Pub Date : 2025-11-26DOI: 10.1109/TEM.2025.3637769
Zheng Li;Yina Li;Jiayu Zhou;Fei Ye;Yuanzhu Zhan
With the rapid advancement of generative AI (Gen-AI), e-commerce platforms like Amazon and Newegg are adopting AI-generated summaries (AIGS) to synthesize online reviews. We analytically study the transformative impact of AIGS on e-commerce with competing retailers by examining its two key effects: the convenience effect, which reduces consumers’ reading costs to access review information, and the filtering effect, which aids consumers in making more informed purchase decisions. Retailers differ in their market reputation (MR), and not all consumers in the market trust AIGS. Our findings suggest that the convenience effect and the filtering effect influence competition in opposite ways. When consumer trust in AIGS is high, its adoption may intensify price competition. We further show that moderately high consumer trust in AIGS enables its adoption to create an all-win outcome for both the platform and competing retailers. Surprisingly, we find that while high trust in AIGS can reinforce high-MR retailers’ advantages (a Matthew effect), the adoption of AIGS under low trust may instead reshape market competition by narrowing these advantages. Interestingly, when AIGS is fully trusted and adoption costs are moderate, it improves social welfare but may lower consumer surplus due to elevated prices.
{"title":"How AI-Generated Summaries of Reviews are Reshaping E-Commerce With Competing Retailers?","authors":"Zheng Li;Yina Li;Jiayu Zhou;Fei Ye;Yuanzhu Zhan","doi":"10.1109/TEM.2025.3637769","DOIUrl":"https://doi.org/10.1109/TEM.2025.3637769","url":null,"abstract":"With the rapid advancement of generative AI (Gen-AI), e-commerce platforms like Amazon and Newegg are adopting AI-generated summaries (AIGS) to synthesize online reviews. We analytically study the transformative impact of AIGS on e-commerce with competing retailers by examining its two key effects: the <italic>convenience effect</i>, which reduces consumers’ reading costs to access review information, and the <italic>filtering effect</i>, which aids consumers in making more informed purchase decisions. Retailers differ in their market reputation (MR), and not all consumers in the market trust AIGS. Our findings suggest that the convenience effect and the filtering effect influence competition in opposite ways. When consumer trust in AIGS is high, its adoption may intensify price competition. We further show that moderately high consumer trust in AIGS enables its adoption to create an all-win outcome for both the platform and competing retailers. Surprisingly, we find that while high trust in AIGS can reinforce high-MR retailers’ advantages (a Matthew effect), the adoption of AIGS under low trust may instead reshape market competition by narrowing these advantages. Interestingly, when AIGS is fully trusted and adoption costs are moderate, it improves social welfare but may lower consumer surplus due to elevated prices.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1116-1131"},"PeriodicalIF":5.2,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982224","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 : 2025-11-26DOI: 10.1109/TEM.2025.3637747
Wen Xin;Yanan Li;Dehong Li;Fei Jing
When a seller sells its product through both direct and platform channels, it is faced with the decision of selecting a supporting service for each channel—either the platform service or the third-party service. In this article, we investigate whether the platform owner allows the seller on it to use the third-party service and further explores which service portfolio should be adopted by the seller. Our results show that the platform never allows the seller to use third-party service if the platform service has a quality advantage or only a slight quality disadvantage. Interestingly, such platform behavior does not necessarily lead to lower consumer surplus or diminished social welfare. In addition, the seller's choice of service portfolio depends on the quality difference between platform and third-party services. When the platform service has a significant quality disadvantage, the seller may adopt either the PT (platform service on the platform and third-party service in the direct channel) or the TT (third-party service in both channels) portfolio. Conversely, if the disadvantage is relatively low or if the platform service has a quality advantage, the seller chooses either the PT or the PP (platform service in both channels) portfolio. Moreover, the seller may opt for a service portfolio that maximizes its profit at the expense of total consumer demand. Notably, service differentiation in the channels (PT or TP) consistently results in higher commission price charged by the platform and greater total consumer demand.
{"title":"Seller's Supporting Service Portfolio Selection in a Dual-Channel Supply Chain","authors":"Wen Xin;Yanan Li;Dehong Li;Fei Jing","doi":"10.1109/TEM.2025.3637747","DOIUrl":"https://doi.org/10.1109/TEM.2025.3637747","url":null,"abstract":"When a seller sells its product through both direct and platform channels, it is faced with the decision of selecting a supporting service for each channel—either the platform service or the third-party service. In this article, we investigate whether the platform owner allows the seller on it to use the third-party service and further explores which service portfolio should be adopted by the seller. Our results show that the platform never allows the seller to use third-party service if the platform service has a quality advantage or only a slight quality disadvantage. Interestingly, such platform behavior does not necessarily lead to lower consumer surplus or diminished social welfare. In addition, the seller's choice of service portfolio depends on the quality difference between platform and third-party services. When the platform service has a significant quality disadvantage, the seller may adopt either the PT (platform service on the platform and third-party service in the direct channel) or the TT (third-party service in both channels) portfolio. Conversely, if the disadvantage is relatively low or if the platform service has a quality advantage, the seller chooses either the PT or the PP (platform service in both channels) portfolio. Moreover, the seller may opt for a service portfolio that maximizes its profit at the expense of total consumer demand. Notably, service differentiation in the channels (PT or TP) consistently results in higher commission price charged by the platform and greater total consumer demand.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"481-494"},"PeriodicalIF":5.2,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830891","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 : 2025-11-24DOI: 10.1109/TEM.2025.3635874
Xiangyu Qin;Bei Wu;Ada Che
The integration of self-healing resources into systems presents a promising strategy for mitigating operational failure risks. However, the practical application of such systems is critically constrained by the absence of quantifiable management tools for precise performance evaluation and cost-effective decision-making. To bridge this gap, we develop a reliability-based management framework tailored for systems with limited self-healing resources that are subject to both natural degradation and multisource shocks. Unlike prior work, our study explicitly models the stochastic consumption of healing resources and the distinction between self-repairable and irreparable shocks. A closed-form reliability function is derived, with its validity confirmed through a dedicated simulation algorithm to ensure analytical rigor. Building on this foundation, we formulate a joint optimization model to simultaneously determine the optimal quantity of healing resources and the frequency of imperfect maintenance actions, with the objective of minimizing the average long-run cost rate under a availability constraint. A case study on self-healing lithium-ion batteries demonstrates that reliability improvements exhibit diminishing marginal returns as healing resources increase, offering a key managerial insight for resource planning. Extensive sensitivity analyses further confirm the robustness of the proposed methods and furnish engineering managers with actionable strategies to maintain a cost-availability balance amid cost fluctuations.
{"title":"Reliability-Based Joint Optimization of Self-Healing Resource and Maintenance Policy for Systems in a Multisource Shock Environment","authors":"Xiangyu Qin;Bei Wu;Ada Che","doi":"10.1109/TEM.2025.3635874","DOIUrl":"https://doi.org/10.1109/TEM.2025.3635874","url":null,"abstract":"The integration of self-healing resources into systems presents a promising strategy for mitigating operational failure risks. However, the practical application of such systems is critically constrained by the absence of quantifiable management tools for precise performance evaluation and cost-effective decision-making. To bridge this gap, we develop a reliability-based management framework tailored for systems with limited self-healing resources that are subject to both natural degradation and multisource shocks. Unlike prior work, our study explicitly models the stochastic consumption of healing resources and the distinction between self-repairable and irreparable shocks. A closed-form reliability function is derived, with its validity confirmed through a dedicated simulation algorithm to ensure analytical rigor. Building on this foundation, we formulate a joint optimization model to simultaneously determine the optimal quantity of healing resources and the frequency of imperfect maintenance actions, with the objective of minimizing the average long-run cost rate under a availability constraint. A case study on self-healing lithium-ion batteries demonstrates that reliability improvements exhibit diminishing marginal returns as healing resources increase, offering a key managerial insight for resource planning. Extensive sensitivity analyses further confirm the robustness of the proposed methods and furnish engineering managers with actionable strategies to maintain a cost-availability balance amid cost fluctuations.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"798-817"},"PeriodicalIF":5.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929664","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 : 2025-11-24DOI: 10.1109/TEM.2025.3636827
Jiaojiao Qin;Annapoornima M. Subramanian;Haydar Yalçın;Tugrul Daim
Research indicates that R&D volatility (RDV) is a significant predictor of firm performance (FP). To date, however, answers on how this relationship manifests remain elusive, and practitioners have no clear guidelines for firms’ R&D spending strategy. By incorporating the strategic change view into the RDV research, this study proposes a nonlinear framework to revisit the effect of RDV on FP. Since the performance implications of a firm’s strategic change are determined by the strategic fit between it and internal capability, we also explore the contingency effect of technical capability in terms of firm digitalization (FD) and Top Management Team (TMT) technical experience (TMTTEC). Based on a panel sample of Chinese listed firms from 2011 to 2023, we find that the impact of RDV on FP is not simply linear, but follows an inverted U-shaped pattern. Moreover, FD moderates this curvilinear effect in a way that shifts it upward. A bibliometric analysis is conducted to situate our findings within the existing literature, and these research findings provide substantial theoretical and practical implications. In conclusion, our research introduces a nonlinear framework for exploring RDV for the first time.
{"title":"Too Much of a Good Thing? Untangling the Relationship Between R&D Volatility and Firm Performance","authors":"Jiaojiao Qin;Annapoornima M. Subramanian;Haydar Yalçın;Tugrul Daim","doi":"10.1109/TEM.2025.3636827","DOIUrl":"https://doi.org/10.1109/TEM.2025.3636827","url":null,"abstract":"Research indicates that R&D volatility (RDV) is a significant predictor of firm performance (FP). To date, however, answers on how this relationship manifests remain elusive, and practitioners have no clear guidelines for firms’ R&D spending strategy. By incorporating the strategic change view into the RDV research, this study proposes a nonlinear framework to revisit the effect of RDV on FP. Since the performance implications of a firm’s strategic change are determined by the strategic fit between it and internal capability, we also explore the contingency effect of technical capability in terms of firm digitalization (FD) and Top Management Team (TMT) technical experience (TMTTEC). Based on a panel sample of Chinese listed firms from 2011 to 2023, we find that the impact of RDV on FP is not simply linear, but follows an inverted U-shaped pattern. Moreover, FD moderates this curvilinear effect in a way that shifts it upward. A bibliometric analysis is conducted to situate our findings within the existing literature, and these research findings provide substantial theoretical and practical implications. In conclusion, our research introduces a nonlinear framework for exploring RDV for the first time.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"572-586"},"PeriodicalIF":5.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830763","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 : 2025-11-24DOI: 10.1109/TEM.2025.3636468
Ying Wei;Shan Lyu;Ruisi Yang
With the emergence of virtual artificial intelligence (AI) streamers, choosing between a virtual streamer and a live streamer is a crucial decision for the brand manufacturer when launching a live-streaming business. This article examines a dual-channel supply chain model consisting of a brand manufacturer, a store retailer, and either an AI streamer or a key opinion leader (KOL) streamer. KOL streamers possess significant advantages over AI streamers due to their ability to interact effectively with consumers and their large fan bases. However, participating in prearranged KOL live-streaming events can incur a hassle cost for consumers, which is not required for AI streamers. In addition, when collaborating with a KOL, the brand manufacturer must pay a fixed payment and a commission rate on revenues generated from live-streaming e-commerce, which can be ignored for AI streamers. Our research indicates that the costs associating with KOLs are a key factor in mode selection. We identify cost thresholds under which the brand manufacturer may prefer the KOL live-streaming mode over the AI mode. We also find that the hassle cost is a double-edged sword for the brand manufacturer. When the hassle cost is high, the KOL mode may still be preferred over AI mode. We then derive the retailer’s mode preference and identify the conditions for achieving a common preference for the brand manufacturer and the retailer in both live-streaming scenarios. Finally, we conduct three extensions: considering product returns, exploring an alternative in-house live-streaming mode to the KOL mode, and examining the implications of an existing online channel. Results show that the findings of the main model remain robust.
{"title":"Virtual or Live? Choosing the Optimal Live-Streaming Mode in a Dual-Channel Supply Chain","authors":"Ying Wei;Shan Lyu;Ruisi Yang","doi":"10.1109/TEM.2025.3636468","DOIUrl":"https://doi.org/10.1109/TEM.2025.3636468","url":null,"abstract":"With the emergence of virtual artificial intelligence (AI) streamers, choosing between a virtual streamer and a live streamer is a crucial decision for the brand manufacturer when launching a live-streaming business. This article examines a dual-channel supply chain model consisting of a brand manufacturer, a store retailer, and either an AI streamer or a key opinion leader (KOL) streamer. KOL streamers possess significant advantages over AI streamers due to their ability to interact effectively with consumers and their large fan bases. However, participating in prearranged KOL live-streaming events can incur a hassle cost for consumers, which is not required for AI streamers. In addition, when collaborating with a KOL, the brand manufacturer must pay a fixed payment and a commission rate on revenues generated from live-streaming e-commerce, which can be ignored for AI streamers. Our research indicates that the costs associating with KOLs are a key factor in mode selection. We identify cost thresholds under which the brand manufacturer may prefer the KOL live-streaming mode over the AI mode. We also find that the hassle cost is a double-edged sword for the brand manufacturer. When the hassle cost is high, the KOL mode may still be preferred over AI mode. We then derive the retailer’s mode preference and identify the conditions for achieving a common preference for the brand manufacturer and the retailer in both live-streaming scenarios. Finally, we conduct three extensions: considering product returns, exploring an alternative in-house live-streaming mode to the KOL mode, and examining the implications of an existing online channel. Results show that the findings of the main model remain robust.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"360-373"},"PeriodicalIF":5.2,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830844","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 : 2025-11-21DOI: 10.1109/TEM.2025.3633709
Atanu Dey;Mamata Jenamani;Arijit De
Electronic waste (E-waste) is an escalating global challenge, with discarded laptops forming a major share of this growing environmental burden. To support sustainable consumption and informed consumer decision-making, this study proposes an unsupervised deep learning framework that ranks refurbished and new laptop brands based on consumer sentiment extracted from online reviews. The framework identifies not only direct product features called aspects (such as battery, display, or customer support) but also experiential dimensions (such as reliability, performance, or overall satisfaction), providing a holistic view of consumer perception. By leveraging a transformer-based multiheaded attention mechanism and part-of-speech tagging, the model extracts rich five-part sentiment structures: aspect/dimension, category, opinion, irrealis (hypotheticals), and sentiment, collectively represented as ACOIS and DCOIS quintuples. These insights feed into a folksonomy-based consumer brand ranking algorithm, which aggregates sentiment scores to rank laptop brands effectively. Unlike traditional models, this framework requires no labeled training data, increasing its adaptability across domains. Comparative evaluations against state-of-the-art supervised and self-supervised models, including large language models, demonstrate superior performance with F1 score improvements of 9%, 6%, and 4% in extracting product aspects, dimensions, and opinions, respectively. The model is applied to a curated dataset comprising new and refurbished laptops within the same price segment. Results show that 40% of refurbished brands appear in the top 25% of recommendations. We ensured the framework’s robustness check, including McNemar’s statistical testing on six subtasks ($text{5/6}$ above 0.05 threshold), ablation studies with two alternative attention mechanisms, and validation against several benchmark methods, confirming framework’s stability.
{"title":"Consumer Sentiment-Driven Product Ranking Using a Feature-Level Deep Learning Approach: The Case of New and Refurbished Laptops","authors":"Atanu Dey;Mamata Jenamani;Arijit De","doi":"10.1109/TEM.2025.3633709","DOIUrl":"https://doi.org/10.1109/TEM.2025.3633709","url":null,"abstract":"Electronic waste (E-waste) is an escalating global challenge, with discarded laptops forming a major share of this growing environmental burden. To support sustainable consumption and informed consumer decision-making, this study proposes an unsupervised deep learning framework that ranks refurbished and new laptop brands based on consumer sentiment extracted from online reviews. The framework identifies not only direct product features called <italic>aspects</i> (such as battery, display, or customer support) but also experiential <italic>dimensions</i> (such as reliability, performance, or overall satisfaction), providing a holistic view of consumer perception. By leveraging a transformer-based multiheaded attention mechanism and part-of-speech tagging, the model extracts rich five-part sentiment structures: <italic>aspect/dimension, category, opinion, irrealis (hypotheticals),</i> and <italic>sentiment</i>, collectively represented as <italic>ACOIS and DCOIS quintuples</i>. These insights feed into a folksonomy-based <italic>consumer brand ranking</i> algorithm, which aggregates sentiment scores to rank laptop brands effectively. Unlike traditional models, this framework requires no labeled training data, increasing its adaptability across domains. Comparative evaluations against state-of-the-art supervised and self-supervised models, including large language models, demonstrate superior performance with F1 score improvements of 9%, 6%, and 4% in extracting product aspects, dimensions, and opinions, respectively. The model is applied to a curated dataset comprising new and refurbished laptops within the same price segment. Results show that 40% of refurbished brands appear in the top 25% of recommendations. We ensured the framework’s robustness check, including McNemar’s statistical testing on six subtasks (<inline-formula><tex-math>$text{5/6}$</tex-math></inline-formula> above 0.05 threshold), ablation studies with two alternative attention mechanisms, and validation against several benchmark methods, confirming framework’s stability.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"510-526"},"PeriodicalIF":5.2,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830857","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 : 2025-11-21DOI: 10.1109/TEM.2025.3635663
Zexuan Shi;Tiaojun Xiao;Yu Ning;Yang Tong
The increasing prevalence of store brands has attracted both retailers and national manufacturers to participate in store brand operations. However, retailers face a strategic decision regarding whether to disclose the national manufacturer's identity information, especially considering the impacts of the brand spillover effect and online reviews. This research investigates the retailer's disclosure strategy by developing a two-period model and exploring how this decision impacts online review ratings. Our findings illustrate that although the brand spillover effect of disclosing the national manufacturer's identity information can elevate consumers’ quality expectations for store brands, the influence of online reviews may diminish or even reverse this benefit. Specifically, a stronger brand spillover effect may adversely affect both parties and thus inhibit the retailer from disclosing such information, which is different from the conventional wisdom. Moreover, we demonstrate that disclosing the national manufacturer's identity is not advantageous for the retailer when the degree of the brand spillover effect is relatively low, consumers are more reference-dependent and rely more on online reviews to formulate quality expectations, and the quality level of the store brand is perceived as moderate. Finally, we consider several extensions, including strategic consumers, distinct pricing power structures, the effect of the national brand's online reviews, asymmetric consumer numbers, and heterogeneous consumer ratings, which either confirm the robustness of our findings or enrich the basic findings.
{"title":"To Disclose or Not? Disclosure of Manufacturer Identity in Store Brand Supply Chains With Brand Spillover Effect and Online Reviews","authors":"Zexuan Shi;Tiaojun Xiao;Yu Ning;Yang Tong","doi":"10.1109/TEM.2025.3635663","DOIUrl":"https://doi.org/10.1109/TEM.2025.3635663","url":null,"abstract":"The increasing prevalence of store brands has attracted both retailers and national manufacturers to participate in store brand operations. However, retailers face a strategic decision regarding whether to disclose the national manufacturer's identity information, especially considering the impacts of the brand spillover effect and online reviews. This research investigates the retailer's disclosure strategy by developing a two-period model and exploring how this decision impacts online review ratings. Our findings illustrate that although the brand spillover effect of disclosing the national manufacturer's identity information can elevate consumers’ quality expectations for store brands, the influence of online reviews may diminish or even reverse this benefit. Specifically, a stronger brand spillover effect may adversely affect both parties and thus inhibit the retailer from disclosing such information, which is different from the conventional wisdom. Moreover, we demonstrate that disclosing the national manufacturer's identity is not advantageous for the retailer when the degree of the brand spillover effect is relatively low, consumers are more reference-dependent and rely more on online reviews to formulate quality expectations, and the quality level of the store brand is perceived as moderate. Finally, we consider several extensions, including strategic consumers, distinct pricing power structures, the effect of the national brand's online reviews, asymmetric consumer numbers, and heterogeneous consumer ratings, which either confirm the robustness of our findings or enrich the basic findings.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"543-557"},"PeriodicalIF":5.2,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830883","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 : 2025-11-20DOI: 10.1109/TEM.2025.3634836
Dong-Young Kim
In today's rapidly evolving business environment, organizations are increasingly leveraging artificial intelligence (AI) to unlock the full potential of Lean Six Sigma (LSS). Although the integration of AI with LSS has been identified as a critical area of research, existing review studies have yet to present an in-depth analysis of this intersection. This study aims to uncover critical knowledge clusters and propose future research directions on the integration of AI with LSS. It also seeks to develop an integrative framework elucidating the role of AI in enhancing an organization's dynamic capabilities. A systematic literature review and bibliometric analysis were conducted to synthesize existing research and identify thematic structures. Our analysis reveals that AI research within the LSS context remains in its early developmental stages, despite the pioneering efforts of scholars. The literature on AI and LSS demonstrates a limited effort to incorporate established theories. This study identifies four distinct knowledge clusters and presents five key propositions that offer valuable directions for future research. This study contributes to the literature by providing a comprehensive overview of AI's role in supporting the implementation of LSS practices. It proposes an AI-supported LSS dynamic capability framework, explaining how AI facilitates the sensing, seizing, and reconfiguring cycle within LSS. To bridge theory and practice, a decision matrix is presented, mapping AI to the DMAIC phases with illustrative industry examples.
{"title":"Artificial Intelligence and Lean Six Sigma: What Have We Learned?","authors":"Dong-Young Kim","doi":"10.1109/TEM.2025.3634836","DOIUrl":"https://doi.org/10.1109/TEM.2025.3634836","url":null,"abstract":"In today's rapidly evolving business environment, organizations are increasingly leveraging artificial intelligence (AI) to unlock the full potential of Lean Six Sigma (LSS). Although the integration of AI with LSS has been identified as a critical area of research, existing review studies have yet to present an in-depth analysis of this intersection. This study aims to uncover critical knowledge clusters and propose future research directions on the integration of AI with LSS. It also seeks to develop an integrative framework elucidating the role of AI in enhancing an organization's dynamic capabilities. A systematic literature review and bibliometric analysis were conducted to synthesize existing research and identify thematic structures. Our analysis reveals that AI research within the LSS context remains in its early developmental stages, despite the pioneering efforts of scholars. The literature on AI and LSS demonstrates a limited effort to incorporate established theories. This study identifies four distinct knowledge clusters and presents five key propositions that offer valuable directions for future research. This study contributes to the literature by providing a comprehensive overview of AI's role in supporting the implementation of LSS practices. It proposes an AI-supported LSS dynamic capability framework, explaining how AI facilitates the sensing, seizing, and reconfiguring cycle within LSS. To bridge theory and practice, a decision matrix is presented, mapping AI to the DMAIC phases with illustrative industry examples.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"587-598"},"PeriodicalIF":5.2,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830894","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 : 2025-11-18DOI: 10.1109/TEM.2025.3634149
Xin Wang;Jianghua Wu
Extreme weather events increasingly disrupt agricultural supply chains (ASCs). Insurance serves as a vital financial buffer, compensating smallholder farmers’ input costs while also enhancing their ability to repay loans. Traditional insurance, due to lengthy verification and delayed payouts, limits the farmers’ quick response to extreme weather. Recently, smart insurance has emerged as a promising solution, leveraging index-based verification and automated payouts to facilitate timely farmer recovery. In this article, we explore the operation of an e-commerce-based ASC under extreme weather, considering dual protection of crop insurance (traditional or smart) and government subsidies (premium or input). We examine the insurer's decision to adopt smart insurance and its alignment with the socially optimal outcome. Our results show that, when farmer recovery efficiency is low, smart insurance is preferred only if traditional verification incurs high costs. Conversely, with efficient farmer recovery, the insurer always adopts smart insurance, which empowers the ASC to sustain under severe weather conditions but may lead to a decline in social welfare due to the associated subsidy expenditure. Furthermore, we analyze the implications of insurance innovation for government subsidy policies. We find that smart insurance can resolve the policy dilemma faced by traditional insurance, where no policy can outperform the other in both farmer well-being and social welfare under severe weather conditions. In contrast, smart insurance enables the input subsidy to achieve a dual-benefit outcome. This finding highlights the potential of insurance innovation in improving government policy effectiveness.
{"title":"Coping With Extreme Weather Risks: Implications of Insurance Innovation and Government Subsidies in Agricultural Supply Chains","authors":"Xin Wang;Jianghua Wu","doi":"10.1109/TEM.2025.3634149","DOIUrl":"https://doi.org/10.1109/TEM.2025.3634149","url":null,"abstract":"Extreme weather events increasingly disrupt agricultural supply chains (ASCs). Insurance serves as a vital financial buffer, compensating smallholder farmers’ input costs while also enhancing their ability to repay loans. Traditional insurance, due to lengthy verification and delayed payouts, limits the farmers’ quick response to extreme weather. Recently, smart insurance has emerged as a promising solution, leveraging index-based verification and automated payouts to facilitate timely farmer recovery. In this article, we explore the operation of an e-commerce-based ASC under extreme weather, considering dual protection of crop insurance (traditional or smart) and government subsidies (premium or input). We examine the insurer's decision to adopt smart insurance and its alignment with the socially optimal outcome. Our results show that, when farmer recovery efficiency is low, smart insurance is preferred only if traditional verification incurs high costs. Conversely, with efficient farmer recovery, the insurer always adopts smart insurance, which empowers the ASC to sustain under severe weather conditions but may lead to a decline in social welfare due to the associated subsidy expenditure. Furthermore, we analyze the implications of insurance innovation for government subsidy policies. We find that smart insurance can resolve the policy dilemma faced by traditional insurance, where no policy can outperform the other in both farmer well-being and social welfare under severe weather conditions. In contrast, smart insurance enables the input subsidy to achieve a dual-benefit outcome. This finding highlights the potential of insurance innovation in improving government policy effectiveness.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"418-431"},"PeriodicalIF":5.2,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830914","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 : 2025-11-17DOI: 10.1109/TEM.2025.3633382
Kehong Chen;Dawei Wang;Shengming Zheng
Amid growing environmental and social pressures, enhancing supply chain sustainability has become a central concern for firms, with supplier environmental, social, and governance practices, particularly green research and development (R&D), playing a pivotal role. However, how dominant firms incentivize supplier green R&D and how such sponsorship influences the interests of different supply chain members remain underexplored. This study develops a Stackelberg game model consisting of a supplier, a dominant core firm (acting as a retailer), and a weak retailer to investigate two widely observed green R&D approaches. The first is supplier-independent R&D, where the supplier bears all R&D costs. The second is core firm-sponsored R&D, which can take the form of either core firm exclusive sponsored R&D (CE), where the benefits are restricted to the core firm, or core firm inclusive sponsored R&D (CI), where the benefits are shared across all downstream firms. The analysis reveals that core firm sponsorship is not always accepted by the supplier, even when it reduces her direct R&D costs. When R&D costs are high, supplier-independent R&D emerges as the equilibrium choice. Only under moderate R&D costs does the supplier accept sponsorship from the core firm, which can lead to win-win or sub-win-win outcomes for the core firm and supplier. However, such sponsorship, especially in its exclusive form, may harm the weak retailer, challenging the common belief that ESG-oriented strategies universally benefit all stakeholders.
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