IEEE Transactions on Engineering Management最新文献_第9页

Structural Characteristics and Formation Mechanisms of Patent Collaboration Networks in China’s Prefabricated Buildings 中国装配式建筑专利协同网络的结构特征及形成机制

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI: 10.1109/TEM.2026.3675483

Fangliang Wang;Min Cheng;Jinsheng Yang

{"title":"Structural Characteristics and Formation Mechanisms of Patent Collaboration Networks in China’s Prefabricated Buildings","authors":"Fangliang Wang;Min Cheng;Jinsheng Yang","doi":"10.1109/TEM.2026.3675483","DOIUrl":"https://doi.org/10.1109/TEM.2026.3675483","url":null,"abstract":"Interorganizational collaborative innovation is crucial for advancing the development of prefabricated buildings (PBs). However, the structural characteristics and formation mechanisms of networks arising from such collaborative innovation remain unexplored. Using patent data on PBs in China (2010–2021), this study constructed patent collaboration networks (PCNs) and examined their structural characteristics using social network analysis and network motif analysis methods. Exponential random graph models were applied to investigate the mechanisms underlying PCN formation. The results show that 1) the PCNs of PBs exhibit small-world and scale-free characteristics without a core-periphery structure, evolving from single-core to multicore aggregation and forming an open and diverse innovation ecosystem; 2) State Grid Corporation of China emerges as the core organization, significantly influencing knowledge flow and resource integration; 3) enterprise-enterprise-enterprise and enterprise–enterprise–university are dominant collaborative innovation patterns for PBs. Enterprises lead PBs’ innovation while universities provide technical support; 4) PCNs’ formation is driven by triadic closure, technology proximity, geography proximity, collaboration breadth, and collaboration depth, whereas organization proximity and innovation capability show inconsistent effects. Robustness tests conducted on PCNs formed by varying time intervals also support these findings. These insights provide strategic guidance for partner selection in PBs’ innovation and inform policy-making for collaborative innovation initiatives.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2500-2515"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665429","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}

引用次数: 0

Patent Quality, Institutions, and Innovation Leadership in FinTech: A Multidimensional Machine Learning Analysis 金融科技领域的专利质量、制度和创新领导力：多维机器学习分析

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-04-02 DOI: 10.1109/TEM.2026.3680345

Mehmet Sahiner;Dionysios Karavidas;Noptanit Chotisarn;Milad Armani Dehghani

{"title":"Patent Quality, Institutions, and Innovation Leadership in FinTech: A Multidimensional Machine Learning Analysis","authors":"Mehmet Sahiner;Dionysios Karavidas;Noptanit Chotisarn;Milad Armani Dehghani","doi":"10.1109/TEM.2026.3680345","DOIUrl":"https://doi.org/10.1109/TEM.2026.3680345","url":null,"abstract":"Firms in technology-intensive industries require reliable tools to evaluate patent quality and guide R&D resource allocation. Conventional indicators such as patent counts or citation totals provide incomplete signals and often privilege volume over strategic substance. This study develops and applies a patent quality index (PQI), a multidimensional framework integrating technological scope, legal robustness, and market responsiveness to capture the intrinsic quality of patents. Using a global dataset of 96 657 FinTech patents filed between 2000 and 2023, the PQI is constructed through complementary statistical and machine-learning methods and applied to compare patent quality across firms and jurisdictions. Results reveal substantial heterogeneity in patent quality, clear tradeoffs between portfolio size and average quality at the firm level, and systematic cross-country differences. Specifically, regulatory quality, corruption control, and foreign direct investment significantly shape national patterns of patent quality, linking firm-level innovation strategies with broader governance environments. By providing a scalable and theory-grounded measure of patent quality, the PQI advances innovation measurement and offers practical guidance for managers, policymakers, and investors seeking to benchmark portfolios, prioritize high-value patents, and design policies that promote substantive innovation. Although demonstrated in the FinTech sector, the framework is adaptable to other technology-driven industries.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2666-2681"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737028","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}

引用次数: 0

iFreightOps: Platform for Data-Driven Freight Transportation Management in Port Operations iFreightOps：港口运营数据驱动货运管理平台

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-03-02 DOI: 10.1109/TEM.2026.3668989

Nima Golghamat Raad;Vamsi Pusapati;Hemanth Sai Yeddulapalli;Ray Wood;Sharan Srinivas;Suchithra Rajendran;Prasad Calyam

{"title":"iFreightOps: Platform for Data-Driven Freight Transportation Management in Port Operations","authors":"Nima Golghamat Raad;Vamsi Pusapati;Hemanth Sai Yeddulapalli;Ray Wood;Sharan Srinivas;Suchithra Rajendran;Prasad Calyam","doi":"10.1109/TEM.2026.3668989","DOIUrl":"https://doi.org/10.1109/TEM.2026.3668989","url":null,"abstract":"Although marine ports frequently face landside congestion events that impact their efficiency, current port management systems often lack comprehensive data-driven solutions for disruption prevention, timely intervention, and effective communication. To address this gap, this study presents <italic>iFreightOps</i>, an integrated, cloud-based analytics and communication platform designed for proactive and reactive congestion management at ports. iFreightOps includes three core modules: 1) a dynamic traffic dashboard for real-time monitoring and forecasting of key indicators, such as truck arrivals, waiting times, and cycle times; 2) a scenario analysis module that employs simulation modeling to evaluate alternative operational and infrastructural strategies under various disruption scenarios; and 3) an incident management module with automated alerts and incident logging to support stakeholder response and future decision-making. The platform has been conceptually designed and experimentally evaluated using data and operational constraints reflective of a major port in the United States, including gate constraints, railway disruptions, and infrastructure limitations. Numerical experiments indicate that iFreightOps can significantly reduce vehicle waiting times during short-term disruptions, with reductions of up to threefold observed in select scenarios through optimized rerouting strategies. Under typical operating conditions, redirecting peak-time traffic to alternative routes resulted in reductions of average waiting and cycle times by 25% and 15%, respectively.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2257-2272"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557874","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}

引用次数: 0

Connecting the Dots: Customizing Digital Supply Chain Finance for Dealers via Signaling-Screening Mechanisms 连接点：通过信号筛选机制为经销商定制数字供应链金融

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-03-13 DOI: 10.1109/TEM.2026.3673851

Hua Song;Xinge Ding;Siqi Han

{"title":"Connecting the Dots: Customizing Digital Supply Chain Finance for Dealers via Signaling-Screening Mechanisms","authors":"Hua Song;Xinge Ding;Siqi Han","doi":"10.1109/TEM.2026.3673851","DOIUrl":"https://doi.org/10.1109/TEM.2026.3673851","url":null,"abstract":"This study identifies a key distinction between digital and traditional supply chain finance (SCF): technology-empowered financial service providers (FSPs) are no longer completely uninformed parties in financing markets. By examining an underexplored digital SCF scenario involving upstream focal firms and financially constrained downstream dealers, we reveal the effective signals that enable downstream small and micro enterprises to access SCF, and explore how and when FSPs use different screens to refine financing decisions. Using a dataset from MYBank, a leading Chinese big tech lender with nationwide coverage and a dominant market share in digital SCF, we find that a dealer’s procurement amount from focal firms is a strong signal, particularly when credit limits are higher. Meanwhile, FSPs utilize stakeholder cues and digital footprints within their ecosystems to refine financing decisions. Three key stakeholder groups—owners, focal firms, and peer dealers—and regional digital financial inclusion influence the effectiveness of procurement signals. The impact of distinct signaling-screening mechanisms varies across firm size, platform registration duration, and regional marketization. Robustness tests, including IV-2SLS regressions, the Heckman two-step method, stringent fixed effects and subsample analysis, validate our findings. The study enriches the SCF literature by revealing the role of supply chain data in credit creation and identifying various signaling-screening mechanisms. It also extends screening theory by illustrating the contingent nature of screens. The findings respond to China’s recent policy initiatives on decentralized supply chain loans and provide guidance for SCF practitioners.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2320-2333"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557624","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}

引用次数: 0

Editorial: Advancing Innovation Ecosystem Research: Measurement Dynamics and Contextual Intelligence 社论：推进创新生态系统研究：测量动力学和情境智能

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-02-17 DOI: 10.1109/TEM.2026.3665869

D. Cetindamar;M. Bliemel;N. Acur;A. Ates;A. McNabola

引用次数: 0

A Systematic Review of Electric Vehicle Supply Chains: Challenges and Research Agenda 电动汽车供应链的系统回顾：挑战与研究议程

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-04-01 DOI: 10.1109/TEM.2026.3679958

Xiaojun Wang;Yudi Zhang;Joseph Amankwah-Amoah

{"title":"A Systematic Review of Electric Vehicle Supply Chains: Challenges and Research Agenda","authors":"Xiaojun Wang;Yudi Zhang;Joseph Amankwah-Amoah","doi":"10.1109/TEM.2026.3679958","DOIUrl":"https://doi.org/10.1109/TEM.2026.3679958","url":null,"abstract":"Among a growing scholarly discourse on electric vehicle (EV) supply chains, much of the existing literature remains fragmented and lacks a clear agenda for future research. Drawing on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, this study presents a systematic review of peer-reviewed research on the EV supply chain published between 2010 and 2025 and yields 501 articles. We combine structured content analysis with topic modeling, which identifies six cross-disciplinary themes: adoption, policy, and market design, EV routing and scheduling, charging infrastructure, shared mobility platforms, grid integration and vehicle-to-grid (V2G) markets, and battery-swapping models. We synthesize the main debates within each theme and surface five cross-cutting challenges that cut horizontally across themes: interoperability and standards; capital intensity and risk allocation; upstream supply, circularity, and asset longevity; data governance; and equity and access. Collectively, our findings provide an integrated framework, a forward-looking research agenda, and impactful scholarly discourse on scaling EV supply chains.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2682-2698"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737024","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}

引用次数: 0

Harnessing AI Capacities for Sustainable Business Models: Empowering Firms Across Economic, Social, and Environmental Dimensions 利用人工智能能力实现可持续商业模式：在经济、社会和环境层面赋予企业权力

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-03-24 DOI: 10.1109/TEM.2026.3677052

Giulia Nevi;Sara Bartoloni;Federica Pascucci;Luca Dezi

{"title":"Harnessing AI Capacities for Sustainable Business Models: Empowering Firms Across Economic, Social, and Environmental Dimensions","authors":"Giulia Nevi;Sara Bartoloni;Federica Pascucci;Luca Dezi","doi":"10.1109/TEM.2026.3677052","DOIUrl":"https://doi.org/10.1109/TEM.2026.3677052","url":null,"abstract":"The rapid advance of artificial intelligence (AI) is transforming industries by enabling cost reduction, personalized solutions, and improved customer service. AI also supports sustainability by facilitating data-driven decisions, operational efficiency, and better management of environmental and social externalities. However, a gap persists between technological potential and the effective adoption of sustainable and responsible business practices. This article explores how AI capacities contribute to sustainable business model (SBM) innovation, focusing on the Italian cosmetics sector. Based on a qualitative analysis of interviews with chief executive officers and senior managers from 11 companies, the article highlights how dynamic capabilities (DCs) powered by AI capacities enable organizations to sustainably enhance value creation, delivery, and capture. The findings identify best practices that leverage AI for sustainability, including product personalization, process automation, and predictive analytics. The article provides actionable insights into how AI-based systems can drive both innovation and sustainability. Building on these insights, the article proposes a theoretical framework that conceptualizes the link between lower order AI capacities, high-order DCs, and SBM innovation. Within this framework, best practices emerge as the operational outcome of AI-enabled DCs: they not only innovate processes and practices but also act as catalysts that transform and reconfigure the business model itself toward sustainability. By contextualizing these insights within a sector-specific framework, the article contributes to the growing body of knowledge on AI's role in supporting sustainable business transformation.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2821-2833"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796174","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}

引用次数: 0

Impact of Unit Carbon Quota Mechanism on Clean Energy Investment Strategies: A Biform Game Analysis 单位碳配额机制对清洁能源投资策略的影响：一个统一博弈分析

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-04-14 DOI: 10.1109/TEM.2026.3683708

Wei Chen;Ruonan Zhu;Jing Chen

引用次数: 0

70 Years of the IEEE Transactions on Engineering Management: Trends and Patterns in Scholarly Publishing IEEE工程管理汇刊70年：学术出版的趋势和模式

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-04-20 DOI: 10.1109/TEM.2026.3685972

Mohammad Sadegh Khorshidi;José M. Merigó;Dilek Cetindamar;T. M. Choi

{"title":"70 Years of the IEEE Transactions on Engineering Management: Trends and Patterns in Scholarly Publishing","authors":"Mohammad Sadegh Khorshidi;José M. Merigó;Dilek Cetindamar;T. M. Choi","doi":"10.1109/TEM.2026.3685972","DOIUrl":"https://doi.org/10.1109/TEM.2026.3685972","url":null,"abstract":"This study presents a comprehensive bibliometric and science mapping analysis of the <sc>IEEE Transactions on Engineering Management</small> (IEEE TEM), one of the longest-standing journals in the field of technology, innovation, and engineering management. Renamed in 1963, IEEE TEM has evolved from its earlier forms as the <italic>Transactions of the IRE Professional Group on Engineering Management</i> (1954) and the <italic>IRE Transactions on Engineering Management</i> (1955–1962). Drawing on data from WoS and Scopus (1954–2024), the study examines publication and citation trends, leading authors, institutions, and countries/regions, as well as the journal’s knowledge structure and thematic evolution. Standard bibliometric techniques—including co-citation, bibliographic coupling, and keyword co-occurrence (VOSviewer; Bibliometrix)—were employed to uncover intellectual foundations, collaborative networks, and emerging research frontiers. Findings highlight the journal’s strong international orientation, increasing interdisciplinarity across business management, industrial engineering, computer science, and information systems, and a marked surge in activity since 2020. Analyses of influential documents, institutions, and topic clusters reveal IEEE TEM’s central role in advancing research on innovation, project management, digital transformation, sustainability, and supply chain systems. This article summarizes IEEE TEM’s historical development and current standing and distills evidence-based implications from the reported indicators and maps.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2994-3010"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828775","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}

引用次数: 0

Transformative Learning Through Organizational Learning Theory in the Gen-AI Landscape: Challenges and Capacities for Organizational Evolution 人工智能时代组织学习理论的变革学习：组织进化的挑战与能力

IF 5.2 3区管理学

IEEE Transactions on Engineering Management Pub Date : 2026-01-01 Epub Date: 2026-04-23 DOI: 10.1109/TEM.2026.3683981

Umair Tanveer;Thinh Gia Hoang;Shamaila Ishaq;Jing Dai

{"title":"Transformative Learning Through Organizational Learning Theory in the Gen-AI Landscape: Challenges and Capacities for Organizational Evolution","authors":"Umair Tanveer;Thinh Gia Hoang;Shamaila Ishaq;Jing Dai","doi":"10.1109/TEM.2026.3683981","DOIUrl":"https://doi.org/10.1109/TEM.2026.3683981","url":null,"abstract":"Generative artificial intelligence (Gen-AI) marks a transformative phase for organizations, creating both major opportunities and challenges. Anchored in organisational learning theory, this study examines how organizations adopt Gen-AI by unpacking the related challenges, learning processes, and required capabilities. Using a multiple-case study design with semistructured interviews across four pioneering organizations, the research highlights the importance of adaptability, rapid decision-making, risk tolerance, and a strong learning culture in shaping organizational learning during Gen-AI adoption. The findings show that Gen-AI creates distinctive learning demands compared with earlier technologies. Its cognitive reasoning and adaptive functionalities require organizations to engage in iterative, continuous learning to integrate these tools into both strategic and operational systems. The study offers practical guidance for organizations seeking to overcome adoption challenges, strengthen learning cultures, and realize Gen-AI's transformative potential. It also contributes to the broader Gen-AI discourse by providing a theoretically grounded and empirically supported roadmap for managing AI integration, including strategies such as iterative feedback loops, risk-tolerant decision-making, and embedding ethical considerations in AI governance.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2939-2949"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796038","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}

引用次数: 0