Pub Date : 2026-02-25DOI: 10.1109/TEM.2026.3667874
Yidan Ma;Bin Shen;Nanzhi Xue;Yifan Cao
Mandatory environmental, social, and governance (ESG) disclosure regulations vary across countries and regions. Some, such as Singapore, require all listed firms to disclose ESG reports (i.e., full ESG disclosure), while others, including the U.K. and the EU, mandate disclosure only for large firms and allow small firms to disclose voluntarily (i.e., partial ESG disclosure). To examine the implications of these regulatory designs, we develop a game-theoretic model with one large firm and one small firm in a competitive market, and assess how disclosure regulations affect firms’ strategic disclosure choices and performance outcomes. Our results reveal that the small firm voluntarily discloses ESG reports only when the penalty for violations falls below a certain threshold. An all-win outcome for the large firm, the small firm, and consumers arises only under a moderate level of penalty intensity, under which both firms disclose ESG reports. Moreover, while partial disclosure can mitigate the competitive disparity between large and small firms (i.e., the Matthew effect), this effect is realized only when the penalty is sufficiently high; otherwise, partial disclosure may even exacerbate this disparity. Overall, our analysis underscores that the interaction between the scope of mandatory disclosure and penalty intensity fundamentally shapes disclosure incentives and market fairness. Therefore, well-aligned regulation can improve transparency while preventing excessive market polarization, which provides valuable insights for policymakers seeking to balance regulatory objectives with market equity.
{"title":"Managing Transparency: ESG Disclosure and Firm Performance","authors":"Yidan Ma;Bin Shen;Nanzhi Xue;Yifan Cao","doi":"10.1109/TEM.2026.3667874","DOIUrl":"https://doi.org/10.1109/TEM.2026.3667874","url":null,"abstract":"Mandatory environmental, social, and governance (ESG) disclosure regulations vary across countries and regions. Some, such as Singapore, require all listed firms to disclose ESG reports (i.e., full ESG disclosure), while others, including the U.K. and the EU, mandate disclosure only for large firms and allow small firms to disclose voluntarily (i.e., partial ESG disclosure). To examine the implications of these regulatory designs, we develop a game-theoretic model with one large firm and one small firm in a competitive market, and assess how disclosure regulations affect firms’ strategic disclosure choices and performance outcomes. Our results reveal that the small firm voluntarily discloses ESG reports only when the penalty for violations falls below a certain threshold. An all-win outcome for the large firm, the small firm, and consumers arises only under a moderate level of penalty intensity, under which both firms disclose ESG reports. Moreover, while partial disclosure can mitigate the competitive disparity between large and small firms (i.e., the Matthew effect), this effect is realized only when the penalty is sufficiently high; otherwise, partial disclosure may even exacerbate this disparity. Overall, our analysis underscores that the interaction between the scope of mandatory disclosure and penalty intensity fundamentally shapes disclosure incentives and market fairness. Therefore, well-aligned regulation can improve transparency while preventing excessive market polarization, which provides valuable insights for policymakers seeking to balance regulatory objectives with market equity.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2180-2192"},"PeriodicalIF":5.2,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440631","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 : 2026-02-20DOI: 10.1109/TEM.2026.3666760
Fatong Shi;Tingting Li
Due to the considerable valuation uncertainty in online shopping, consumers often return products after purchase. To reduce consumer returns, retailers provide detailed product information, referred to as uncertainty-reducing (UR) information, to mitigate consumers’ valuation uncertainty. Even so, consumers cannot fully ascertain their true valuations before purchase. Furthermore, they often discover that the actual utility after purchase deviates from their prepurchase expectations, leading to perceived losses or gains, which is referred to as reference-dependence. This study considers a monopolistic online retailer selling products to consumers with valuation uncertainty. The retailer can provide UR information to consumers who receive an imperfect signal (good or bad) indicating their true valuation types. We examine the retailer’s optimal information provision strategy for rational and reference-dependent consumers, respectively. Furthermore, we analyze the interplay between the retailer’s provision of UR information and the consumers’ reference-dependent behavior. The results show that, for rational consumers, the retailer can always provide information, and higher information accuracy is more advantageous. However, with reference-dependent behavior, even if providing information is optimal, overly accurate information may harm the retailer’s interests. Additionally, providing overly accurate information can exacerbate the return behavior of reference-dependent consumers. Moreover, we analyze consumer surplus under the retailer’s optimal information provision strategy and find that the strategy of providing UR information and offering full market coverage can lead to a win–win outcome for the retailer and the consumers. To ensure analytical rigor, we extend the analysis by considering a positive return hassle cost of consumers and demonstrate that all key insights remain qualitatively robust.
{"title":"Information Provision Strategy of Retailers Under Consumer Reference-Dependent Behavior","authors":"Fatong Shi;Tingting Li","doi":"10.1109/TEM.2026.3666760","DOIUrl":"https://doi.org/10.1109/TEM.2026.3666760","url":null,"abstract":"Due to the considerable valuation uncertainty in online shopping, consumers often return products after purchase. To reduce consumer returns, retailers provide detailed product information, referred to as uncertainty-reducing (UR) information, to mitigate consumers’ valuation uncertainty. Even so, consumers cannot fully ascertain their true valuations before purchase. Furthermore, they often discover that the actual utility after purchase deviates from their prepurchase expectations, leading to perceived losses or gains, which is referred to as reference-dependence. This study considers a monopolistic online retailer selling products to consumers with valuation uncertainty. The retailer can provide UR information to consumers who receive an imperfect signal (good or bad) indicating their true valuation types. We examine the retailer’s optimal information provision strategy for rational and reference-dependent consumers, respectively. Furthermore, we analyze the interplay between the retailer’s provision of UR information and the consumers’ reference-dependent behavior. The results show that, for rational consumers, the retailer can always provide information, and higher information accuracy is more advantageous. However, with reference-dependent behavior, even if providing information is optimal, overly accurate information may harm the retailer’s interests. Additionally, providing overly accurate information can exacerbate the return behavior of reference-dependent consumers. Moreover, we analyze consumer surplus under the retailer’s optimal information provision strategy and find that the strategy of providing UR information and offering full market coverage can lead to a win–win outcome for the retailer and the consumers. To ensure analytical rigor, we extend the analysis by considering a positive return hassle cost of consumers and demonstrate that all key insights remain qualitatively robust.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1973-1988"},"PeriodicalIF":5.2,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440553","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 : 2026-02-20DOI: 10.1109/TEM.2026.3665982
Chunyu Bao;Min Li
For firms committed to environmentally friendly (green) innovation, the launch strategy—particularly the choice of timing and product portfolio—critically affects both market opportunities and reliability risks. We develop a two-period model to examine how early launch decisions, mixed-product strategies, and proactive regulatory compliance interact under potential recalls. Our findings indicate that early launch is viable only when sufficient reliability is ensured, and that retaining mature products mitigates recall-related losses through a “risk-sharing effect.” In addition, contrary to the traditional view that government regulation acts as a constraint, our study reveals that the “reliability assurance” aspect of government regulation can actually incentivize the firm to launch green innovative products earlier. Furthermore, the extension not only confirms the robustness of our model but also constructively suggests that strategic partial market withdrawal can internally motivate a firm’s green innovation. These insights provide both theoretical and practical guidance for designing green product launch strategies that balance speed, performance, and risk.
{"title":"Launch Strategy for Green Innovative Products Considering Recall Risk: Timing and Product Portfolio Decisions","authors":"Chunyu Bao;Min Li","doi":"10.1109/TEM.2026.3665982","DOIUrl":"https://doi.org/10.1109/TEM.2026.3665982","url":null,"abstract":"For firms committed to environmentally friendly (green) innovation, the launch strategy—particularly the choice of timing and product portfolio—critically affects both market opportunities and reliability risks. We develop a two-period model to examine how early launch decisions, mixed-product strategies, and proactive regulatory compliance interact under potential recalls. Our findings indicate that early launch is viable only when sufficient reliability is ensured, and that retaining mature products mitigates recall-related losses through a “risk-sharing effect.” In addition, contrary to the traditional view that government regulation acts as a constraint, our study reveals that the “reliability assurance” aspect of government regulation can actually incentivize the firm to launch green innovative products earlier. Furthermore, the extension not only confirms the robustness of our model but also constructively suggests that strategic partial market withdrawal can internally motivate a firm’s green innovation. These insights provide both theoretical and practical guidance for designing green product launch strategies that balance speed, performance, and risk.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1959-1972"},"PeriodicalIF":5.2,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362537","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}
In contemporary online multiplayer environments, disparities in social capital between new and repeat participants give rise to pronounced social-comparison dynamics. New players, who lack both experiential knowledge and accumulated in-game resources, often experience psychological inferiority when interacting with more seasoned participants. This effect diminishes the incentive for potential newcomers to engage with the game, thereby threatening the long-term sustainability and growth of the community. To mitigate it, an increasing number of game providers have adopted behavior-based quality discrimination (BBQD) strategies, through which they grant enhanced in-game benefits to new players in order to bolster their engagement. In this article, we develop a two-period, game-theoretic framework in which two horizontally differentiated game providers set constant prices for their respective multiplayer games. We examine the strategic interplay between social-comparison effects and the adoption of BBQD strategies. Contrary to the conventional wisdom in behavior-based discrimination (BBD) literature (including behavior-based quality discrimination and price discrimination), where BBD typically reduces firm profitability in duopoly markets, our analysis shows that the incorporation of social-comparison effects allows providers to increase both pricing power and profitability under BBQD. Furthermore, we find that providers can benefit from implementing BBQD when upward comparison is strong while may fall into prisoner’s dilemma when upward comparison is weak. Finally, we figure out the impact of BBQD strategies on consumer surplus and social welfare. We further verify the robustness of the above results through several model extensions relaxing baseline assumptions.
{"title":"Implications of Quality Discrimination in Multiplayer Games: The Role of Social Comparison Effect","authors":"Lingyu Qiao;Wansheng Tang;Xiaoqing Fan;Jianxiong Zhang","doi":"10.1109/TEM.2026.3665888","DOIUrl":"https://doi.org/10.1109/TEM.2026.3665888","url":null,"abstract":"In contemporary online multiplayer environments, disparities in social capital between new and repeat participants give rise to pronounced social-comparison dynamics. New players, who lack both experiential knowledge and accumulated in-game resources, often experience psychological inferiority when interacting with more seasoned participants. This effect diminishes the incentive for potential newcomers to engage with the game, thereby threatening the long-term sustainability and growth of the community. To mitigate it, an increasing number of game providers have adopted behavior-based quality discrimination (BBQD) strategies, through which they grant enhanced in-game benefits to new players in order to bolster their engagement. In this article, we develop a two-period, game-theoretic framework in which two horizontally differentiated game providers set constant prices for their respective multiplayer games. We examine the strategic interplay between social-comparison effects and the adoption of BBQD strategies. Contrary to the conventional wisdom in behavior-based discrimination (BBD) literature (including behavior-based quality discrimination and price discrimination), where BBD typically reduces firm profitability in duopoly markets, our analysis shows that the incorporation of social-comparison effects allows providers to increase both pricing power and profitability under BBQD. Furthermore, we find that providers can benefit from implementing BBQD when upward comparison is strong while may fall into prisoner’s dilemma when upward comparison is weak. Finally, we figure out the impact of BBQD strategies on consumer surplus and social welfare. We further verify the robustness of the above results through several model extensions relaxing baseline assumptions.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1989-2003"},"PeriodicalIF":5.2,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440590","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}
Understanding how the public engages with messages from organizations is key to getting people to adopt sustainable technological innovation. This study looks at what drives social media interactions with sustainable technological innovation content in both developed and developing countries using language processing, econometric modeling, and machine learning. The study chose hydrogen fuel cell vehicles as an example. By studying large amounts of social media data, the research finds important themes and factors that shape public engagement. The results show that an ideal balance between simple text and technical information leads to higher engagement. Engagement trends vary by region: short-term issues attract more attention in developed countries, while both short- and long-term energy topics appeal to developing countries. Emotional factors like trust and fear play a big role in engagement across different settings. The study provides insights into how messages from organizations can influence public attitudes toward new sustainable technologies. It also gives practical tips to improve content and boost engagement. This way, organizations and policymakers can make better decisions that promote sustainable technology. Overall, the research helps us understand how communication affects the public's interest in renewable energy.
{"title":"Drivers of Social Media Engagement on Organizational Communication on Sustainable Technological Innovation: Insights From Developed and Developing Countries","authors":"Swagato Chatterjee;Arpita Ghatak;Anirudh Kumar Meena;Piyush Meena","doi":"10.1109/TEM.2026.3665711","DOIUrl":"https://doi.org/10.1109/TEM.2026.3665711","url":null,"abstract":"Understanding how the public engages with messages from organizations is key to getting people to adopt sustainable technological innovation. This study looks at what drives social media interactions with sustainable technological innovation content in both developed and developing countries using language processing, econometric modeling, and machine learning. The study chose hydrogen fuel cell vehicles as an example. By studying large amounts of social media data, the research finds important themes and factors that shape public engagement. The results show that an ideal balance between simple text and technical information leads to higher engagement. Engagement trends vary by region: short-term issues attract more attention in developed countries, while both short- and long-term energy topics appeal to developing countries. Emotional factors like trust and fear play a big role in engagement across different settings. The study provides insights into how messages from organizations can influence public attitudes toward new sustainable technologies. It also gives practical tips to improve content and boost engagement. This way, organizations and policymakers can make better decisions that promote sustainable technology. Overall, the research helps us understand how communication affects the public's interest in renewable energy.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1944-1958"},"PeriodicalIF":5.2,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362358","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 : 2026-02-16DOI: 10.1109/TEM.2026.3665195
Zhe Zhang;Xinyi Chen
Despite the growing attention given to applicants’ reactions to the utilization of artificial intelligence (AI) within organizational recruitment contexts, scholars have paid little attention to the emotional mechanism between AI decision-making (especially negative) and applicants’ procedural justice perception, as well as how the perception affects their subsequent behavior. Drawing on the cognitive–functional model of discrete negative emotions, in this article, we propose and empirically test the effect of anger triggered by AI-driven negative hiring decisions on applicants’ procedural justice perception and complaint behavior. General support for the model testing is obtained from two studies, namely, a laboratory experiment (N = 97) that uses technology for facial expression analysis (i.e., FaceReader 8.0) and an online scenario-based experiment (N = 270). The rigor of our findings is enhanced by collecting data from multiple participant sources, using FaceReader and self-reports in a complementary manner to measure anger, and by introducing control variables for robustness checks. We find that AI-driven negative decisions, rather than human-driven ones, elicit greater anger from applicants. Notably, this effect is weakened for applicants with extensive prior AI experience. Moreover, anger suppresses applicants’ perceptions of procedural justice, thus yielding a distinct effect on their complaint behavior. Our study contributes to the understanding of the effect of AI-driven negative hiring decisions on applicants’ emotional, cognitive, and behavioral reactions. It also sheds light on the influence of applicants’ prior AI experience in reducing the negative effect of AI-driven negative hiring decisions on applicants’ subsequent responses.
{"title":"How Dare You Refuse Me? How and When AI Negative Hiring Decision Influences Applicants’ Reactions","authors":"Zhe Zhang;Xinyi Chen","doi":"10.1109/TEM.2026.3665195","DOIUrl":"https://doi.org/10.1109/TEM.2026.3665195","url":null,"abstract":"Despite the growing attention given to applicants’ reactions to the utilization of artificial intelligence (AI) within organizational recruitment contexts, scholars have paid little attention to the emotional mechanism between AI decision-making (especially negative) and applicants’ procedural justice perception, as well as how the perception affects their subsequent behavior. Drawing on the cognitive–functional model of discrete negative emotions, in this article, we propose and empirically test the effect of anger triggered by AI-driven negative hiring decisions on applicants’ procedural justice perception and complaint behavior. General support for the model testing is obtained from two studies, namely, a laboratory experiment (<italic>N</i> = 97) that uses technology for facial expression analysis (i.e., FaceReader 8.0) and an online scenario-based experiment (<italic>N</i> = 270). The rigor of our findings is enhanced by collecting data from multiple participant sources, using FaceReader and self-reports in a complementary manner to measure anger, and by introducing control variables for robustness checks. We find that AI-driven negative decisions, rather than human-driven ones, elicit greater anger from applicants. Notably, this effect is weakened for applicants with extensive prior AI experience. Moreover, anger suppresses applicants’ perceptions of procedural justice, thus yielding a distinct effect on their complaint behavior. Our study contributes to the understanding of the effect of AI-driven negative hiring decisions on applicants’ emotional, cognitive, and behavioral reactions. It also sheds light on the influence of applicants’ prior AI experience in reducing the negative effect of AI-driven negative hiring decisions on applicants’ subsequent responses.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1918-1928"},"PeriodicalIF":5.2,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362546","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 : 2026-02-16DOI: 10.1109/TEM.2026.3664073
Francisco J. Santos-Arteaga;Raquel Marín;Madjid Tavana;Debora Di Caprio
Digitalization strategies emerge through interactions among producers, consumers, and institutions across value chains, leading to gains in productivity, the introduction of new patents, and innovations in products and processes. Dynamic capabilities theory extends the static resource-based view of the firm by incorporating learning and innovation in evolving environments. Its collaborative extension has recently been introduced to analyze the behavior of corporate dynamics within evolving ecosystems shaped by information and communication technologies and firms’ digitalization strategies. However, empirical evaluations of how digital value chains influence firm-level productivity and innovation remain limited, relying on static efficiency evaluation frameworks. We apply a dynamic slacks-based data envelopment analysis model to a panel of 1369 Spanish manufacturing firms progressing through the emergence and consolidation phases of digitalization that have characterized the early decades of the twenty-first century. We examine how efficiency and competitiveness evolve, finding that larger firms and those in more complex technological sectors show more varied behavior but lower inefficiency. The findings offer implications for theory, best practices, and digitalization policies. The robustness of our analysis has been validated by demonstrating substantial differences between the patterns obtained under the standard static and our dynamic evaluation environments.
{"title":"On the Convergence-Club Nature of Competitiveness and Efficiency Across Firms by Technological Complexity and Size","authors":"Francisco J. Santos-Arteaga;Raquel Marín;Madjid Tavana;Debora Di Caprio","doi":"10.1109/TEM.2026.3664073","DOIUrl":"https://doi.org/10.1109/TEM.2026.3664073","url":null,"abstract":"Digitalization strategies emerge through interactions among producers, consumers, and institutions across value chains, leading to gains in productivity, the introduction of new patents, and innovations in products and processes. Dynamic capabilities theory extends the static resource-based view of the firm by incorporating learning and innovation in evolving environments. Its collaborative extension has recently been introduced to analyze the behavior of corporate dynamics within evolving ecosystems shaped by information and communication technologies and firms’ digitalization strategies. However, empirical evaluations of how digital value chains influence firm-level productivity and innovation remain limited, relying on static efficiency evaluation frameworks. We apply a dynamic slacks-based data envelopment analysis model to a panel of 1369 Spanish manufacturing firms progressing through the emergence and consolidation phases of digitalization that have characterized the early decades of the twenty-first century. We examine how efficiency and competitiveness evolve, finding that larger firms and those in more complex technological sectors show more varied behavior but lower inefficiency. The findings offer implications for theory, best practices, and digitalization policies. The robustness of our analysis has been validated by demonstrating substantial differences between the patterns obtained under the standard static and our dynamic evaluation environments.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2035-2049"},"PeriodicalIF":5.2,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440628","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 : 2026-02-13DOI: 10.1109/TEM.2026.3664541
Jianyu Zhao;Lulu Zhang;Xi Xi;Wei Liu
Driven by the digital economy, cross-disciplinary knowledge integration is accelerating, fostering innovation and collaboration across supply chains. This transformation has also intensified dependencies on critical knowledge flows, exposing supply chains to heightened risks of disruption and making resilience an increasingly important managerial and policy concern. To address this challenge, this study examines the robustness and recoverability of knowledge-based supply chains by modeling their underlying knowledge structures as combinatorial networks. Using data from China's strategic emerging industries, we apply percolation models to evaluate network resilience by simulating two primary forms of structural disruption: node failure and edge blockage. To capture how different disruptions propagate through the network, five node-targeted and five edge-targeted attack strategies are simulated, and the corresponding recovery pathways are evaluated. Robustness is validated via two complementary indicators, namely, the relative size of the largest connected component and network efficiency, to ensure the consistency of the results. The findings reveal substantial heterogeneity in the criticality of nodes and edges, depending on network configuration, and demonstrate that tailored recovery strategies can markedly reduce resilience loss. By integrating proactive defense with recovery optimization, the proposed framework both contributes to advancing theoretical approaches and offers practical implications for strengthening the resilience of knowledge-based supply chains.
{"title":"Combinatorial Network Resilience in Knowledge-Based Supply Chains: Optimal Recovery Strategies Under Targeted Attacks","authors":"Jianyu Zhao;Lulu Zhang;Xi Xi;Wei Liu","doi":"10.1109/TEM.2026.3664541","DOIUrl":"https://doi.org/10.1109/TEM.2026.3664541","url":null,"abstract":"Driven by the digital economy, cross-disciplinary knowledge integration is accelerating, fostering innovation and collaboration across supply chains. This transformation has also intensified dependencies on critical knowledge flows, exposing supply chains to heightened risks of disruption and making resilience an increasingly important managerial and policy concern. To address this challenge, this study examines the robustness and recoverability of knowledge-based supply chains by modeling their underlying knowledge structures as combinatorial networks. Using data from China's strategic emerging industries, we apply percolation models to evaluate network resilience by simulating two primary forms of structural disruption: node failure and edge blockage. To capture how different disruptions propagate through the network, five node-targeted and five edge-targeted attack strategies are simulated, and the corresponding recovery pathways are evaluated. Robustness is validated via two complementary indicators, namely, the relative size of the largest connected component and network efficiency, to ensure the consistency of the results. The findings reveal substantial heterogeneity in the criticality of nodes and edges, depending on network configuration, and demonstrate that tailored recovery strategies can markedly reduce resilience loss. By integrating proactive defense with recovery optimization, the proposed framework both contributes to advancing theoretical approaches and offers practical implications for strengthening the resilience of knowledge-based supply chains.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"1889-1903"},"PeriodicalIF":5.2,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362469","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}
One-third of food produced for human consumption is wasted, with households in high-income countries responsible for nearly half of this total. This study examines user engagement with three major U.K. food waste reduction campaigns on X (formerly Twitter), analyzing more than 50 000 tweets collected over seven years. Using sentiment analysis, topic modeling (TM), and feature-importance classification (random forest, support vector machines, Naïve Bayes, and gradient boosting), the study identifies the content attributes that are most strongly associated with higher engagement. Practical advice (e.g., food storage tips), community mobilization, and food sharing consistently generate the highest levels of interaction, while the strategic use of hashtags, mentions, and influencer endorsements amplifies engagement. This article makes three theoretical contributions. First, it advances agenda-setting theory by demonstrating how long-term digital campaigns curate content to elevate food waste as a salient public issue, with distinct strategies (aligning messages with organizational vision, offering actionable tips, and promoting food sharing) driving engagement. Second, it extends framing theory by showing how platform-native tools associate with interaction, with tweets using either hashtags or mentions (but not both) achieving greater resonance, suggesting that simplified framing may be more effective in crowded digital spaces. Third, it offers a methodological contribution by integrating feature importance analysis with sentiment and TM, moving beyond descriptive metrics to predictive insights into which tweet attributes are most strongly associated with higher engagement. For practitioners, the study provides a cost-effective framework for dynamically adapting campaign content, equipping engineering managers and policy makers to design more impactful, scalable, and sustainable digital interventions that promote public engagement and behavioural change.
{"title":"Feature Classification Methods for Measuring User Engagement in Social Media Campaigns","authors":"Soujanya Krishnamurthy;Fotios Misopoulos;Eric K.H. Leung;Katerina Antonopoulou","doi":"10.1109/TEM.2026.3664701","DOIUrl":"https://doi.org/10.1109/TEM.2026.3664701","url":null,"abstract":"One-third of food produced for human consumption is wasted, with households in high-income countries responsible for nearly half of this total. This study examines user engagement with three major U.K. food waste reduction campaigns on X (formerly Twitter), analyzing more than 50 000 tweets collected over seven years. Using sentiment analysis, topic modeling (TM), and feature-importance classification (random forest, support vector machines, Naïve Bayes, and gradient boosting), the study identifies the content attributes that are most strongly associated with higher engagement. Practical advice (e.g., food storage tips), community mobilization, and food sharing consistently generate the highest levels of interaction, while the strategic use of hashtags, mentions, and influencer endorsements amplifies engagement. This article makes three theoretical contributions. First, it advances agenda-setting theory by demonstrating how long-term digital campaigns curate content to elevate food waste as a salient public issue, with distinct strategies (aligning messages with organizational vision, offering actionable tips, and promoting food sharing) driving engagement. Second, it extends framing theory by showing how platform-native tools associate with interaction, with tweets using either hashtags or mentions (but not both) achieving greater resonance, suggesting that simplified framing may be more effective in crowded digital spaces. Third, it offers a methodological contribution by integrating feature importance analysis with sentiment and TM, moving beyond descriptive metrics to predictive insights into which tweet attributes are most strongly associated with higher engagement. For practitioners, the study provides a cost-effective framework for dynamically adapting campaign content, equipping engineering managers and policy makers to design more impactful, scalable, and sustainable digital interventions that promote public engagement and behavioural change.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"73 ","pages":"2127-2144"},"PeriodicalIF":5.2,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440630","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 : 2026-02-12DOI: 10.1109/TEM.2026.3664256
Ajax Persaud;Javid Zare;H. M. Belal
Many studies on big data analytics provide empirical evidence showing that big data analytics capabilities (BDAC) do not always result in value creation or competitive advantage. This study sheds light on how entrepreneurial orientation (EO) mediates the relationship between BDAC and value creation in small- and medium-sized enterprises (SMEs). The study employed a mixed-method design, integrating desk research, executive interviews, and a survey of 447 Canadian SME leaders. Partial least squares structural equation modeling analysis, supported by reliability and validity diagnostics, common-method-bias tests, and model-fit assessments, ensured the robustness of results and enabled triangulation across methods. Findings reveal that EO mediates the BDAC–value creation relationship, suggesting that SMEs with stronger EO leverage BDAC more effectively. Thus, developing EO is essential for unlocking BDAC’s value-creation potential. A practical implication for senior executives is that employing EO—proactiveness, risk-taking, and innovativeness—can lead to better leverage of BDAC insights for value creation. A theoretical implication is that EO can complement other organizational capabilities, such as an analytics culture, to extract greater value from BDAC. The empirical evidence provided in this study complements previous studies that have examined other mediators, such as analytics culture and absorptive capacity. It provides additional insights into how SMEs can leverage their BDAC for value creation by being more innovative, proactive, and encouraging risk-taking across the organization.
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