多重并发影响,采用QCA方法则有助于理解产生高知识重组创造的多重等效合作研发组态[21]。其二,采取线性回归方法,合作研发要素在解释知识重组创造时仅是替代或者累加关系,并非完全等效关系,研究结果难免偏倚。而QCA方法适合探究多个合作研发要素对涉农企业知识重组创造的复杂机制。本文采用fsQCA方法,因为其可将数据校准为连续变量,保留原始数据特征。
其次,通过fsQCA与回归分析的混合方法探讨复杂中介模型,即运用fsQCA得到涉农企业高知识重组创造的组态Xi,将其作为自变量,依次检验Xi→M(知识重组创造)、Xi→Y(创新型创业机会实现)、M→Y的因果链。
3.2 样本选择和数据来源
本文数据来自Wind、CNRDS、CCAD数据库、沪深证交所、国家知识产权局,具体来说:
首先,通过CCAD得到上市涉农企业名单。
其次,因合作研发维系时间在3-5年,故前因变量和中介变量需同时满足3-5年时间范围:前因变量选取2018-2021年数据,中介变量选取2019-2022年数据。此外,考虑到专利申请的滞后性,因变量则滞后一年,即2020-2023年数据。为尽可能避免偶然因素的误差影响,本文所有变量以连续四年均值来测度。
然后,从沪深证交所获得上市涉农企业2018-2023年年报;结合涉农企业代码从CNRDS中获得2018-2022年涉农企业合作研发申请的发明、实用新型专利数据,以及2019-2023年专利转让、被引用数据;从国家知识产权局发布的《全国知识产权发展状况报告》获得省份2018-2021年知识产权保护指数;从Wind获得2020-2023年涉农企业财务数据。
最后,为保障研究可靠性,对数据进行预处理:(1)剔除所有ST、*ST以及企业年龄小于1的样本;(2)剔除所有年份未进行合作研发的样本;(3)剔除部分数据缺失样本,同时保障QCA研究与后续回归分析中样本的一致性,共得到94家涉农企业。
3.3 变量的测量、校准
前因变量的测量方式如下所述:
(1)合作研发导向(CIO)的测量采取文本分析法。首先,通过短词组合,即合作词库[22]:“合作、团结、联合、配合、协作、协同、协力、合力、互助、分享、共享、同舟共济、沟通、交流、双赢”;以及研发词库[23]:“R&D、研发、创新、研究、开发、研制、科研、预研、设计、创造、实验、试验、技术、专利、工艺、新项目、新产品、新业务、知识产权、科技成果、科技投入”。在此基础上,综合利用FastText词向量方法与GloVe词向量方法进一步识别不包含在上述词库中的核心词组:“合作攻关、联合攻关、协作攻关、协同攻关、合作育种、产学研、共建研发平台、共建技术中心、共建实验室”等,同时借鉴哈工大停用词。运用Jieba工具对年报分词,利用Python软件得出2018-2021年合作研发导向关键词词频(两类短词组合共同出现及核心词组出现频次)来度量合作研发导向,并进行加1取对数处理。
(2)合作研发能力(CIA)从知识互补性和知识替代性两个方面测度[24]。根据2018-2021年涉农企业合作申请的发明专利、实用新型专利分类号前4位来测度,以知识互补性、知识替代性的均值来测度其合作研发能力。
(3)知识产权保护(IPP)采用《全国知识产权发展状况报告》中披露的2018-2021年知识产权保护指数。市场环境(ME)选取2018-2021年樊纲发布的要素市场化指数。
中介变量知识重组创造(KRC)的测量。提取涉农企业在2019-2022年共同申请专利的IPC分类号前4位作为知识元素类别,通过与其专利申请年之前的专利IPC分类号进行比较,若知识元素组合是首次出现,则将该专利认定为知识重组创造[25]。通过计算上述专利申请个数加1取对数来测度知识重组创造。
结果变量创新型创业机会实现(IEO)的测量。从技术供给、市场需求和市场竞争优势三个维度测量[26]:技术供给以涉农企业2020-2023年专利转让个数和剔除自引后被引用次数归一化后的均值来测度;市场需求采用内容分析法,对涉农企业2020-2023年年报内提及的非相关多元化业务(如果与企业主营业务所属国民经济行业大类代码不同,则被视为新市场)个数来测度;市场竞争优势以涉农企业该年度经行业中值调整后的主营收入增长率来测度。再采取三者归一化后的均值加1取对数来测度创新型创业机会实现。
同时,采用直接校准法校准变量,锚点值设定为样本数据的95%、50%和5%分位数值,针对交叉点的数值统一校准为0.501,各变量校准锚点如表1所示。
4 实证分析
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4.1 fsQCA分析
(1)单个必要条件分析
在组态分析之前,首先对各前因条件进行必要性分析。表2为必要性分析结果,可知四个前因变量的一致性均低于0.9,表明没有任何一个前因是知识重组创造的必要条件,需进一步借助组态分析来判断不同前因组合对知识重组创造的影响。
(2)组态的充分性分析
为保证研究科学性,本文在求解中间解进行反事实分析时,对各前因条件与知识重组创造的关系不做方向性设定。遵循至少保留75%的案例要求,将案例频数阈值设定为3,PRI阈值为0.60,组态一致性门槛值为0.80,大于0.80意味着该组态能够充分解释高/非高水平知识重组创造。表3展示了实现高/非高水平知识重组创造的组态分析结果:在动机、能力、触发器前因变量的交互作用下,高水平知识重组创造存在3种组态,总体一致性为0.822,高于最低接受度0.80,总体覆盖率为0.426,解释了42.6%高知识重组创造的原因。考虑到QCA方法的因果非对称性,进一步探讨非高水平知识重组创造的前因变量组态,发现其存在1种组态,总体一致性为0.841,总体覆盖率为0.393。
从单个前因条件看,合作研发导向作为每个组态中的核心条件存在,这印证了合作研发动机是触发企业开展合作研发的重要前因。为了更好地比较不同组态之间的差异,故归纳导致高水平知识重组创造的3种合作研发组态,如图2所示,同时围绕吸收能力理论进行分析:
模式1:导向驱动型合作研发组态,即在清晰的合作方向和明确的合作战略指引下开展的合作研发。组态1被纳入此模式中,呈现下述情境:低水平的知识产权保护、市场情境挫伤了涉农企业开展合作研发的积极性,同时低水平的合作研发能力阻碍其知识获取和内化,其希冀通过高水平合作研发导向,在清晰的战略指引、明确的合作方向下思考如何有效整合合作主体间的知识,以触发知识重组创造。
模式2:市场驱动型合作研发组态,即在较为完善的要素市场情境、较强的合作研发动机、合作研发能力驱动下开展的合作研发。组态2被纳入此模式中,呈现下述情境:低水平的知识产权保护导致涉农企业对合作研发资源的投入持谨慎态度,导致市场中大量同质性产品出现,涉农企业尝试通过跨组织知识互动来获取异质性知识,并与现有知识进行组合来实现互补性利用和替代性优化,而良好市场环境要求涉农企业必须快速响应顾客需求,推动了知识的快速流动和有效吸收,进而催化知识重组创造。
模式3:制度驱动型合作研发组态,即在相对健全的知识产权保护制度情境、较强的合作研发动机、合作研发能力驱动下开展的合作研发。组态3被纳入此模式中,呈现下述情境:有别于模式2,此模式下涉农企业的市场需求较为稳定,在完善的知识产权保护制度下,不仅政策支持为其合作研发提供稳定的知识共享、利用环境,同时合作主体无须担忧知识泄露、侵权现象,降低了双方在合作研发中的风险感知,并且促进不同来源知识的吸收,实现了知识的聚变性重组创造。
(3)稳健性检验
本文从最低案例阈值、PRI值和一致性阈值进行结论的稳健性检验,如表4所示。
首先,将最低案例阈值从3调整到4,将PRI值0.60调整到0.65,均得到1条组态与现有组态S1基本一致;然后,将一致性阈值0.8调整到0.86,产生2条组态与现有组态中S2、S3一致,故从最低案例、PRI、一致性阈值方面看,研究结论均具有一定的稳健性。
4.2 回归分析
(1)描述性统计
自变量是产生高知识重组创造的合作研发组态,即通过涉农企业在每种合作研发组态的集合隶属度来测量。本研究将发明专利数量(IP)、实用新型专利数量(UP)、总资产增长率(GR)、融资约束(FC)、研发投入比率(RDI)作为控制变量。各变量描述性统计见表5。
(2)不同合作研发组态的知识重组创造与创新型创业机会实现
本文通过方差膨胀系数VIF检验变量间的共线性,结果显示各变量的VIF值均低于10,说明变量间多重共线性不严重。其次,就合作研发组态与创新型创业机会实现进行回归分析。表6结果显示,3种驱动高知识重组创造的合作研发组态均正向影响创新型创业机会实现。
(3)稳健性检验
本文通过去除极端值和替换因变量(市场竞争优势子维度以赫芬达尔指数代替)的方式对3种高知识重组创造的合作研发组态与创新型创业机会实现间的关系进行稳健性检验,如表7所示,结果可以佐证本研究各变量显著性、系数方向均是稳健的。
(4)机制分析
本文纳入中介变量知识重组创造进行回归分析,回归结果见表8。模型10是知识重组创造与创新型创业机会实现的关系;模型11—13是考虑知识重组创造后,3种合作研发组态对创新型创业机会实现的影响。
模型10—13结果可知,3种合作研发组态、知识重组创造与创新型创业机会实现间存在复杂中介效应,其中,知识重组创造和3种合作研发组态的回归系数显著为正,佐证了3种合作研发组态通过知识重组创造促进创新型创业机会实现。
结合表3组态分析结果,本文就合作研发组态对创新型创业机会实现的复杂中介论述如下:以组态S1为主的涉农企业虽然面临低水平的知识产权保护和市场环境,以及合作研发能力制约,但通过高水平的合作研发导向进行组织间知识共享,触发了知识重组创造,从而促进了创新型创业机会实现。同理,组态S2和组态S3虽然面临低水平的知识产权保护或要素市场环境,但通过高水平的合作研发能力和合作研发导向聚合了多样性知识,从而催化了知识重组创造,并实现了更多创新型创业机会。
综上所述,本文提出如下3个命题:
命题1:驱动涉农企业高知识重组创造的3种合作研发组态与创新型创业机会实现间存在复杂中介效应,并且3种组态均能通过驱动高知识重组创造间接促进创新型创业机会实现。
命题2:合作研发导向是驱动涉农企业高知识重组创造的核心条件。
命题3:涉农企业在高合作研发导向和高合作研发能力情况下,知识产权保护和市场环境对其高知识重组创造分别发挥了不同的触发器作用。
5 研究结论、贡献及展望
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5.1 研究结论
本研究以进行合作研发的94家涉农企业为样本,结合组态视角、吸收能力理论和复杂中介方法,深入分析了合作研发动机、能力、触发器要素组合如何驱动创新型创业机会实现,以及知识重组创造所发挥的作用,得出如下三点结论:
第一,基于Fogg行为理论将涉农企业合作研发要素归纳为合作研发动机、合作研发能力和合作研发触发器(知识产权保护和市场环境)。
第二,组态分析结果表明:涉农企业合作研发影响高知识重组创造存在3种组态,可归纳为导向驱动型、市场驱动型和制度驱动型,3种组态以殊途同归方式触发高知识重组创造。其中,合作研发导向是核心条件,若合作研发导向和合作研发能力均处于高水平,知识产权保护和市场环境对高知识重组创造分别发挥了不同的触发器作用。
第三,回归分析和复杂中介结果表明:3种合作研发组态可直接促进创新型创业机会实现,也可通过触发高知识重组创造间接促进创新型创业机会实现。
5.2 理论贡献
本文理论贡献包括如下三点:
第一,结合Fogg行为理论和涉农企业合作研发相关研究提炼了涉农企业合作研发“动机—能力—触发器”要素框架,归纳了其合作研发要素驱动知识重组创造的多重等效路径。Fogg行为理论多用于探讨组织间知识共享[27]等问题,本文基于既有研究将涉农企业合作研发要素提炼为合作研发导向(动机)、合作研发能力(能力)、合作研发触发器(知识产权保护和市场环境),有助于学者更为全面地理解其合作研发要素构成。而合作研发要素与知识重组既有研究多探讨企业合作能力[28]等单一因素对知识重组的影响,本文基于组态视角发现合作研发动机、能力和触发器要素可通过3种组态促进高知识重组创造,采取组态视角不仅能够克服线性回归模型的不足和内生性干扰,同时也有助于打开“动机—能力—触发器”要素框架中多重条件影响知识重组创造的“黑箱”。
第二,揭示了知识重组创造在涉农企业不同合作研发组态与创新型创业机会实现间存在的复杂中介作用。既有研究虽探讨了涉农企业合作研发单一要素对创业机会数量的影响[3],但并未就合作研发组态如何直接、间接影响其创新型创业机会实现等复杂问题进行探讨。本文发现知识重组创造在涉农企业3种合作研发组态与其创新型创业机会实现间存在复杂中介作用,该复杂中介模型的探讨对于揭示合作研发组态影响创业机会实现机理具有研究价值。
第三,通过解释涉农企业合作研发组态通过知识重组创造促进创新型创业机会实现,深化了吸收能力理论在创业研究中的应用。尽管本文并未考虑吸收能力,但涉农企业知识重组创造本质是对外部知识的识别和整合。既有研究虽提及企业通过吸收能力可获取更多创业机会[2],本研究进一步指出涉农企业创新型创业机会实现依赖于其如何将外部获取知识与内部知识进行创造性重组,这为理解企业如何通过吸收知识实现创新型创业机会提供了解释,从而深化了吸收能力理论在创业研究中的应用。
5.3 实践启示
本文的实践启示包括如下三点:
首先,着力塑造涉农企业的合作研发导向。合作研发导向是涉农企业高知识重组创造组态中的核心要素,通过培育合作研发导向并联合高校等主体共建研发中心,才能实现技术供给与市场需求的精准匹配,进而推动知识重组创造,并促进创新型创业机会实现。
其次,政府应进一步优化制度环境或市场环境建设,以发挥市场与制度的协同作用。通过有效市场与有为政府的协同来降低涉农企业合作研发风险,鼓励试错性的知识重组,从而更精准匹配涉农企业创新型创业机会所需。
最后,涉农企业和政府都应坚持系统论思维以发挥合作研发要素的组合效应。复杂因果应简化为前因组合,这是导致结果出现的重要原因。因此不能割裂地看待合作研发的单一要素,而需要通过有效“组合”来发挥要素合力,以充分触发知识重组创造,实现更多创新型创业机会。
5.4 局限及未来展望
本文存在如下不足:第一,前因变量的选择存在一定片面性,未来可进一步挖掘相关变量,以提高结论的普适性;第二,缺乏内生性分析,复杂中介效应可能存在反向因果、变量遗漏、样本选择性偏差等问题,后续可采用倾向得分匹配法、工具变量法进行深入检验;第三,未直接探讨吸收能力的作用边界,后续可将吸收能力作为调节变量,以丰富研究推论。
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2026年第44卷第10期
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  • 接收时间：2025-07-26
  • 首发时间：2026-10-10
  • 出版时间：2026-10-15
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  • 收稿日期：2025-07-26
  • 修回日期：2026-06-08
基金
国家自然科学基金资助项目(72572072)
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    吉林大学商学与管理学院, 吉林长春 130012

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陈姿颖(1997-),女,博士研究生,E-mail:。
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