Journal of Innovation & Knowledge最新文献_第5页

A geometric-causal knowledge framework for quantifying and simulating counterfactual credit risk cascades under supply chain shocks 供应链冲击下量化和模拟反事实信用风险级联的几何因果知识框架

IF 18.1 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-07-18 DOI: 10.1016/j.jik.2026.101114

Huinan Xu, Jianming Yu, Fulan Ye, Xiaojian Yuan, Jianjun Tong

引用次数: 0

Sustainability uncertainty and the dynamics of green asset markets 可持续性、不确定性和绿色资产市场的动态

IF 14 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-07-01 Epub Date: 2026-03-02 DOI: 10.1016/j.jik.2026.100999

Keyu Luo, Lijuan Peng, Fei Zhou

{"title":"Sustainability uncertainty and the dynamics of green asset markets","authors":"Keyu Luo, Lijuan Peng, Fei Zhou","doi":"10.1016/j.jik.2026.100999","DOIUrl":"10.1016/j.jik.2026.100999","url":null,"abstract":"<div><div>This study investigates how different dimensions of uncertainty shape the volatility and interconnectedness of green financial markets, with particular attention to the Sustainability Uncertainty Index (ESGUI) as a novel driver of asset dynamics. We employ Glosten–Jagannathan–Runkle generalised autoregressive conditional heteroskedasticity mixed data sampling and dynamic conditional correlation mixed data sampling models to analyse three representative green asset classes − environmental, social, and governance equities; clean energy stocks; and green bonds − across four uncertainty regimes, including ESGUI, climate policy uncertainty, global economic policy uncertainty, and geopolitical risk. Results show that green assets exhibit strong volatility persistence and asymmetric responses to shocks, with ESGUI emerging as the most dominant and consistent driver across markets. Moreover, uncertainty shocks significantly influence cross-asset correlations in heterogeneous ways, with ESGUI persistently increasing correlations across all asset pairs. A principal component analysis-based composite factor confirms the aggregate relevance of uncertainty shocks while highlighting heterogeneous transmission channels, particularly the resilience of green bonds as a diversification instrument. The findings emphasise the importance of adopting flexible and adaptive diversification strategies that reflect prevailing uncertainty regimes for investors, and ensuring transparent and credible disclosure to stabilise capital flows and strengthen the resilience of sustainable finance for policymakers.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"15 ","pages":"Article 100999"},"PeriodicalIF":14.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Government as network architect or facilitator? Structural versus relational intervention efficacy in innovation network 政府是网络架构师还是促进者？创新网络结构干预效能与关系干预效能

IF 14 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-07-01 Epub Date: 2026-02-14 DOI: 10.1016/j.jik.2026.100976

Yiling Chen, Benlu Hai, Guanju Chen

{"title":"Government as network architect or facilitator? Structural versus relational intervention efficacy in innovation network","authors":"Yiling Chen, Benlu Hai, Guanju Chen","doi":"10.1016/j.jik.2026.100976","DOIUrl":"10.1016/j.jik.2026.100976","url":null,"abstract":"<div><div>This study applies network intervention theory to conceptualize two governmental strategies: structural intervention and relational intervention. Our aim is to elucidate the mechanism through which government, acting as a network architect (through structural intervention that alters network connections) or as a facilitator (through relational intervention that builds trust and collaboration to induce connections), influences other actors and steers existing networks toward more effective structures or processes. Focusing on industry-university-research (IUR) innovation networks characterized by goal heterogeneity, this study examines the efficacy of government network interventions. Using survey data from manufacturing enterprises in China, we found that goal heterogeneity positively affects cooperative innovation performance. Moreover, interactions involving inter-organizational resources, knowledge, and information serve a mediatory function in this relationship. While the government’s structural and relational interventions function complementarily in networks dominated by resource interaction, they exhibit mutually offsetting or weakening substitutive effects in networks dominated by knowledge and information interactions. This study extends the application of network intervention theory to the domain of innovation network studies, and evidence from China confirms the complex dynamics of governmental network intervention. The findings reveal both synergistic and conflicting effects between intervention strategies and offer decision-making insights for governments in designing effective innovation policies.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"15 ","pages":"Article 100976"},"PeriodicalIF":14.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148737219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Key predictors of entrepreneurship and innovation via the 3C framework: A data-driven machine learning analysis 通过3C框架预测创业和创新的关键因素：数据驱动的机器学习分析

IF 15.5 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-06-01 Epub Date: 2026-02-09 DOI: 10.1016/j.jik.2026.100963

Yangjie Huang , Yajing Bu , Lina Wei , Xuebo Zhu , Lingling Xu

{"title":"Key predictors of entrepreneurship and innovation via the 3C framework: A data-driven machine learning analysis","authors":"Yangjie Huang , Yajing Bu , Lina Wei , Xuebo Zhu , Lingling Xu","doi":"10.1016/j.jik.2026.100963","DOIUrl":"10.1016/j.jik.2026.100963","url":null,"abstract":"<div><div>Entrepreneurship represents a pivotal pathway for transforming knowledge into innovation and economic value; however, the factors underlying this process remain a “black box”. Drawing on the Cognition, Capability, and Commitment (3C) framework, this study employs a data-driven machine learning approach—specifically, a decision tree algorithm—to systematically identify the potential predictors of Total Early-stage Entrepreneurial Activity (TEA). Using primary data from more than 70,000 individuals in the 2020 Global Entrepreneurship Monitor (GEM), supplemented with 2021 data for robustness checks, the results suggest that various forms of entrepreneurial cognition, such as necessity-driven motivations, family tradition, and perceptions of opportunity scarcity, emerge as prominent potential predictors. In contrast, capability-related variables exhibit comparatively lower predictive weights. These findings may advance theoretical understanding of entrepreneurial patterns and offer potential actionable implications for policymakers seeking to foster entrepreneurship. Moreover, they highlight the utility of machine learning in uncovering complex, non-linear interactions between motivational and contextual factors, thereby providing complementary exploratory insights beyond conventional GEM-based analyses.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"14 ","pages":"Article 100963"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transition to electric vehicles and the location of key battery-related activities 向电动汽车过渡与电池相关的关键活动地点

IF 15.5 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-06-01 Epub Date: 2026-01-20 DOI: 10.1016/j.jik.2026.100951

Jesús F. Lampón, Miguel A. Crespo-Velando, María del Mar Rodríguez-Domínguez, Hugo Pérez-Moure

引用次数: 0

Digital orientation and the hybrid tipping point: Balancing in-person and remote work 数字导向和混合引爆点：平衡面对面和远程工作

IF 15.5 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-06-01 Epub Date: 2026-01-21 DOI: 10.1016/j.jik.2026.100949

Francesca Sanguineti, Costanza Baldrighi, Stefano Denicolai, Pietro Previtali

引用次数: 0

The implications of innovation in traditional industries: Revitalizing mature economic sectors 传统产业创新的意义：振兴成熟的经济部门

IF 15.5 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-06-01 Epub Date: 2026-01-28 DOI: 10.1016/j.jik.2026.100959

Alexandru-Mihai Bugheanu , Oana Pricopoaia , Camelia Madalina Beldiman , Ion Popa , Nicoleta Cristache , Dorin Iancu , Ioana Lazarescu

{"title":"The implications of innovation in traditional industries: Revitalizing mature economic sectors","authors":"Alexandru-Mihai Bugheanu , Oana Pricopoaia , Camelia Madalina Beldiman , Ion Popa , Nicoleta Cristache , Dorin Iancu , Ioana Lazarescu","doi":"10.1016/j.jik.2026.100959","DOIUrl":"10.1016/j.jik.2026.100959","url":null,"abstract":"<div><div>This research explores the implications of innovation in traditional industries in response to the need to deploy digital technologies to revitalize mature economic sectors. Despite being essential to regional and national economies, traditional industries face challenges due to globalization and new technologies. Embracing modern technologies can enable such industries to respond to these challenges, thereby revitalizing mature economic sectors and creating opportunities for economic growth. This study emphasizes the value of working with local communities to preserve craft techniques and the role of innovations in vocational training covering business management and digital marketing. The study also emphasizes the importance of partnerships between traditional manufacturers and companies in modern industries and the creation of innovative products that combine advanced technologies with traditional manufacturing methods. Furthermore, it emphasizes the relevance of implementing green innovations and their impact on the sustainability of traditional industries. SmartPLS 4 software was used to perform partial least squares structural equation modeling (PLS-SEM). The analysis provides detailed indications of how technological innovations can transform traditional industries. The results can be applied to support the adoption of innovative strategies to foster the development and modernization of traditional industries. The study has implications on many levels, from developing regional economies and improving the sustainability of traditional industries to creating opportunities for training and cross-sector collaboration.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"14 ","pages":"Article 100959"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building the efficiency bridge: Artificial intelligence innovation, high-growth firms and industry competition through governance 搭建效率桥梁：人工智能创新、高增长企业和通过治理实现的行业竞争

IF 15.5 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-06-01 Epub Date: 2026-01-28 DOI: 10.1016/j.jik.2026.100961

Xikai Yu , Kanghao Chen , Zongqiang Ren

{"title":"Building the efficiency bridge: Artificial intelligence innovation, high-growth firms and industry competition through governance","authors":"Xikai Yu , Kanghao Chen , Zongqiang Ren","doi":"10.1016/j.jik.2026.100961","DOIUrl":"10.1016/j.jik.2026.100961","url":null,"abstract":"<div><div>High-growth firms serve as vital drivers of economic vitality; however, sustaining their rapid growth remains a formidable challenge. Although researchers widely regard artificial intelligence (AI) innovation as a key competitive advantage, the specific mechanisms through which it fosters high-growth firms remain underexplored. We analyse 20,178 firm–year observations from China (2007–2022) using technology affordance actualisation theory to investigate this relationship. Results show that AI innovation drives high growth primarily by enhancing firm efficiency. Particularly, we identify a critical contingency: the intensity of industry competition negatively moderates the effect of AI innovation on firm efficiency. A novel finding reveals that board size conditions whether this moderating effect persists. Specifically, industry competition undermines efficiency gains only in firms with small boards, thereby hindering high growth. By contrast, large boards nullify this negative effect, enabling AI innovation to drive firms towards high-growth status even under competitive pressure. Our study theoretically advances the literature by elucidating the contingent role of industry competition and establishing board size as a crucial governance mechanism that determines the success of AI innovation in promoting high growth.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"14 ","pages":"Article 100961"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital leadership, digital platform capability and digital business model innovation of start-ups 初创企业的数字化领导力、数字化平台能力和数字化商业模式创新

IF 15.5 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-06-01 Epub Date: 2026-02-04 DOI: 10.1016/j.jik.2026.100970

Xiaozhou Ding

{"title":"Digital leadership, digital platform capability and digital business model innovation of start-ups","authors":"Xiaozhou Ding","doi":"10.1016/j.jik.2026.100970","DOIUrl":"10.1016/j.jik.2026.100970","url":null,"abstract":"<div><div>In the digital economy era, digital business model innovation is an important way for start-ups to participate in market competition and improve organizational performance. However, as the core driving force for promoting disruptive innovation in organizations, the mechanisms by which digital leadership can stimulate digital business model innovation remain limited. To address this gap, based on the upper echelons theory, dynamic capability theory, and business model innovation theory, a theoretical model is proposed to examine the impact of digital leadership on digital business model innovation. The study focuses on the mediating role of digital platform capability and the moderating roles of digital self-efficacy. Hierarchical regression analysis is applied to data from 376 Chinese start-ups. The results show that digital positively influences digital business model innovation. The relationship is fully mediated by digital platform capability. Digital self-efficacy moderates the relationship between digital leadership and digital platform capability and between digital leadership and digital business model innovation. This study contributes to the literature on digital leadership and business model innovation. It also provides practical insights for start-ups to achieve digital business model innovation and sustainability development in the digital context.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"14 ","pages":"Article 100970"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When does artificial intelligence innovation pay in servitization? A combined organizational learning and socio-technical systems perspective 人工智能创新什么时候会带来服务化？结合组织学习和社会技术系统的观点

IF 15.5 1区管理学

Journal of Innovation & Knowledge Pub Date : 2026-06-01 Epub Date: 2026-01-30 DOI: 10.1016/j.jik.2026.100960

Kaining Yan , Xue Pang , Qingxue Li , Ge Tian , Xinyu Dong

{"title":"When does artificial intelligence innovation pay in servitization? A combined organizational learning and socio-technical systems perspective","authors":"Kaining Yan , Xue Pang , Qingxue Li , Ge Tian , Xinyu Dong","doi":"10.1016/j.jik.2026.100960","DOIUrl":"10.1016/j.jik.2026.100960","url":null,"abstract":"<div><div>Artificial intelligence (AI) innovation is often celebrated as a powerful catalyst for servitization, yet its revenue implications remain paradoxical and underexplored. Building on organizational learning theory and socio-technical systems theory, this study theorizes and tests the nonlinear influence of AI innovation on firms’ service revenue, using panel data from 796 Chinese manufacturing firms between 2015 and 2022. It reveals an inverted U-shaped relationship, suggesting that while AI innovation initially enhances revenue through exploratory learning and customer value creation, excessive reliance may induce organizational rigidity and misaligned priorities that erode service outcomes. Moreover, coordination capability stabilizes AI innovation by moderating both its benefits and risks, while human capital amplifies these dual effects. These findings transcend the prevailing view of AI as a uniformly positive enabler, offering a more nuanced understanding of how organizational and technical subsystems interact to shape the trajectory of service-led growth.</div></div>","PeriodicalId":46792,"journal":{"name":"Journal of Innovation & Knowledge","volume":"14 ","pages":"Article 100960"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0