Artificial intelligence (AI) is transforming how consumers search, interpret, and apply information, presenting both opportunities and challenges for engineering management in designing, managing, and using AI-enabled search systems. Despite substantial research, studies on AI-enabled consumer information search remain fragmented, providing limited integration of insights across technological, behavioral, and managerial perspectives. To address this, we conduct a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol and grounded theory, identifying four thematic areas: Information Search and AI recommendation, AI conversation, AI anthropomorphism, and AI retrieval, which are structured into a search-theme mapping framework to organize themes across the consumer search stages. The findings under each theme are synthesized to reveal research gaps, derive future research propositions, and highlight managerial implications. This synthesis shows how engineering managers can apply AI recommendation strategies, message framing, anthropomorphic design, modality, cognitive load, and generative AI timing to optimize consumer search, decision-making, and engagement. Overall, the study synthesizes fragmented knowledge, advances theoretical understanding, and provides a structured foundation for guiding future research and managerial practice in engineering management.
{"title":"Artificial Intelligence and Consumer Information Search: A Review, Synthesis, and Propositions","authors":"Sivaranjan Murugesan;Bharadhwaj Sivakumaran;Piyush Sharma;Laxminarayanan Ganesan","doi":"10.1109/TEM.2025.3648018","DOIUrl":"https://doi.org/10.1109/TEM.2025.3648018","url":null,"abstract":"Artificial intelligence (AI) is transforming how consumers search, interpret, and apply information, presenting both opportunities and challenges for engineering management in designing, managing, and using AI-enabled search systems. Despite substantial research, studies on AI-enabled consumer information search remain fragmented, providing limited integration of insights across technological, behavioral, and managerial perspectives. To address this, we conduct a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol and grounded theory, identifying four thematic areas: Information Search and AI recommendation, AI conversation, AI anthropomorphism, and AI retrieval, which are structured into a search-theme mapping framework to organize themes across the consumer search stages. The findings under each theme are synthesized to reveal research gaps, derive future research propositions, and highlight managerial implications. This synthesis shows how engineering managers can apply AI recommendation strategies, message framing, anthropomorphic design, modality, cognitive load, and generative AI timing to optimize consumer search, decision-making, and engagement. Overall, the study synthesizes fragmented knowledge, advances theoretical understanding, and provides a structured foundation for guiding future research and managerial practice in engineering management.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1100-1115"},"PeriodicalIF":5.2,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982173","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 : 2025-12-25DOI: 10.1109/TEM.2025.3648352
Renzhi Gao;Shujie Liu;Xiangwei Kong;Gang Chen
Image analysis provides richer, more intuitive, and easily understandable insights, making it an increasingly indispensable tool in business. The exponential growth of image data has inspired management researchers to uncover the stories from image analysis. This study conducts a review of advanced management literature to examine how image analysis empowers managerial decisions. Drawing on information processing theory, we discuss the process of addressing management problems through image analysis from image perception, image encoding, and image-based decision-making. We reveal that image analysis provides novel perspectives, innovative methods, and emerging challenges for management, while also highlighting current research gaps in the application of image analysis within the management field. This study provides foundational guidelines for future explorations and innovations in image analysis for managerial decisions.
{"title":"Every Picture Tells a Managerial Story: A Review on Image Analysis Empowering Managerial Decisions","authors":"Renzhi Gao;Shujie Liu;Xiangwei Kong;Gang Chen","doi":"10.1109/TEM.2025.3648352","DOIUrl":"https://doi.org/10.1109/TEM.2025.3648352","url":null,"abstract":"Image analysis provides richer, more intuitive, and easily understandable insights, making it an increasingly indispensable tool in business. The exponential growth of image data has inspired management researchers to uncover the stories from image analysis. This study conducts a review of advanced management literature to examine how image analysis empowers managerial decisions. Drawing on information processing theory, we discuss the process of addressing management problems through image analysis from image perception, image encoding, and image-based decision-making. We reveal that image analysis provides novel perspectives, innovative methods, and emerging challenges for management, while also highlighting current research gaps in the application of image analysis within the management field. This study provides foundational guidelines for future explorations and innovations in image analysis for managerial decisions.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1132-1146"},"PeriodicalIF":5.2,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982169","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 : 2025-12-23DOI: 10.1109/TEM.2025.3647087
Yue Chen;Xuting Sun
A supply chain system comprises both upstream and downstre am members, and in many contexts, a final product assembly (FPA) step is required before products reach the market. Deciding whether and when this FPA step should be undertaken by upstream or downstream supply chain members has become a practical and timely challenge, particularly as 3-D printing technologies now enable and support flexible FPA processes. In this article, we tackle this critical question by developing analytical models grounded in game theory. In our basic model, we consider a supply chain with a single supplier and a single retailer selling a seasonal product. The FPA step incurs costs that vary depending on which supply chain agent performs the task. Our analysis reveals that, from the perspective of the entire supply chain system, the optimal assignment of FPA responsibility is uniquely determined by the respective FPA costs of the members. To achieve this optimal supply chain outcome, we need to identify the best-suited member to undertake the FPA process and coordinate the supply chain accordingly. To this end, we propose the use of a buyback contract and analytically derive its contract bounds. To generate further insights and demonstrate the robustness of our results, we extend our analysis to cover two additional scenarios: markets with two products and contexts where the retailer offers make-to-order services for FPA. Our findings confirm that the core results from the baseline model remain valid while also yielding several new insights. We believe that the results of this study not only make substantial contributions to the literature on supply chain systems but also offer actionable guidance to practitioners and systems’ engineers on how to optimally decide which agent should assume the FPA task within supply chain systems.
{"title":"Strategic Decisions for Final Product Assembly in 3-D Printing Supply Chains: A Supply Chain Positioning Perspective","authors":"Yue Chen;Xuting Sun","doi":"10.1109/TEM.2025.3647087","DOIUrl":"https://doi.org/10.1109/TEM.2025.3647087","url":null,"abstract":"A supply chain system comprises both upstream and downstre am members, and in many contexts, a final product assembly (FPA) step is required before products reach the market. Deciding whether and when this FPA step should be undertaken by upstream or downstream supply chain members has become a practical and timely challenge, particularly as 3-D printing technologies now enable and support flexible FPA processes. In this article, we tackle this critical question by developing analytical models grounded in game theory. In our basic model, we consider a supply chain with a single supplier and a single retailer selling a seasonal product. The FPA step incurs costs that vary depending on which supply chain agent performs the task. Our analysis reveals that, from the perspective of the entire supply chain system, the optimal assignment of FPA responsibility is uniquely determined by the respective FPA costs of the members. To achieve this optimal supply chain outcome, we need to identify the best-suited member to undertake the FPA process and coordinate the supply chain accordingly. To this end, we propose the use of a buyback contract and analytically derive its contract bounds. To generate further insights and demonstrate the robustness of our results, we extend our analysis to cover two additional scenarios: markets with two products and contexts where the retailer offers make-to-order services for FPA. Our findings confirm that the core results from the baseline model remain valid while also yielding several new insights. We believe that the results of this study not only make substantial contributions to the literature on supply chain systems but also offer actionable guidance to practitioners and systems’ engineers on how to optimally decide which agent should assume the FPA task within supply chain systems.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1331-1347"},"PeriodicalIF":5.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026458","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 : 2025-12-23DOI: 10.1109/TEM.2025.3647474
Mengchu Li;Xu Guan;Ruixiao Dong
When placing product promotion in contents, creators face the challenge of balancing the limited resources between creating high-quality content and integrating advertising seamlessly to mitigate advertising disutility. We conceptualize a creator on a user-generated content platform who maximizes both traffic-based revenue and commissions from selling products. Our study investigates how the platform leverages content incentives to influence the creator’s devotion to content creation as well as viewers’ engagement. Our modeling analysis reveals several interesting findings. We observe that the platform provides high incentives when content quality is low, but reduces them once quality exceeds a certain threshold. Correspondingly, the creator first devotes full effort to content creation and then shifts more effort to advertising integration. Our results suggest that an increase in the creator’s efficiency in mitigating advertising disutility or the commission reduces the creator’s motivation in content creation, ultimately lowering the platform’s profit and consumer surplus. However, the creator does not always benefit from these improvements. In fact, the profit can diminish if the creator’s efficiency in advertising integration or the commission becomes high enough. Furthermore, we demonstrate the robustness of our insights under several key model extensions, including platform sales commissions and asymmetric cost structure.
{"title":"Designing Platform Incentives for Balancing Content Creation and Advertising Integration","authors":"Mengchu Li;Xu Guan;Ruixiao Dong","doi":"10.1109/TEM.2025.3647474","DOIUrl":"https://doi.org/10.1109/TEM.2025.3647474","url":null,"abstract":"When placing product promotion in contents, creators face the challenge of balancing the limited resources between creating high-quality content and integrating advertising seamlessly to mitigate advertising disutility. We conceptualize a creator on a user-generated content platform who maximizes both traffic-based revenue and commissions from selling products. Our study investigates how the platform leverages content incentives to influence the creator’s devotion to content creation as well as viewers’ engagement. Our modeling analysis reveals several interesting findings. We observe that the platform provides high incentives when content quality is low, but reduces them once quality exceeds a certain threshold. Correspondingly, the creator first devotes full effort to content creation and then shifts more effort to advertising integration. Our results suggest that an increase in the creator’s efficiency in mitigating advertising disutility or the commission reduces the creator’s motivation in content creation, ultimately lowering the platform’s profit and consumer surplus. However, the creator does not always benefit from these improvements. In fact, the profit can diminish if the creator’s efficiency in advertising integration or the commission becomes high enough. Furthermore, we demonstrate the robustness of our insights under several key model extensions, including platform sales commissions and asymmetric cost structure.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1316-1330"},"PeriodicalIF":5.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026494","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 : 2025-12-23DOI: 10.1109/TEM.2025.3647213
Minghao Zhu;Chen Liang;Peter K. C. Lee;Andy C. L. Yeung;Honggeng Zhou
In the face of escalating environmental challenges and growing regulatory and stakeholder pressures, improving firms’ environmental performance has become an essential strategic objective. Digital technologies (DTs) are increasingly viewed as transformative tools that can support firms in achieving sustainability goals. However, despite growing interest, existing empirical evidence on the impact of DTs deployment on environmental performance remains fragmented and inconclusive. Moreover, limited empirical research has examined how DTs interact with human-centered production systems, such as lean management, to shape environmental outcomes. Addressing these gaps, this study draws on the natural-resource-based view to investigate whether and how DTs deployment enhances firms’ environmental performance, and how this relationship is moderated by lean production and environmental leadership. Using longitudinal data from publicly listed firms in China, our analysis reveals that DTs deployment has a significant positive effect on environmental performance, and this effect is amplified in firms exhibiting higher levels of lean production and environmental leadership. These findings remain robust across various estimation strategies, including alternative variable specifications, instrumental variable methods, Heckman two-step correction, and a quasi-natural experiment. By providing large-scale empirical evidence on the environmental implications of digital transformation and its interaction with lean practices, this study contributes to the emerging literature on Industry 4.0 and sustainable operations, offering actionable insights for managers and policymakers committed to green transition.
{"title":"Does Digital Technologies Deployment Promote Environmental Performance? Evidence From China","authors":"Minghao Zhu;Chen Liang;Peter K. C. Lee;Andy C. L. Yeung;Honggeng Zhou","doi":"10.1109/TEM.2025.3647213","DOIUrl":"https://doi.org/10.1109/TEM.2025.3647213","url":null,"abstract":"In the face of escalating environmental challenges and growing regulatory and stakeholder pressures, improving firms’ environmental performance has become an essential strategic objective. Digital technologies (DTs) are increasingly viewed as transformative tools that can support firms in achieving sustainability goals. However, despite growing interest, existing empirical evidence on the impact of DTs deployment on environmental performance remains fragmented and inconclusive. Moreover, limited empirical research has examined how DTs interact with human-centered production systems, such as lean management, to shape environmental outcomes. Addressing these gaps, this study draws on the natural-resource-based view to investigate whether and how DTs deployment enhances firms’ environmental performance, and how this relationship is moderated by lean production and environmental leadership. Using longitudinal data from publicly listed firms in China, our analysis reveals that DTs deployment has a significant positive effect on environmental performance, and this effect is amplified in firms exhibiting higher levels of lean production and environmental leadership. These findings remain robust across various estimation strategies, including alternative variable specifications, instrumental variable methods, Heckman two-step correction, and a quasi-natural experiment. By providing large-scale empirical evidence on the environmental implications of digital transformation and its interaction with lean practices, this study contributes to the emerging literature on Industry 4.0 and sustainable operations, offering actionable insights for managers and policymakers committed to green transition.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"712-725"},"PeriodicalIF":5.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886620","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 : 2025-12-22DOI: 10.1109/TEM.2025.3646635
Jack Adams;Ozgur Dedehayir;Saku J. Mäkinen;J. Roland Ortt
The ability of innovation ecosystems to deliver desired economic output, particularly under conditions of uncertainty shaped by market shifts, competitive change, and regulatory pressure, concerns all ecosystem stakeholders. Understanding innovation ecosystem performance, therefore, emerges as an important topic for scholars, managers, and policymakers. The objective of this article is to propose a conceptual framework of ecosystem performance that builds on the inherent connection between system-level outcomes and the performance of all components that constitute the ecosystem. To this end, we apply a socio-technical lens to identify performance-deficient social or technical components known as “reverse salient” that influence the performance of the ecosystem as a whole. Our case study of a regional Australian food innovation ecosystem identifies numerous reverse salients that inhibit ecosystem performance as the system transitions from its current focus on high-quality produce to a future state characterized by increased output capacity and value-added offerings. We categorize these reverse salients as those associated with “actors” in the ecosystem, “connections” between actors, and “resources” flowing among them. While these categories align with the ecosystem-as-structure perspective, our findings additionally underscore the moderating role of ecosystem “leadership” and “rules of engagement” that can themselves act as reverse salients when misaligned. We present a conceptual model that integrates these insights and offer a set of propositions to guide future empirical research.
{"title":"Toward a Framework of Innovation Ecosystem Performance: A Case Study","authors":"Jack Adams;Ozgur Dedehayir;Saku J. Mäkinen;J. Roland Ortt","doi":"10.1109/TEM.2025.3646635","DOIUrl":"https://doi.org/10.1109/TEM.2025.3646635","url":null,"abstract":"The ability of innovation ecosystems to deliver desired economic output, particularly under conditions of uncertainty shaped by market shifts, competitive change, and regulatory pressure, concerns all ecosystem stakeholders. Understanding innovation ecosystem performance, therefore, emerges as an important topic for scholars, managers, and policymakers. The objective of this article is to propose a conceptual framework of ecosystem performance that builds on the inherent connection between system-level outcomes and the performance of all components that constitute the ecosystem. To this end, we apply a socio-technical lens to identify performance-deficient social or technical components known as “reverse salient” that influence the performance of the ecosystem as a whole. Our case study of a regional Australian food innovation ecosystem identifies numerous reverse salients that inhibit ecosystem performance as the system transitions from its current focus on high-quality produce to a future state characterized by increased output capacity and value-added offerings. We categorize these reverse salients as those associated with “actors” in the ecosystem, “connections” between actors, and “resources” flowing among them. While these categories align with the ecosystem-as-structure perspective, our findings additionally underscore the moderating role of ecosystem “leadership” and “rules of engagement” that can themselves act as reverse salients when misaligned. We present a conceptual model that integrates these insights and offer a set of propositions to guide future empirical research.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"740-752"},"PeriodicalIF":5.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886599","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 : 2025-12-19DOI: 10.1109/TEM.2025.3646221
Meiting Lin;Hugo K. S. Lam
Artificial intelligence (AI) has been increasingly adopted by firms for different organizational purposes. While such AI adoption is expected to affect firm performance, it may also have different effects on firms’ stakeholders, such as employees, shareholders, and customers. To provide a more comprehensive understanding of the stakeholder implications of AI adoption, we conduct a systematic review of 84 relevant papers published in business journals over the past eight years (2017–2024). Our review suggests that firms’ AI adoption does have different, sometimes conflicting, effects on different stakeholder groups. We also uncover several limitations of the extant literature and develop an integrative AI Impacts Multiple Stakeholders (AIMS) framework that summarizes important directions for future research. Our AIMS framework emphasizes the need to consider underexplored stakeholder groups, such as suppliers and competitors, as well as the trade-offs and dynamics among different stakeholder groups induced by AI adoption. Our framework also encourages future research to move beyond examining the direct impact of AI adoption on stakeholders by investigating when (contextual factors) and why (underlying mechanisms) AI adoption affects stakeholders, thereby advancing the literature on AI–stakeholder relationships. Finally, we discuss the implications for future operations management research, encouraging scholars to adopt a supply chain perspective to study the stakeholder implications of firms’ AI adoption.
{"title":"How Does Firms’ Artificial Intelligence Adoption Affect Different Stakeholders? A Systematic Review and the AIMS Framework","authors":"Meiting Lin;Hugo K. S. Lam","doi":"10.1109/TEM.2025.3646221","DOIUrl":"https://doi.org/10.1109/TEM.2025.3646221","url":null,"abstract":"Artificial intelligence (AI) has been increasingly adopted by firms for different organizational purposes. While such AI adoption is expected to affect firm performance, it may also have different effects on firms’ stakeholders, such as employees, shareholders, and customers. To provide a more comprehensive understanding of the stakeholder implications of AI adoption, we conduct a systematic review of 84 relevant papers published in business journals over the past eight years (2017–2024). Our review suggests that firms’ AI adoption does have different, sometimes conflicting, effects on different stakeholder groups. We also uncover several limitations of the extant literature and develop an integrative AI Impacts Multiple Stakeholders (AIMS) framework that summarizes important directions for future research. Our AIMS framework emphasizes the need to consider underexplored stakeholder groups, such as suppliers and competitors, as well as the trade-offs and dynamics among different stakeholder groups induced by AI adoption. Our framework also encourages future research to move beyond examining the direct impact of AI adoption on stakeholders by investigating when (contextual factors) and why (underlying mechanisms) AI adoption affects stakeholders, thereby advancing the literature on AI–stakeholder relationships. Finally, we discuss the implications for future operations management research, encouraging scholars to adopt a supply chain perspective to study the stakeholder implications of firms’ AI adoption.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"832-846"},"PeriodicalIF":5.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929430","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 : 2025-12-19DOI: 10.1109/TEM.2025.3646488
Tana Siqin;Qian Zhao;Song-Man Wu
Sustainable supply chain operations that emphasize environmental and social welfare have drawn significant attention. Supply chain members and governments are increasingly implementing incentive strategies to promote sustainability. This article develops a game-theoretical model of a sustainable supply chain to examine the effectiveness of cooperative advertising under different government subsidy schemes. We consider that the supply chain consists of a green manufacturer and a retailer who sells a green product through advertising. The manufacturer can share advertising costs with the retailer, a strategy known as cooperative advertising, while the government can offer either the advertising subsidy or the consumption subsidy to support sustainability. Our analytical findings uncover that cooperative advertising may not always benefit the sustainable supply chain. Without the government subsidy, cooperative advertising is beneficial to the manufacturer, consumers, and the government only when the negative effect of cooperative advertising is mild; however, it is always detrimental to the retailer. In contrast, when a government subsidy is provided, not engaging in cooperative advertising is always superior. On the other hand, we find that government subsidies are always effective in facilitating sustainable supply chain operations, with the consumption subsidy outperforming the advertising subsidy. We further extend the model to explore the scenarios under 1) the manufacturer encroachment and 2) a marginal advertising cost. We find the major findings remain valid in two extensions. This study not only contributes to the existing literature on sustainable supply chain management, but also offers practical insights for firms and governments to design incentive mechanisms.
{"title":"Cooperative Advertising in a Sustainable Supply Chain: The Role of Government Subsidies","authors":"Tana Siqin;Qian Zhao;Song-Man Wu","doi":"10.1109/TEM.2025.3646488","DOIUrl":"https://doi.org/10.1109/TEM.2025.3646488","url":null,"abstract":"Sustainable supply chain operations that emphasize environmental and social welfare have drawn significant attention. Supply chain members and governments are increasingly implementing incentive strategies to promote sustainability. This article develops a game-theoretical model of a sustainable supply chain to examine the effectiveness of cooperative advertising under different government subsidy schemes. We consider that the supply chain consists of a green manufacturer and a retailer who sells a green product through advertising. The manufacturer can share advertising costs with the retailer, a strategy known as cooperative advertising, while the government can offer either the advertising subsidy or the consumption subsidy to support sustainability. Our analytical findings uncover that cooperative advertising may not always benefit the sustainable supply chain. Without the government subsidy, cooperative advertising is beneficial to the manufacturer, consumers, and the government only when the negative effect of cooperative advertising is mild; however, it is always detrimental to the retailer. In contrast, when a government subsidy is provided, not engaging in cooperative advertising is always superior. On the other hand, we find that government subsidies are always effective in facilitating sustainable supply chain operations, with the consumption subsidy outperforming the advertising subsidy. We further extend the model to explore the scenarios under 1) the manufacturer encroachment and 2) a marginal advertising cost. We find the major findings remain valid in two extensions. This study not only contributes to the existing literature on sustainable supply chain management, but also offers practical insights for firms and governments to design incentive mechanisms.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1194-1209"},"PeriodicalIF":5.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026526","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 : 2025-12-10DOI: 10.1109/TEM.2025.3642877
Dar-Zen Chen;Hsu-Chuan Chang;Mu-Hsuan Huang;Chung-Huei Kuan;Chun-Chieh Wang
This study examines how public funding influences research and development (R&D) outcomes by analyzing the performance of government-interest (GI) patents—those that explicitly acknowledge government support—relative to non-GI patents. Using a comprehensive patentometric assessment of U.S. patent data, the study challenges the conventional belief that government-backed patents inherently yield superior results. While GI patents tend to emphasize foundational and publicly aligned research, non-GI patents often outperform them in terms of citation influence and technological impact, particularly in market-driven contexts. To interpret these patterns, the study introduces a quadrant-based framework grounded in four complementary theories: the triple helix model, Pasteur’s quadrant, organizational learning theory, and resource dependence theory. This framework supports a strategic understanding of how different recipients utilize government support, highlighting patterns of efficient resource use, latent potential, and underperformance. The findings suggest that public funding alone does not guarantee high-impact innovation; rather, success depends on how effectively recipient organizations align funding strategies with their internal capabilities and long-term goals. The study underscores the importance of differentiated funding approaches and improved organizational absorptive capacity. By incorporating metrics such as patent citations, concentration indices, and temporal performance, the research offers a multidimensional view of the effectiveness of public R&D investment. The results provide actionable guidance for both funders and recipients in shaping policies and strategies that maximize the societal and economic returns on public R&D investments.
{"title":"Does It Matter Where the Funds Go? A Sectoral Analysis of U.S. Government-Interest Patents","authors":"Dar-Zen Chen;Hsu-Chuan Chang;Mu-Hsuan Huang;Chung-Huei Kuan;Chun-Chieh Wang","doi":"10.1109/TEM.2025.3642877","DOIUrl":"https://doi.org/10.1109/TEM.2025.3642877","url":null,"abstract":"This study examines how public funding influences research and development (R&D) outcomes by analyzing the performance of government-interest (GI) patents—those that explicitly acknowledge government support—relative to non-GI patents. Using a comprehensive patentometric assessment of U.S. patent data, the study challenges the conventional belief that government-backed patents inherently yield superior results. While GI patents tend to emphasize foundational and publicly aligned research, non-GI patents often outperform them in terms of citation influence and technological impact, particularly in market-driven contexts. To interpret these patterns, the study introduces a quadrant-based framework grounded in four complementary theories: the triple helix model, Pasteur’s quadrant, organizational learning theory, and resource dependence theory. This framework supports a strategic understanding of how different recipients utilize government support, highlighting patterns of efficient resource use, latent potential, and underperformance. The findings suggest that public funding alone does not guarantee high-impact innovation; rather, success depends on how effectively recipient organizations align funding strategies with their internal capabilities and long-term goals. The study underscores the importance of differentiated funding approaches and improved organizational absorptive capacity. By incorporating metrics such as patent citations, concentration indices, and temporal performance, the research offers a multidimensional view of the effectiveness of public R&D investment. The results provide actionable guidance for both funders and recipients in shaping policies and strategies that maximize the societal and economic returns on public R&D investments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"726-739"},"PeriodicalIF":5.2,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886596","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}
This article presents a data-driven framework for policy-oriented benchmarking and catalyzation of innovation productivity across 65 U.S. metropolitan areas. The study achieves a high level of research rigor by integrating multiple complementary analytical modules and systematically validating results through robust statistical and diagnostic tests. Specifically, the methodological design synthesizes three components: First, a multifeature selection pipeline that combines random forest, select K-best, and recursive feature elimination to ensure statistically reliable identification of innovation determinants; second, kernel principal component analysis with parameterized kernel functions optimized to capture complex nonlinear interdependencies among innovation factors; and third, a particle swarm optimization-enhanced gradient boosting machine that delivers exceptional predictive accuracy ($R^{2}$ = 0.984, RMSE = 358.0) while demonstrating minimal overfitting ($R^{2}$ differential between training and testing = 0.016). Rigor is further reinforced through systematic residual analysis and comprehensive sensitivity analysis, which together provide robust validation of the framework's reliability. These diagnostics reveal significant regional disparities in innovation performance relative to model predictions, with striking counterintuitive results. Several metropolitan areas substantially outperform expectations through strategic ecosystem alignment and policy coherence, while others exhibit considerable innovation deficits despite apparent structural advantages. Sensitivity analysis identifies STEM education infrastructure as the most influential driver of innovation, challenging conventional policy assumptions. The empirically validated framework equips engineering managers and policymakers with actionable, quantitative insights for designing targeted interventions, allocating resources effectively, and transforming underperforming regions into resilient innovation ecosystems through evidence-based strategies.
{"title":"A Predictive Analytics Framework for Policy-Driven Benchmarking and Promotion of Innovation Productivity in U.S. Cities","authors":"Inam Ullah Khan;Khaled Abdelghany;Terrance Pohlen;Gautam Das;Eric Griffin;Victor Fishman","doi":"10.1109/TEM.2025.3642738","DOIUrl":"https://doi.org/10.1109/TEM.2025.3642738","url":null,"abstract":"This article presents a data-driven framework for policy-oriented benchmarking and catalyzation of innovation productivity across 65 U.S. metropolitan areas. The study achieves a high level of research rigor by integrating multiple complementary analytical modules and systematically validating results through robust statistical and diagnostic tests. Specifically, the methodological design synthesizes three components: First, a multifeature selection pipeline that combines random forest, select K-best, and recursive feature elimination to ensure statistically reliable identification of innovation determinants; second, kernel principal component analysis with parameterized kernel functions optimized to capture complex nonlinear interdependencies among innovation factors; and third, a particle swarm optimization-enhanced gradient boosting machine that delivers exceptional predictive accuracy (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> = 0.984, RMSE = 358.0) while demonstrating minimal overfitting (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> differential between training and testing = 0.016). Rigor is further reinforced through systematic residual analysis and comprehensive sensitivity analysis, which together provide robust validation of the framework's reliability. These diagnostics reveal significant regional disparities in innovation performance relative to model predictions, with striking counterintuitive results. Several metropolitan areas substantially outperform expectations through strategic ecosystem alignment and policy coherence, while others exhibit considerable innovation deficits despite apparent structural advantages. Sensitivity analysis identifies STEM education infrastructure as the most influential driver of innovation, challenging conventional policy assumptions. The empirically validated framework equips engineering managers and policymakers with actionable, quantitative insights for designing targeted interventions, allocating resources effectively, and transforming underperforming regions into resilient innovation ecosystems through evidence-based strategies.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"753-765"},"PeriodicalIF":5.2,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11296910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886557","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}
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