Pub Date : 2025-12-22DOI: 10.1109/TEM.2025.3646635
Jack Adams;Ozgur Dedehayir;Saku J. Mäkinen;J. Roland Ortt
The ability of innovation ecosystems to deliver desired economic output, particularly under conditions of uncertainty shaped by market shifts, competitive change, and regulatory pressure, concerns all ecosystem stakeholders. Understanding innovation ecosystem performance, therefore, emerges as an important topic for scholars, managers, and policymakers. The objective of this article is to propose a conceptual framework of ecosystem performance that builds on the inherent connection between system-level outcomes and the performance of all components that constitute the ecosystem. To this end, we apply a socio-technical lens to identify performance-deficient social or technical components known as “reverse salient” that influence the performance of the ecosystem as a whole. Our case study of a regional Australian food innovation ecosystem identifies numerous reverse salients that inhibit ecosystem performance as the system transitions from its current focus on high-quality produce to a future state characterized by increased output capacity and value-added offerings. We categorize these reverse salients as those associated with “actors” in the ecosystem, “connections” between actors, and “resources” flowing among them. While these categories align with the ecosystem-as-structure perspective, our findings additionally underscore the moderating role of ecosystem “leadership” and “rules of engagement” that can themselves act as reverse salients when misaligned. We present a conceptual model that integrates these insights and offer a set of propositions to guide future empirical research.
{"title":"Toward a Framework of Innovation Ecosystem Performance: A Case Study","authors":"Jack Adams;Ozgur Dedehayir;Saku J. Mäkinen;J. Roland Ortt","doi":"10.1109/TEM.2025.3646635","DOIUrl":"https://doi.org/10.1109/TEM.2025.3646635","url":null,"abstract":"The ability of innovation ecosystems to deliver desired economic output, particularly under conditions of uncertainty shaped by market shifts, competitive change, and regulatory pressure, concerns all ecosystem stakeholders. Understanding innovation ecosystem performance, therefore, emerges as an important topic for scholars, managers, and policymakers. The objective of this article is to propose a conceptual framework of ecosystem performance that builds on the inherent connection between system-level outcomes and the performance of all components that constitute the ecosystem. To this end, we apply a socio-technical lens to identify performance-deficient social or technical components known as “reverse salient” that influence the performance of the ecosystem as a whole. Our case study of a regional Australian food innovation ecosystem identifies numerous reverse salients that inhibit ecosystem performance as the system transitions from its current focus on high-quality produce to a future state characterized by increased output capacity and value-added offerings. We categorize these reverse salients as those associated with “actors” in the ecosystem, “connections” between actors, and “resources” flowing among them. While these categories align with the ecosystem-as-structure perspective, our findings additionally underscore the moderating role of ecosystem “leadership” and “rules of engagement” that can themselves act as reverse salients when misaligned. We present a conceptual model that integrates these insights and offer a set of propositions to guide future empirical research.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"740-752"},"PeriodicalIF":5.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/TEM.2025.3646221
Meiting Lin;Hugo K. S. Lam
Artificial intelligence (AI) has been increasingly adopted by firms for different organizational purposes. While such AI adoption is expected to affect firm performance, it may also have different effects on firms’ stakeholders, such as employees, shareholders, and customers. To provide a more comprehensive understanding of the stakeholder implications of AI adoption, we conduct a systematic review of 84 relevant papers published in business journals over the past eight years (2017–2024). Our review suggests that firms’ AI adoption does have different, sometimes conflicting, effects on different stakeholder groups. We also uncover several limitations of the extant literature and develop an integrative AI Impacts Multiple Stakeholders (AIMS) framework that summarizes important directions for future research. Our AIMS framework emphasizes the need to consider underexplored stakeholder groups, such as suppliers and competitors, as well as the trade-offs and dynamics among different stakeholder groups induced by AI adoption. Our framework also encourages future research to move beyond examining the direct impact of AI adoption on stakeholders by investigating when (contextual factors) and why (underlying mechanisms) AI adoption affects stakeholders, thereby advancing the literature on AI–stakeholder relationships. Finally, we discuss the implications for future operations management research, encouraging scholars to adopt a supply chain perspective to study the stakeholder implications of firms’ AI adoption.
{"title":"How Does Firms’ Artificial Intelligence Adoption Affect Different Stakeholders? A Systematic Review and the AIMS Framework","authors":"Meiting Lin;Hugo K. S. Lam","doi":"10.1109/TEM.2025.3646221","DOIUrl":"https://doi.org/10.1109/TEM.2025.3646221","url":null,"abstract":"Artificial intelligence (AI) has been increasingly adopted by firms for different organizational purposes. While such AI adoption is expected to affect firm performance, it may also have different effects on firms’ stakeholders, such as employees, shareholders, and customers. To provide a more comprehensive understanding of the stakeholder implications of AI adoption, we conduct a systematic review of 84 relevant papers published in business journals over the past eight years (2017–2024). Our review suggests that firms’ AI adoption does have different, sometimes conflicting, effects on different stakeholder groups. We also uncover several limitations of the extant literature and develop an integrative AI Impacts Multiple Stakeholders (AIMS) framework that summarizes important directions for future research. Our AIMS framework emphasizes the need to consider underexplored stakeholder groups, such as suppliers and competitors, as well as the trade-offs and dynamics among different stakeholder groups induced by AI adoption. Our framework also encourages future research to move beyond examining the direct impact of AI adoption on stakeholders by investigating when (contextual factors) and why (underlying mechanisms) AI adoption affects stakeholders, thereby advancing the literature on AI–stakeholder relationships. Finally, we discuss the implications for future operations management research, encouraging scholars to adopt a supply chain perspective to study the stakeholder implications of firms’ AI adoption.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"832-846"},"PeriodicalIF":5.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/TEM.2025.3646488
Tana Siqin;Qian Zhao;Song-Man Wu
Sustainable supply chain operations that emphasize environmental and social welfare have drawn significant attention. Supply chain members and governments are increasingly implementing incentive strategies to promote sustainability. This article develops a game-theoretical model of a sustainable supply chain to examine the effectiveness of cooperative advertising under different government subsidy schemes. We consider that the supply chain consists of a green manufacturer and a retailer who sells a green product through advertising. The manufacturer can share advertising costs with the retailer, a strategy known as cooperative advertising, while the government can offer either the advertising subsidy or the consumption subsidy to support sustainability. Our analytical findings uncover that cooperative advertising may not always benefit the sustainable supply chain. Without the government subsidy, cooperative advertising is beneficial to the manufacturer, consumers, and the government only when the negative effect of cooperative advertising is mild; however, it is always detrimental to the retailer. In contrast, when a government subsidy is provided, not engaging in cooperative advertising is always superior. On the other hand, we find that government subsidies are always effective in facilitating sustainable supply chain operations, with the consumption subsidy outperforming the advertising subsidy. We further extend the model to explore the scenarios under 1) the manufacturer encroachment and 2) a marginal advertising cost. We find the major findings remain valid in two extensions. This study not only contributes to the existing literature on sustainable supply chain management, but also offers practical insights for firms and governments to design incentive mechanisms.
{"title":"Cooperative Advertising in a Sustainable Supply Chain: The Role of Government Subsidies","authors":"Tana Siqin;Qian Zhao;Song-Man Wu","doi":"10.1109/TEM.2025.3646488","DOIUrl":"https://doi.org/10.1109/TEM.2025.3646488","url":null,"abstract":"Sustainable supply chain operations that emphasize environmental and social welfare have drawn significant attention. Supply chain members and governments are increasingly implementing incentive strategies to promote sustainability. This article develops a game-theoretical model of a sustainable supply chain to examine the effectiveness of cooperative advertising under different government subsidy schemes. We consider that the supply chain consists of a green manufacturer and a retailer who sells a green product through advertising. The manufacturer can share advertising costs with the retailer, a strategy known as cooperative advertising, while the government can offer either the advertising subsidy or the consumption subsidy to support sustainability. Our analytical findings uncover that cooperative advertising may not always benefit the sustainable supply chain. Without the government subsidy, cooperative advertising is beneficial to the manufacturer, consumers, and the government only when the negative effect of cooperative advertising is mild; however, it is always detrimental to the retailer. In contrast, when a government subsidy is provided, not engaging in cooperative advertising is always superior. On the other hand, we find that government subsidies are always effective in facilitating sustainable supply chain operations, with the consumption subsidy outperforming the advertising subsidy. We further extend the model to explore the scenarios under 1) the manufacturer encroachment and 2) a marginal advertising cost. We find the major findings remain valid in two extensions. This study not only contributes to the existing literature on sustainable supply chain management, but also offers practical insights for firms and governments to design incentive mechanisms.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1194-1209"},"PeriodicalIF":5.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1109/TEM.2025.3642877
Dar-Zen Chen;Hsu-Chuan Chang;Mu-Hsuan Huang;Chung-Huei Kuan;Chun-Chieh Wang
This study examines how public funding influences research and development (R&D) outcomes by analyzing the performance of government-interest (GI) patents—those that explicitly acknowledge government support—relative to non-GI patents. Using a comprehensive patentometric assessment of U.S. patent data, the study challenges the conventional belief that government-backed patents inherently yield superior results. While GI patents tend to emphasize foundational and publicly aligned research, non-GI patents often outperform them in terms of citation influence and technological impact, particularly in market-driven contexts. To interpret these patterns, the study introduces a quadrant-based framework grounded in four complementary theories: the triple helix model, Pasteur’s quadrant, organizational learning theory, and resource dependence theory. This framework supports a strategic understanding of how different recipients utilize government support, highlighting patterns of efficient resource use, latent potential, and underperformance. The findings suggest that public funding alone does not guarantee high-impact innovation; rather, success depends on how effectively recipient organizations align funding strategies with their internal capabilities and long-term goals. The study underscores the importance of differentiated funding approaches and improved organizational absorptive capacity. By incorporating metrics such as patent citations, concentration indices, and temporal performance, the research offers a multidimensional view of the effectiveness of public R&D investment. The results provide actionable guidance for both funders and recipients in shaping policies and strategies that maximize the societal and economic returns on public R&D investments.
{"title":"Does It Matter Where the Funds Go? A Sectoral Analysis of U.S. Government-Interest Patents","authors":"Dar-Zen Chen;Hsu-Chuan Chang;Mu-Hsuan Huang;Chung-Huei Kuan;Chun-Chieh Wang","doi":"10.1109/TEM.2025.3642877","DOIUrl":"https://doi.org/10.1109/TEM.2025.3642877","url":null,"abstract":"This study examines how public funding influences research and development (R&D) outcomes by analyzing the performance of government-interest (GI) patents—those that explicitly acknowledge government support—relative to non-GI patents. Using a comprehensive patentometric assessment of U.S. patent data, the study challenges the conventional belief that government-backed patents inherently yield superior results. While GI patents tend to emphasize foundational and publicly aligned research, non-GI patents often outperform them in terms of citation influence and technological impact, particularly in market-driven contexts. To interpret these patterns, the study introduces a quadrant-based framework grounded in four complementary theories: the triple helix model, Pasteur’s quadrant, organizational learning theory, and resource dependence theory. This framework supports a strategic understanding of how different recipients utilize government support, highlighting patterns of efficient resource use, latent potential, and underperformance. The findings suggest that public funding alone does not guarantee high-impact innovation; rather, success depends on how effectively recipient organizations align funding strategies with their internal capabilities and long-term goals. The study underscores the importance of differentiated funding approaches and improved organizational absorptive capacity. By incorporating metrics such as patent citations, concentration indices, and temporal performance, the research offers a multidimensional view of the effectiveness of public R&D investment. The results provide actionable guidance for both funders and recipients in shaping policies and strategies that maximize the societal and economic returns on public R&D investments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"726-739"},"PeriodicalIF":5.2,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a data-driven framework for policy-oriented benchmarking and catalyzation of innovation productivity across 65 U.S. metropolitan areas. The study achieves a high level of research rigor by integrating multiple complementary analytical modules and systematically validating results through robust statistical and diagnostic tests. Specifically, the methodological design synthesizes three components: First, a multifeature selection pipeline that combines random forest, select K-best, and recursive feature elimination to ensure statistically reliable identification of innovation determinants; second, kernel principal component analysis with parameterized kernel functions optimized to capture complex nonlinear interdependencies among innovation factors; and third, a particle swarm optimization-enhanced gradient boosting machine that delivers exceptional predictive accuracy ($R^{2}$ = 0.984, RMSE = 358.0) while demonstrating minimal overfitting ($R^{2}$ differential between training and testing = 0.016). Rigor is further reinforced through systematic residual analysis and comprehensive sensitivity analysis, which together provide robust validation of the framework's reliability. These diagnostics reveal significant regional disparities in innovation performance relative to model predictions, with striking counterintuitive results. Several metropolitan areas substantially outperform expectations through strategic ecosystem alignment and policy coherence, while others exhibit considerable innovation deficits despite apparent structural advantages. Sensitivity analysis identifies STEM education infrastructure as the most influential driver of innovation, challenging conventional policy assumptions. The empirically validated framework equips engineering managers and policymakers with actionable, quantitative insights for designing targeted interventions, allocating resources effectively, and transforming underperforming regions into resilient innovation ecosystems through evidence-based strategies.
{"title":"A Predictive Analytics Framework for Policy-Driven Benchmarking and Promotion of Innovation Productivity in U.S. Cities","authors":"Inam Ullah Khan;Khaled Abdelghany;Terrance Pohlen;Gautam Das;Eric Griffin;Victor Fishman","doi":"10.1109/TEM.2025.3642738","DOIUrl":"https://doi.org/10.1109/TEM.2025.3642738","url":null,"abstract":"This article presents a data-driven framework for policy-oriented benchmarking and catalyzation of innovation productivity across 65 U.S. metropolitan areas. The study achieves a high level of research rigor by integrating multiple complementary analytical modules and systematically validating results through robust statistical and diagnostic tests. Specifically, the methodological design synthesizes three components: First, a multifeature selection pipeline that combines random forest, select K-best, and recursive feature elimination to ensure statistically reliable identification of innovation determinants; second, kernel principal component analysis with parameterized kernel functions optimized to capture complex nonlinear interdependencies among innovation factors; and third, a particle swarm optimization-enhanced gradient boosting machine that delivers exceptional predictive accuracy (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> = 0.984, RMSE = 358.0) while demonstrating minimal overfitting (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> differential between training and testing = 0.016). Rigor is further reinforced through systematic residual analysis and comprehensive sensitivity analysis, which together provide robust validation of the framework's reliability. These diagnostics reveal significant regional disparities in innovation performance relative to model predictions, with striking counterintuitive results. Several metropolitan areas substantially outperform expectations through strategic ecosystem alignment and policy coherence, while others exhibit considerable innovation deficits despite apparent structural advantages. Sensitivity analysis identifies STEM education infrastructure as the most influential driver of innovation, challenging conventional policy assumptions. The empirically validated framework equips engineering managers and policymakers with actionable, quantitative insights for designing targeted interventions, allocating resources effectively, and transforming underperforming regions into resilient innovation ecosystems through evidence-based strategies.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"753-765"},"PeriodicalIF":5.2,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11296910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/TEM.2025.3640362
Andrea Patricia Iglesias-Pardo;Jose Moyano-Fuentes;Juan Manuel Maqueira Marin;Daniel Luiz de Mattos Nascimento
This study presents a comprehensive review of the current literature on supply chain (SC) responsiveness capabilities enabled by Industry 4.0 (I4.0) technologies, focusing on flexibility and agility as core dimensions. A systematic literature review was conducted using the Web of Science and Scopus databases, identifying 237 studies that addressed SC flexibility and 206 that addressed agility. The findings reveal distinct interrelationships between specific I4.0 technologies and SC responsiveness capabilities, highlighting the need for an integrated perspective beyond isolated technological applications. Drawing on dynamic capabilities theory, this work proposes a novel conceptual framework that systematically maps enabling I4.0 technologies to the sensing, seizing, and transforming processes underpinning SC agility and flexibility. In doing so, the study identifies critical research gaps and offers a structured foundation for future empirical and theoretical developments. The proposed framework enhances understanding of the synergistic potential of I4.0 technologies and supports strategic decision-making in SC digital transformation.
本研究对工业4.0 （I4.0）技术支持的供应链（SC）响应能力的当前文献进行了全面回顾，重点关注灵活性和敏捷性作为核心维度。使用Web of Science和Scopus数据库进行了系统的文献综述，确定了237项研究涉及SC灵活性，206项研究涉及敏捷性。研究结果揭示了特定工业4.0技术与供应链响应能力之间明显的相互关系，强调了超越孤立技术应用的综合视角的必要性。根据动态能力理论，这项工作提出了一个新的概念框架，系统地将工业4.0技术映射到支持SC敏捷性和灵活性的传感、捕获和转换过程。在此过程中，该研究确定了关键的研究差距，并为未来的实证和理论发展提供了结构化的基础。提出的框架增强了对工业4.0技术协同潜力的理解，并支持供应链数字化转型的战略决策。
{"title":"Exploring the Impact of Industry 4.0 Information Technologies on Supply Chain Responsiveness: A Dynamic Capabilities Theory Perspective","authors":"Andrea Patricia Iglesias-Pardo;Jose Moyano-Fuentes;Juan Manuel Maqueira Marin;Daniel Luiz de Mattos Nascimento","doi":"10.1109/TEM.2025.3640362","DOIUrl":"https://doi.org/10.1109/TEM.2025.3640362","url":null,"abstract":"This study presents a comprehensive review of the current literature on supply chain (SC) responsiveness capabilities enabled by Industry 4.0 (I4.0) technologies, focusing on flexibility and agility as core dimensions. A systematic literature review was conducted using the Web of Science and Scopus databases, identifying 237 studies that addressed SC flexibility and 206 that addressed agility. The findings reveal distinct interrelationships between specific I4.0 technologies and SC responsiveness capabilities, highlighting the need for an integrated perspective beyond isolated technological applications. Drawing on dynamic capabilities theory, this work proposes a novel conceptual framework that systematically maps enabling I4.0 technologies to the sensing, seizing, and transforming processes underpinning SC agility and flexibility. In doing so, the study identifies critical research gaps and offers a structured foundation for future empirical and theoretical developments. The proposed framework enhances understanding of the synergistic potential of I4.0 technologies and supports strategic decision-making in SC digital transformation.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"783-797"},"PeriodicalIF":5.2,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11288013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/TEM.2025.3640147
Shun Li;Li Li;Kun Zhang
Video platforms have increasingly been observed forming partnerships with other service providers (e.g., digital music service provider) through the launch of membership bundles. This article analytically investigates such strategic choices of competing video platforms regarding two paid membership strategies, i.e., standard plans and membership bundles. In the basic model, the chosen service provider is assumed to exhibit similar horizontal attributes as the video platform. Therefore, compared to standard plans, membership bundles reinforce the intensity of mismatches, even though they bring incremental benefits. Our study unveils the following intriguing findings. First, when the incremental value consumers receive from membership bundles is sufficiently small (large), both platforms prefer to adopt standard plans (membership bundles). Second, in the scenario where the incremental value is moderate, both platforms can be better off by adopting asymmetric membership strategies if network effects are strong. Furthermore, we demonstrate that the provision of membership bundles will intensify platform competition. This result implies that the provision of membership bundles may hurt platforms' profitability when network effects are very weak and the incremental value is not very large. However, given that membership bundles can increase social welfare, policymakers may encourage their applications. In the model extensions, the robustness of our results is tested. We allow for alternative preference correlations across bundled services, endogenize the revenue-sharing rate via the incremental value created by bundling, and introduce asymmetric intensities of network effects for different plans.
{"title":"Standard Plans or Membership Bundles? Competitive Paid Membership Strategies for Video Platforms","authors":"Shun Li;Li Li;Kun Zhang","doi":"10.1109/TEM.2025.3640147","DOIUrl":"https://doi.org/10.1109/TEM.2025.3640147","url":null,"abstract":"Video platforms have increasingly been observed forming partnerships with other service providers (e.g., digital music service provider) through the launch of membership bundles. This article analytically investigates such strategic choices of competing video platforms regarding two paid membership strategies, i.e., standard plans and membership bundles. In the basic model, the chosen service provider is assumed to exhibit similar horizontal attributes as the video platform. Therefore, compared to standard plans, membership bundles reinforce the intensity of mismatches, even though they bring incremental benefits. Our study unveils the following intriguing findings. First, when the incremental value consumers receive from membership bundles is sufficiently small (large), both platforms prefer to adopt standard plans (membership bundles). Second, in the scenario where the incremental value is moderate, both platforms can be better off by adopting asymmetric membership strategies if network effects are strong. Furthermore, we demonstrate that the provision of membership bundles will intensify platform competition. This result implies that the provision of membership bundles may hurt platforms' profitability when network effects are very weak and the incremental value is not very large. However, given that membership bundles can increase social welfare, policymakers may encourage their applications. In the model extensions, the robustness of our results is tested. We allow for alternative preference correlations across bundled services, endogenize the revenue-sharing rate via the incremental value created by bundling, and introduce asymmetric intensities of network effects for different plans.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"599-611"},"PeriodicalIF":5.2,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830822","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 : 2025-12-08DOI: 10.1109/TEM.2025.3641649
Judy Y. H. Huang;Erica Z. Y. Liu;Eric T. G. Wang;James J. Jiang
Information technology (IT)-enabled transformation (ITT) programs are increasingly vulnerable to creeping disruptions. Previous studies have proposed a range of project team resilience activities to address types of disruption, suggesting that different team resilience capacities require different resources. Drawing on the capacity view of team resilience, this study adopts a mixed-methods research approach. The qualitative phase reveals that 1) goal volatility is the most common creeping disruption, while anticipating change, planning adjustment, and adapting to new practices are the three components of ITT program team resilience; and 2) change potency, program psychological safety, business understanding, and IT improvisation are key resources in fostering ITT program resilience capacity. A quantitative survey of 177 ITT programs was used to externally validate and extend the qualitative findings. Results confirmed that ITT program team resilience significantly enhances program performance, particularly under conditions of high program goal volatility, and four identified resources are positively associated with ITT program resilience capacity. This study extends the literature on IT project management by distinguishing program team resilience in the ITT program context from project team resilience.
{"title":"Thriving Amid Creeping Disruption: A Study of IT-Enabled Transformation Program Team Resilience","authors":"Judy Y. H. Huang;Erica Z. Y. Liu;Eric T. G. Wang;James J. Jiang","doi":"10.1109/TEM.2025.3641649","DOIUrl":"https://doi.org/10.1109/TEM.2025.3641649","url":null,"abstract":"Information technology (IT)-enabled transformation (ITT) programs are increasingly vulnerable to creeping disruptions. Previous studies have proposed a range of project team resilience activities to address types of disruption, suggesting that different team resilience capacities require different resources. Drawing on the capacity view of team resilience, this study adopts a mixed-methods research approach. The qualitative phase reveals that 1) goal volatility is the most common creeping disruption, while anticipating change, planning adjustment, and adapting to new practices are the three components of ITT program team resilience; and 2) change potency, program psychological safety, business understanding, and IT improvisation are key resources in fostering ITT program resilience capacity. A quantitative survey of 177 ITT programs was used to externally validate and extend the qualitative findings. Results confirmed that ITT program team resilience significantly enhances program performance, particularly under conditions of high program goal volatility, and four identified resources are positively associated with ITT program resilience capacity. This study extends the literature on IT project management by distinguishing program team resilience in the ITT program context from project team resilience.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"766-782"},"PeriodicalIF":5.2,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885033","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 : 2025-12-08DOI: 10.1109/TEM.2025.3641381
Shan Jiang;Florence Yean Yng Ling;Jianyao Jia
Drawing on the conservation of resources theory and the cognitive–affective processing system framework, in this article, we investigate how relational governance boosts project team resilience through a dual–path mediation mechanism. Employing hierarchical multiple regression and fuzzy-set qualitative comparative analysis, empirical results reveal that team reflexivity and team affective identification fully mediate the relationship between relational governance and project team resilience, with a significant serial mediation effect flowing sequentially from team reflexivity to team affective identification. Furthermore, project complexity emerges as a boundary condition, enhancing the mediating role of team reflexivity while exerting no significant moderating effect on the affective identification pathway. A series of additional robustness checks was conducted for each method within the mixed-methods design, further reinforcing the overall rigor and reliability of the results. These findings advance the theoretical understanding of how resilience develops, unfolds, and is shaped in project teams, affording novel insights to the body of knowledge in engineering project management. In terms of practical application, this study highlights the need for project managers to prioritize relational governance strategies to foster project team resilience through cultivating reflective practices and shared affective bonds while calibrating these strategies according to the contextual complexities of project-based engineering environments.
{"title":"Unlocking Project Team Resilience: Relational Governance as Antecedent Through Reflexivity and Affective Identification","authors":"Shan Jiang;Florence Yean Yng Ling;Jianyao Jia","doi":"10.1109/TEM.2025.3641381","DOIUrl":"https://doi.org/10.1109/TEM.2025.3641381","url":null,"abstract":"Drawing on the conservation of resources theory and the cognitive–affective processing system framework, in this article, we investigate how relational governance boosts project team resilience through a dual–path mediation mechanism. Employing hierarchical multiple regression and fuzzy-set qualitative comparative analysis, empirical results reveal that team reflexivity and team affective identification fully mediate the relationship between relational governance and project team resilience, with a significant serial mediation effect flowing sequentially from team reflexivity to team affective identification. Furthermore, project complexity emerges as a boundary condition, enhancing the mediating role of team reflexivity while exerting no significant moderating effect on the affective identification pathway. A series of additional robustness checks was conducted for each method within the mixed-methods design, further reinforcing the overall rigor and reliability of the results. These findings advance the theoretical understanding of how resilience develops, unfolds, and is shaped in project teams, affording novel insights to the body of knowledge in engineering project management. In terms of practical application, this study highlights the need for project managers to prioritize relational governance strategies to foster project team resilience through cultivating reflective practices and shared affective bonds while calibrating these strategies according to the contextual complexities of project-based engineering environments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"640-653"},"PeriodicalIF":5.2,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830847","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 : 2025-12-05DOI: 10.1109/TEM.2025.3640875
Omaymah Almashaleh;Omid Fatahi Valilai
Green digital marketing is critical for advancing sustainability in the textile sector. As brands aim to reduce their environmental impact and engage ethically conscious consumers, identifying effective social-media formats is essential. This study proposes a causal inference framework that integrates double machine learning (DML), a method for estimating treatment effects in high dimensional observational data, with the DoWhy platform for treatment effect estimation and refutation testing. The framework controls for sentiment polarity, posting time, weekday/weekend status, and sustainability keywords, ensuring robust average treatment effect estimates. Empirical analysis reveals that Instagram Reels produce the strongest positive impact on engagement, measured as the combined number of likes and comments for each post. In contrast, Videos and Carousel Albums reduce interaction. Among all estimation methods tested, the DML model produced comparatively precise and stable estimates, yielding narrower confidence intervals (CIs) and stronger refutation performance than the baseline approaches. The study provides strong causal evidence; practical generalization should consider platform dynamics and potential unobserved influences. Across 20 768 posts in 2024, DML yields tighter CIs and smaller placebo errors than OLS/PSM/PSS/NDIM. Robustness is demonstrated via bootstrap CIs, and placebo effects are examined through permuted treatments, random and hidden commoncause refuters, and subset analyzes. Effects generalize within the window and context, and temporal or platform limits were noted.
{"title":"Causal Drivers of Sustainable Social Media Engagement in the Textile Industry: A Double Machine Learning Approach","authors":"Omaymah Almashaleh;Omid Fatahi Valilai","doi":"10.1109/TEM.2025.3640875","DOIUrl":"https://doi.org/10.1109/TEM.2025.3640875","url":null,"abstract":"Green digital marketing is critical for advancing sustainability in the textile sector. As brands aim to reduce their environmental impact and engage ethically conscious consumers, identifying effective social-media formats is essential. This study proposes a causal inference framework that integrates double machine learning (DML), a method for estimating treatment effects in high dimensional observational data, with the <monospace>DoWhy</monospace> platform for treatment effect estimation and refutation testing. The framework controls for sentiment polarity, posting time, weekday/weekend status, and sustainability keywords, ensuring robust average treatment effect estimates. Empirical analysis reveals that Instagram Reels produce the strongest positive impact on engagement, measured as the combined number of likes and comments for each post. In contrast, Videos and Carousel Albums reduce interaction. Among all estimation methods tested, the DML model produced comparatively precise and stable estimates, yielding narrower confidence intervals (CIs) and stronger refutation performance than the baseline approaches. The study provides strong causal evidence; practical generalization should consider platform dynamics and potential unobserved influences. Across 20 768 posts in 2024, DML yields tighter CIs and smaller placebo errors than OLS/PSM/PSS/NDIM. Robustness is demonstrated via bootstrap CIs, and placebo effects are examined through permuted treatments, random and hidden commoncause refuters, and subset analyzes. Effects generalize within the window and context, and temporal or platform limits were noted.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"495-509"},"PeriodicalIF":5.2,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：