Pub Date : 2026-01-16DOI: 10.1109/TEM.2026.3654537
Juanjuan Qin;Yanan Wang;Ziping Wang
This study investigate a multilevel supply chain subject to cap-and-trade regulation, involving a capital-constrained supplier, a manufacturer, and a retailer. Blockchain technology enhances trust among core firms, cross-level firms, and banks, thereby enabling financing for cross-level firms, and strengthening consumer trust in low-carbon products. This study explores the advance payment without blockchain technology ($AN$ mode), and two blockchain-enabled financing modes: advance payment with cross-level financing ($ACB$ mode) and advance payment with bank financing ($ABB$ mode). First, both $ABB$ and $ACB$ modes reduce carbon emissions more than the $AN$ mode, while which mode achieves greater total supply chain abatement depends on the bank interest rate and carbon abatement efficiency. Blockchain-enabled financing benefits all supply chain participants when blockchain operation costs are low, achieving win–win–win outcomes compared with the $AN$ mode. These results remain robust even when carbon abatement efficiencies are heterogeneous. Second, when all supply chain participants adopt blockchain, an equilibrium emerges where the supplier, manufacturer, and retailer choose the same financing mode. Interestingly, when blockchain operation costs are low and carbon abatement efficiency is high, the $ACB$ mode enables all participants to attain higher profits. Finally, when the supplier’s initial capital and the retailer’s interest rate are either low or high, the $ACB$ mode is the supplier’s optimal choice under blockchain technology. These findings provide managerial guidance for strategically selecting financing modes and adopting blockchain technology. The reliability of the conclusions is verified through sensitivity analyses.
{"title":"Emission Reduction and Financing Decisions in Blockchain-Enabled Supply Chains Under Cross-Level Financing","authors":"Juanjuan Qin;Yanan Wang;Ziping Wang","doi":"10.1109/TEM.2026.3654537","DOIUrl":"https://doi.org/10.1109/TEM.2026.3654537","url":null,"abstract":"This study investigate a multilevel supply chain subject to cap-and-trade regulation, involving a capital-constrained supplier, a manufacturer, and a retailer. Blockchain technology enhances trust among core firms, cross-level firms, and banks, thereby enabling financing for cross-level firms, and strengthening consumer trust in low-carbon products. This study explores the advance payment without blockchain technology (<inline-formula><tex-math>$AN$</tex-math></inline-formula> mode), and two blockchain-enabled financing modes: advance payment with cross-level financing (<inline-formula><tex-math>$ACB$</tex-math></inline-formula> mode) and advance payment with bank financing (<inline-formula><tex-math>$ABB$</tex-math></inline-formula> mode). First, both <inline-formula><tex-math>$ABB$</tex-math></inline-formula> and <inline-formula><tex-math>$ACB$</tex-math></inline-formula> modes reduce carbon emissions more than the <inline-formula><tex-math>$AN$</tex-math></inline-formula> mode, while which mode achieves greater total supply chain abatement depends on the bank interest rate and carbon abatement efficiency. Blockchain-enabled financing benefits all supply chain participants when blockchain operation costs are low, achieving win–win–win outcomes compared with the <inline-formula><tex-math>$AN$</tex-math></inline-formula> mode. These results remain robust even when carbon abatement efficiencies are heterogeneous. Second, when all supply chain participants adopt blockchain, an equilibrium emerges where the supplier, manufacturer, and retailer choose the same financing mode. Interestingly, when blockchain operation costs are low and carbon abatement efficiency is high, the <inline-formula><tex-math>$ACB$</tex-math></inline-formula> mode enables all participants to attain higher profits. Finally, when the supplier’s initial capital and the retailer’s interest rate are either low or high, the <inline-formula><tex-math>$ACB$</tex-math></inline-formula> mode is the supplier’s optimal choice under blockchain technology. These findings provide managerial guidance for strategically selecting financing modes and adopting blockchain technology. The reliability of the conclusions is verified through sensitivity analyses.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1376-1389"},"PeriodicalIF":5.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082008","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}
Pay-per-click (PPC) advertising has become a dominant tool for e-tailers to increase product exposure and boost sales. This study investigates a multiproduct newsvendor-like problem that jointly optimizes advertising budget allocation and order quantity decisions. A key feature of the problem is the consideration of the spillover effect of PPC advertising, where the demand for a product depends not only on its own ad clicks, but also on those of related products. We introduce a linear demand function to capture this dependence and validate its predictive power using real-world data. The theoretical analyses reveal that the spillover effect leads to nontrivial interactions between advertising and ordering decisions, while also amplifying demand uncertainty and complicating the computation of the optimal solutions. To address these challenges, we consider two heuristic methods. The first follows the common practice of neglecting the spillover effect, while the second simplifies it by approximating random ad clicks with their mean value. Numerical experiments demonstrate that the first heuristic can lead to substantial performance loss (up to 28.72%), whereas the second yields near-optimal solutions with performance loss within 1%. We further conduct robustness tests with respect to ad-click distributions, market conditions, and ad-click uncertainty to evaluate the performances of both heuristic methods under different scenarios. A sensitivity analysis to assess the impact of changing key parameters (e.g., the coefficients of spillover effects and advertising budget) is also developed to provide more managerial insights.
{"title":"Spillover Effect Matters: A Multiproduct Newsvendor-Like Model With Pay-Per-Click Advertising","authors":"Yugang Yu;Hongyan Zhang;Zhao Cai;Ye Shi;Qitong Zhao","doi":"10.1109/TEM.2026.3651194","DOIUrl":"https://doi.org/10.1109/TEM.2026.3651194","url":null,"abstract":"Pay-per-click (PPC) advertising has become a dominant tool for e-tailers to increase product exposure and boost sales. This study investigates a multiproduct newsvendor-like problem that jointly optimizes advertising budget allocation and order quantity decisions. A key feature of the problem is the consideration of the spillover effect of PPC advertising, where the demand for a product depends not only on its own ad clicks, but also on those of related products. We introduce a linear demand function to capture this dependence and validate its predictive power using real-world data. The theoretical analyses reveal that the spillover effect leads to nontrivial interactions between advertising and ordering decisions, while also amplifying demand uncertainty and complicating the computation of the optimal solutions. To address these challenges, we consider two heuristic methods. The first follows the common practice of neglecting the spillover effect, while the second simplifies it by approximating random ad clicks with their mean value. Numerical experiments demonstrate that the first heuristic can lead to substantial performance loss (up to 28.72%), whereas the second yields near-optimal solutions with performance loss within 1%. We further conduct robustness tests with respect to ad-click distributions, market conditions, and ad-click uncertainty to evaluate the performances of both heuristic methods under different scenarios. A sensitivity analysis to assess the impact of changing key parameters (e.g., the coefficients of spillover effects and advertising budget) is also developed to provide more managerial insights.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1259-1273"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026355","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 : 2026-01-12DOI: 10.1109/TEM.2026.3651889
Melanie E. Kreye
This research investigates how the supply-chain configurations of service offerings in business-to-consumer (B2C) markets enable manufacturers to implement circular practices. We differentiate offerings by service complexity as relevant starting points for this purpose. Following best practices in methodological rigor, we provide empirical evidence from five cases in the household-appliances industry. We detail the respective supply chain (SC) configurations of service offerings and of circular practices. In addition, we identify three mechanisms by which the SC configurations of service offerings connect to the SC configurations of circular practices: complement, enable, and undermine. This research contributes to the debate on the connections between servitization and circularity in B2C markets. Specifically, we identify the supply-chain configuration of service-based business models and circular practices showing their downstream and upstream effects. This enabled us to identify the mechanisms connecting service offerings to circular practices via their respective supply-chain configurations.
{"title":"Implementing Circular Practices Through Supply-Chain Configurations of Service Offerings for Consumer Products","authors":"Melanie E. Kreye","doi":"10.1109/TEM.2026.3651889","DOIUrl":"https://doi.org/10.1109/TEM.2026.3651889","url":null,"abstract":"This research investigates how the supply-chain configurations of service offerings in business-to-consumer (B2C) markets enable manufacturers to implement circular practices. We differentiate offerings by service complexity as relevant starting points for this purpose. Following best practices in methodological rigor, we provide empirical evidence from five cases in the household-appliances industry. We detail the respective supply chain (SC) configurations of service offerings and of circular practices. In addition, we identify three mechanisms by which the SC configurations of service offerings connect to the SC configurations of circular practices: complement, enable, and undermine. This research contributes to the debate on the connections between servitization and circularity in B2C markets. Specifically, we identify the supply-chain configuration of service-based business models and circular practices showing their downstream and upstream effects. This enabled us to identify the mechanisms connecting service offerings to circular practices via their respective supply-chain configurations.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1224-1239"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026446","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 : 2026-01-12DOI: 10.1109/TEM.2025.3648578
Muhammad Usman;Nadia Zahoor;Erhan Boğan;Muhammad Waheed Akhtar;Bekir Dedeoğlu
Given the growing emphasis on business sustainability and environmental management aimed at protecting the natural environment, this study, drawing insights from organizational information processing theory, investigates the direct association of artificial intelligence-supported Big Data analytics (AI-BDA) with organizations’ green core competencies as well as the indirect association through green data-driven decision-making culture (GDDC). In addition, the role of leader conscientiousness as an important boundary condition is examined. Data collected from 339 managers is analyzed using structural equation modeling in Mplus (8.8). Our findings indicate that AI-BDA has both direct and indirect positive relationships with green core competencies. Moreover, leader conscientiousness moderated the direct impact of AI-BDA on GDDC. Supplementary semi-structured interviews with 12 senior managers provide contextual validation and illustrate how AI-BDA, green data-driven culture, and leader conscientiousness jointly shape green core competencies in practice. Our research provides actionable insights that empower organizations to develop green core competencies, thereby enhancing their impact on initiatives aimed at preserving the natural environment. By integrating technological capabilities with leadership traits, this study highlights a pathway for organizations to align digital transformation with environmental sustainability goals, thereby advancing their corporate responses to environmental challenges.
{"title":"Big Data Analytics and Green Core Competencies: Important Role of Data-Driven Decision-Making Culture and Leader Conscientiousness","authors":"Muhammad Usman;Nadia Zahoor;Erhan Boğan;Muhammad Waheed Akhtar;Bekir Dedeoğlu","doi":"10.1109/TEM.2025.3648578","DOIUrl":"https://doi.org/10.1109/TEM.2025.3648578","url":null,"abstract":"Given the growing emphasis on business sustainability and environmental management aimed at protecting the natural environment, this study, drawing insights from organizational information processing theory, investigates the direct association of artificial intelligence-supported Big Data analytics (AI-BDA) with organizations’ green core competencies as well as the indirect association through green data-driven decision-making culture (GDDC). In addition, the role of leader conscientiousness as an important boundary condition is examined. Data collected from 339 managers is analyzed using structural equation modeling in Mplus (8.8). Our findings indicate that AI-BDA has both direct and indirect positive relationships with green core competencies. Moreover, leader conscientiousness moderated the direct impact of AI-BDA on GDDC. Supplementary semi-structured interviews with 12 senior managers provide contextual validation and illustrate how AI-BDA, green data-driven culture, and leader conscientiousness jointly shape green core competencies in practice. Our research provides actionable insights that empower organizations to develop green core competencies, thereby enhancing their impact on initiatives aimed at preserving the natural environment. By integrating technological capabilities with leadership traits, this study highlights a pathway for organizations to align digital transformation with environmental sustainability goals, thereby advancing their corporate responses to environmental challenges.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1363-1375"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082075","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}
Blockchain has gained significant attention, but operations literature lacks an integrated framework capturing its adoption dynamics. This article addresses the gap through a multicase study, proposing a framework that examines drivers, enablers, resistors, mechanisms, and outcomes of blockchain adoption in supply chains. The process is segmented into three phases—preadoption, early adoption, and full adoption—across technological, organizational, and environmental dimensions. Findings show organizational and environmental factors dominate preadoption, while enablers and resistors shape early adoption. In full adoption, mechanisms such as resilience, trust, visibility, information sharing, and cost reduction deliver operational benefits. The article offers valuable insights for researchers and practitioners to better understand and implement blockchain in supply chain operations.
{"title":"A Framework for Understanding Blockchain Adoption in Supply Chain Operations: A Multicase Study","authors":"Alok Raj;Tsan-Ming Choi;Rajeev Ranjan Kumar;Shikha Aggarwal","doi":"10.1109/TEM.2026.3653443","DOIUrl":"https://doi.org/10.1109/TEM.2026.3653443","url":null,"abstract":"Blockchain has gained significant attention, but operations literature lacks an integrated framework capturing its adoption dynamics. This article addresses the gap through a multicase study, proposing a framework that examines drivers, enablers, resistors, mechanisms, and outcomes of blockchain adoption in supply chains. The process is segmented into three phases—preadoption, early adoption, and full adoption—across technological, organizational, and environmental dimensions. Findings show organizational and environmental factors dominate preadoption, while enablers and resistors shape early adoption. In full adoption, mechanisms such as resilience, trust, visibility, information sharing, and cost reduction deliver operational benefits. The article offers valuable insights for researchers and practitioners to better understand and implement blockchain in supply chain operations.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1390-1401"},"PeriodicalIF":5.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082143","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 : 2026-01-05DOI: 10.1109/TEM.2025.3650452
Jinzhao Shi;Kewen Jing;Xiaoping Xu;Li Zhou;Qiang Du;T. C. E. Cheng
In the green order financing (GOF), suppliers must first obtain “green certification” from core enterprises to secure preferential bank loans for eco-friendly orders. This paper examines the setting of entry threshold for GOF and proposes incentives to coordinate the GOF system. We study a GOF system consisting of a bank, a large retailer, and a capital-constrained supplier. The retailer, acting on behalf of the bank, sets the greenness entry threshold for the supplier, leveraging its informational advantages regarding the supplier’s environmental performance. We find that the supplier will opt for GOF only when the entry threshold set by the retailer is below a critical value; otherwise, it will continue using traditional order financing (TOF). In a Stackelberg game with the retailer as the leader, a unique equilibrium emerges under which GOF is implemented, leading to Pareto improvements and enhanced greenness. The bank also benefits from providing GOF, provided that it sets a reasonable interest rate—no lower than a certain threshold. If the rate falls below this threshold, external government incentives will be necessary. We demonstrate that the government will only refrain from subsidizing the bank if the supplier’s cost coefficient of green investment is extremely high while the bank’s GOF interest rate is exceedingly low. Otherwise, the subsidies will generate Pareto improvements for all three GOF members and achieve greater social welfare than with TOF. Finally, we conduct numerical studies and extend the analysis to new scenarios, further confirming the robustness of the results.
{"title":"Entry Threshold Setting and Incentive Design for Green Order Financing","authors":"Jinzhao Shi;Kewen Jing;Xiaoping Xu;Li Zhou;Qiang Du;T. C. E. Cheng","doi":"10.1109/TEM.2025.3650452","DOIUrl":"https://doi.org/10.1109/TEM.2025.3650452","url":null,"abstract":"In the green order financing (GOF), suppliers must first obtain “green certification” from core enterprises to secure preferential bank loans for eco-friendly orders. This paper examines the setting of entry threshold for GOF and proposes incentives to coordinate the GOF system. We study a GOF system consisting of a bank, a large retailer, and a capital-constrained supplier. The retailer, acting on behalf of the bank, sets the greenness entry threshold for the supplier, leveraging its informational advantages regarding the supplier’s environmental performance. We find that the supplier will opt for GOF only when the entry threshold set by the retailer is below a critical value; otherwise, it will continue using traditional order financing (TOF). In a Stackelberg game with the retailer as the leader, a unique equilibrium emerges under which GOF is implemented, leading to Pareto improvements and enhanced greenness. The bank also benefits from providing GOF, provided that it sets a reasonable interest rate—no lower than a certain threshold. If the rate falls below this threshold, external government incentives will be necessary. We demonstrate that the government will only refrain from subsidizing the bank if the supplier’s cost coefficient of green investment is extremely high while the bank’s GOF interest rate is exceedingly low. Otherwise, the subsidies will generate Pareto improvements for all three GOF members and achieve greater social welfare than with TOF. Finally, we conduct numerical studies and extend the analysis to new scenarios, further confirming the robustness of the results.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1240-1258"},"PeriodicalIF":5.2,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026408","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 : 2026-01-01DOI: 10.1109/TEM.2025.3649091
Muhammad Zia Ul Haq;Tahir Abbas Syed;Usman Akbar;Haris Aslam
This study explores the critical interplay between supply chain analytics (SCA), the Triple-A supply chain framework (agility, adaptability, and alignment), and supply chain resilience (SCR), addressing a significant gap in both theory and practice. While SCA has emerged as a transformative tool for managing disruptions through data-driven insights, its potential to foster resilience remains underexplored without the integration of dynamic organizational capabilities. Grounded in the dynamic capability’s theory, this research examines how SCA enhances Triple-A capabilities, which in turn drive SCR by enabling organizations to sense, respond to, and adapt to disruptions. Using a dual-study approach, our findings show that while alignment strongly predicts agility, it does not directly enhance adaptability, challenging conventional wisdom that greater alignment inherently strengthens both capabilities. These results suggest the need to reconsider linear assumptions about capability building in supply chains. Multiple analytical approaches and robustness checks, including alternative model specifications, different estimation techniques, and subgroup analyses, were performed to ensure research rigor. The consistent results across these analyses confirm the robustness and generalizability of the study’s findings. This study advances not only theoretical understanding by connecting SCA with the Triple-A supply chain and resilience but also provides actionable insights for practitioners to align investments in analytics with strategic organizational capabilities. By addressing these interconnections, this research contributes to bridging the gap between digital transformation and resilient supply chain practices, offering a robust framework for navigating uncertainty.
{"title":"Triple-A Supply Chains: The Bridge Between Supply Chain Analytics and Supply Chain Resilience","authors":"Muhammad Zia Ul Haq;Tahir Abbas Syed;Usman Akbar;Haris Aslam","doi":"10.1109/TEM.2025.3649091","DOIUrl":"https://doi.org/10.1109/TEM.2025.3649091","url":null,"abstract":"This study explores the critical interplay between supply chain analytics (SCA), the Triple-A supply chain framework (agility, adaptability, and alignment), and supply chain resilience (SCR), addressing a significant gap in both theory and practice. While SCA has emerged as a transformative tool for managing disruptions through data-driven insights, its potential to foster resilience remains underexplored without the integration of dynamic organizational capabilities. Grounded in the dynamic capability’s theory, this research examines how SCA enhances Triple-A capabilities, which in turn drive SCR by enabling organizations to sense, respond to, and adapt to disruptions. Using a dual-study approach, our findings show that while alignment strongly predicts agility, it does not directly enhance adaptability, challenging conventional wisdom that greater alignment inherently strengthens both capabilities. These results suggest the need to reconsider linear assumptions about capability building in supply chains. Multiple analytical approaches and robustness checks, including alternative model specifications, different estimation techniques, and subgroup analyses, were performed to ensure research rigor. The consistent results across these analyses confirm the robustness and generalizability of the study’s findings. This study advances not only theoretical understanding by connecting SCA with the Triple-A supply chain and resilience but also provides actionable insights for practitioners to align investments in analytics with strategic organizational capabilities. By addressing these interconnections, this research contributes to bridging the gap between digital transformation and resilient supply chain practices, offering a robust framework for navigating uncertainty.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1210-1223"},"PeriodicalIF":5.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026507","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}
Environmental, social, and governance (ESG) influences corporate financial performance (CFP), though the effectiveness varies notably across industry sectors. Employing multiple linear regression and multiperiod differences-in-differences (DID), this article empirically examines the differential impacts by comparing European chemical and software industries. Our framework distinguishes between actual ESG performance metrics and voluntary ESG disclosure, and reveals distinct pathways through which sustainability practices and reporting affect CFP outcomes. We find that actual ESG performance impacts CFP more in the chemical industry than in software. Conversely, ESG disclosure, even when not reflecting true performance, provides immediate and sustained market value benefits to software companies. Despite earlier and more comprehensive non-financial reporting by chemical companies, no significant financial effects emerged in the immediate four years. Multiple robustness tests are employed to address the potential selection problem inherent in economic observational data, and the model results are interpreted with due caution. The empirical findings further advance the understanding of ESG–CFP mechanisms by revealing the complex balance between regulatory compliance, sustainability investments, and financial outcomes.
{"title":"The Impact of Environmental, Social, and Governance on Corporate Financial Performance: A Cross-Industry Perspective","authors":"Wei Liu;Nicholas Dacre;Hao Dong;Jiuh-Biing Sheu;Qin Zhou","doi":"10.1109/TEM.2025.3649696","DOIUrl":"https://doi.org/10.1109/TEM.2025.3649696","url":null,"abstract":"Environmental, social, and governance (ESG) influences corporate financial performance (CFP), though the effectiveness varies notably across industry sectors. Employing multiple linear regression and multiperiod differences-in-differences (DID), this article empirically examines the differential impacts by comparing European chemical and software industries. Our framework distinguishes between actual ESG performance metrics and voluntary ESG disclosure, and reveals distinct pathways through which sustainability practices and reporting affect CFP outcomes. We find that actual ESG performance impacts CFP more in the chemical industry than in software. Conversely, ESG disclosure, even when not reflecting true performance, provides immediate and sustained market value benefits to software companies. Despite earlier and more comprehensive non-financial reporting by chemical companies, no significant financial effects emerged in the immediate four years. Multiple robustness tests are employed to address the potential selection problem inherent in economic observational data, and the model results are interpreted with due caution. The empirical findings further advance the understanding of ESG–CFP mechanisms by revealing the complex balance between regulatory compliance, sustainability investments, and financial outcomes.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1162-1175"},"PeriodicalIF":5.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982221","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 : 2025-12-30DOI: 10.1109/TEM.2025.3649531
Mirco Peron;Srinivas Talluri
Additive Manufacturing (AM) has been extensively studied for spare parts management. The literature concurs that producing AM spare parts close to the point of use (i.e., on-site) offers both economic and environmental advantages compared to off-site production, particularly when spare parts are produced through printing hubs, which is the current industrial practice. However, this might not always hold true: different countries have different energy costs and emit different amounts of CO2-eq to produce one kWh. Consequently, it may be preferable to produce spare parts in a country having low energy costs and/or depending less on fossil fuels, even if far from the point of use (i.e., off-site). Such decisions should also consider governmental carbon-reduction schemes (e.g., carbon taxes) whereby the more a firm emits, the more it pays. However, there is a lack of research encompassing all these aspects when deciding whether to produce AM spare parts on- or off-site. This work fills this gap, identifying if and when off-site production of AM spare parts is preferable from both economic and environmental perspectives. Contrary to claims in the literature, the results show that off-site production is sometimes preferable, particularly when its unitary energy cost is lower than that of on-site production. Interestingly, due to current low carbon tax values, the on-site/off-site production decision is driven only by economic considerations. Robustness checks confirmed the findings’ reliability. Notably, adopting off-site AM production, when advantageous, results in substantial savings, as demonstrated numerically and through a case study considering 6000+ spare parts.
{"title":"On-Site or Off-Site Additive Manufacturing Spare Parts Production? An Economic and Environmental Analysis Encompassing Country Carbon Tax Policy, Energy Mix, and Availability","authors":"Mirco Peron;Srinivas Talluri","doi":"10.1109/TEM.2025.3649531","DOIUrl":"https://doi.org/10.1109/TEM.2025.3649531","url":null,"abstract":"Additive Manufacturing (AM) has been extensively studied for spare parts management. The literature concurs that producing AM spare parts close to the point of use (i.e., on-site) offers both economic and environmental advantages compared to off-site production, particularly when spare parts are produced through printing hubs, which is the current industrial practice. However, this might not always hold true: different countries have different energy costs and emit different amounts of CO<sub>2-eq</sub> to produce one kWh. Consequently, it may be preferable to produce spare parts in a country having low energy costs and/or depending less on fossil fuels, even if far from the point of use (i.e., off-site). Such decisions should also consider governmental carbon-reduction schemes (e.g., carbon taxes) whereby the more a firm emits, the more it pays. However, there is a lack of research encompassing all these aspects when deciding whether to produce AM spare parts on- or off-site. This work fills this gap, identifying if and when off-site production of AM spare parts is preferable from both economic and environmental perspectives. Contrary to claims in the literature, the results show that off-site production is sometimes preferable, particularly when its unitary energy cost is lower than that of on-site production. Interestingly, due to current low carbon tax values, the on-site/off-site production decision is driven only by economic considerations. Robustness checks confirmed the findings’ reliability. Notably, adopting off-site AM production, when advantageous, results in substantial savings, as demonstrated numerically and through a case study considering 6000+ spare parts.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1176-1193"},"PeriodicalIF":5.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026444","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}
Accurate prediction of raw material prices helps enterprises optimize procurement, control costs, and enhance profits. Yet, the interplay of factors, such as supply and demand imbalances, market volatility, and abrupt disruptions, still poses a significant challenge. To address these challenges, in this article, we propose a novel hybrid framework for price prediction, called EFD-CBGT, that combines deep learning and large language model (LLM). First of all, we leverage LLMs internalized rich knowledge and contextual reasoning capabilities to generate high-quality textual data using interactive querying. The textual data are then converted into low-dimensional, high-value features using financial BERT and deep sparse autoencoder. Second, we employ the empirical wavelet transform to create a stationary numerical series from the most strongly correlated features of raw material prices. Finally, by incorporating a featurewise attention module, we use four deep learning models to extract local, bidirectional temporal, temporal, and global features. We conduct comparative experiments, ablation studies, significance tests, and robustness analyses on three real datasets from a leading lithium-ion battery manufacturer in China. The experimental results demonstrate the effectiveness and robustness of EFD-CBGT.
{"title":"A Novel Hybrid Price Prediction Method Using Multimodal Deep Learning and LLM for New Energy Power Raw Materials","authors":"Xuhui Zhu;Muzi Li;Pingfan Xia;Hao Lei;Zhanglin Peng","doi":"10.1109/TEM.2025.3649729","DOIUrl":"https://doi.org/10.1109/TEM.2025.3649729","url":null,"abstract":"Accurate prediction of raw material prices helps enterprises optimize procurement, control costs, and enhance profits. Yet, the interplay of factors, such as supply and demand imbalances, market volatility, and abrupt disruptions, still poses a significant challenge. To address these challenges, in this article, we propose a novel hybrid framework for price prediction, called EFD-CBGT, that combines deep learning and large language model (LLM). First of all, we leverage LLMs internalized rich knowledge and contextual reasoning capabilities to generate high-quality textual data using interactive querying. The textual data are then converted into low-dimensional, high-value features using financial BERT and deep sparse autoencoder. Second, we employ the empirical wavelet transform to create a stationary numerical series from the most strongly correlated features of raw material prices. Finally, by incorporating a featurewise attention module, we use four deep learning models to extract local, bidirectional temporal, temporal, and global features. We conduct comparative experiments, ablation studies, significance tests, and robustness analyses on three real datasets from a leading lithium-ion battery manufacturer in China. The experimental results demonstrate the effectiveness and robustness of EFD-CBGT.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1348-1362"},"PeriodicalIF":5.2,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082009","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}
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