IEEE Transactions on Engineering Management最新文献

{"title":"Implementing Artificial Intelligence Consumer Experience Tools in Supply Chains","authors":"Ming Cheng;Bin Shen;Hau-Ling Chan","doi":"10.1109/TEM.2024.3525412","DOIUrl":"https://doi.org/10.1109/TEM.2024.3525412","url":null,"abstract":"In the e-marketplace, consumers can know how products fit their preferences using artificial intelligence consumer experience tools (AI-CE), which have been adopted by the retailer and/or supplier. Motivated by e-commerce practices, the question of who provides AI-CE is optimal for the supply chain remains unclear. We evaluate the impacts of implementing AI-CE on the supply chain. The supplier sells two horizontally differentiated products. One is sold through the retailer, and one is sold directly on the supplier's own channel. Our analytical results show that the optimal AI-CE implementation strategy of the retailer and supplier depends on consumer sensitivity toward the privacy of using AI-CE. To realize a win–win outcome for the supplier and retailer, the retailer (supplier) should implement AI-CE, but the supplier (retailer) should not when consumer sensitivity toward privacy is low (high). Surprisingly, when consumer sensitivity toward privacy is moderate, neither the supplier nor the retailer should implement AI-CE. The reason is that joint implementation will eliminate the positive effect of comparative disadvantage and channel competition. Moreover, we find that the enhancement in the precision of AI-CE may either benefit or harm the supplier and retailer. Our study guides AI-CE implementation in e-commerce supply chains.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"717-729"},"PeriodicalIF":4.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667571","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}

{"title":"Improving Energy Efficiency Through the Digital Economy in Mainland China: Easier Said Than Done","authors":"Chenchen Huang;Boqiang Lin","doi":"10.1109/TEM.2024.3524514","DOIUrl":"https://doi.org/10.1109/TEM.2024.3524514","url":null,"abstract":"Enhancing energy efficiency (EE) is thought to be an essential strategy for reaching carbon neutrality objectives. The emergence of the digital economy (DE) has given EE fresh momentum. However, the relationship between the DE and EE is controversial. This research builds a fixed effect panel stochastic frontier model to examine how the DE affects EE by analyzing data from 30 provinces in China between 2011 and 2021. The study finds that the DE significantly and positively affects EE. This conclusion still holds in a series of robustness tests. Mechanism analysis finds that the DE improves EE by promoting the substitution of electricity for other energy sources. Heterogeneity analysis shows that the role of the DE in improving EE is more vital in regions with low coal proportion, low urbanization levels, and high human capital levels. This study offers new evidence for related studies and provides policy recommendations for carbon neutrality and sustainable development.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"326-334"},"PeriodicalIF":4.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976073","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}

{"title":"Balancing Cognitions and Emotions to Advance Lean Corporate Programs","authors":"Danilo Pesce;Paolo Neirotti","doi":"10.1109/TEM.2024.3522570","DOIUrl":"https://doi.org/10.1109/TEM.2024.3522570","url":null,"abstract":"This study investigates how cognitive and emotional mechanisms drive advancement in lean corporate programs, addressing a key yet underexplored factor in continuous improvement. While existing research documents lean program successes, it often overlooks the role of cognitive and emotional factors in program progression. Using a phenomenological approach, we integrate individual and plant-level data through both qualitative and quantitative methods. Focusing on a lean corporate program implemented across 22 Italian plants of a leading global carmaker, we employ a structural equation model to assess the relationships among lean organizing principles, cognitive involvement, emotional perceptions, and lean program advancement. Three key insights emerge: 1) A comprehensive application of lean organizing principles enhances employees’ cognitive involvement in lean practices; 2) Cognitive involvement is essential for advancing lean corporate programs; 3) Positive emotional states—such as pride, self-efficacy, and perceived fairness—significantly mediate the relationship between cognitive involvement and lean advancement. This study contributes to lean management literature by demonstrating that positive emotions are critical for sustaining lean progress. By bridging the knowledge-based view with behavioral theories, we provide researchers and practitioners with a nuanced understanding of the interplay between cognitive and emotional factors in advancing lean programs.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"363-377"},"PeriodicalIF":4.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993284","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}

{"title":"Supply Chain Disruptions and Need for Resilience: SMEs Direct/Indirect Exporting and Rapid Internationalization","authors":"Timothy Kiessling;Marina Dabić;Sandeep Yadav;Nicolas Huck;Jane F. Maley","doi":"10.1109/TEM.2024.3514701","DOIUrl":"https://doi.org/10.1109/TEM.2024.3514701","url":null,"abstract":"In this article, we explore how small- and medium-sized enterprises (SMEs) handle supply chain disruptions, focusing on their choices between direct and indirect exporting and the pace of internationalization. While past scholars have extensively examined exporting in uncertainty and disruption, they have neglected SMEs, exposing a gap in the extant literature. Drawing on the organizational resilience concept supported by the theoretical lens of dynamic capabilities, the study focuses on resilience in preparing and reconfiguring the supply chain to manage disruptive events. The analysis of 2484 SMEs from 23 countries reveals that SMEs engaging in direct exporting are likely to internationalize more swiftly, influenced by local factors such as corruption, informal competition, and political instability. This study examines a major weakness in the extant literature by casting light on SMEs exporting behavior in disruptive events and underscores the need for resilience-building strategies in SME supply chains to manage future face market disruptions and volatile events.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"115-133"},"PeriodicalIF":4.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937847","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}

{"title":"An Evaluation Framework for Machine Learning and Data Science-Based Financial Strategies: A Case Study-Driven Decision Model","authors":"Mohammadsaleh Saadatmand;Tugrul Daim;Carlos Mena;Haydar Yalcin;Gulin Bolatan;Manali Chatterjee","doi":"10.1109/TEM.2024.3522313","DOIUrl":"https://doi.org/10.1109/TEM.2024.3522313","url":null,"abstract":"Big data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of <italic>economic and financial foundations</i>, <italic>data perspective</i>, <italic>features perspective</i>, <italic>modeling perspective</i>, and <italic>performance perspective</i>. The research methodology applied was the hierarchical decision model (HDM) to provide a 360° view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"349-362"},"PeriodicalIF":4.6,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993283","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}

{"title":"Does Digital Economy Investment Promote Sustainable Competitiveness by Creating New Industry?","authors":"Lei Shen;Xiaodi Zhao;Debadrita Panda;Vinit Parida","doi":"10.1109/TEM.2024.3521542","DOIUrl":"https://doi.org/10.1109/TEM.2024.3521542","url":null,"abstract":"This study explores how governmental and regional investments in the digital economy foster the emergence of new industries and the region's sustainable competitiveness. In particular, we focus on the creation of the big data comprehensive pilot zone (BDCPZ) in China that serves as a proxy for digital economy advancement initiatives. Employing a quasi-experimental design and a panel dataset from 31 Chinese provinces (2009–2020), this study uses the difference-in-differences approach to evaluate the impact of BDCPZ on the region's competitiveness. Our findings show that enhanced digital economy advancement significantly boosts the creation of new industries, such as the creative industry. This study advances understanding by linking economic development and the business environment and conducting a heterogeneity analysis to identify the most influential characteristics for new industry creation. These characteristics are categories that fall under regional ecosystems—including talent mobility, innovation culture, and financial institutions—and act as mediators of this relationship, offering valuable insights for stakeholders to enhance regional sustainable competitiveness and innovation. \u0000<p><i>Managerial relevance statement:</i> This study offers valuable managerial insights for managers, policymakers, and industrial networks to apply in practice. By understanding the importance of regional talent mobility and strong innovation culture, managers in traditional engineering firms can experiment with novel strategies like rotational mobilizing programs to increase agility and diversify into creative sectors. Policymakers can support these efforts in three ways: by incentivizing digital economy advancements like automation, application of emerging technologies through tax breaks and grants; by establishing cross-sector innovation hubs where traditional industries collaborate with tech startups and creative enterprises; and by focusing on training and reskilling programs. Finally, industrial networks, i.e. consultancy networks drive strategy and collaboration, are encouraged to educate financial institutions on how to assess creative industry projects and facilitate talent mobility by linking traditional engineering firms with SMEs and startups to acquire valuable cross-sector experience, foster innovation, and contribute to the development of new industries.</p>","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"295-307"},"PeriodicalIF":4.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938540","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}

{"title":"Does Fintech Adoption Improve Sustainable Supply Chain Management? An Innovation-Intensive Environment Perspective","authors":"Fu Jia;Jiguang Guo;Lujie Chen;Nazrul Islam","doi":"10.1109/TEM.2024.3515471","DOIUrl":"https://doi.org/10.1109/TEM.2024.3515471","url":null,"abstract":"Financial technology, or Fintech, is used to describe disruptive new technologies that help firms manage their financial operations and improve the cost effectiveness of customer services. However, the role of Fintech adoption in sustainable supply chain management performance is unclear. Using the panel data from Chinese A-share listed companies from 2018 to 2022, in this article, we empirically demonstrate that Fintech adoption can significantly improve sustainable supply chain management performance. This improvement is primarily attributed to Fintech's ability to enhance information transparency and refine decision-making capabilities, thereby fostering sustainability. Furthermore, green innovation, digital innovation policies, top management team forward-looking innovation orientation, and persistent innovation orientation strengthen the relationship between Fintech adoption and sustainable supply chain management performance. Green innovation enables firms to comply with environmental regulations and gain a competitive advantage, thereby facilitating the integration of Fintech into sustainable supply chain management. Digital innovation policies provide regulatory support and establish norms, creating a conducive environment for technology applications. The innovation orientation of the top management team drives the continuous optimization of supply chain processes and services through Fintech. This research contributes to the current industrial sustainability debate by integrating Fintech adoption and sustainable supply chain management from an innovation-intensive environment perspective.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"210-226"},"PeriodicalIF":4.6,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938291","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}

{"title":"A Primer on Out-of-the-Box AI Marketing Mix Models","authors":"Macarena Estevez;María Teresa Ballestar;Jorge Sainz","doi":"10.1109/TEM.2024.3519172","DOIUrl":"https://doi.org/10.1109/TEM.2024.3519172","url":null,"abstract":"Marketing mix modeling (MMM) optimizes budget allocation and determines the return on advertising investment through market response analysis. MMM are vital tools to help marketers define their marketing strategies according to the firm's business and marketing objectives while reducing uncertainty in the decision-making process. As AI and automated MMM out-of-the-box packages gain popularity among marketers, it has become evident there is a theoretical and empirical gap in the understanding of the benefits and inconveniences of these new methods over traditional econometric models. To shed light on these questions, two different models using the same database from a telecommunications firm have been developed and tested using a traditional econometric model and Robyn, an AI-powered open-sourced MMM package from meta marketing science. The research compares both methods’ development processes and subsequent outputs from different perspectives: technical, business, and practical. It shows the advantages and shortcomings of each, providing insightful recommendations for academics and practitioners to navigate through the process of adoption of econometric and AI models for budget allocation decision-making. Econometric models are easy to explain and replicate, while AI complexity from the combination of several methods, their parametrization, and the random initialization of iterations during training, hinders its explainability.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"282-294"},"PeriodicalIF":4.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10804651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938542","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}

{"title":"Surfing With the Tides: How Does Dual Strategic Orientation Enhance Organizational Resilience Through Resource Orchestration? A Moderated Mediation Model","authors":"Haiwen Li;Junying Liu;Bo Xia;Yanyan Hong","doi":"10.1109/TEM.2024.3518585","DOIUrl":"https://doi.org/10.1109/TEM.2024.3518585","url":null,"abstract":"Organizational resilience (ORE) is crucial for businesses to cope with disruptions and achieve long-term survival in volatile environments. Strategic orientation (SO), which serves as a beacon for the long-term development of firms, has a significant influence on ORE. However, existing research remains inconclusive regarding the relationship between SO and ORE. This article seeks to investigate the mechanisms through which the emerging dimensions of SO—digital orientation (DO) and environmental orientation (EO)—impact ORE, drawing on resource orchestration (ROR) theory and organizational learning theory. In particular, we explore the mediating role of ROR in explaining the diverse pathways from DO and EO to ORE under the boundary conditions of organizational memory (OM) and environmental hostility (EH). This research tests the proposed hypotheses using a mixed-methods approach with a sample of 304 Chinese firms. The hierarchical multiple regression results indicate that both DO and EO positively affect ORE, with ROR serving as a full mediator. More interestingly, OM positively moderates the indirect effects of DO and EO on ORE via ROR, whereas EH only strengthens the indirect relationship between DO and ORE via ROR. Furthermore, the fuzzy set qualitative comparative analysis results reinforce these findings by revealing that four configurations of these variables can achieve high ORE. This study helps enrich the literature on SO and ORE and provides practical insights to guide firms in enhancing ORE.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"252-266"},"PeriodicalIF":4.6,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938541","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}

{"title":"Unraveling Generative AI Adoption in Enterprise Digital Platforms: The Effect of Institutional Pressures and the Moderating Role of Internal and External Environments","authors":"Lin Zhang;Zhen Shao;Bin Chen;Jose Benitez","doi":"10.1109/TEM.2024.3513773","DOIUrl":"https://doi.org/10.1109/TEM.2024.3513773","url":null,"abstract":"Despite the transformative potential of generative artificial intelligence (AI) systems within enterprise digital platforms, there still exist gaps in understanding the challenges and strategies associated with their adoption. Addressing this pressing issue, this article draws upon institutional theory to delve into the drivers influencing firms’ adoption of generative AI within their enterprise digital platforms. Leveraging survey data collected from 328 firms that have implemented digital platforms to support their business operations, we find that institutional pressures positively influence generative AI adoption within their enterprise digital platforms. Furthermore, we identify salient moderating effects of policy uncertainty and innovative culture in shaping the relationships. Our research findings make a substantial contribution to AI literature by illuminating the potential challenges and strategies toward generative AI adoption as well as reassessing the application of institutional theory within the digital landscape.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"335-348"},"PeriodicalIF":4.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976131","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}