Journal of Engineering and Technology Management最新文献

{"title":"Transforming business models across digital platforms: Exploring the role of artificial intelligence","authors":"Maria Elena Latino,&nbsp;Giacomo Marzi,&nbsp;Daniel Trabucchi,&nbsp;Francesca Grippa,&nbsp;Ted Ladd","doi":"10.1016/j.jengtecman.2026.101983","DOIUrl":"10.1016/j.jengtecman.2026.101983","url":null,"abstract":"<div><div>The convergence of artificial intelligence (AI) and digital platforms is reconfiguring how organizations create, deliver, and capture value. As platform-based business models proliferate across industries, AI introduces new mechanisms of learning, data-driven value creation, and ecosystem evolution within two-sided and multi-sided platforms. This special issue assembles eight studies that examine AI-enabled platform business model innovation from diverse theoretical, methodological, and sectoral perspectives, including agri-food sustainability, semiconductor manufacturing, ESG rating platforms, open-source ecosystems, B2B platforms, global innovation dynamics, entertainment industry leadership, and blockchain-enabled decentralized systems. This editorial situates these contributions within the broader research trajectory on digital transformation and platform thinking and develops a structured synthesis that highlights recurring patterns across studies. Building on this synthesis, it proposes an integrative conceptual <em>Framework for AI-driven Platform Business Model Transformation,</em> conceptualizing the dynamic alignment and co-evolution among AI roles, platform configurations, and value creation mechanisms. In particular, the framework advances a processual understanding of platform transformation and identifies data externalities as central driver of learning-based value creation. Finally, the editorial outlines a future research agenda grounded in the insights of the special issue, emphasizing the need for further investigation of co-evolutionary dynamics, governance mechanisms, and the theoretical and methodological approaches required to explain AI-enabled platform innovation.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101983"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148958182","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}

{"title":"The effect of digital platform adoption on the resilience of innovation ecosystems: An empirical analysis using social media data","authors":"Andrea Ancona,&nbsp;Myriam Caratù,&nbsp;Giuseppe Ceci,&nbsp;Antonio Iovanella","doi":"10.1016/j.jengtecman.2026.101992","DOIUrl":"10.1016/j.jengtecman.2026.101992","url":null,"abstract":"<div><div>The increasing complexity of innovation processes has led to the conceptualization of innovation ecosystems as complex systems. This study focuses on one underexplored property of such systems: their resilience to exogenous shocks. Specifically, we explore how digital platform adoption – accelerated since the COVID-19 pandemic – can facilitate internal reorganization within ecosystems following a shock by examining its effect on key systemic properties such as openness, diversity, coherence, and flexibility. Using network-based metrics and tests, we analyze a startup ecosystem on Twitter, advancing theoretical understanding of innovation ecosystem resilience and offering practical insights into the role of digital platforms.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101992"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creative cities, innovation and technological development – can urban communities be good examples in sustainable development?","authors":"Paulo Reis Mourão,&nbsp;Bruno Blanco-Varela","doi":"10.1016/j.jengtecman.2026.101989","DOIUrl":"10.1016/j.jengtecman.2026.101989","url":null,"abstract":"<div><div>The UNESCO Creative Cities Network (UCCN) represents a global initiative that aligns cultural and creative industries (CCIs) with sustainable development goals through innovation and technological integration. This study explores the uneven global distribution of Creative Cities by examining how national-level innovation systems, technological capacity, and socio-economic structures influence city nominations. Using Poisson fixed effects, Poisson pseudo maximum likelihood, and generalized linear models, we assess the impact of variables such as GDP per capita, human capital, urban density, and institutional quality. Our findings reveal that while economic development and strong human capital are key enablers, high institutional quality in innovation does not necessarily correlate with more UNESCO Creative City designations. This paradox underscores the need for more inclusive and context-sensitive innovation policies. The results contribute to the debate on how CCIs can better leverage innovation and technology to foster sustainable urban development and cultural resilience.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101989"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148567781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Managing innovation performance in Industry 5.0: The role of environmental turbulence, dynamic capabilities, and AI acceptance","authors":"Bilal Afzal,&nbsp;Ana Beatriz Hernandez Lara,&nbsp;Anna Visvizi,&nbsp;Xiaoni Li","doi":"10.1016/j.jengtecman.2026.101987","DOIUrl":"10.1016/j.jengtecman.2026.101987","url":null,"abstract":"<div><div>This paper examines how market and technological turbulence (MT, TT) influence AI-enabled inbound and outbound open innovation (IOI, OOI), their mediating effects on innovation performance (IP), and the moderating roles of effort expectancy (EE) and performance expectancy (PE) within the Industry 5.0 framework. A dual-method approach combining PLS-SEM and NCA was applied to survey data from 245 industry professionals collected between November 2024 and February 2025. Both IOI and OOI significantly enhance IP, with OOI emerging as the stronger predictor. MT positively influences both IOI and OOI, while TT affects only OOI. EE and PE moderate the turbulence–IOI relationship, and several mediation effects through open innovation are supported. The study extends Dynamic Capability Theory (DCT) and UTAUT by integrating AI-enabled open innovation into the Industry 5.0 paradigm, emphasizing the human factor in innovation management.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101987"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148567810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agentic AI and labor disruption: A comprehensive review, measurement, and policy framework","authors":"Maikel Leon","doi":"10.1016/j.jengtecman.2026.101993","DOIUrl":"10.1016/j.jengtecman.2026.101993","url":null,"abstract":"<div><div>Artificial intelligence has evolved from rule-based systems to agentic AI: autonomous agents capable of perception, reasoning, and interaction with minimal human supervision. This transformation positions AI as a general-purpose technology with profound implications for labor markets. Autonomous agent capability is accelerating; the length of tasks frontier agents can complete unaided has been doubling roughly every seven months, yet measurement of labor effects has not kept pace, leaving managers and policymakers reliant on indices that capture only job elimination. This article reviews agentic AI through an interdisciplinary lens, integrating insights from computer science, economics, and organizational studies. It introduces the Labor Disruption Index (LDI), a composite framework capturing four dimensions of technological impact: displacement, automation potential, productivity enhancement, and new task creation. Unlike prior automation-risk indices that focus solely on job elimination, the LDI incorporates both negative and positive effects, enabling the identification of net outcomes at the occupational level. Each component is operationalized using existing datasets, and calibration procedures are demonstrated with sensitivity analyses across alternative weighting schemes. The analysis is organized through a micro–meso–macro framework: the micro level concerns agent design and technical capabilities; the meso level concerns organizational adoption patterns and governance structures; and the macro level concerns economy-wide impacts and policy responses. The synthesis draws on peer-reviewed empirical studies, including randomized controlled trials, adoption surveys, and macroeconomic projections. Findings reveal substantial heterogeneity across sectors: some roles face net displacement while others benefit from productivity gains and task creation. Applications are analyzed across healthcare, finance, manufacturing, legal services, education, and government. The article concludes with policy recommendations and an interdisciplinary research agenda organized around the LDI, advancing the measurement of labor disruption and providing actionable tools for scholars, managers, and policymakers.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101993"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635187","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}

{"title":"Entrepreneurial support organizations in the new space economy: Location, peers, and experience effects on startup performance","authors":"David Hernando-Tomé,&nbsp;Enrique Acebo,&nbsp;Julio Abad-González,&nbsp;José-Ángel Miguel-Dávila","doi":"10.1016/j.jengtecman.2026.101990","DOIUrl":"10.1016/j.jengtecman.2026.101990","url":null,"abstract":"<div><div>Entrepreneurial support organizations (ESOs), such as incubators and accelerators, have rapidly become integral to modern entrepreneurial ecosystems. This study investigates how organizational and contextual factors affect the performance of technology startups accelerated under the European Space Agency’s BIC program from 2005 to 2025. Utilizing negative binomial regression models on a dataset of 1326 European New Space startups, we examine the roles of ESO geographical location, cohort density, and institutional experience in shaping startup outcomes. Our findings indicate that: ESO geographical location significantly shapes startup employment size; accumulated ESO institutional experience is a robust predictor of startup employment growth; and cohort characteristics show secondary effects. Grounded in the Resource-Based View, these insights enhance our understanding of the value and effectiveness of ESOs in technology-intensive sectors, particularly the New Space industry.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101990"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148567783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating and orchestrating Web 3.0 innovation challenges: A theory of cross-chain ecosystem strategy","authors":"Tuan-Dung Tran,&nbsp;Phuong-Dai Bui,&nbsp;Van-Hau Pham","doi":"10.1016/j.jengtecman.2026.101988","DOIUrl":"10.1016/j.jengtecman.2026.101988","url":null,"abstract":"<div><div>Web 3.0 technologies present fundamental challenges to established theories of platform strategy and organizational design, yet the organizational forms enabling decentralized innovation remain theoretically underexamined. This paper reconceptualizes cross-chain bridges, which enable value and message transfer across independent blockchain networks, not as passive technical utilities but as novel business models orchestrating decentralized innovation ecosystems. Integrating platform economics, governance theory, and business model innovation, we develop a theoretical framework for understanding how these ecosystems create, capture, and coordinate value in the absence of hierarchical authority. Empirically, we construct a high-fidelity analytical sample drawn from a large corpus of cross-chain transactions spanning multiple protocols over a multi-year observation window, applying strict filtering heuristics that exclude automated Maximal Extractable Value (MEV) activity, wash trading, and low-value transfers to isolate genuine user-driven economic activity relevant to governance and ecosystem outcomes. This on-chain dataset is complemented by a structured survey of ecosystem practitioners and systematic post-mortem analysis of failed protocols. The study yields three theoretical contributions. First, a configurational theory identifies distinct business model archetypes whose competitive differentiation derives from supermodular complementarities among liquidity depth, developer ecosystems, and governance legitimacy. Second, emergent coordination mechanisms enable ecosystem orchestration without central control, though token-based governance remains vulnerable to contestation when institutional safeguards are insufficient, necessitating self-correcting mechanisms. Third, an inverted U-shaped relationship between decentralization and performance reveals that a graduated governance transition optimally balances development velocity with long-term legitimacy. These findings reconceptualize value analysis in decentralized markets, shifting attention from firm-level appropriation to ecosystem-level coordination as the primary determinant of competitive sustainability.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101988"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148567782","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}

{"title":"From technology to transformation: How AI governance initiatives drives sustainability through managerial and organizational mediation","authors":"Siriprapha Jitanugoon,&nbsp;Pittinun Puntha","doi":"10.1016/j.jengtecman.2026.101996","DOIUrl":"10.1016/j.jengtecman.2026.101996","url":null,"abstract":"<div><div>This study examines how artificial intelligence (AI) enables sustainability transformation in small and medium-sized enterprises (SMEs) in emerging economies. Using sequential mixed-methods design and drawing on Organizational Change (OC) and Organizational Transformation (OT) theories, the study of SMEs shows that AI drives sustainability through managerial enablement and organizational mechanisms. Strategic sensemaking, ethical framing, and participatory communication translate AI capabilities into sustainability goals, while governance routines and feedback systems institutionalize adaptive learning. The study contributes by integrating OC–OT perspectives and offers practical guidance for designing adaptive, ethically grounded AI-driven governance initiatives in resource-constrained contexts.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101996"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635189","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}

{"title":"The impacts of ambidextrous learning balance and synergy on innovation quality","authors":"Zhongtao Zhao,&nbsp;Zhaofeng Yu,&nbsp;Lilei Zhang,&nbsp;Jing Tian","doi":"10.1016/j.jengtecman.2026.101991","DOIUrl":"10.1016/j.jengtecman.2026.101991","url":null,"abstract":"<div><div>Drawing on organizational learning theory and the knowledge-based view, this study addresses the underexplored question of how ambidextrous learning affects innovation quality, particularly the moderating role of knowledge resources. We deconstruct ambidextrous learning into two dimensions, namely, balance and synergy, and we empirically investigate their effects on innovation quality using a sample of 463 listed manufacturing firms in China. We further incorporate the knowledge stock and research and development (R&amp;D) partner diversity as critical moderators to clarify the relevant boundary conditions. The results reveal an inverted U-shaped relationship between ambidextrous learning balance and innovation quality, while ambidextrous learning synergy has a positive effect on innovation quality. Moreover, the positive effect of synergy is weakened by both the knowledge stock and R&amp;D partner diversity. Heterogeneity analyses indicate that the negative moderating effect of the knowledge stock is stronger in high-tech industries and in highly dynamic environments, whereas the negative moderating effect of R&amp;D partner diversity is more salient in academic‑led and weak-tie collaborations. These findings extend organizational learning theory by explaining the dual mechanisms through which ambidextrous learning influences innovation quality and by revealing the contextual constraints imposed by knowledge resources.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101991"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of digital technology investment rhythm on disruptive innovation: An organizational resilience perspective","authors":"Liang Ma,&nbsp;Yufei Yao,&nbsp;Wenhui Li","doi":"10.1016/j.jengtecman.2026.101997","DOIUrl":"10.1016/j.jengtecman.2026.101997","url":null,"abstract":"<div><div>Disruptive innovation is recognized as an effective strategy for breaking established paradigms and achieving substantial growth through technological advancement.While existing literature highlights digital technology as a critical catalyst for enhancing firms’ innovation capabilities,research on the relationship between digital technology investment rhythm and disruptive innovation has been largely overlooked.Using empirical data from Chinese A-share manufacturing firms (2013–2023),we demonstrate that a sustained rhythm of digital technology investment promotes disruptive innovation.This relationship is mediated by resource reconstruction capability and resource allocation disparity.Moreover,organizational resilience positively moderates this link.Ultimately,our findings provide nuanced understanding of how firms orchestrate the digital technology investment rhythm to drive disruptive innovation.</div></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":"81 ","pages":"Article 101997"},"PeriodicalIF":4.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148735667","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}