This article investigates the role of digital technologies as circular economy enablers. We review extant literature to identify 15 digital technologies and characterize their enabling role along two dimensions: functions and circular product lifecycle. An exploratory empirical analysis has been conducted, leveraging a large sample of cases, to investigate the role of digital technologies in enabling circularity through those two dimensions. As a result, we develop our novel integrative framework that illustrates how each digital technology enables distinct functions along the product lifecycle. The integrative framework shows 131 examples of widely implemented best practices and provides a comprehensive vision of the functions that each digital technology enables along the whole product lifecycle. Among the others, we find that a broader set of digital technologies enables two functions, namely connect and optimize. We contribute to the academic debate in the nexus between circularity and digital technologies and we urge scholars to deploy the lens of the functions and a comprehensive perspective over the product lifecycle to fully understand the potentialities offered by digital technologies to achieve circularity. Our integrative framework represents a compass to managers striving to understand how to leverage digital technologies to implement circular economy.
{"title":"Digital Technologies as Enablers of Circular Economy: An Exploratory Analysis of Functions and Product Lifecycle","authors":"Lucrezia Sgambaro;Davide Chiaroni;Federico Frattini","doi":"10.1109/TEM.2025.3605250","DOIUrl":"https://doi.org/10.1109/TEM.2025.3605250","url":null,"abstract":"This article investigates the role of digital technologies as circular economy enablers. We review extant literature to identify 15 digital technologies and characterize their enabling role along two dimensions: functions and circular product lifecycle. An exploratory empirical analysis has been conducted, leveraging a large sample of cases, to investigate the role of digital technologies in enabling circularity through those two dimensions. As a result, we develop our novel integrative framework that illustrates how each digital technology enables distinct functions along the product lifecycle. The integrative framework shows 131 examples of widely implemented best practices and provides a comprehensive vision of the functions that each digital technology enables along the whole product lifecycle. Among the others, we find that a broader set of digital technologies enables two functions, namely <italic>connect</i> and <italic>optimize</i>. We contribute to the academic debate in the nexus between circularity and digital technologies and we urge scholars to deploy the lens of the functions and a comprehensive perspective over the product lifecycle to fully understand the potentialities offered by digital technologies to achieve circularity. Our integrative framework represents a compass to managers striving to understand how to leverage digital technologies to implement circular economy.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"4051-4066"},"PeriodicalIF":5.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210050","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 : 2025-09-01DOI: 10.1109/TEM.2025.3604850
Yu-Xin Feng;Bon-Gang Hwang
Various emerging technologies are attracting great attention and discussion these days, such as mixed reality headsets and ChatGPT. However, the associated risk perceptions remain underexplored, posing potential barriers to public acceptance and adoption. This study aims to systematically review the current research state on risk perception in the context of emerging technologies. A total of 403 articles were selected and analyzed to identify publication trends and key research themes. The analysis reveals a surge in scholarly interest from 2020 onward, with AI technology being the predominant focus of risk perception research. Key themes encompass the characteristics of technological risk perception, including its manifestations, determinants, and dimensions. It further examines the interaction between risk perception and emerging technologies. Based on the findings, research gaps and future research directions are proposed. This review advances the psychological understanding of emerging technologies by synthesizing fragmented research on risk perception. It will support the development of practical strategies for effective risk communication and responsible technology deployment.
{"title":"Charting the Unseen: A Systematic Review of Risk Perception in Emerging Technologies","authors":"Yu-Xin Feng;Bon-Gang Hwang","doi":"10.1109/TEM.2025.3604850","DOIUrl":"https://doi.org/10.1109/TEM.2025.3604850","url":null,"abstract":"Various emerging technologies are attracting great attention and discussion these days, such as mixed reality headsets and ChatGPT. However, the associated risk perceptions remain underexplored, posing potential barriers to public acceptance and adoption. This study aims to systematically review the current research state on risk perception in the context of emerging technologies. A total of 403 articles were selected and analyzed to identify publication trends and key research themes. The analysis reveals a surge in scholarly interest from 2020 onward, with AI technology being the predominant focus of risk perception research. Key themes encompass the characteristics of technological risk perception, including its manifestations, determinants, and dimensions. It further examines the interaction between risk perception and emerging technologies. Based on the findings, research gaps and future research directions are proposed. This review advances the psychological understanding of emerging technologies by synthesizing fragmented research on risk perception. It will support the development of practical strategies for effective risk communication and responsible technology deployment.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3832-3848"},"PeriodicalIF":5.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073435","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 : 2025-08-29DOI: 10.1109/TEM.2025.3604286
Siyu Li;Xing Shen;Lu Yang;Baofeng Huo
As products and services become increasingly complex, manufacturers need to communicate vision with their supply chain (SC) collaborators to effectively lead them and mobilize resources in SCs. This study proposes the concept of SC rich media vision communication, classifies it into internal, supplier, and customer rich media vision communication, and investigates their antecedents and performance consequences considering the moderating impacts of environmental dynamism. Using data collected from 200 Chinese manufacturers, we find that internal rich media vision communication positively affects supplier and customer rich media vision communication. All three types of rich media vision communication contribute to improved economic performance. Environmental dynamism strengthens the positive relationship between customer rich media vision communication and economic performance, but weakens the link between supplier rich media vision communication and economic performance. Additionally, long-term relationship orientation with supplier/customer enhances supplier/customer rich media vision communication, and team culture facilitates internal rich media vision communication. This research advances the understanding of communicating vision with SC collaborators by introducing the concept of SC rich media vision communication, identifying its internal and external drivers, evaluating its impact on economic performance, and clarifying its effect boundary.
{"title":"Painting a Rosy Picture? The Impact of Supply Chain Rich Media Vision Communication on Economic Performance: The Signaling Theory Perspective","authors":"Siyu Li;Xing Shen;Lu Yang;Baofeng Huo","doi":"10.1109/TEM.2025.3604286","DOIUrl":"https://doi.org/10.1109/TEM.2025.3604286","url":null,"abstract":"As products and services become increasingly complex, manufacturers need to communicate vision with their supply chain (SC) collaborators to effectively lead them and mobilize resources in SCs. This study proposes the concept of SC rich media vision communication, classifies it into internal, supplier, and customer rich media vision communication, and investigates their antecedents and performance consequences considering the moderating impacts of environmental dynamism. Using data collected from 200 Chinese manufacturers, we find that internal rich media vision communication positively affects supplier and customer rich media vision communication. All three types of rich media vision communication contribute to improved economic performance. Environmental dynamism strengthens the positive relationship between customer rich media vision communication and economic performance, but weakens the link between supplier rich media vision communication and economic performance. Additionally, long-term relationship orientation with supplier/customer enhances supplier/customer rich media vision communication, and team culture facilitates internal rich media vision communication. This research advances the understanding of communicating vision with SC collaborators by introducing the concept of SC rich media vision communication, identifying its internal and external drivers, evaluating its impact on economic performance, and clarifying its effect boundary.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3985-4005"},"PeriodicalIF":5.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210093","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 : 2025-08-29DOI: 10.1109/TEM.2025.3590299
Chandra S. Mishra
This study examines the relationship between firm innovativeness and acquisition frequency, considering the moderating roles of managerial ability and risk propensity. Innovative firms engage in acquisitions to integrate external knowledge. We refine this perspective further by proposing a curvilinear relationship, where excessive R&D intensity may reduce acquisition frequency due to high internalization costs and a reduced reliance on external innovations. Using a panel dataset of serial acquirers, we test the hypothesis that managerial ability strengthens the link between innovation and acquisitions, particularly in large firms where resource coordination is critical. In addition, we find that firms with higher managerial risk propensity are more likely to use acquisitions as a risk diversification strategy. Still, this effect weakens in highly leveraged firms where financial constraints limit acquisition activity. Our findings challenge the prevailing notion that firms deficient in research and development (R&D) dominate acquisition activities. The findings offer valuable managerial implications, guiding firms in balancing internal R&D investments with external acquisitions, optimizing managerial leadership for mergers and acquisitions success, and adapting acquisition strategies to dynamic industry conditions.
{"title":"Acquiring Growth: How Innovative Firms Leverage M&As for Competitive Advantage","authors":"Chandra S. Mishra","doi":"10.1109/TEM.2025.3590299","DOIUrl":"https://doi.org/10.1109/TEM.2025.3590299","url":null,"abstract":"This study examines the relationship between firm innovativeness and acquisition frequency, considering the moderating roles of managerial ability and risk propensity. Innovative firms engage in acquisitions to integrate external knowledge. We refine this perspective further by proposing a curvilinear relationship, where excessive R&D intensity may reduce acquisition frequency due to high internalization costs and a reduced reliance on external innovations. Using a panel dataset of serial acquirers, we test the hypothesis that managerial ability strengthens the link between innovation and acquisitions, particularly in large firms where resource coordination is critical. In addition, we find that firms with higher managerial risk propensity are more likely to use acquisitions as a risk diversification strategy. Still, this effect weakens in highly leveraged firms where financial constraints limit acquisition activity. Our findings challenge the prevailing notion that firms deficient in research and development (R&D) dominate acquisition activities. The findings offer valuable managerial implications, guiding firms in balancing internal R&D investments with external acquisitions, optimizing managerial leadership for mergers and acquisitions success, and adapting acquisition strategies to dynamic industry conditions.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3633-3649"},"PeriodicalIF":5.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918161","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 : 2025-08-26DOI: 10.1109/TEM.2025.3603183
Chin-Yi Lin;Tzu-Liang Tseng;Honglun Xu
In today’s volatile geopolitical environment and heightened emphasis on sustainability, effective supplier selection must simultaneously handle cost, delivery risks, and environmental + social + governance (ESG) considerations. This article proposes a GPT-augmented Bayesian reinforcement learning (i-SUP) framework, which integrates 1) GPT to extract real-time risk signals from unstructured text (news, social media), 2) Bayesian- best–worst method to capture expert uncertainty and produce robust multicriteria weights, 3) Bayesian belief networks (BBNs) for continuously updated disruption probabilities, 4) reinforcement learning (RL) for dynamic monthly or weekly order allocation, and 5) NSGA-II for long-horizon multiobjective contract planning. By combining semantic risk detection with Bayesian updates and RL-based adaptive decision-making, i-SUP (intelligent supplier selection system) dynamically adjusts to emergent risks (e.g., tariffs, labor unrest), while concurrently balancing ESG imperatives and cost efficiency. Empirical validation in the semiconductor industry—characterized by tight geopolitical sensitivity and high ESG demands—shows that i-SUP significantly reduces disruptions and ESG incidents relative to static or cost-only methods. Moreover, ablation analyses confirm that removing any single module (GPT, BBN, RL, or NSGA-II) undermines performance, demonstrating the necessity of a fully integrated pipeline. The findings underscore i-SUP’s ability to enhance supplier resilience and sustainability in a wide range of globalized supply networks that face evolving textual risk signals and multidimensional objectives.
{"title":"GPT-Augmented Bayesian Reinforcement Learning Framework for Multiobjective Supplier Selection","authors":"Chin-Yi Lin;Tzu-Liang Tseng;Honglun Xu","doi":"10.1109/TEM.2025.3603183","DOIUrl":"https://doi.org/10.1109/TEM.2025.3603183","url":null,"abstract":"In today’s volatile geopolitical environment and heightened emphasis on sustainability, effective supplier selection must simultaneously handle cost, delivery risks, and environmental + social + governance (ESG) considerations. This article proposes a GPT-augmented Bayesian reinforcement learning (i-SUP) framework, which integrates 1) GPT to extract real-time risk signals from unstructured text (news, social media), 2) Bayesian- best–worst method to capture expert uncertainty and produce robust multicriteria weights, 3) Bayesian belief networks (BBNs) for continuously updated disruption probabilities, 4) reinforcement learning (RL) for dynamic monthly or weekly order allocation, and 5) NSGA-II for long-horizon multiobjective contract planning. By combining semantic risk detection with Bayesian updates and RL-based adaptive decision-making, i-SUP (intelligent supplier selection system) dynamically adjusts to emergent risks (e.g., tariffs, labor unrest), while concurrently balancing ESG imperatives and cost efficiency. Empirical validation in the semiconductor industry—characterized by tight geopolitical sensitivity and high ESG demands—shows that i-SUP significantly reduces disruptions and ESG incidents relative to static or cost-only methods. Moreover, ablation analyses confirm that removing any single module (GPT, BBN, RL, or NSGA-II) undermines performance, demonstrating the necessity of a fully integrated pipeline. The findings underscore i-SUP’s ability to enhance supplier resilience and sustainability in a wide range of globalized supply networks that face evolving textual risk signals and multidimensional objectives.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3779-3804"},"PeriodicalIF":5.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073199","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 : 2025-08-25DOI: 10.1109/TEM.2025.3602008
Mengqi Li;Dengfeng Li;Lixiao Wei
With the maturity of digital technology, manufacturing platforms that achieve the connection between productions and sales are becoming increasingly common. For a two-level manufacturing platform supply chain comprising a manufacturing platform, a manufacturer of the check-in platform, and retailers, this article constructs a noncooperative–cooperative biform game model to study the green R&D with technology spillovers and pricing in co-opetition situation. The co-opetition is reflected in price competition and green competition between the manufacturing platform and the manufacturer, and the revenue sharing between online and offline channels. The coupling mechanism between competition and cooperation is revealed. Cooperation stability is proved by convexity of the cooperative game and individual rationality of allocation values. By a numerical example, our results reveal that increasing online channel acceptance will decrease the manufacturing platform’s profit, which is surprising and counterintuitive. A unidirectional technology spillover will reduce the price, green R&D level, and profit of the enterprise. But moderate technology spillover of the manufacturer can improve social welfare. In order to accomplish a win-win situation for enterprises and society, the manufacturing platform can get low-level technology spillover of the manufacturer by green R&D cooperation. Our article provides theoretical guidance for the co-opetition in platform supply chain.
{"title":"Manufacturing Platform’s Pricing and Green R&D With Technology Spillovers Under Supply Chain Co-opetition","authors":"Mengqi Li;Dengfeng Li;Lixiao Wei","doi":"10.1109/TEM.2025.3602008","DOIUrl":"https://doi.org/10.1109/TEM.2025.3602008","url":null,"abstract":"With the maturity of digital technology, manufacturing platforms that achieve the connection between productions and sales are becoming increasingly common. For a two-level manufacturing platform supply chain comprising a manufacturing platform, a manufacturer of the check-in platform, and retailers, this article constructs a noncooperative–cooperative biform game model to study the green R&D with technology spillovers and pricing in co-opetition situation. The co-opetition is reflected in price competition and green competition between the manufacturing platform and the manufacturer, and the revenue sharing between online and offline channels. The coupling mechanism between competition and cooperation is revealed. Cooperation stability is proved by convexity of the cooperative game and individual rationality of allocation values. By a numerical example, our results reveal that increasing online channel acceptance will decrease the manufacturing platform’s profit, which is surprising and counterintuitive. A unidirectional technology spillover will reduce the price, green R&D level, and profit of the enterprise. But moderate technology spillover of the manufacturer can improve social welfare. In order to accomplish a win-win situation for enterprises and society, the manufacturing platform can get low-level technology spillover of the manufacturer by green R&D cooperation. Our article provides theoretical guidance for the co-opetition in platform supply chain.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3849-3863"},"PeriodicalIF":5.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078656","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}
Drawing on institutional theory, this study disentangles the intricate relationships between environmental laws, corporate environmental ethics (CEE), green process innovation (GPI), and environmental performance. Using survey data from manufacturing firms in Pakistan, the study finds that environmental laws have a positive influence on GPI, and this association is mediated by CEE. Furthermore, the effects of environmental laws on environmental performance are sequentially mediated by CEE and GPI. In addition, institutional support plays a positive moderating role in enhancing the effects of environmental laws on CEE, as well as the effects of CEE on GPI. Theoretical, practical, and policy-related contributions are offered.
{"title":"Do Environmental Laws Matter for Corporate Ethics and Green Process Innovation in Environment Performance? The Moderating Role of Institutional Support","authors":"Huda Khan;Joseph Amankwah-Amoah;Benjamin Laker;Richard Lee;Deepak Sardana","doi":"10.1109/TEM.2025.3597927","DOIUrl":"https://doi.org/10.1109/TEM.2025.3597927","url":null,"abstract":"Drawing on institutional theory, this study disentangles the intricate relationships between environmental laws, corporate environmental ethics (CEE), green process innovation (GPI), and environmental performance. Using survey data from manufacturing firms in Pakistan, the study finds that environmental laws have a positive influence on GPI, and this association is mediated by CEE. Furthermore, the effects of environmental laws on environmental performance are sequentially mediated by CEE and GPI. In addition, institutional support plays a positive moderating role in enhancing the effects of environmental laws on CEE, as well as the effects of CEE on GPI. Theoretical, practical, and policy-related contributions are offered.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3678-3687"},"PeriodicalIF":5.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990183","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 : 2025-08-19DOI: 10.1109/TEM.2025.3600381
Milad Rahmati
The rapid expansion of electric vehicle (EV) charging infrastructure brings with it an increasing reliance on software systems for managing control logic, communication protocols, and real-time decision-making. As these systems grow more complex and interconnected, ensuring their operational reliability becomes essential—not only for individual charging stations but for maintaining broader energy grid stability and safety. This study introduces a new framework that models software reliability within EV charging systems, combining probabilistic techniques and explainable artificial intelligence (XAI) to improve failure prediction and monitoring transparency. By employing Bayesian reliability analysis and dynamic runtime observation, the proposed method identifies latent software vulnerabilities and offers interpretable diagnostic feedback, even under uncertain operating conditions. Unlike prior work focused primarily on hardware resilience or energy optimization, our research emphasizes control software robustness and the visibility of system behavior during operation. To validate the framework, we simulate an EV charging network featuring real-time data flows and multiple failure scenarios. Results show that our model enhances system stability, extends the average time between software failures, and facilitates faster issue diagnosis—all without compromising explainability. This contribution supports ongoing national efforts in clean energy transition, infrastructure modernization, and cyber-physical system safety by offering a scalable, modular, and intelligible approach to software reliability assurance in EV environments.
{"title":"Explainable Reliability Modeling and Runtime Monitoring of Software Systems in Electric Vehicle Charging Infrastructure","authors":"Milad Rahmati","doi":"10.1109/TEM.2025.3600381","DOIUrl":"https://doi.org/10.1109/TEM.2025.3600381","url":null,"abstract":"The rapid expansion of electric vehicle (EV) charging infrastructure brings with it an increasing reliance on software systems for managing control logic, communication protocols, and real-time decision-making. As these systems grow more complex and interconnected, ensuring their operational reliability becomes essential—not only for individual charging stations but for maintaining broader energy grid stability and safety. This study introduces a new framework that models software reliability within EV charging systems, combining probabilistic techniques and explainable artificial intelligence (XAI) to improve failure prediction and monitoring transparency. By employing Bayesian reliability analysis and dynamic runtime observation, the proposed method identifies latent software vulnerabilities and offers interpretable diagnostic feedback, even under uncertain operating conditions. Unlike prior work focused primarily on hardware resilience or energy optimization, our research emphasizes control software robustness and the visibility of system behavior during operation. To validate the framework, we simulate an EV charging network featuring real-time data flows and multiple failure scenarios. Results show that our model enhances system stability, extends the average time between software failures, and facilitates faster issue diagnosis—all without compromising explainability. This contribution supports ongoing national efforts in clean energy transition, infrastructure modernization, and cyber-physical system safety by offering a scalable, modular, and intelligible approach to software reliability assurance in EV environments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3667-3677"},"PeriodicalIF":5.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934404","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 : 2025-08-19DOI: 10.1109/TEM.2025.3600490
Yunbing Li;Jie Wu;Yong Zha
Excessive traffic consumption creates anxiety about traffic costs and encourages the popularity of data sponsorship, a business model in which internet service providers (ISPs) encourage content providers (CPs) to subsidize consumers’ mobile traffic costs. In practice, content with data sponsorship may be output at higher or lower resolution. We propose a game-theoretic model in which three cooperation options exist between the ISP and CP: Case N (no data subsidization is allowed), Case L (allowing the CP to subsidize low-resolution content), and Case H (allowing the CP to subsidize high-resolution content). We find that the ISP chooses Case H when the ad-revenue rate and degree of increased viewing cost for low-resolution content compared with high-resolution content (DIC) and degree of increased traffic for high-resolution content compared with low-resolution content (DIT) are high. However, the ISP chooses Case L when DIC and DIT are low and Case N when the ad-revenue rate is low. The CP offers full subsidization to cover consumers’ traffic costs under Case L but only partially subsidizes data under Case H. In addition, the Pareto zone shows that a large ad-revenue rate and a low DIC allow Case L to benefit both the ISP and CP, but a large DIC can let Case H benefit both parties, which sheds light on the motivation behind ISP–CP cooperation from a new perspective. We further identify the conditions under which consumer surplus and social welfare can benefit from a data plan.
{"title":"Sponsored Data: A Game-Theoretic Model With Content Provider Content Quality Differentiation","authors":"Yunbing Li;Jie Wu;Yong Zha","doi":"10.1109/TEM.2025.3600490","DOIUrl":"https://doi.org/10.1109/TEM.2025.3600490","url":null,"abstract":"Excessive traffic consumption creates anxiety about traffic costs and encourages the popularity of data sponsorship, a business model in which internet service providers (ISPs) encourage content providers (CPs) to subsidize consumers’ mobile traffic costs. In practice, content with data sponsorship may be output at higher or lower resolution. We propose a game-theoretic model in which three cooperation options exist between the ISP and CP: Case N (no data subsidization is allowed), Case L (allowing the CP to subsidize low-resolution content), and Case H (allowing the CP to subsidize high-resolution content). We find that the ISP chooses Case H when the ad-revenue rate and degree of increased viewing cost for low-resolution content compared with high-resolution content (DIC) and degree of increased traffic for high-resolution content compared with low-resolution content (DIT) are high. However, the ISP chooses Case L when DIC and DIT are low and Case N when the ad-revenue rate is low. The CP offers full subsidization to cover consumers’ traffic costs under Case L but only partially subsidizes data under Case H. In addition, the Pareto zone shows that a large ad-revenue rate and a low DIC allow Case L to benefit both the ISP and CP, but a large DIC can let Case H benefit both parties, which sheds light on the motivation behind ISP–CP cooperation from a new perspective. We further identify the conditions under which consumer surplus and social welfare can benefit from a data plan.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3805-3816"},"PeriodicalIF":5.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073200","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 : 2025-08-19DOI: 10.1109/TEM.2025.3598853
Xiang Li;Yue Wang;Daniel Y. Mo
Mass customization has emerged as a viable smart manufacturing strategy to deliver tailor-made products with the efficiency of mass production. It significantly impacts a company’s research, development, and engineering functions by fostering innovation in product design, manufacturing processes, and supply chain management. A critical challenge in mass customization is developing a user-friendly choice navigation process that enables customers to identify customized designs with minimal burden and complexity. This article addresses this challenge by proposing a novel approach to choice navigation that maps customer needs expressed in natural language to suitable product attribute choices. We tackle data sparsity issues by leveraging the extensive amount of online product-review text to mine customer needs and preferences. External domain knowledge in the product domain is distilled using conceptual graphs. We then develop a convolutional neural network-based structure and a transfer learning procedure to integrate this domain knowledge with contextual semantic information from the review and needs text. Our extensive experiments show that the approach’s effectiveness and robustness in the needs-attributes mapping, and demonstrate its potential to improve user-friendliness and customer satisfaction in mass customization systems.
{"title":"A Domain Knowledge Integrated Convolutional Neural Network for Translating Customer Needs Into Configuration Choices in Mass Customization","authors":"Xiang Li;Yue Wang;Daniel Y. Mo","doi":"10.1109/TEM.2025.3598853","DOIUrl":"https://doi.org/10.1109/TEM.2025.3598853","url":null,"abstract":"Mass customization has emerged as a viable smart manufacturing strategy to deliver tailor-made products with the efficiency of mass production. It significantly impacts a company’s research, development, and engineering functions by fostering innovation in product design, manufacturing processes, and supply chain management. A critical challenge in mass customization is developing a user-friendly choice navigation process that enables customers to identify customized designs with minimal burden and complexity. This article addresses this challenge by proposing a novel approach to choice navigation that maps customer needs expressed in natural language to suitable product attribute choices. We tackle data sparsity issues by leveraging the extensive amount of online product-review text to mine customer needs and preferences. External domain knowledge in the product domain is distilled using conceptual graphs. We then develop a convolutional neural network-based structure and a transfer learning procedure to integrate this domain knowledge with contextual semantic information from the review and needs text. Our extensive experiments show that the approach’s effectiveness and robustness in the needs-attributes mapping, and demonstrate its potential to improve user-friendliness and customer satisfaction in mass customization systems.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"3567-3583"},"PeriodicalIF":5.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914105","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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