Pub Date : 2025-12-22DOI: 10.1016/j.jengtecman.2025.101936
Mingjun Chen , Jianya Zhou
The application of digital finance in green technology innovation in small- and medium-sized enterprises (SME) has not been leveraged to the fullest extent. Based on innovation ecosystem, digital empowerment, and long-tail effect theories, this study constructs a model of digital finance driving SMEs’ green technology innovation using data from county-level units in China. The findings reveal that digital finance partially drives SMEs’ green technology innovation. Besides revealing digital finance’s role as a key driver of green technology innovation in SMEs, this study provides practical insights into combining digital finance with green technology innovation.
{"title":"Digital finance-driven green technology innovation: Evidence from Chinese SMEs","authors":"Mingjun Chen , Jianya Zhou","doi":"10.1016/j.jengtecman.2025.101936","DOIUrl":"10.1016/j.jengtecman.2025.101936","url":null,"abstract":"<div><div>The application of digital finance in green technology innovation in small- and medium-sized enterprises (SME) has not been leveraged to the fullest extent. Based on innovation ecosystem, digital empowerment, and long-tail effect theories, this study constructs a model of digital finance driving SMEs’ green technology innovation using data from county-level units in China. The findings reveal that digital finance partially drives SMEs’ green technology innovation. Besides revealing digital finance’s role as a key driver of green technology innovation in SMEs, this study provides practical insights into combining digital finance with green technology innovation.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"79 ","pages":"Article 101936"},"PeriodicalIF":3.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Latecomer firms often face persistent disadvantages relative to frontier firms, giving rise to a neglected but critical phenomenon we term technological backlog—the accumulated potential for catch-up created by long-term lag. Prior research largely overlooks how this backlog can be released and transformed into productivity growth, especially in the digital era. This study addresses this gap by examining whether, how, and under what conditions digital transformation enables latecomers to release their technological backlog. Using panel data on Chinese listed manufacturing firms (2004–2022), we find that digital transformation does not directly promote productivity growth; rather, it functions as a contingent catalyst that unlocks technological backlog and indirectly enhances productivity. The mechanism operates through strengthened absorptive capacity, with intangible assets serving as a vital complement. Heterogeneity analyses further reveal that the effect is stronger for state-owned, older, and larger firms, as well as those in high-tech, digitally advanced, and technology-intensive industries. By introducing technological backlog as a new lens for catch-up theory and reframing digital transformation as a conditional catalyst rather than a universal driver, this study advances theoretical debates on catch-up and digital transformation while offering practical guidance for managers and policymakers on designing effective digital catch-up strategies.
{"title":"Releasing the technological backlog: The role of digital transformation","authors":"Hao Gao, Rongjie Lv, Xinkai Wu, Yiming Zhang, Chenyu Guo","doi":"10.1016/j.jengtecman.2025.101935","DOIUrl":"10.1016/j.jengtecman.2025.101935","url":null,"abstract":"<div><div>Latecomer firms often face persistent disadvantages relative to frontier firms, giving rise to a neglected but critical phenomenon we term technological backlog—the accumulated potential for catch-up created by long-term lag. Prior research largely overlooks how this backlog can be released and transformed into productivity growth, especially in the digital era. This study addresses this gap by examining whether, how, and under what conditions digital transformation enables latecomers to release their technological backlog. Using panel data on Chinese listed manufacturing firms (2004–2022), we find that digital transformation does not directly promote productivity growth; rather, it functions as a contingent catalyst that unlocks technological backlog and indirectly enhances productivity. The mechanism operates through strengthened absorptive capacity, with intangible assets serving as a vital complement. Heterogeneity analyses further reveal that the effect is stronger for state-owned, older, and larger firms, as well as those in high-tech, digitally advanced, and technology-intensive industries. By introducing technological backlog as a new lens for catch-up theory and reframing digital transformation as a conditional catalyst rather than a universal driver, this study advances theoretical debates on catch-up and digital transformation while offering practical guidance for managers and policymakers on designing effective digital catch-up strategies.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"79 ","pages":"Article 101935"},"PeriodicalIF":3.9,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.jengtecman.2025.101921
Ariful Islam , Md Asadul Islam , Francesca Dal Mas , Justyna Fijałkowska , Mahfuzur Rahman , Maurizio Massaro
Researchers have been exploring an effective framework for achieving competitive advantage for many years, specifically tailored to small and medium-sized enterprises (SMEs) to ensure their long-term survival. The recent surge in advanced technologies, particularly artificial intelligence (AI), has made their debates more challenging. Thus, the study proposes a conceptual framework specifically designed to leverage AI for long-term competitive advantage in SMEs, examining their business models through this lens. This study conducts a systematic literature review (SLR) to cover a broad range of relevant literature within a final sample of 69 articles. The SLR method was chosen to integrate research in a systematic, transparent, and reproducible way. For qualitative analysis and framework derivation, the study draws on a thematic ontological analysis. The study identifies multiple research streams at the intersection of advanced technology and entrepreneurship aimed at enhancing the competitiveness of SMEs. The primary outcome of this study is the development of a comprehensive business model framework, encompassing both external antecedents (namely, market and industry dynamics, technological infrastructure, government policies and support, strategic alliances, socio-cultural factors) and internal antecedents (digital leadership, dynamic capabilities/adaptability, entrepreneurial mindset, data strategy, growth/resilience), ultimately contributing to sustainable performance. Practically, the study provides a comprehensive avenue for SME owners and managers to adopt and use AI in business strategies and operations. Based on the results, SMEs can implement automation and machine learning to streamline business processes, minimize manual labor, and boost overall operational efficiency. More theoretical and practical implications, along with limitations and future directions, are also discussed, revealing multiple theoretical gateways and an agenda for subsequent empirical work.
{"title":"Configuring AI-guided sustainable competitive advantage for SMEs through business model innovation: A systematic literature review approach","authors":"Ariful Islam , Md Asadul Islam , Francesca Dal Mas , Justyna Fijałkowska , Mahfuzur Rahman , Maurizio Massaro","doi":"10.1016/j.jengtecman.2025.101921","DOIUrl":"10.1016/j.jengtecman.2025.101921","url":null,"abstract":"<div><div>Researchers have been exploring an effective framework for achieving competitive advantage for many years, specifically tailored to small and medium-sized enterprises (SMEs) to ensure their long-term survival. The recent surge in advanced technologies, particularly artificial intelligence (AI), has made their debates more challenging. Thus, the study proposes a conceptual framework specifically designed to leverage AI for long-term competitive advantage in SMEs, examining their business models through this lens. This study conducts a systematic literature review (SLR) to cover a broad range of relevant literature within a final sample of 69 articles. The SLR method was chosen to integrate research in a systematic, transparent, and reproducible way. For qualitative analysis and framework derivation, the study draws on a thematic ontological analysis. The study identifies multiple research streams at the intersection of advanced technology and entrepreneurship aimed at enhancing the competitiveness of SMEs. The primary outcome of this study is the development of a comprehensive business model framework, encompassing both external antecedents (namely, market and industry dynamics, technological infrastructure, government policies and support, strategic alliances, socio-cultural factors) and internal antecedents (digital leadership, dynamic capabilities/adaptability, entrepreneurial mindset, data strategy, growth/resilience), ultimately contributing to sustainable performance. Practically, the study provides a comprehensive avenue for SME owners and managers to adopt and use AI in business strategies and operations. Based on the results, SMEs can implement automation and machine learning to streamline business processes, minimize manual labor, and boost overall operational efficiency. More theoretical and practical implications, along with limitations and future directions, are also discussed, revealing multiple theoretical gateways and an agenda for subsequent empirical work.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"78 ","pages":"Article 101921"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.jengtecman.2025.101920
Maria Chiara De Lorenzi, Marta Menegoli, Maria Laura Giangrande
This study presents an original and relevant exploration of how platform-based companies can leverage Artificial Intelligence to address sustainability challenges, particularly in compliance with European Sustainable Development Goals. Starting by the observation of platformization as a process bringing to Business Model Innovation and focusing on the intersection of Artificial Intelligence and sustainability, the study fills a critical gap in existing literature, which often overlooks the specific implications of Artificial Intelligence integration in various business scenarios. A comprehensive conceptual framework that guides the study’s investigation into Artificial Intelligence as a boost for the twin transition with a focus on advancing stakeholder legitimacy within agri-food platform-based companies was designed. The study was based on a qualitative research approach through a three-phases methodology. The results of study derived from a Systematic Literature Review within content analysis and multiple case study that using desktop analysis on a 52 platform-based agri-food companies sample. Follow a discussion through inside-out and outside-in perspective underline results evidence in order to build academic evidence for practice and empirical evidence for academia. The use of Artificial Intelligence in sustainable practices provides concrete evidence that enhances the understanding of how value is created, captured, and delivered within platform-based business models for sustainability. Moreover, the impact of these AI-driven practices on stakeholder perceptions offers updated empirical insights into Stakeholder Theory, particularly regarding the practical mechanisms through which normative legitimacy is built or undermined in the context of advanced technologies. The research agenda outlines critical avenues for future investigation. These directions aim to deepen our understanding of the complex interplay between advanced technologies, sustainable transformation, and societal acceptance.
{"title":"Igniting twin transition through artificial intelligence and stakeholder value: The case of platform-based agri-food companies","authors":"Maria Chiara De Lorenzi, Marta Menegoli, Maria Laura Giangrande","doi":"10.1016/j.jengtecman.2025.101920","DOIUrl":"10.1016/j.jengtecman.2025.101920","url":null,"abstract":"<div><div>This study presents an original and relevant exploration of how platform-based companies can leverage Artificial Intelligence to address sustainability challenges, particularly in compliance with European Sustainable Development Goals. Starting by the observation of platformization as a process bringing to Business Model Innovation and focusing on the intersection of Artificial Intelligence and sustainability, the study fills a critical gap in existing literature, which often overlooks the specific implications of Artificial Intelligence integration in various business scenarios. A comprehensive conceptual framework that guides the study’s investigation into Artificial Intelligence as a boost for the twin transition with a focus on advancing stakeholder legitimacy within agri-food platform-based companies was designed. The study was based on a qualitative research approach through a three-phases methodology. The results of study derived from a Systematic Literature Review within content analysis and multiple case study that using desktop analysis on a 52 platform-based agri-food companies sample. Follow a discussion through inside-out and outside-in perspective underline results evidence in order to build academic evidence for practice and empirical evidence for academia. The use of Artificial Intelligence in sustainable practices provides concrete evidence that enhances the understanding of how value is created, captured, and delivered within platform-based business models for sustainability. Moreover, the impact of these AI-driven practices on stakeholder perceptions offers updated empirical insights into Stakeholder Theory, particularly regarding the practical mechanisms through which normative legitimacy is built or undermined in the context of advanced technologies. The research agenda outlines critical avenues for future investigation. These directions aim to deepen our understanding of the complex interplay between advanced technologies, sustainable transformation, and societal acceptance.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"78 ","pages":"Article 101920"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.jengtecman.2025.101927
Costanza Mariani, Mauro Mancini
Recent advancements in Artificial Intelligence (AI) have intensified discussions about job transformation, with growing evidence that many professional roles will be significantly affected. This paper examines the impact of analytical AI systems on project management, focusing on how data-driven, algorithmic tools influence both the quantitative and qualitative dimensions of project management activities. While existing studies often highlight the potential of AI in this domain, they frequently concentrate on generative AI or overlook project managers’ own expectations regarding whether analytical AI will replace or augment their work. Using the Nominal Group Technique, this study investigates which project management activities practitioners expect to be replaced, supported, or remain unaffected by analytical AI. The findings reveal that although analytical AI is not anticipated to replace project managers, it is likely to reshape how tasks are executed. As a result, project managers will increasingly need to develop new skills and competencies to remain competitive in an AI-enhanced project environment.
{"title":"Beyond replacement: How project managers perceive the transformative role of AI in project work","authors":"Costanza Mariani, Mauro Mancini","doi":"10.1016/j.jengtecman.2025.101927","DOIUrl":"10.1016/j.jengtecman.2025.101927","url":null,"abstract":"<div><div>Recent advancements in Artificial Intelligence (AI) have intensified discussions about job transformation, with growing evidence that many professional roles will be significantly affected. This paper examines the impact of analytical AI systems on project management, focusing on how data-driven, algorithmic tools influence both the quantitative and qualitative dimensions of project management activities. While existing studies often highlight the potential of AI in this domain, they frequently concentrate on generative AI or overlook project managers’ own expectations regarding whether analytical AI will replace or augment their work. Using the Nominal Group Technique, this study investigates which project management activities practitioners expect to be replaced, supported, or remain unaffected by analytical AI. The findings reveal that although analytical AI is not anticipated to replace project managers, it is likely to reshape how tasks are executed. As a result, project managers will increasingly need to develop new skills and competencies to remain competitive in an AI-enhanced project environment.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"78 ","pages":"Article 101927"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.jengtecman.2025.101917
Maria Alice Moreira Trindade , Pietro De Giovanni
Craftsmen traditionally relied on proximity markets and localized territories to conduct their business without heavy investments in digital solutions. However, the Covid-19 pandemic necessitated the adoption of digital solutions to access markets also giving unexpected opportunities like, for example, international exposure. Such investments required a complete change in the craftsmen's business strategies and persisted in the post-pandemic period requesting continuous and new investments. In this study, we employ Structural Equation Modeling on a sample of 762 Italian craftsmen to investigate how the post-Covid business strategies influence the investments in both digital marketing and digital technologies and their subsequent impact on performance. Furthermore, this study seeks to explore the advantages that craftsmen derive from subscribing to post-Covid commercial and digital-based contracts with strategic partners that joined the business model due to the pandemic. By doing so, we demonstrate the profound influence of these contract types on firms' business strategies and digital-related adoption paths and contribute to a better understanding of the factors driving technology adoption in craftsmen industry.
{"title":"Craftsmen and digital transformation: Business strategies and contracts in a post-Covid world","authors":"Maria Alice Moreira Trindade , Pietro De Giovanni","doi":"10.1016/j.jengtecman.2025.101917","DOIUrl":"10.1016/j.jengtecman.2025.101917","url":null,"abstract":"<div><div>Craftsmen traditionally relied on proximity markets and localized territories to conduct their business without heavy investments in digital solutions. However, the Covid-19 pandemic necessitated the adoption of digital solutions to access markets also giving unexpected opportunities like, for example, international exposure. Such investments required a complete change in the craftsmen's business strategies and persisted in the post-pandemic period requesting continuous and new investments. In this study, we employ Structural Equation Modeling on a sample of 762 Italian craftsmen to investigate how the post-Covid business strategies influence the investments in both digital marketing and digital technologies and their subsequent impact on performance. Furthermore, this study seeks to explore the advantages that craftsmen derive from subscribing to post-Covid commercial and digital-based contracts with strategic partners that joined the business model due to the pandemic. By doing so, we demonstrate the profound influence of these contract types on firms' business strategies and digital-related adoption paths and contribute to a better understanding of the factors driving technology adoption in craftsmen industry.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"78 ","pages":"Article 101917"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of artificial intelligence (AI) into digital platforms is transforming the way businesses tackle environmental, social and governance (ESG) issues. This study investigates how AI can enable platform business models (Platform BMs) to create, deliver and capture ESG-related value, with a particular focus on the ESG rating industry. Using the Platform Business Model Canvas as a conceptual framework, and conducting a comparative analysis of six case studies, the research identifies three distinct configurations of AI-enabled Platform BMs: (1) ESG data wrangling and integration; (2) financial analysis and provision of ESG data to investors and companies; and (3) compliance and management of ESG issues in supply chains. Each configuration embeds specific mechanisms, such as predictive analytics, compliance automation and stakeholder coordination, through which AI can support ESG-oriented business innovation. Based on these findings, the study proposes four theoretical propositions that clarify the relationships between AI capabilities, data governance, and ESG value creation within platform ecosystems. The paper advances the academic understanding of the relationship between AI and sustainability and provides a typology to inform the strategic development of ESG-focused digital platforms.
{"title":"Integrating AI and ESG in digital platforms: New profiles of platform-based business models","authors":"Giulia Nevi , Raffaella Montera , Nicola Cucari , Francesco Laviola","doi":"10.1016/j.jengtecman.2025.101913","DOIUrl":"10.1016/j.jengtecman.2025.101913","url":null,"abstract":"<div><div>The integration of artificial intelligence (AI) into digital platforms is transforming the way businesses tackle environmental, social and governance (ESG) issues. This study investigates how AI can enable platform business models (Platform BMs) to create, deliver and capture ESG-related value, with a particular focus on the ESG rating industry. Using the Platform Business Model Canvas as a conceptual framework, and conducting a comparative analysis of six case studies, the research identifies three distinct configurations of AI-enabled Platform BMs: (1) ESG data wrangling and integration; (2) financial analysis and provision of ESG data to investors and companies; and (3) compliance and management of ESG issues in supply chains. Each configuration embeds specific mechanisms, such as predictive analytics, compliance automation and stakeholder coordination, through which AI can support ESG-oriented business innovation. Based on these findings, the study proposes four theoretical propositions that clarify the relationships between AI capabilities, data governance, and ESG value creation within platform ecosystems. The paper advances the academic understanding of the relationship between AI and sustainability and provides a typology to inform the strategic development of ESG-focused digital platforms.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"78 ","pages":"Article 101913"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.jengtecman.2025.101915
Louisa Heiduk , Philipp Frey , Lysander Weiss , Dominik K. Kanbach
Within the domain of corporate entrepreneurship, external corporate venturing (CV) offers firms different avenues for strategic renewal, yet research comparing its different modes remains limited. This study examines two external CV approaches—Corporate Venture Capital (CVC) and Venture Clienting (VCL)—to investigate how they differentially and complementarily facilitate strategic renewal. The study applies a flexible pattern-matching approach to abductively compare empirical insights from 99 semi-structured interviews with CV managers to existing theory on strategic corporate venturing. The findings show that CVC often primarily functions as an exploratory external CV mode oriented toward future growth, characterized by strong strategic and external linkages, proactive sensing, and investment-led innovation processes. In contrast, VCL tends to serve as an exploitative external CV mode focused on near-term operational enhancement through tight business-unit integration, structured piloting, and implementation activities. While both modes support certain aspects of strategic renewal, the findings suggest that orchestrated jointly, CVC and VCL have the potential to facilitate distributed ambidexterity, enabling companies to balance exploration and exploitation across time horizons to renew current and future competitive advantages. The study contributes to the literature by empirically conceptualizing complementary configurations of CVC and VCL for a distributed architecture for strategic renewal and by advancing the configurational view of external CV through a formalized configuration space that links structural features to heterogeneous ambidextrous behaviours.
{"title":"External corporate venturing for strategic renewal: A comparative study of corporate venture capital and corporate venture clienting","authors":"Louisa Heiduk , Philipp Frey , Lysander Weiss , Dominik K. Kanbach","doi":"10.1016/j.jengtecman.2025.101915","DOIUrl":"10.1016/j.jengtecman.2025.101915","url":null,"abstract":"<div><div>Within the domain of corporate entrepreneurship, external corporate venturing (CV) offers firms different avenues for strategic renewal, yet research comparing its different modes remains limited. This study examines two external CV approaches—Corporate Venture Capital (CVC) and Venture Clienting (VCL)—to investigate how they differentially and complementarily facilitate strategic renewal. The study applies a flexible pattern-matching approach to abductively compare empirical insights from 99 semi-structured interviews with CV managers to existing theory on strategic corporate venturing. The findings show that CVC often primarily functions as an exploratory external CV mode oriented toward future growth, characterized by strong strategic and external linkages, proactive sensing, and investment-led innovation processes. In contrast, VCL tends to serve as an exploitative external CV mode focused on near-term operational enhancement through tight business-unit integration, structured piloting, and implementation activities. While both modes support certain aspects of strategic renewal, the findings suggest that orchestrated jointly, CVC and VCL have the potential to facilitate distributed ambidexterity, enabling companies to balance exploration and exploitation across time horizons to renew current and future competitive advantages. The study contributes to the literature by empirically conceptualizing complementary configurations of CVC and VCL for a distributed architecture for strategic renewal and by advancing the configurational view of external CV through a formalized configuration space that links structural features to heterogeneous ambidextrous behaviours.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"78 ","pages":"Article 101915"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.jengtecman.2025.101925
Duo Jin , Yang Yang , Yang Liu
Drawing on inferential learning theory and situated artificial intelligence (AI) theory, we posit that both “good” peers (those exhibiting higher levels of green innovation) and “bad” peers (those accruing higher environmental penalties) exert positive influences on a focal firm's green innovation. Moreover, we propose that firm’s AI orientation will exert a dual moderating effect: It will enhance inferential learning from “good” peers by accelerating the learning process, yet diminish the learning derived from “bad” peers by amplifying the inherent myopia of inferential learning. Empirical and robustness tests using 4089 firms with 29,402 firm-year observations from 2010 to 2020 largely support our theory. These findings shed light on AI augmented inferential learning theory as well as the nuanced peer effects on green innovation in the age of AI.
{"title":"AI augmented inferential learning from both the 'good' and 'bad' for green innovation: Evidence from China","authors":"Duo Jin , Yang Yang , Yang Liu","doi":"10.1016/j.jengtecman.2025.101925","DOIUrl":"10.1016/j.jengtecman.2025.101925","url":null,"abstract":"<div><div>Drawing on inferential learning theory and situated artificial intelligence (AI) theory, we posit that both “good” peers (those exhibiting higher levels of green innovation) and “bad” peers (those accruing higher environmental penalties) exert positive influences on a focal firm's green innovation. Moreover, we propose that firm’s AI orientation will exert a dual moderating effect: It will enhance inferential learning from “good” peers by accelerating the learning process, yet diminish the learning derived from “bad” peers by amplifying the inherent myopia of inferential learning. Empirical and robustness tests using 4089 firms with 29,402 firm-year observations from 2010 to 2020 largely support our theory. These findings shed light on AI augmented inferential learning theory as well as the nuanced peer effects on green innovation in the age of AI.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"78 ","pages":"Article 101925"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.jengtecman.2025.101923
Mehdi Fatemi , Shohreh Nasri , Sepehr Ghazinoory
The rapid advancement of artificial intelligence (AI) reinforces the necessity of adopting global perspectives on innovation. Conventional frameworks (national innovation systems, global value chains, and ecosystem models) remain useful, yet they are limited in their ability to capture the complexity, dynamism, and asymmetrical interactions that characterize transnational AI ecosystems. This article introduces the innovation biosphere framework as a response to this theoretical gap, a conceptual approach that foregrounds the non-equilibrium dynamics, role fluidity, and co-evolutionary processes that characterize today's transnational AI landscape. Employing a metaphor research strategy, the article systematically maps key actors (leader, systemic intermediary, umbrella, and fundamental), interactions (cooperation, competition, prey/predator, and commensalism), and evolution mechanisms (environmental adaptation, innovation-driven evolution, performance improvement, and directional change) onto global innovation dynamics. Accordingly, leader actors (e.g., OpenAI and DeepMind) drive deep learning and language processing breakthroughs. Google and IBM, as systemic intermediary actors, spread AI across sectors, while umbrella actors, including Alphabet and Tencent, nurture AI startups and foster innovation at multiple scales. Fundamental actors contribute foundational research, regulation, and ethical frameworks. Furthermore, cooperative efforts (e.g., Partnership on AI) foster joint technological advancements, while competitive dynamics among tech giants stimulate rapid AI progress. Prey-predator and commensalism relationships illustrate interactions characterized by asymmetries in power or resources. Finally, evolution mechanisms include environmental adaptation, observed in AI's pandemic-driven growth, and innovation-driven evolution marked by leaps in NLP model capabilities. Performance improvement results from cross-sector contributions, like Nvidia's influence on autonomous vehicles, while geopolitical disruptions trigger directional changes.
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