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Article(id=1309226335151685865, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, 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=1740931200000, receivedDateStr=2025-03-03, revisedDate=1745942400000, revisedDateStr=2025-04-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1790073482549, onlineDateStr=2026-09-22, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790073482549, onlineIssueDateStr=2026-09-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790073482549, creator=13701087609, updateTime=1790073482549, updator=13701087609, issue=Issue{id=1309226306445865584, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='6', pageStart='1121', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790073475705, creator='13701087609', updateTime=1790073520980, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309226496414286647, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309226496414286648, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1252, endPage=1263, ext={EN=ArticleExt(id=1309226336858767594, articleId=1309226335151685865, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=Influencing factors and configurational pathways of disruptive green innovation in manufacturing enterprises, columnId=1309226334803550422, journalTitle=Studies in Science of Science, columnName=Innovation Explorations, runingTitle=null, highlight=null, articleAbstract=

Facing escalating environmental challenges and global competition, manufacturing enterprises must adopt disruptive green innovation strategies to achieve sustainable economic and environmental benefits. Disruptive green innovation refers to the process where enterprises introduce green products or services with different performance attributes from those required by mainstream consumers, initially targeting low-end or niche markets, and gradually improving the product to disrupt the mainstream market. Despite its importance in enhancing sustainable competitiveness and meeting the "dual carbon" goals, disruptive green innovation faces challenges due to its complexity, high risks, and uncertainty. This raises an important question: What factors lead to the more effective implementation of disruptive green innovation, and how do these factors interact?

This study adopts a mixed-methods approach by combining case studies of BYD and Luyuan with fuzzy-set qualitative comparative analysis (fsQCA), to explore the factors driving disruptive green innovation. The results of case study identify several key drivers, including innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource coordination, and big data analytics capability. Using survey data from 332 manufacturing enterprises in strategic emerging industries, this study applies fsQCA to uncover configurational pathways driving disruptive green innovation. Key findings include: (1) Innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource orchestration, and big data analytics capability constitute core antecedents of disruptive green innovation. (2) No single antecedent is a necessary condition for disruptive green innovation. (3) Six configurational paths enabling high disruptive green innovation are categorized into four types: knowledge-data-driven pathway under cooperation dominance, knowledge-R&D-driven pathway under competition dominance, R&D-data-driven pathway under coopetition dominance, capability-led driven pathway, with substitutable relationships among element combinations under specific conditions. (4) Non-high disruptive green innovation configurations fall into two types: collaboration-resource-data-constrained and knowledge-R&D-data-constrained. This research provides theoretical and practical insights into how enterprises achieve disruptive green innovation across diverse contexts.

This study makes several theoretical contributions. First, this study enriches the research on antecedent factors of disruptive green innovation by case study. Currently, research on the drivers of disruptive green innovation is still in its early stages. Through exploratory case studies, this study investigates key antecedents of disruptive green innovation and expands the understanding of this concept. Second, this study also identifies the configuration paths driving disruptive green innovation, which improves and deepens the analytical paradigm of its formation mechanism. Existing studies on the antecedents of disruptive green innovation have rarely focused on the joint effects of multiple factors. Given the complexity and systemic nature of disruptive green innovation, this study applies the fsQCA method from a configurational perspective to explore the co-action of factors such as innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource orchestration, and big data analytics capability. By adopting a mixed-methods approach, this study offers a more comprehensive interpretation of the research model, revealing results that might be overlooked by a single method. This approach helps to improve the understanding of how enterprises conduct disruptive green innovation.

From a practical perspective, this study suggests that enterprises should adjust their innovation paths based on their strengths and market demands, optimizing their roles and strategies within the innovation ecosystem, especially when facing international competition. Digital technologies, particularly big data analytics capability, are identified as key drivers for disruptive green innovation. Enterprises should also strengthen coopetition relationships with partners, engage in cross-sector collaboration, and share knowledge to enhance green innovation. Additionally, investing in green R&D and improving resource coordination are essential for achieving sustainable green innovation.

, authors=Xiao-hua XIN, Yan-ying SHANG, authorsList=Xiao-hua XIN, Yan-ying SHANG, authorCompany=null, correspAuthors=Yan-ying SHANG, 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=1309226350490255707, articleId=1309226335151685865, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=制造企业颠覆性绿色创新的影响因素及作用路径, columnId=1309226334996488408, journalTitle=科学学研究, columnName=创新探索, runingTitle=null, highlight=null, articleAbstract=

面对日益严峻的环境挑战与国际竞争,制造企业亟须采用颠覆性绿色创新战略以实现可持续的经济效益和环境效益。采用混合研究方法,首先通过对比亚迪和绿源的案例分析提炼出企业颠覆性绿色创新的影响因素,而后基于战略性新兴产业中的332家制造企业的调研数据,运用fsQCA方法识别驱动企业颠覆性绿色创新的作用路径。研究发现:(1)创新生态系统竞合关系、绿色知识获取、绿色研发投入、环境资源协奏和大数据分析能力是企业颠覆性绿色创新的主要前因变量;(2)单一前因变量不构成颠覆性绿色创新的必要条件;(3)驱动企业高颠覆性绿色创新的6条组态路径可分为四大类:合作主导下的知识-数据驱动型、竞争主导下的知识-研发驱动型、竞合主导下的研发-数据驱动型、能力主导驱动型,且在一定条件下,实现高颠覆性绿色创新的要素组合之间具有替代关系;(4)产生非高颠覆性绿色创新的组态解可归为合作-资源-数据抑制型与知识-研发-数据抑制型2类。本文对解释不同情境下企业实现颠覆性绿色创新的作用路径具有重要的理论与实践意义。

, authors=辛晓华, 尚晏莹, authorsList=辛晓华, 尚晏莹, authorCompany=null, correspAuthors=尚晏莹, authorNote=

辛晓华(1994-),女,讲师。

, correspAuthorsNote=
尚晏莹(1996-),女,讲师,E-mail:。
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辛晓华(1994-),女,讲师。

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辛晓华(1994-),女,讲师。

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Journal of Business Research, 2019, 105:49-60., articleTitle=Strategic orientations, developmental culture, and big data capability, refAbstract=null)], funds=[Fund(id=1309226355762495879, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, awardId=24JK0150, language=CN, fundingSource=陕西省教育厅一般专项科学研究计划项目(24JK0150), fundOrder=null, country=null), Fund(id=1309226355837993352, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, awardId=20JG022, language=CN, fundingSource=陕西省教育厅人文社会科学专项(20JG022), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1309226350779662684, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, xref=null, ext=[AuthorCompanyExt(id=1309226350783856989, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, companyId=1309226350779662684, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China), AuthorCompanyExt(id=1309226350792245598, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, companyId=1309226350779662684, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=西安理工大学经济与管理学院, 陕西西安 710054)])], figs=[ArticleFig(id=1309226352235086193, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Figure 1(a), caption=Development timeline and key events of BYD, figureFileSmall=5f1xk1RJO3vwBhNC/rIHrw==, figureFileBig=3/ZTcgx6OBp4kFc8YbrVLg==, tableContent=null), ArticleFig(id=1309226352348332402, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=图1(a), caption=比亚迪汽车发展历程及关键性事件, figureFileSmall=5f1xk1RJO3vwBhNC/rIHrw==, figureFileBig=3/ZTcgx6OBp4kFc8YbrVLg==, tableContent=null), ArticleFig(id=1309226352549658995, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Figure 1(b), caption=Development timeline and key events of Luyuan, figureFileSmall=JcgqU4ieO+q3By0zHTrNNQ==, figureFileBig=/dnBXXwjB4AcWpnTOkf4xg==, tableContent=null), ArticleFig(id=1309226352616767860, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=图1(b), caption=绿源电动车发展历程及关键性事件, figureFileSmall=JcgqU4ieO+q3By0zHTrNNQ==, figureFileBig=/dnBXXwjB4AcWpnTOkf4xg==, tableContent=null), ArticleFig(id=1309226352675488117, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Figure 2, caption=Configurational analysis model, figureFileSmall=jxWbT6qaUvAKTqUPrkbGNw==, figureFileBig=XU1/2OF/BqPKmSQIVz1USg==, tableContent=null), ArticleFig(id=1309226352755179894, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=图2, caption=组态分析模型, figureFileSmall=jxWbT6qaUvAKTqUPrkbGNw==, figureFileBig=XU1/2OF/BqPKmSQIVz1USg==, tableContent=null), ArticleFig(id=1309226352843260279, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 1, caption=

Basic characteristics of BYD and Luyuan

, figureFileSmall=null, figureFileBig=null, tableContent=
基本特征 比亚迪 绿源
成立时间 1995年 1997年
产业类型 制造业 制造业
主营业务领域 汽车、电子、新能源、轨道交通 电动自行车、电动轻便摩托车、电动摩托车
颠覆性绿色创
新的起始时间		 2003年进入汽车领域,2006年开始涉足新能源汽车领域 1997年开始开拓电动自行车的新市场
产品市场规模 2022年比亚迪汽车全年销量为186.85万辆,
占全球新能源汽车份额的18.4%,位列第一		 2020-2022年绿源的产品销量分别为147.2万辆、
194.8万辆、242.4万辆
), ArticleFig(id=1309226352922952056, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表1, caption=

比亚迪和绿源企业的基本特征

, figureFileSmall=null, figureFileBig=null, tableContent=
基本特征 比亚迪 绿源
成立时间 1995年 1997年
产业类型 制造业 制造业
主营业务领域 汽车、电子、新能源、轨道交通 电动自行车、电动轻便摩托车、电动摩托车
颠覆性绿色创
新的起始时间		 2003年进入汽车领域,2006年开始涉足新能源汽车领域 1997年开始开拓电动自行车的新市场
产品市场规模 2022年比亚迪汽车全年销量为186.85万辆,
占全球新能源汽车份额的18.4%,位列第一		 2020-2022年绿源的产品销量分别为147.2万辆、
194.8万辆、242.4万辆
), ArticleFig(id=1309226352990060921, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 2, caption=

Evidence list of core constructs in the disruptive phase of non-mainstream markets

, figureFileSmall=null, figureFileBig=null, tableContent=
案例企业 核心构念 典型证据举例
比亚迪 创新生态系统合作关系 2008年到2012年期间,中国新能源汽车产业基于这个平台生态系统,企业之间建立合作……实现了快速增长
创新生态系统竞争关系 合资车企大量涌入,价格也逐渐下探,进一步压缩了自主品牌的发展空间,其实是很困难的……
绿源 创新生态系统合作关系 上游供应商的电池出现质量问题……卖出去的绿源电动车电池遭到了退货,合作的经销商也率先反水
创新生态系统竞争关系 上海的千鹤、苏州的小羚羊、南京的大陆鸽这些国有控股企业都在长三角成立
比亚迪 绿色知识获取 通过大规模的兼并收购,比亚迪在短时间内具备了新能源汽车整车和零部件的自主研发、设计及制造能力
绿色研发投入 我们在电池和电动汽车技术领域投入了大量资金,……
环境资源协奏 比亚迪通过业务外包、采购等与供应链上下游企业保持稳定的交易关系,整合资源拼凑出完整的电动车生产线……
绿源 绿色知识获取 对绿色出行相关的政策要清楚了解……当时创始人就注意到一些大城市开始禁止汽油机车……
绿色研发投入 绿源前期一直选择的是做电动自行车的产品设计和研发,鼓励各种各样的新技术进入到这个新的领域
环境资源协奏 当时浙江有长兴天能、宁波协成这些企业,可以买到蓄电池、电机……绿源对这些资源进行了有效整合和利用
), ArticleFig(id=1309226354667782522, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表2, caption=

非主流市场颠覆阶段核心构念的证据列表示例

, figureFileSmall=null, figureFileBig=null, tableContent=
案例企业 核心构念 典型证据举例
比亚迪 创新生态系统合作关系 2008年到2012年期间,中国新能源汽车产业基于这个平台生态系统,企业之间建立合作……实现了快速增长
创新生态系统竞争关系 合资车企大量涌入,价格也逐渐下探,进一步压缩了自主品牌的发展空间,其实是很困难的……
绿源 创新生态系统合作关系 上游供应商的电池出现质量问题……卖出去的绿源电动车电池遭到了退货,合作的经销商也率先反水
创新生态系统竞争关系 上海的千鹤、苏州的小羚羊、南京的大陆鸽这些国有控股企业都在长三角成立
比亚迪 绿色知识获取 通过大规模的兼并收购,比亚迪在短时间内具备了新能源汽车整车和零部件的自主研发、设计及制造能力
绿色研发投入 我们在电池和电动汽车技术领域投入了大量资金,……
环境资源协奏 比亚迪通过业务外包、采购等与供应链上下游企业保持稳定的交易关系,整合资源拼凑出完整的电动车生产线……
绿源 绿色知识获取 对绿色出行相关的政策要清楚了解……当时创始人就注意到一些大城市开始禁止汽油机车……
绿色研发投入 绿源前期一直选择的是做电动自行车的产品设计和研发,鼓励各种各样的新技术进入到这个新的领域
环境资源协奏 当时浙江有长兴天能、宁波协成这些企业,可以买到蓄电池、电机……绿源对这些资源进行了有效整合和利用
), ArticleFig(id=1309226354751668603, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 3, caption=

Evidence list of core constructs in the disruptive phase of mainstream markets

, figureFileSmall=null, figureFileBig=null, tableContent=
案例企业 核心构念 典型证据举例
比亚迪 创新生态系统合作关系 电动车一起来以后,整个生态就起来了,产业链各种零组件,像正极材料、负极材料、隔膜纸、定检仪也都全部拉起来了
创新生态系统竞争关系 那些新势力,像蔚小理……互联网转型的车企可能会对比亚迪有冲击
绿源 创新生态系统合作关系 在产学研方面,绿源与浙江大学、浙江工业大学、中国科技大学、合肥工业大学等院校建立了密切合作关系……
创新生态系统竞争关系 ……越来越多的企业将目光锁定电动自行车这个新兴行业,市场竞争加剧,呈现多品牌竞争的局面
比亚迪 绿色知识获取 比亚迪也学习别的企业做电池、电机或其他新能源领域的先进技术与丰富经验,这对我们研发与生产新能源汽车很有帮助
绿色研发投入 2022年对新能源方面的研发投入非常大,招了很多研发人员。很多部门原来只有100人,现在已经有800人……
环境资源协奏 ……积极协调生产各环节资源提升产量,以满足(新能源汽车)订单需求
大数据分析能力 每辆车我都能在后台查到你的转速、里程、发动机运行、怎么运转的,后台把数据收集起来去做分析,然后做创新优化策略,比如车怎么跑起来更省油……
绿源 绿色知识获取 通过对一些市场信息的了解和对技术知识的学习,我们取消了简易款电动车型的脚踏、链条和飞轮,在前部做成了一个宽敞的脚踏板
绿色研发投入 绿源投入200多万元引进先进设备进行电池等技术研发……
环境资源协奏 我们核心竞争力不是简单将人力、物力和财力叠加在一起,而是将这些资源通过创新整合、组织协调和应变管理等方法进一步集成和加工……
大数据分析能力 对商品制造、用户经营、服务体验这些数据进行收集、整理、分析,提前预判客户需求
), ArticleFig(id=1309226354822971772, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表3, caption=

主流市场颠覆阶段核心构念的证据列表示例

, figureFileSmall=null, figureFileBig=null, tableContent=
案例企业 核心构念 典型证据举例
比亚迪 创新生态系统合作关系 电动车一起来以后,整个生态就起来了,产业链各种零组件,像正极材料、负极材料、隔膜纸、定检仪也都全部拉起来了
创新生态系统竞争关系 那些新势力,像蔚小理……互联网转型的车企可能会对比亚迪有冲击
绿源 创新生态系统合作关系 在产学研方面,绿源与浙江大学、浙江工业大学、中国科技大学、合肥工业大学等院校建立了密切合作关系……
创新生态系统竞争关系 ……越来越多的企业将目光锁定电动自行车这个新兴行业,市场竞争加剧,呈现多品牌竞争的局面
比亚迪 绿色知识获取 比亚迪也学习别的企业做电池、电机或其他新能源领域的先进技术与丰富经验,这对我们研发与生产新能源汽车很有帮助
绿色研发投入 2022年对新能源方面的研发投入非常大,招了很多研发人员。很多部门原来只有100人,现在已经有800人……
环境资源协奏 ……积极协调生产各环节资源提升产量,以满足(新能源汽车)订单需求
大数据分析能力 每辆车我都能在后台查到你的转速、里程、发动机运行、怎么运转的,后台把数据收集起来去做分析,然后做创新优化策略,比如车怎么跑起来更省油……
绿源 绿色知识获取 通过对一些市场信息的了解和对技术知识的学习,我们取消了简易款电动车型的脚踏、链条和飞轮,在前部做成了一个宽敞的脚踏板
绿色研发投入 绿源投入200多万元引进先进设备进行电池等技术研发……
环境资源协奏 我们核心竞争力不是简单将人力、物力和财力叠加在一起,而是将这些资源通过创新整合、组织协调和应变管理等方法进一步集成和加工……
大数据分析能力 对商品制造、用户经营、服务体验这些数据进行收集、整理、分析,提前预判客户需求
), ArticleFig(id=1309226354906857853, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 4, caption=

Calibration anchors for condition and outcome variables

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研究变量 锚点
完全隶属 交叉点 完全不隶属
条件变量 COO 4.7500 4.1250 3.0000
COM 4.7500 4.2500 2.9125
GKA 4.7250 4.1667 3.0000
GRDI 4.6667 4.0000 2.3333
ERO 5.0000 4.3333 2.6667
BDAC 4.6250 4.2500 3.1250
结果变量 DGI 4.8000 3.8000 2.6000
), ArticleFig(id=1309226354990743934, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表4, caption=

条件变量和结果变量的校准锚点

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研究变量 锚点
完全隶属 交叉点 完全不隶属
条件变量 COO 4.7500 4.1250 3.0000
COM 4.7500 4.2500 2.9125
GKA 4.7250 4.1667 3.0000
GRDI 4.6667 4.0000 2.3333
ERO 5.0000 4.3333 2.6667
BDAC 4.6250 4.2500 3.1250
结果变量 DGI 4.8000 3.8000 2.6000
), ArticleFig(id=1309226355066241407, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 5, caption=

Analysis of necessary conditions for high disruptive green innovation

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前因条件 高颠覆性绿色创新
一致性 覆盖度
COO 0.7976 0.7407
~COO 0.5281 0.5589
COM 0.7229 0.7285
~COM 0.5958 0.5786
GKA 0.7888 0.7635
~GKA 0.5327 0.5388
GRDI 0.7902 0.7333
~GRDI 0.5248 0.5558
ERO 0.7422 0.8159
~ERO 0.6505 0.5849
BDAC 0.7735 0.7518
~BDAC 0.5405 0.5444
), ArticleFig(id=1309226355141738880, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表5, caption=

高颠覆性绿色创新的必要条件分析

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件 高颠覆性绿色创新
一致性 覆盖度
COO 0.7976 0.7407
~COO 0.5281 0.5589
COM 0.7229 0.7285
~COM 0.5958 0.5786
GKA 0.7888 0.7635
~GKA 0.5327 0.5388
GRDI 0.7902 0.7333
~GRDI 0.5248 0.5558
ERO 0.7422 0.8159
~ERO 0.6505 0.5849
BDAC 0.7735 0.7518
~BDAC 0.5405 0.5444
), ArticleFig(id=1309226355213042049, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 6, caption=

Analysis of necessary conditions for non-high disruptive green innovation

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前因条件 非高颠覆性绿色创新
一致性 覆盖度
COO 0.5921 0.5618
~COO 0.7267 0.7858
COM 0.5754 0.5926
~COM 0.7364 0.7308
GKA 0.5538 0.5477
~GKA 0.7609 0.7864
GRDI 0.5895 0.5590
~GRDI 0.7188 0.7778
ERO 0.5482 0.6157
~ERO 0.8361 0.7682
BDAC 0.5573 0.5534
~BDAC 0.7500 0.7719
), ArticleFig(id=1309226355288539522, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表6, caption=

非高颠覆性绿色创新的必要条件分析

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件 非高颠覆性绿色创新
一致性 覆盖度
COO 0.5921 0.5618
~COO 0.7267 0.7858
COM 0.5754 0.5926
~COM 0.7364 0.7308
GKA 0.5538 0.5477
~GKA 0.7609 0.7864
GRDI 0.5895 0.5590
~GRDI 0.7188 0.7778
ERO 0.5482 0.6157
~ERO 0.8361 0.7682
BDAC 0.5573 0.5534
~BDAC 0.7500 0.7719
), ArticleFig(id=1309226355368231299, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 7, caption=

Configurations for generating high disruptive green innovation

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件 高颠覆性绿色创新
D1a D1b D2a D2b D3 D4
COO
COM
GKA
GRDI
ERO
BDAC
一致性 0.8713 0.9016 0.8811 0.9182 0.9190 0.9466
原始覆盖度 0.5511 0.5724 0.5404 0.5078 0.5097 0.2763
唯一覆盖度 0.0100 0.0298 0.0356 0.0120 0.0231 0.0160
总体一致性 0.8569
总体覆盖度 0.6966
), ArticleFig(id=1309226355477283204, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表7, caption=

产生高颠覆性绿色创新的组态

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件 高颠覆性绿色创新
D1a D1b D2a D2b D3 D4
COO
COM
GKA
GRDI
ERO
BDAC
一致性 0.8713 0.9016 0.8811 0.9182 0.9190 0.9466
原始覆盖度 0.5511 0.5724 0.5404 0.5078 0.5097 0.2763
唯一覆盖度 0.0100 0.0298 0.0356 0.0120 0.0231 0.0160
总体一致性 0.8569
总体覆盖度 0.6966
), ArticleFig(id=1309226355561169285, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=EN, label=Table 8, caption=

Configurations for generating non-high disruptive green innovation

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件 非高颠覆性绿色创新
N1a N1b N2a N2b
COO
COM
GKA
GRDI
ERO
BDAC
一致性 0.9002 0.9180 0.9183 0.9429
原始覆盖度 0.5535 0.4780 0.4857 0.2847
唯一覆盖度 0.0673 0.0231 0.0308 0.0054
总体一致性 0.8867
总体覆盖度 0.6129
), ArticleFig(id=1309226355657638278, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226335151685865, language=CN, label=表8, caption=

产生非高颠覆性绿色创新的组态

, figureFileSmall=null, figureFileBig=null, tableContent=
前因条件 非高颠覆性绿色创新
N1a N1b N2a N2b
COO
COM
GKA
GRDI
ERO
BDAC
一致性 0.9002 0.9180 0.9183 0.9429
原始覆盖度 0.5535 0.4780 0.4857 0.2847
唯一覆盖度 0.0673 0.0231 0.0308 0.0054
总体一致性 0.8867
总体覆盖度 0.6129
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制造企业颠覆性绿色创新的影响因素及作用路径
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辛晓华 , 尚晏莹
科学学研究 | 创新探索 2026,44(6): 1252-1263
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科学学研究 |创新探索 2026 , 44 (6) : 1252 -1263
制造企业颠覆性绿色创新的影响因素及作用路径
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辛晓华, 尚晏莹
作者信息
  • 西安理工大学经济与管理学院, 陕西西安 710054
通讯作者:
尚晏莹(1996-),女,讲师,E-mail:。
作者简介:

辛晓华(1994-),女,讲师。

Influencing factors and configurational pathways of disruptive green innovation in manufacturing enterprises
Xiao-hua XIN, Yan-ying SHANG
Affiliations
  • School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China
出版时间: 2026-06-15
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摘要
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面对日益严峻的环境挑战与国际竞争,制造企业亟须采用颠覆性绿色创新战略以实现可持续的经济效益和环境效益。采用混合研究方法,首先通过对比亚迪和绿源的案例分析提炼出企业颠覆性绿色创新的影响因素,而后基于战略性新兴产业中的332家制造企业的调研数据,运用fsQCA方法识别驱动企业颠覆性绿色创新的作用路径。研究发现:(1)创新生态系统竞合关系、绿色知识获取、绿色研发投入、环境资源协奏和大数据分析能力是企业颠覆性绿色创新的主要前因变量;(2)单一前因变量不构成颠覆性绿色创新的必要条件;(3)驱动企业高颠覆性绿色创新的6条组态路径可分为四大类:合作主导下的知识-数据驱动型、竞争主导下的知识-研发驱动型、竞合主导下的研发-数据驱动型、能力主导驱动型,且在一定条件下,实现高颠覆性绿色创新的要素组合之间具有替代关系;(4)产生非高颠覆性绿色创新的组态解可归为合作-资源-数据抑制型与知识-研发-数据抑制型2类。本文对解释不同情境下企业实现颠覆性绿色创新的作用路径具有重要的理论与实践意义。

颠覆性绿色创新  /  创新生态系统竞合关系  /  大数据分析能力  /  模糊集定性比较分析

Facing escalating environmental challenges and global competition, manufacturing enterprises must adopt disruptive green innovation strategies to achieve sustainable economic and environmental benefits. Disruptive green innovation refers to the process where enterprises introduce green products or services with different performance attributes from those required by mainstream consumers, initially targeting low-end or niche markets, and gradually improving the product to disrupt the mainstream market. Despite its importance in enhancing sustainable competitiveness and meeting the "dual carbon" goals, disruptive green innovation faces challenges due to its complexity, high risks, and uncertainty. This raises an important question: What factors lead to the more effective implementation of disruptive green innovation, and how do these factors interact?

This study adopts a mixed-methods approach by combining case studies of BYD and Luyuan with fuzzy-set qualitative comparative analysis (fsQCA), to explore the factors driving disruptive green innovation. The results of case study identify several key drivers, including innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource coordination, and big data analytics capability. Using survey data from 332 manufacturing enterprises in strategic emerging industries, this study applies fsQCA to uncover configurational pathways driving disruptive green innovation. Key findings include: (1) Innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource orchestration, and big data analytics capability constitute core antecedents of disruptive green innovation. (2) No single antecedent is a necessary condition for disruptive green innovation. (3) Six configurational paths enabling high disruptive green innovation are categorized into four types: knowledge-data-driven pathway under cooperation dominance, knowledge-R&D-driven pathway under competition dominance, R&D-data-driven pathway under coopetition dominance, capability-led driven pathway, with substitutable relationships among element combinations under specific conditions. (4) Non-high disruptive green innovation configurations fall into two types: collaboration-resource-data-constrained and knowledge-R&D-data-constrained. This research provides theoretical and practical insights into how enterprises achieve disruptive green innovation across diverse contexts.

This study makes several theoretical contributions. First, this study enriches the research on antecedent factors of disruptive green innovation by case study. Currently, research on the drivers of disruptive green innovation is still in its early stages. Through exploratory case studies, this study investigates key antecedents of disruptive green innovation and expands the understanding of this concept. Second, this study also identifies the configuration paths driving disruptive green innovation, which improves and deepens the analytical paradigm of its formation mechanism. Existing studies on the antecedents of disruptive green innovation have rarely focused on the joint effects of multiple factors. Given the complexity and systemic nature of disruptive green innovation, this study applies the fsQCA method from a configurational perspective to explore the co-action of factors such as innovation ecosystem coopetition, green knowledge acquisition, green R&D investment, environmental resource orchestration, and big data analytics capability. By adopting a mixed-methods approach, this study offers a more comprehensive interpretation of the research model, revealing results that might be overlooked by a single method. This approach helps to improve the understanding of how enterprises conduct disruptive green innovation.

From a practical perspective, this study suggests that enterprises should adjust their innovation paths based on their strengths and market demands, optimizing their roles and strategies within the innovation ecosystem, especially when facing international competition. Digital technologies, particularly big data analytics capability, are identified as key drivers for disruptive green innovation. Enterprises should also strengthen coopetition relationships with partners, engage in cross-sector collaboration, and share knowledge to enhance green innovation. Additionally, investing in green R&D and improving resource coordination are essential for achieving sustainable green innovation.

disruptive green innovation  /  innovation ecosystem coopetition  /  big data analytics capability  /  fuzzy-set qualitative comparative analysis (fsQCA)
辛晓华, 尚晏莹. 制造企业颠覆性绿色创新的影响因素及作用路径. 科学学研究, 2026 , 44 (6) : 1252 -1263 .
Xiao-hua XIN, Yan-ying SHANG. Influencing factors and configurational pathways of disruptive green innovation in manufacturing enterprises[J]. Studies in Science of Science, 2026 , 44 (6) : 1252 -1263 .
正文
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面对日益严峻的环境挑战与激烈的国际竞争,在“双碳”的战略背景下,制造企业不仅需要在技术和产品层面进行绿色创新,更需要通过颠覆性绿色创新模式实现跨越式转型和市场突围。颠覆性绿色创新是指企业通过提供与主流消费者所需性能属性不同的绿色产品或服务,最初扎根于低端市场或新市场等非主流市场,随着技术和资源积累不断改善产品性能属性,进而逐渐侵蚀并颠覆主流市场的一种绿色创新[1]。例如,绿源电动自行车最初面向城市中的中老年人和女性群体开辟小规模的新兴市场,随后持续改进产品性能并进行市场开拓,成功“挤占”了摩托车等产品的市场。尽管颠覆性绿色创新已成为制造企业提升可持续竞争力与实现“双碳”目标的关键举措[2],但由于其高复杂性、高风险性和不确定性,企业开展颠覆性绿色创新更容易面临资源匮乏与动力不足的挑战。由此产生的重要问题是:面对技术飞速发展和环保意识增强带来的历史性机遇,哪些因素引致了制造企业更有效地实施颠覆性绿色创新?又是如何作用的?
现有文献对颠覆性绿色创新的前因研究尚处在起步阶段。Shang等[3]通过案例研究揭示了颠覆性绿色创新在不同阶段的驱动因素,包括企业家精神、行业内竞争、行业间竞争、补贴政策、技术和市场。徐建中等[2]基于净效应分析逻辑实证检验了齐美尔联接、知识整合、知识隐匿和知识共享对颠覆性绿色技术创新的影响。可见,已有研究虽对颠覆性绿色创新的影响因素做了部分探索,但并不充分,尤其缺乏对多因素协同驱动颠覆性绿色创新的作用路径的剖析。模糊集定性比较分析(fsQCA)是基于整体视角分析多要素组态效应的一种研究范式,可揭示内外部要素间相互作用、“多重并发”驱动颠覆性绿色创新的组态路径[4]。鉴于此,本研究首先通过文献研究与案例研究提炼颠覆性绿色创新的前因要素,继而基于问卷调查收集战略性新兴产业制造企业的数据,运用fsQCA方法探索多要素协同联动产生颠覆性绿色创新的作用路径。一方面力求丰富颠覆性绿色创新的相关理论,另一方面为制造企业“因企制宜”实施颠覆性绿色创新提供实践启示。
1 文献综述
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颠覆性绿色创新是指在主流市场引起重大变化或颠覆,同时显著减少对环境负面影响的新的产品或服务,这些产品或服务最初提供的价值属性对主流市场不具吸引力,但被低端、高端或新市场等非主流市场的客户重视,随着产品性能的完善,不断吸引主流市场客户,进而侵蚀在位者市场[1]。由于颠覆性绿色创新前因研究较少,且其属于颠覆性创新与绿色创新的子集,本文进一步借鉴相关领域前因研究进行归纳参考。
首先,在颠覆性创新研究领域,现有文献主要从组织内生驱动要素与外生情境条件两大维度展开理论建构。聚焦组织内生驱动要素,已有研究表明,社交媒体战略能力[5]、动态能力与吸收能力[6]对颠覆性创新起重要作用。外部情境视角下,已有研究发现强合作关系网络会阻碍颠覆性技术引进[7],而商业生态系统对颠覆性创新的实现有重要作用[8]。其次,既有对绿色创新影响因素的研究可大致归纳为外部因素(如,政府、消费者[9])、内部因素(如,绿色知识整合能力[10])和内外部因素结合三类。值得一提的是,越来越多文献关注到企业内部因素与外部因素对绿色创新的协同作用[11]。
由上可知,现有颠覆性创新和绿色创新的前因研究为本文奠定了一定的理论基础。颠覆性绿色创新作为一种能够重新定义市场、改变竞争格局的绿色创新,其前定因素可能亦由外部因素和内部因素两部分构成。然而,现有文献对其驱动因素探索较少,更鲜有通过组态这一整体性视角探讨内、外部因素联动对颠覆性绿色创新的作用路径,忽视了颠覆性绿色创新的系统性与整体性。因此,本文将从整体性视角出发,采用案例分析与fsQCA的混合研究方法,识别内、外部因素协同驱动颠覆性绿色创新的组态路径,以进一步深化对颠覆性绿色创新前因要素的理解。
2 研究设计
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2.1 研究方法
fsQCA方法采取整体的分析视角,把研究对象视为条件变量不同组合方式的组态,通过集合分析发现要素组态与结果的集合关系,以更有效解释非对称性、多重并发因果和多路径等效性等复杂关系的管理问题[12]。同时,fsQCA适用于连续变量,且能处理100以上的大样本[12]。本文试图揭示内、外部影响因素协同驱动企业颠覆性绿色创新的复杂管理问题,样本数为332,适合采用fsQCA方法进行分析。
2.2 基于探索性案例分析的变量构建
参考张明与杜运周[13]的研究,本文采用文献归纳法与现象总结法进行颠覆性绿色创新的前因条件选择。首先,在文献综述部分,通过文献归纳法梳理了颠覆性创新、绿色创新以及颠覆性绿色创新的影响因素。接着,为提高与企业实践的相关性,进一步采用探索性案例分析(即现象总结法)完善和提炼颠覆性绿色创新的影响因素。
遵循理论抽样原则,本文选择新能源汽车行业的比亚迪股份有限公司(以下简称:比亚迪)以及电动自行车行业的浙江绿源电动车有限公司(以下简称:绿源)作为探索性案例研究对象。主要原因如下:第一,比亚迪和绿源是典型的颠覆性绿色创新企业(从低端/新市场进入,后逐渐侵蚀主流市场),且两个企业既具有一定的相似性(主要业务均涉及电池、电机等绿色技术的研发和应用),又具备各自行业的差异性。第二,比亚迪和绿源满足研究中数据的可获取性与完整性原则。企业基本情况见表1。
为保障研究的信度,本文基于多种数据来源收集数据以形成“三角验证”,主要包括:(1)半结构化访谈。本文累计访谈了两家企业的共6位中高层管理人员,包括研究院的项目经理、研发工程师,以及产品开发部、装配部、市场部的部门经理。每位访谈者的采访时间为1.5-2小时。(2)企业内部资料。主要包括不同时期的产品介绍及主要技术性能指标、企业年度报告、业务报告以及宣传手册等。(3)外部资料。主要包括文献材料,新闻和采访材料,及公共数据资料等。
在此基础上,以产品和市场作为关键事件,将比亚迪汽车和绿源电动车的颠覆性绿色创新过程分为两个阶段,见图1(a)和图1(b)。
接着,通过编码的方式对企业数据资料进行挖掘,为避免偏见或忽略重要信息,作者和团队另外两名博士对原始案例资料中的词句同时进行分解、编码并进行结果比对,从而实现逻辑化和概念化以提炼核心要素,归纳最终的影响因素。表2和表3分别为非主流市场颠覆阶段与主流市场颠覆阶段的部分编码示例。
编码结果进一步明确了创新生态系统竞争关系、创新生态系统合作关系、绿色知识获取、绿色研发投入、环境资源协奏和大数据分析能力等因素对企业颠覆性绿色创新的影响。
另外,基于文献梳理可知,企业颠覆性绿色创新的前因可分为外部环境因素和组织内部因素两大类。业界实践也表明,外部环境因素和组织内部因素均对企业颠覆性绿色创新有促进作用。因此,本文基于内、外部因素的整体性视角,构建组织层面和环境层面多因素联动影响企业颠覆性绿色创新的组态模型(如图2所示)。
2.3 样本选择
本文选取战略性新兴产业中的制造企业为问卷调研对象,原因如下:(1)战略性新兴产业是实现我国“双碳”目标的重要支持产业,其以重大技术突破和重大发展需求为基础,是新兴科技与新兴产业的深入融合,具有更高的不确定性,这为产生颠覆性绿色创新创造了良好条件。(2)制造企业产生的工业“三废”被认为是对环境产生负面影响的主要因素,其在环境领域面临更为严格的行政管控,这使得战略性新兴产业中的制造企业更容易采取颠覆性绿色创新模式。
2.4 问卷设计与数据收集
本文主要借鉴杨震宁和赵红[14]以及Shu等[15]的研究,形成创新生态系统合作关系(COO)与创新生态系统竞争关系(COM)两个维度的基础量表;对绿色知识获取(GKA)的测量借鉴李晶钰和沈灏[16]的研究,形成6个题项的量表;基于Cui和Wang[17]的研究,从研发设备、研发人员与研发经费3个方面对绿色研发投入(GRDI)进行测量;借鉴Wang等[18]的测量量表,并结合Zahoor和Gerged[19]对环境资源的内容界定,从环境资源的吸收、整合和利用三方面测量环境资源协奏(ERO);借鉴Akhtar等[20]的研究,从数据资源的协调和利用、数据处理的技术和技能、数据见解的获取等方面对大数据分析能力(BDAC)进行测度;借鉴Govindarajan和Kopalle[21]的量表,并引入环保和绿色概念,构建颠覆性绿色创新(DGI)的测量量表,涵盖产品的颠覆程度、创新频率和水平、及市场颠覆过程等方面。
为获得具有代表性的样本数据,本文根据战略性新兴产业集群分布特点,主要在环渤海地区、长江三角洲地区、珠江三角洲地区、中部地区、西部地区和其他地区等6个区域的战略性新兴产业中的制造企业中进行发放。在问卷调研对象方面,问卷填写者为企业的中高层管理者,同时确保每个企业只收集一份样本。本文共发放问卷450份,回收389份,剔除填写前后不一致(如反向题项与正常题项答案相同)、关键题项填写不完整、或作答呈现明显规律性的问卷,最终得到有效问卷332份,有效回收率为73.8%。
3 数据分析
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3.1 信效度检验
本文通过SPSS 22.0计算Cronbach’s α信度系数,同时使用CITC净化题项。结果显示,各变量的Cronbach’s α系数均大于0.7,删除某一题项后的Cronbach’s α系数并未提高,且所有测量题项的CITC值均大于0.5,说明量表具有较好的内部一致性。同时,各变量的CR值介于0.760-0.877之间,均大于0.7,说明量表具有较好的组合信度。综上,本文的量表具有良好的信度。
本文根据Hinkin及Loeser等的建议编制量表,同时在问卷正式内容前阐明调研背景、目的,以及对匿名性和保密性的承诺,以保证量表较高的内容效度。其次,本文运用AMOS 24.0对模型进行验证性因子分析,计算七因子模型适配指标,结果可被接受(χ2=708.19, df=474, χ2/df=1.494<3, RMSEA=0.039<0.08, CFI=0.954>0.9, TLI=0.948>0.9, IFI=0.954>0.9),说明量表具有较好的收敛效度;再次,通过验证性因子分析比较单因子模型、三因子模型、四因子模型、五因子模型、六因子模型和七因子模型之间的拟合指标,结果显示,七因子模型拟合度显著优于其他模型的模型适配度,说明量表较好的区分效度。
3.2 变量校准
由于组态分析的分析对象是集合而非变量,故需先对变量值进行校准,以使原始测量具有可解释的集合意义[12]。本文涉及变量均为连续变量,故而采用直接校准法。参考已有研究[22]并结合数据的实际分布,本文将3个校准点分别设定为各个变量的95%分位数(完全隶属)、50%分位数(交叉点)与5%分位数(完全不隶属),具体锚点如表4所示。
此外,在校准时,需避免案例在条件上的模糊集隶属分数为0.5的情形,为了不影响分析结果,本文采取Fiss的建议[4],对低于1的模糊隶属分数均加上0.001的常数以降低偏差。
3.3 单个条件的必要性分析
本文首先评估创新生态系统合作关系、创新生态系统竞争关系、绿色知识获取、绿色研发投入、环境资源协奏和大数据分析能力6个前因条件及其“非集”状态对“高颠覆性绿色创新”及“非高颠覆性绿色创新”结果发生的必要性。
由表5和表6可知,当“高颠覆性绿色创新”/“非高颠覆性绿色创新”为结果变量时,各条件变量的一致性数值均低于0.9,表明没有条件构成高/非高颠覆性绿色创新的必要条件。
3.4 高颠覆性绿色创新的充分性分析
条件组态的充分性分析可揭示引致结果产生的多重因素构成的可能路径。首先,参考已有研究的做法以及数据特征,本文将一致性阈值确定为0.8[4]。由于本文有332个案例样本,频数阈值设定为3。通过fsQCA软件的真值表分析可产生复杂解、简约解和中间解三类解。结果汇报通常以中间解为主,并结合简约解区分核心条件与边缘条件。
由表7可知,产生高颠覆性绿色创新的组态有6种,单个组态的一致性分别是0.8713、0.9016、0.8811、0.9182、0.9190、0.9466,均超出阈值0.8。总体解的一致性为0.8569,即在所有满足6类条件组态的企业创新发展案例中,存在85.69%的企业颠覆性绿色创新均呈现高水平。解的总体覆盖度为0.6966,说明6个组态解释了约70%的高颠覆性绿色创新的原因。
结合核心条件与边缘条件的结果分析,本文识别了6条产生高颠覆性绿色创新的路径,并将其归纳为4种类型。
合作主导下的知识-数据驱动型。该驱动方案对应组态D1a(创新生态系统合作关系*创新生态系统竞争关系*绿色知识获取*大数据分析能力)和D1b(创新生态系统合作关系*绿色知识获取*环境资源协奏*大数据分析能力)。组态D1a反映的是,企业通过创新生态网络合作,借助知识共享与大数据分析获取绿色知识及互补资源,驱动颠覆性绿色创新,激发创新活力。该组态也证实了颉茂华等[23]提出的企业创新发展路径,即长城汽车在成长阶段通过扩大网络联结规模,从外部获取互补资源和技术,使得联结结构与关键资源获取呈现联动效应,进而激发企业创新积极性。组态D1b反映的是,在创新生态系统良好合作关系支持下,企业通过提高绿色知识获取、环境资源协奏和大数据分析能力来高效且高质地获取、整合和利用内外部资源,进而驱动颠覆性绿色创新。
竞争主导下的知识-研发驱动型。该驱动方案包括组态D2a(创新生态系统合作关系*创新生态系统竞争关系*绿色知识获取*绿色研发投入)和D2b(创新生态系统竞争关系*绿色知识获取*绿色研发投入*环境资源协奏*大数据分析能力)。组态D2a反映的是,在创新生态系统中面临激烈竞争关系的企业,通过进一步加强绿色研发投入以增强自身创新实力,同时通过与创新生态系统中其他伙伴建立合作关系,并积极搜寻和获取组织外部与绿色环保相关的知识,进而驱动颠覆性绿色创新。组态D2b反映的是,无论企业与创新生态系统伙伴是否具有强合作关系,在高强度竞争关系压力下,企业能够通过全方位提升组织内部能力,包括绿色知识获取、绿色研发投入、环境资源协奏和大数据分析能力,来实现颠覆性绿色创新。这意味着创新生态系统竞争关系对企业而言既是压力也是能力发展和创新实现的驱动力[24]。
竞合主导下的研发-数据驱动型。组态D3(创新生态系统合作关系*创新生态系统竞争关系*绿色研发投入*环境资源协奏*大数据分析能力)表示在创新生态系统中企业面临的竞争关系与合作关系均强、企业绿色研发投入高、环境资源协奏强且大数据分析能力强,这类企业能够产生高颠覆性绿色创新。这意味着在创新生态系统中竞争与合作关系相互依赖,相互促进,利益相关者之间形成愈来愈复杂的网络联结,企业不仅通过环境资源协奏更好地整合和利用内外部资源,同时基于大数据分析能力不断捕捉产品开发方向[25],通过加大绿色研发投入提升自身技术实力与核心竞争力,5个要素之间相互衔接并联动作用,共同促进企业实现颠覆性绿色创新。
能力主导驱动型。组态D4(~创新生态系统合作关系*~创新生态系统竞争关系*绿色知识获取*绿色研发投入*~环境资源协奏*大数据分析能力)表示在创新生态系统合作关系欠佳、创新生态系统竞争关系不强、环境资源协奏欠佳的情况下,良好的绿色知识获取能力、高的绿色研发投入与强的大数据分析能力之间的联动也能产生高颠覆性绿色创新。这一组态反映了自力更生的企业创新发展状态,即企业通过积极获取外部绿色知识拓展资源池,通过加大绿色研发投入增加技术储备,并利用大数据分析能力有效识别并及时抓住创新机遇,三者相互促进,共同演化,为企业颠覆性绿色创新发展注入源源不断的活力与驱动力。该组态适用于复杂性相对较低的颠覆性绿色创新,拓宽了对颠覆性绿色创新的理解。
3.5 非高颠覆性绿色创新的充分性分析
考虑到QCA方法的因果非对称性,本文进一步检验了产生非高颠覆性绿色创新的组态方案。由表8可知,产生非高颠覆性绿色创新的组态有4种,单个组态的一致性分别是0.9002、0.9180、0.9183、0.9429,均超出阈值0.8。总体解的一致性为0.8867,总体覆盖度为0.6129,说明4种组态解释了约61%产生非高颠覆性绿色创新的原因。
由表8可知,本文识别了4条产生非高颠覆性绿色创新的组态解,根据核心条件可归为两大类。第一类为“合作-资源-数据抑制型”,包括组态N1a(~创新生态系统合作关系*~绿色知识获取*~环境资源协奏*~大数据分析能力)和N1b(~创新生态系统合作关系*~创新生态系统竞争关系*~绿色研发投入*~环境资源协奏*~大数据分析能力)。组态N1a表明企业在创新生态系统中未建立良好的合作关系,不从外部获取绿色知识和资源,也不对已有资源进行整理和重新配置,且不关注大数据分析能力的作用时,将会抑制颠覆性绿色创新。组态N1b意味着不与创新生态系统中其他主体产生连接,且不关注自身资源和数据方面的能力提升,不进行技术研发与积累,颠覆性绿色创新将难以被推进。第二类为“知识-研发-数据抑制型”,包括组态N2a(~创新生态系统竞争关系*~绿色知识获取*~绿色研发投入*~环境资源协奏*~大数据分析能力)和N2b(~创新生态系统合作关系*创新生态系统竞争关系*~绿色知识获取*~绿色研发投入*~大数据分析能力)。组态N2a反映的是,即使企业没有竞争压力,当绿色知识获取、绿色研发投入、环境资源协奏和大数据分析能力等不存在时,企业也无法实现颠覆性绿色创新。组态N2b反映的是,即使受到创新生态系统竞争关系的倒逼和激励,企业不通过建立合作关系获取外部绿色知识和资源,也不通过绿色研发投入提升自身资源禀赋,且不利用大数据分析能力提供洞察或提高效率,将不利于颠覆性绿色创新。
值得注意的是,结合产生高颠覆性绿色创新的组态路径的核心条件可以发现,在数字经济时代,大数据分析能力成为了驱动企业颠覆性绿色创新的关键要素。
3.6 前因条件的替代分析
对比组态D1a和组态D1b可以发现,创新生态系统竞争关系程度高与环境资源协奏水平高之间存在相互替代关系。具体而言,对于创新生态系统合作关系密切且绿色知识获取能力和大数据分析能力强的企业,环境资源协奏有助于企业有效地构建、捆绑和管理资源,而良好的创新生态系统竞争关系也能激励企业快速整合和利用资源,两者殊途同归,最终促进企业实现颠覆性绿色创新。进一步,对比组态D2a和组态D2b可以发现,对于创新生态系统竞争关系激烈且绿色知识获取和绿色研发投入强的企业,创新生态系统合作关系程度高可以和高环境资源协奏能力与高水平大数据分析能力的组合形成“殊途同归”的替代关系。
3.7 稳健性检验
鉴于QCA是一种集合论方法,本文采用集合论特定的方法进行稳健性检验。原始一致性阈值决定了进入最小化分析过程中的真值表行数,进而会影响最终分析结果。本文通过调整一致性阈值(从0.8提高为0.9)再次展开分析[22],以检验充分性分析结果的稳健性。结果并未发生明显改变,说明高/非高颠覆性绿色创新的充分性分析结果具有稳健性。
4 研究结论与启示
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4.1 主要研究结论
(1)通过文献分析及对比亚迪和绿源的探索性案例研究,识别出制造企业颠覆性绿色创新的影响因素包括:创新生态系统竞合关系,以及绿色知识获取、绿色研发投入、环境资源协奏和大数据分析能力等企业内部能力。(2)通过fsQCA分析发现,驱动制造企业颠覆性绿色创新的组态路径包括4大类:合作主导下的知识-数据驱动型、竞争主导下的知识-研发驱动型、竞合主导下的研发-数据驱动型、能力主导驱动型。(3)在一定条件下,实现高颠覆性绿色创新的要素组合之间具有替代关系:在创新生态系统合作关系密切且绿色知识获取能力和大数据分析能力强的企业,创新生态系统竞争关系程度高与环境资源协奏水平高之间可通过替代效应实现高颠覆性绿色创新;对于创新生态系统竞争关系激烈且绿色知识获取和绿色研发投入强的企业,创新生态系统合作关系程度高可以和高环境资源协奏能力与高水平大数据分析能力的组合通过替代效应实现高颠覆性绿色创新。(4)产生非高颠覆性绿色创新的4种组态解,可归为:合作-资源-数据抑制型与知识-研发-数据抑制型两类。(5)大数据分析能力是实现制造企业颠覆性绿色创新的核心要素。数字经济时代下,大数据分析能力对企业颠覆性绿色创新的作用已不容忽视。
4.2 管理启示
(1)“因企制宜”实施颠覆性绿色创新。制造企业应精准评估内外部条件,根据自身的优势和不足,通过“因企制宜”的策略调整创新路径。具体来说,企业应根据自身的核心竞争力、资源储备以及市场定位,灵活调整其在创新生态系统竞合关系中的角色与策略。同时,通过对不同内部因素(如绿色知识获取、绿色研发投入、大数据分析能力等)进行合理的资源分配,在多因素协同的基础上,逐步实现从局部优化到整体协调。尤其面对日益激烈的国际竞争和不确定的市场需求,企业应根据自身特定的情境和需求,优化各要素的组合和互动关系,以形成符合其长期战略目标的创新路径。
(2)培育制造企业的数字技术与能力。数字经济时代下,大数据分析能力成为了驱动制造企业颠覆性绿色创新的关键要素。企业需构建大数据平台实现高效数据整合,强化数据人才引培以驱动创新,深化技术合作促进数据共享与跨界融合,从而提升绿色创新竞争力。
(3)重视创新生态系统中的竞合关系。研究发现,创新生态系统竞合关系在推动企业颠覆性绿色创新过程中起着至关重要的作用。因此,制造企业需积极创建和维护多方合作与竞争并存的创新生态系统,在构建颠覆性绿色创新战略时,注重与其他企业、科研机构和政府等多方合作,在合作中获取知识和资源,同时利用竞争激发创新动力。
(4)加强制造企业环保导向的相关能力。绿色知识获取、绿色研发投入和环境资源协奏被识别为推动颠覆性绿色创新的关键前因变量。首先,企业需与能提供绿色知识的外部组织建立紧密联系、不断拓宽绿色知识获取渠道、提高外部绿色知识获取效率和质量,以为企业提供精准的颠覆性绿色创新方向。其次,企业应加大对绿色技术研发的投入,建立绿色知识管理体系,通过持续的技术创新和跨领域知识融合,不断提升企业的颠覆性绿色创新能力。此外,企业还应通过数字化技术增强对资源的协同管理能力,最大化环境资源的利用效率,提升颠覆性绿色创新的整体效益。
4.3 研究局限与未来展望
首先,受研究资源约束,每家受访企业仅由一位中高层管理者填写问卷全部题项,尽管Harman单因子检验表明不存在显著的同源性偏差,但单一受访者带来的潜在认知偏差仍是不可忽视的方法局限。后续研究可邀请同一企业的不同管理者分别填写,以减少同源性偏差的影响。其次,尽管问卷调查方法在测量维度覆盖度和样本获取便利性方面具有优势,但自评式量表固有的主观性偏差可能影响变量间关系的精确估计。未来研究可探寻测量创新生态系统竞合关系、企业内部能力和颠覆性绿色创新的客观代理指标,补充二手数据检验本文的研究结果。
基金
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  • 陕西省教育厅一般专项科学研究计划项目(24JK0150)
  • 陕西省教育厅人文社会科学专项(20JG022)
文献
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2026年第44卷第6期
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  • 接收时间：2025-03-03
  • 首发时间：2026-09-22
  • 出版时间：2026-06-15
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  • 收稿日期：2025-03-03
  • 修回日期：2025-04-30
基金
陕西省教育厅一般专项科学研究计划项目(24JK0150)
陕西省教育厅人文社会科学专项(20JG022)
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    西安理工大学经济与管理学院, 陕西西安 710054

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尚晏莹(1996-),女,讲师,E-mail:。
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