Digital technologies have radically altered the nature of innovation and its development process. Innovations in the digital age are characterized by a reliance on a network of diverse actors who champion innovation. Despite the important role of these innovation actors’ social networks in enabling interactions and collaboration for developing innovation, extant research lacks a synthesis of knowledge on innovation actors’ social networks in the digital age. We provide a multidisciplinary literature review on the social network of actors championing innovation and its interplay with digital technology based on a theoretical framework derived from social network research. Our analysis gives insights into the flows and structure of innovation actors’ social networks. We find that digital technology has predominantly led to gradual changes in innovation actors’ activities by functioning as a catalyst. Concerning the characteristics of innovation actors’ networks, we find more fundamental changes in the agency of digital innovation as innovation actors form communities and perform activities collectively. Building on the literature on digital technology and digital innovation, we identify promising avenues for future research. Overall, our literature review contributes by proposing a new conceptual model of innovation actors’ social networks in the digital age and providing an agenda for future research.
{"title":"Understanding the Role of Innovation Actors’ Social Network in the Digital Age: A Literature Review and Avenues for Future Research","authors":"Katharina Drechsler;Victoria Reibenspiess;Andreas Eckhardt;Heinz-Theo Wagner","doi":"10.1109/TEM.2025.3526703","DOIUrl":"https://doi.org/10.1109/TEM.2025.3526703","url":null,"abstract":"Digital technologies have radically altered the nature of innovation and its development process. Innovations in the digital age are characterized by a reliance on a network of diverse actors who champion innovation. Despite the important role of these innovation actors’ social networks in enabling interactions and collaboration for developing innovation, extant research lacks a synthesis of knowledge on innovation actors’ social networks in the digital age. We provide a multidisciplinary literature review on the social network of actors championing innovation and its interplay with digital technology based on a theoretical framework derived from social network research. Our analysis gives insights into the flows and structure of innovation actors’ social networks. We find that digital technology has predominantly led to gradual changes in innovation actors’ activities by functioning as a catalyst. Concerning the characteristics of innovation actors’ networks, we find more fundamental changes in the agency of digital innovation as innovation actors form communities and perform activities collectively. Building on the literature on digital technology and digital innovation, we identify promising avenues for future research. Overall, our literature review contributes by proposing a new conceptual model of innovation actors’ social networks in the digital age and providing an agenda for future research.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"409-425"},"PeriodicalIF":4.6,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/TEM.2024.3525105
Jingwen Wu;Yuting Yan;Shuaian Wang;Lu Zhen
The increasing pressure on global supply chains to reduce carbon emissions has driven the need for sustainable supply chain network design (SSCND). This article proposes an innovative framework for SSCND that optimizes facility location and scale decisions under uncertainty using blockchain technology. By incorporating cap-and-trade regulations and carbon trading into a mixed-integer linear programming model, the article addresses both the economic and environmental objectives of supply chains. A two-stage stochastic programming approach is employed to optimize the SSCND. The first stage focuses on facility location decisions and the second stage on production adjustment, transportation, and carbon trading under demand uncertainty. The carbon trading decisions are integrated into the model by assigning a monetary value to carbon dioxide emissions and allowing for dynamic adjustments to real-time environmental impacts. A primal decomposition algorithm is introduced to address the computational challenges involved in solving the two-stage stochastic programming model. Numerical experiments based on data derived from SAIC Motor Corporation's supply chain demonstrate the effectiveness of the model and algorithm. This article provides an efficient approach for integrating environmental sustainability into supply chain management, offering valuable insights for industries aiming to achieve carbon neutrality
{"title":"Optimizing Blockchain-Enabled Sustainable Supply Chains","authors":"Jingwen Wu;Yuting Yan;Shuaian Wang;Lu Zhen","doi":"10.1109/TEM.2024.3525105","DOIUrl":"https://doi.org/10.1109/TEM.2024.3525105","url":null,"abstract":"The increasing pressure on global supply chains to reduce carbon emissions has driven the need for sustainable supply chain network design (SSCND). This article proposes an innovative framework for SSCND that optimizes facility location and scale decisions under uncertainty using blockchain technology. By incorporating cap-and-trade regulations and carbon trading into a mixed-integer linear programming model, the article addresses both the economic and environmental objectives of supply chains. A two-stage stochastic programming approach is employed to optimize the SSCND. The first stage focuses on facility location decisions and the second stage on production adjustment, transportation, and carbon trading under demand uncertainty. The carbon trading decisions are integrated into the model by assigning a monetary value to carbon dioxide emissions and allowing for dynamic adjustments to real-time environmental impacts. A primal decomposition algorithm is introduced to address the computational challenges involved in solving the two-stage stochastic programming model. Numerical experiments based on data derived from SAIC Motor Corporation's supply chain demonstrate the effectiveness of the model and algorithm. This article provides an efficient approach for integrating environmental sustainability into supply chain management, offering valuable insights for industries aiming to achieve carbon neutrality","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"426-445"},"PeriodicalIF":4.6,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/TEM.2024.3502372
Tsan-Ming Choi
It is my big honor to be appointed as the new Editor-in-Chief of IEEE Transactions on Engineering Management (IEEE-TEM). In this editorial article, some updates of the journal are presented. We also share some editorial policies of the journal as well as publication requirements for full-length original research papers. Finally, some simple tips are provided to help authors better prepare their papers for publication in IEEE-TEM.
{"title":"Editorial: IEEE Transactions on Engineering Management 2025","authors":"Tsan-Ming Choi","doi":"10.1109/TEM.2024.3502372","DOIUrl":"https://doi.org/10.1109/TEM.2024.3502372","url":null,"abstract":"It is my big honor to be appointed as the new Editor-in-Chief of \u0000<sc>IEEE Transactions on Engineering Management</small>\u0000 (IEEE-TEM). In this editorial article, some updates of the journal are presented. We also share some editorial policies of the journal as well as publication requirements for full-length original research papers. Finally, some simple tips are provided to help authors better prepare their papers for publication in IEEE-TEM.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"1-3"},"PeriodicalIF":4.6,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1109/TEM.2024.3524514
Chenchen Huang;Boqiang Lin
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.
提高能源效率（EE）被认为是实现碳中和目标的基本战略。数字经济（DE）的出现为 EE 注入了新的动力。然而，数字经济与能源效率之间的关系却存在争议。本研究建立了一个固定效应面板随机前沿模型，通过分析 2011 年至 2021 年中国 30 个省份的数据，研究了数字经济如何影响能源效率。研究发现，可持续发展指数对能源效率有明显的正向影响。这一结论在一系列稳健性检验中仍然成立。机理分析发现，能源效率提高是通过促进电力对其他能源的替代来实现的。异质性分析表明，在煤炭比例低、城市化水平低和人力资本水平高的地区，可再生能源免税政策在改善能源效率方面的作用更为重要。本研究为相关研究提供了新的证据，并为碳中和与可持续发展提供了政策建议。
{"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}
Pub Date : 2024-12-31DOI: 10.1109/TEM.2024.3522570
Danilo Pesce;Paolo Neirotti
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.
{"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}
Pub Date : 2024-12-31DOI: 10.1109/TEM.2024.3514701
Timothy Kiessling;Marina Dabić;Sandeep Yadav;Nicolas Huck;Jane F. Maley
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.
{"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}
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 economic and financial foundations, data perspective, features perspective, modeling perspective, and performance perspective. 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.
{"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}
Pub Date : 2024-12-23DOI: 10.1109/TEM.2024.3521542
Lei Shen;Xiaodi Zhao;Debadrita Panda;Vinit Parida
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.
Managerial relevance statement: 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.
{"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}
Pub Date : 2024-12-19DOI: 10.1109/TEM.2024.3515471
Fu Jia;Jiguang Guo;Lujie Chen;Nazrul Islam
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.
{"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}
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.
{"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}
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