Pub Date : 2026-02-12DOI: 10.1109/TEM.2026.3664330
Manisha Tiwari
The healthcare sector's vulnerability to societal disruptions was highlighted by the COVID-19 pandemic and supply chain challenges. This study applies Upper Echelon Theory (UET) to crisis contexts, focusing on Sustainable Development Goal 3 (Good Health and Well-Being). Unlike prior work that emphasized leaders’ immediate actions, this research examines how organizational culture influences the link between crisis leadership (CL) and healthcare supply chain resilience. Survey data from 179 participants using structural equation modelling reveal a nonlinear relationship: balancing flexibility and control in organizational culture is vital for supply chain resilience. Effective CL, backed by a strong organizational culture, boosts supply chain robustness and adaptability. In addition, 18 qualitative interviews identified key traits for crisis leaders: communication, transparency, resilience, trust, and empathy. The findings underline the crucial role of CL and balanced organizational culture in overcoming supply chain disruptions. The study recommends that managers and policymakers cultivate such cultures to enhance resilience. Limitations are noted, and future research should further explore how culture and leadership interact in uncertain environments.
{"title":"Unlocking Effective Crisis Leadership Amidst Healthcare Supply Chain Disruptions: The Nonlinear Effect of Organizational Culture","authors":"Manisha Tiwari","doi":"10.1109/TEM.2026.3664330","DOIUrl":"https://doi.org/10.1109/TEM.2026.3664330","url":null,"abstract":"The healthcare sector's vulnerability to societal disruptions was highlighted by the COVID-19 pandemic and supply chain challenges. This study applies Upper Echelon Theory (UET) to crisis contexts, focusing on Sustainable Development Goal 3 (Good Health and Well-Being). Unlike prior work that emphasized leaders’ immediate actions, this research examines how organizational culture influences the link between crisis leadership (CL) and healthcare supply chain resilience. Survey data from 179 participants using structural equation modelling reveal a nonlinear relationship: balancing flexibility and control in organizational culture is vital for supply chain resilience. Effective CL, backed by a strong organizational culture, boosts supply chain robustness and adaptability. In addition, 18 qualitative interviews identified key traits for crisis leaders: communication, transparency, resilience, trust, and empathy. The findings underline the crucial role of CL and balanced organizational culture in overcoming supply chain disruptions. The study recommends that managers and policymakers cultivate such cultures to enhance resilience. Limitations are noted, and future research should further explore how culture and leadership interact in uncertain environments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2145-2162"},"PeriodicalIF":5.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-11DOI: 10.1109/TEM.2026.3663984
Chung-Jen Chen;You-Xiang Song;Cheng Hsueh
In this article, we investigate how firms determine whether to adopt integrated-platform configurations—blending physical and digital operations—versus operating through a single modality. While prior research has focused predominantly on digital platforms, less attention has been paid to what drives the choice to pursue more complex, hybrid models. Drawing on social network theory and environmental contingency perspectives, we examine how firms’ network positions—specifically centrality and brokerage—influence their likelihood of pursuing platform integration. We also explore how these relationships are moderated by industry-level conditions, including environmental munificence and dynamism. Using a panel dataset of 424 publicly listed U.S. firms from 2013 to 2024, we develop a continuous platform integration score derived from firm disclosures and validated using machine learning techniques, including bidirectional encoder representations from transformers (BERT)opic and Sentence-BERT. To estimate causal relationships, we employ Arellano–Bond dynamic panel models. Results show that both centrality and brokerage increase the likelihood of integration, but their effects are contingent on the external environment. To ensure a high level of research rigor, we also conduct extensive robustness checks, including alternative variable specifications and industry exclusion tests. This study contributes to platform and engineering management literature by offering a nuanced understanding of platform design in complex organizational and environmental contexts.
{"title":"Strategic Platform Configuration: Network Position, Environmental Conditions, and Integration Decisions","authors":"Chung-Jen Chen;You-Xiang Song;Cheng Hsueh","doi":"10.1109/TEM.2026.3663984","DOIUrl":"https://doi.org/10.1109/TEM.2026.3663984","url":null,"abstract":"In this article, we investigate how firms determine whether to adopt integrated-platform configurations—blending physical and digital operations—versus operating through a single modality. While prior research has focused predominantly on digital platforms, less attention has been paid to what drives the choice to pursue more complex, hybrid models. Drawing on social network theory and environmental contingency perspectives, we examine how firms’ network positions—specifically centrality and brokerage—influence their likelihood of pursuing platform integration. We also explore how these relationships are moderated by industry-level conditions, including environmental munificence and dynamism. Using a panel dataset of 424 publicly listed U.S. firms from 2013 to 2024, we develop a continuous platform integration score derived from firm disclosures and validated using machine learning techniques, including bidirectional encoder representations from transformers (BERT)opic and Sentence-BERT. To estimate causal relationships, we employ Arellano–Bond dynamic panel models. Results show that both centrality and brokerage increase the likelihood of integration, but their effects are contingent on the external environment. To ensure a high level of research rigor, we also conduct extensive robustness checks, including alternative variable specifications and industry exclusion tests. This study contributes to platform and engineering management literature by offering a nuanced understanding of platform design in complex organizational and environmental contexts.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1904-1917"},"PeriodicalIF":5.2,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-11DOI: 10.1109/TEM.2026.3658087
Ruihui Yu;T. C. Edwin Cheng;XiaoYan Xu
This study investigates how artificial intelligence (AI)-driven endogenous innovation enables Chinese manufacturing firms to upgrade their positions in global value chains (GVCs). Based on survey data from 287 firms, we identify a core mechanism through which AI alleviates resource constraints by improving technical efficiency, supporting data-driven decision-making, and facilitating knowledge recombination. This mechanism helps firms overcome low-end lock-in and move toward higher value activities. Our analysis reveals two key findings that contrast with established views. First, the primary internal driver of innovation is organizational innovation culture rather than individual entrepreneurship, refining the traditional Schumpeterian paradigm’s emphasis on the entrepreneur. Second, while absorptive capacity strengthens process and product upgrading, it does not support functional upgrading, revealing a disconnect between technological capability and governance power. The study contributes theoretically by clarifying the linkages among AI capabilities, endogenous innovation, and GVC upgrading. For managers, it underscores the importance of cultivating an innovation-oriented culture within the organization, while leveraging external market pressures and policy support to build a robust foundation in data, algorithms, and computing power. All findings are validated through structural equation modeling and robustness checks, providing reliable insights for both research and practice.
{"title":"Artificial Intelligence Fueling Endogenous Innovation: Evidence on Global Value Chain Upgrading in Chinese Manufacturing Firms","authors":"Ruihui Yu;T. C. Edwin Cheng;XiaoYan Xu","doi":"10.1109/TEM.2026.3658087","DOIUrl":"https://doi.org/10.1109/TEM.2026.3658087","url":null,"abstract":"This study investigates how artificial intelligence (AI)-driven endogenous innovation enables Chinese manufacturing firms to upgrade their positions in global value chains (GVCs). Based on survey data from 287 firms, we identify a core mechanism through which AI alleviates resource constraints by improving technical efficiency, supporting data-driven decision-making, and facilitating knowledge recombination. This mechanism helps firms overcome low-end lock-in and move toward higher value activities. Our analysis reveals two key findings that contrast with established views. First, the primary internal driver of innovation is organizational innovation culture rather than individual entrepreneurship, refining the traditional Schumpeterian paradigm’s emphasis on the entrepreneur. Second, while absorptive capacity strengthens process and product upgrading, it does not support functional upgrading, revealing a disconnect between technological capability and governance power. The study contributes theoretically by clarifying the linkages among AI capabilities, endogenous innovation, and GVC upgrading. For managers, it underscores the importance of cultivating an innovation-oriented culture within the organization, while leveraging external market pressures and policy support to build a robust foundation in data, algorithms, and computing power. All findings are validated through structural equation modeling and robustness checks, providing reliable insights for both research and practice.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2163-2179"},"PeriodicalIF":5.2,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-10DOI: 10.1109/TEM.2026.3662602
{"title":"2025 Index IEEE Transactions on Engineering Management Vol. 72","authors":"","doi":"10.1109/TEM.2026.3662602","DOIUrl":"https://doi.org/10.1109/TEM.2026.3662602","url":null,"abstract":"","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"1-66"},"PeriodicalIF":5.2,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11389218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175699","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 : 2026-02-09DOI: 10.1109/TEM.2026.3662723
Pawat Tansurat;Nathasit Gerdsri
Technology roadmapping (TRM) is a widely used strategic planning tool in technology-intensive industries. However, the conventional roadmapping approach faces limitations under conditions of high complexity and uncertainty. This study proposes an integrated framework that combines system dynamics (SD) with scenario-based TRM to enhance roadmap robustness and adaptability. By leveraging SD’s capability to model nonlinear interactions and simulate multiple future conditions, the framework provides quantitative insights that strengthen scenario development and support evidence-based decision making. A step-by-step application is demonstrated through the development of roadmaps for future medical devices and technologies. Verification and validation of the SD model confirm its reliability prior to translating simulation outcomes into scenario-based roadmaps. The results demonstrate how the integrated approach facilitates the development of alternative technology roadmaps, representing potential development pathways that better anticipate dynamic changes, enhance stakeholder alignment, and reduce the time required to assess the impacts of shifting conditions. The proposed framework improves the capability of technology roadmapping to address complex and uncertain environments, supporting organizations in mitigating risks, identifying emerging opportunities, and strengthening their strategic resilience.
{"title":"Applying System Dynamics to Enhance Scenario-Based Technology Roadmapping: Analysis Framework and Case Demonstration","authors":"Pawat Tansurat;Nathasit Gerdsri","doi":"10.1109/TEM.2026.3662723","DOIUrl":"https://doi.org/10.1109/TEM.2026.3662723","url":null,"abstract":"Technology roadmapping (TRM) is a widely used strategic planning tool in technology-intensive industries. However, the conventional roadmapping approach faces limitations under conditions of high complexity and uncertainty. This study proposes an integrated framework that combines system dynamics (SD) with scenario-based TRM to enhance roadmap robustness and adaptability. By leveraging SD’s capability to model nonlinear interactions and simulate multiple future conditions, the framework provides quantitative insights that strengthen scenario development and support evidence-based decision making. A step-by-step application is demonstrated through the development of roadmaps for future medical devices and technologies. Verification and validation of the SD model confirm its reliability prior to translating simulation outcomes into scenario-based roadmaps. The results demonstrate how the integrated approach facilitates the development of alternative technology roadmaps, representing potential development pathways that better anticipate dynamic changes, enhance stakeholder alignment, and reduce the time required to assess the impacts of shifting conditions. The proposed framework improves the capability of technology roadmapping to address complex and uncertain environments, supporting organizations in mitigating risks, identifying emerging opportunities, and strengthening their strategic resilience.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1872-1888"},"PeriodicalIF":5.2,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/TEM.2026.3661929
Xiaochen Yue;Xiaoqi Lyu;Min Tian
As artificial intelligence (AI) becomes increasingly embedded in organizational operations, it affects employees in dual ways, creating opportunities for skill development while posing challenges related to job security. Firms implementing AI therefore confront with a tradeoff between technological advancement and employee-related concerns. This study investigates the relationship between AI implementation and employee welfare through the lens of the attention-based view (ABV), which explains how shifts in managerial attention shape employee welfare during AI implementation. Using a panel sample of Chinese publicly listed manufacturers between 2010 and 2020, we identify a U-shaped relationship between AI implementation and employee welfare. To ensure methodological rigor, we address endogeneity issues, and conduct extensive robustness checks using alternative measures and model specifications, subsample analyses, propensity score matching, and placebo tests. We further find that this curvilinear pattern is steepened by humane orientation while flattened by organizational inertia. This study contributes to ABV by advancing an attention-based microfoundation that explains the technology-employee tension through dynamic managerial attention flows. It also contributes to AI implementation literature by conceptualizing AI implementation as a dynamic and adaptive organizational process. This study also provides actionable implications for firms to deploy AI in ways that balance technological advancement with employee well-being.
{"title":"The Impact of AI Implementation on Employee Welfare: An Attention-Based View","authors":"Xiaochen Yue;Xiaoqi Lyu;Min Tian","doi":"10.1109/TEM.2026.3661929","DOIUrl":"https://doi.org/10.1109/TEM.2026.3661929","url":null,"abstract":"As artificial intelligence (AI) becomes increasingly embedded in organizational operations, it affects employees in dual ways, creating opportunities for skill development while posing challenges related to job security. Firms implementing AI therefore confront with a tradeoff between technological advancement and employee-related concerns. This study investigates the relationship between AI implementation and employee welfare through the lens of the attention-based view (ABV), which explains how shifts in managerial attention shape employee welfare during AI implementation. Using a panel sample of Chinese publicly listed manufacturers between 2010 and 2020, we identify a U-shaped relationship between AI implementation and employee welfare. To ensure methodological rigor, we address endogeneity issues, and conduct extensive robustness checks using alternative measures and model specifications, subsample analyses, propensity score matching, and placebo tests. We further find that this curvilinear pattern is steepened by humane orientation while flattened by organizational inertia. This study contributes to ABV by advancing an attention-based microfoundation that explains the technology-employee tension through dynamic managerial attention flows. It also contributes to AI implementation literature by conceptualizing AI implementation as a dynamic and adaptive organizational process. This study also provides actionable implications for firms to deploy AI in ways that balance technological advancement with employee well-being.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1710-1722"},"PeriodicalIF":5.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/TEM.2026.3658353
Wenzhi Tang;Shaofu Du;Chenyang Gou;Li Hu
Incumbents with proprietary technologies often use patents to protect the monopoly right, while some adjust to the technology opening strategy to maintain the market dominance of the leading technology and market expansion. New entrants also face a dilemma: imitate open-source technology for mitigating the risks and costs of technology R&D or innovate to differentiate, considering their technology absorption and transformation capabilities. We build a three-stage Stackelberg model to analyze the strategic interplay involving an incumbent who owns exclusive technology and a potential entrant under technology uncertainty, revealing equilibrium outcomes in technology strategies and production decisions. Equilibrium results indicate that with moderate technology absorption and transformation capability, the incumbent discloses its technology for market expansion to foster imitative entrants, whereas with a weak capability, the technology opening strategy becomes irrelevant as the entrant innovates regardless. Surprisingly, the technology closing strategy forces the strong absorption capability entrant to engage in technology innovation. Moreover, the incumbent defends market share by adjusting production, which paradoxically benefits consumers. Welfare analysis indicates that technology monopoly or excessive innovation costs reduce industry profits and consumer surplus. We propose a flexible subsidy scheme based on the technology transformation rate and innovation cost to incentivize innovation and opening. Extensions on vertical differentiation encroachment and market uncertainty validate our base model.
{"title":"Exclusive or Inclusive? Technology Opening, Innovation, and Imitation Strategies Under Uncertainty","authors":"Wenzhi Tang;Shaofu Du;Chenyang Gou;Li Hu","doi":"10.1109/TEM.2026.3658353","DOIUrl":"https://doi.org/10.1109/TEM.2026.3658353","url":null,"abstract":"Incumbents with proprietary technologies often use patents to protect the monopoly right, while some adjust to the technology opening strategy to maintain the market dominance of the leading technology and market expansion. New entrants also face a dilemma: imitate open-source technology for mitigating the risks and costs of technology R&D or innovate to differentiate, considering their technology absorption and transformation capabilities. We build a three-stage Stackelberg model to analyze the strategic interplay involving an incumbent who owns exclusive technology and a potential entrant under technology uncertainty, revealing equilibrium outcomes in technology strategies and production decisions. Equilibrium results indicate that with moderate technology absorption and transformation capability, the incumbent discloses its technology for market expansion to foster imitative entrants, whereas with a weak capability, the technology opening strategy becomes irrelevant as the entrant innovates regardless. Surprisingly, the technology closing strategy forces the strong absorption capability entrant to engage in technology innovation. Moreover, the incumbent defends market share by adjusting production, which paradoxically benefits consumers. Welfare analysis indicates that technology monopoly or excessive innovation costs reduce industry profits and consumer surplus. We propose a flexible subsidy scheme based on the technology transformation rate and innovation cost to incentivize innovation and opening. Extensions on vertical differentiation encroachment and market uncertainty validate our base model.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1929-1943"},"PeriodicalIF":5.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/TEM.2026.3661838
Faqi Xie;Xiang Li
With the development of Big Data technology, firms are able to collect data on consumer preferences and use it to personalize pricing. Personalized pricing, a tool that is heavily used in practice, is studied in the existing literature mainly in the context of symmetric information that takes into account consumer preferences. However, the personalized pricing under both complete and incomplete information when considering consumers' sensitivity to time slack (STTS) that affects consumer preferences is rarely examined. We develop a two-period game model of two carriers with asymmetric STTS information to capture carriers' freight rate decisions (personalized and nonpersonalized price) and effects of STTS information transparency. We find that when an information-advantaged (IA) carrier is initially more competitive in the market, where it can poach consumers from an information-disadvantaged (ID) carrier in the second period but not vice versa, STTS information transparency and leadership transition (i.e., the process in which the ID carrier changes from an initial market follower to a market leader) do not affect the IA carrier's personalized pricing but reduce that of the ID carrier. In equilibrium, when consumers' STTS is of the high type, information transparency increases the IA carrier's profit but may reduce the ID carrier's profit. We also find that when consumers' STTS is low, leadership transition increases the IA carrier's profit and decreases the ID carrier's profit. Our research provides guidance on carriers' personalized pricing and whether the ID carrier should strive to become the market leader. At the same time, our study provides a basis for carriers to determine the impact of information transparency in the shipping industry.
{"title":"Shipping to Customers With Asymmetric Carriers Under Personalized Pricing","authors":"Faqi Xie;Xiang Li","doi":"10.1109/TEM.2026.3661838","DOIUrl":"https://doi.org/10.1109/TEM.2026.3661838","url":null,"abstract":"With the development of Big Data technology, firms are able to collect data on consumer preferences and use it to personalize pricing. Personalized pricing, a tool that is heavily used in practice, is studied in the existing literature mainly in the context of symmetric information that takes into account consumer preferences. However, the personalized pricing under both complete and incomplete information when considering consumers' sensitivity to time slack (STTS) that affects consumer preferences is rarely examined. We develop a two-period game model of two carriers with asymmetric STTS information to capture carriers' freight rate decisions (personalized and nonpersonalized price) and effects of STTS information transparency. We find that when an information-advantaged (IA) carrier is initially more competitive in the market, where it can poach consumers from an information-disadvantaged (ID) carrier in the second period but not vice versa, STTS information transparency and leadership transition (i.e., the process in which the ID carrier changes from an initial market follower to a market leader) do not affect the IA carrier's personalized pricing but reduce that of the ID carrier. In equilibrium, when consumers' STTS is of the high type, information transparency increases the IA carrier's profit but may reduce the ID carrier's profit. We also find that when consumers' STTS is low, leadership transition increases the IA carrier's profit and decreases the ID carrier's profit. Our research provides guidance on carriers' personalized pricing and whether the ID carrier should strive to become the market leader. At the same time, our study provides a basis for carriers to determine the impact of information transparency in the shipping industry.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2004-2034"},"PeriodicalIF":5.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/TEM.2026.3661903
Tianxiu Zhang;Zhijian Cui;Xiaojin Liu
Strategic quality management (SQM) and innovation are both critical for achieving competitive advantage, yet their interrelationship remains insufficiently understood. To address this gap, this study examines the distinct effects of SQM on exploitative and exploratory innovation, as well as the moderating role of operational complexity in shaping these effects. We compile a longitudinal dataset of Chinese listed firms covering the period 2007–2020, supplemented with firm-level quality award records from China’s quality award systems. Subsequently, we employ the eXtreme Gradient Boosting algorithm to develop a predictive measure of SQM. We then combine this measure with ordinary least squares (OLS) regression to assess the causal relationships among the variables. Our findings show that SQM positively influences exploitative innovation but negatively affects exploratory innovation. Notably, operational complexity, operationalized as business diversification and supply chain geographic dispersion, significantly weakens SQM’s adverse effect on exploratory innovation. These findings reconcile conflicting theoretical perspectives, demonstrating that the strategic design of operational contexts needs to balance the standardization needs inherent in SQM with the flexibility required for exploratory innovation. This study advances the literature by conceptualizing SQM as a strategic capability whose innovation outcomes depend on the type of innovation. It also offers actionable insights for managers seeking to integrate quality management and innovation in dynamic environments. Our results are robust across a series of supplementary analyses, strengthening their reliability and generalizability.
{"title":"How Strategic Quality Management Shapes Innovation: The Moderating Role of Operational Complexity","authors":"Tianxiu Zhang;Zhijian Cui;Xiaojin Liu","doi":"10.1109/TEM.2026.3661903","DOIUrl":"https://doi.org/10.1109/TEM.2026.3661903","url":null,"abstract":"Strategic quality management (SQM) and innovation are both critical for achieving competitive advantage, yet their interrelationship remains insufficiently understood. To address this gap, this study examines the distinct effects of SQM on exploitative and exploratory innovation, as well as the moderating role of operational complexity in shaping these effects. We compile a longitudinal dataset of Chinese listed firms covering the period 2007–2020, supplemented with firm-level quality award records from China’s quality award systems. Subsequently, we employ the eXtreme Gradient Boosting algorithm to develop a predictive measure of SQM. We then combine this measure with ordinary least squares (OLS) regression to assess the causal relationships among the variables. Our findings show that SQM positively influences exploitative innovation but negatively affects exploratory innovation. Notably, operational complexity, operationalized as business diversification and supply chain geographic dispersion, significantly weakens SQM’s adverse effect on exploratory innovation. These findings reconcile conflicting theoretical perspectives, demonstrating that the strategic design of operational contexts needs to balance the standardization needs inherent in SQM with the flexibility required for exploratory innovation. This study advances the literature by conceptualizing SQM as a strategic capability whose innovation outcomes depend on the type of innovation. It also offers actionable insights for managers seeking to integrate quality management and innovation in dynamic environments. Our results are robust across a series of supplementary analyses, strengthening their reliability and generalizability.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1681-1694"},"PeriodicalIF":5.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/TEM.2026.3662135
Song Ding;Zhijian Cai;Huahan Zhang
The provincial transportation carbon emissions (TCEs) in China exhibit spatiotemporal heterogeneous characteristics and have become increasingly unpredictable in recent years. To this end, this work proposes a multisource, multiprocess forecasting model, named recursive DFD-PINN, encompassing three crucial components of the discovery of fractional-derivative dynamics, physics-informed neural networks (PINNs), and a recursive mechanism. The newly proposed model is typically designed for forecast tasks of heterogeneous time series dynamics. This model can identify exclusive evolutionary equations describing the provincial TCE systems, leveraging PINN for unified solutions and incorporating short-term temporal dependencies and new updates. Empirically, the comparative experiments indicate that recursive DFD-PINN outperforms six prevailing benchmarks in terms of forecasting accuracy, stability, and robustness against outlier predictions. Furthermore, robustness checks are performed through Monte-Carlo simulations to validate the proposed framework’s stability against randomness in real-world applications. Lastly, the fusion efficacy analysis is conducted by ablation studies to validate the synergy and effectiveness of the fusing components. Supported by the empirical evidence, recursive DFD-PINN is employed to forecast China’s provincial- and national-level TCEs, offering the interpretable use of the hybrid model and informed decision-making for transportation decarbonization.
{"title":"A Recursive Discovery of Fractional Dynamics and Its Informed Neural Network Framework With Applications to Heterogeneous Transportation Carbon Emission Forecast","authors":"Song Ding;Zhijian Cai;Huahan Zhang","doi":"10.1109/TEM.2026.3662135","DOIUrl":"https://doi.org/10.1109/TEM.2026.3662135","url":null,"abstract":"The provincial transportation carbon emissions (TCEs) in China exhibit spatiotemporal heterogeneous characteristics and have become increasingly unpredictable in recent years. To this end, this work proposes a multisource, multiprocess forecasting model, named recursive DFD-PINN, encompassing three crucial components of the discovery of fractional-derivative dynamics, physics-informed neural networks (PINNs), and a recursive mechanism. The newly proposed model is typically designed for forecast tasks of heterogeneous time series dynamics. This model can identify exclusive evolutionary equations describing the provincial TCE systems, leveraging PINN for unified solutions and incorporating short-term temporal dependencies and new updates. Empirically, the comparative experiments indicate that recursive DFD-PINN outperforms six prevailing benchmarks in terms of forecasting accuracy, stability, and robustness against outlier predictions. Furthermore, robustness checks are performed through Monte-Carlo simulations to validate the proposed framework’s stability against randomness in real-world applications. Lastly, the fusion efficacy analysis is conducted by ablation studies to validate the synergy and effectiveness of the fusing components. Supported by the empirical evidence, recursive DFD-PINN is employed to forecast China’s provincial- and national-level TCEs, offering the interpretable use of the hybrid model and informed decision-making for transportation decarbonization.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1855-1871"},"PeriodicalIF":5.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299604","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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