Pub Date : 2024-10-30DOI: 10.1109/TEM.2024.3488183
Han Huang;Jie Xiong;Lu Xu;Zhe Yuan;Chun Liu
The rapid advancement of Chinese complex products and systems (CoPSs) enterprises marks their transition into a post-catch-up phase, challenging the conventional theories of catch-up. In this article, we employ a configurational approach to explore the intricate relationships between catch-up environments and strategies, specifically focusing on the distinct paths of state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) within the CoPS sector. Utilizing fuzzy-set qualitative comparative analysis with data sourced from the EU industrial research and development (R&D) investment scoreboard (2017–2020) and corresponding Chinese-listed companies, our research identifies diverse catch-up configurations for SOEs, characterized by “complexity adoption” and “complexity decipher” models. In contrast, non-SOEs encounter challenges in strategically adapting to environmental shifts, which affects their catch-up strategies. Our findings emphasize the critical role of strategic alignment with external conditions, technological learning, and resource utilization in achieving successful catch-up in CoPS. These configurations enable SOEs to effectively align internal resources with external opportunities, resulting in superior catch-up performance. In contrast, non-SOEs encounter significant obstacles in adapting to environmental changes and optimizing resource utilization, which hinders their ability to attain similar successes. Moreover, our study sheds light on specific challenges faced by non-SOEs in responding to environmental shifts. This enriched understanding provides valuable theoretical insights into the catch-up of latecomer CoPS enterprises and has practical implications for both policymakers and business practitioners.
{"title":"Catch-Up in Complex Products and Systems: A Fuzzy-Set Qualitative Comparative Analysis of China's Equipment Manufacturing Industry","authors":"Han Huang;Jie Xiong;Lu Xu;Zhe Yuan;Chun Liu","doi":"10.1109/TEM.2024.3488183","DOIUrl":"https://doi.org/10.1109/TEM.2024.3488183","url":null,"abstract":"The rapid advancement of Chinese complex products and systems (CoPSs) enterprises marks their transition into a post-catch-up phase, challenging the conventional theories of catch-up. In this article, we employ a configurational approach to explore the intricate relationships between catch-up environments and strategies, specifically focusing on the distinct paths of state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) within the CoPS sector. Utilizing fuzzy-set qualitative comparative analysis with data sourced from the EU industrial research and development (R&D) investment scoreboard (2017–2020) and corresponding Chinese-listed companies, our research identifies diverse catch-up configurations for SOEs, characterized by “complexity adoption” and “complexity decipher” models. In contrast, non-SOEs encounter challenges in strategically adapting to environmental shifts, which affects their catch-up strategies. Our findings emphasize the critical role of strategic alignment with external conditions, technological learning, and resource utilization in achieving successful catch-up in CoPS. These configurations enable SOEs to effectively align internal resources with external opportunities, resulting in superior catch-up performance. In contrast, non-SOEs encounter significant obstacles in adapting to environmental changes and optimizing resource utilization, which hinders their ability to attain similar successes. Moreover, our study sheds light on specific challenges faced by non-SOEs in responding to environmental shifts. This enriched understanding provides valuable theoretical insights into the catch-up of latecomer CoPS enterprises and has practical implications for both policymakers and business practitioners.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15375-15389"},"PeriodicalIF":4.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672015","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 : 2024-10-30DOI: 10.1109/TEM.2024.3479413
Carlos Carbajal-Pina;Nuran Acur
This article explores the imperative role of responsible innovation (RI) in guiding the development and integration of emerging technologies within society. With technological progress rapidly advancing, it becomes ever more imperative to confront the complex ethical, social, economic, and environmental challenges that accompany these innovations. This research highlights the significance of RI practices, advocating for a principle-based approach to technology development. It asserts that such an approach, which is rooted in societal values and ethical standards, is crucial for nurturing long-term societal well-being and fostering trust in new technologies, as opposed to relying solely on rule-based technology development governed by regulations. Our analysis begins with the definition of each RI principle, followed by an exploration of its relevance in addressing innovation dilemmas. Our case studies involve various sectors, including artificial intelligence, connected autonomous vehicles, and quantum computing, to showcase the practical application of these principles in real-world contexts. Through these instances, we highlight the efficacy of RI in both mitigating risks and maximizing the benefits of new technologies. In addition, the article investigates the dynamics of implementing RI within organizational frameworks and innovation processes. It provides strategic recommendations for technology managers and policymakers on how to operationalize RI principles effectively, emphasizing the need for an adaptive approach that is responsive to the rapid changes in the technology landscape. The study contributes to the academic discourse by highlighting that proactive engagement with RI can lead to more sustainable and socially acceptable technology outcomes. Ultimately, this research underscores the importance of responsible practices in innovation, proposing that a principled approach to technology development is essential for fostering long-term societal well-being and trust in new technologies.
{"title":"From Principles to Practice: Responsible Implementation of Emerging Technologies Through Innovation Dilemmas","authors":"Carlos Carbajal-Pina;Nuran Acur","doi":"10.1109/TEM.2024.3479413","DOIUrl":"https://doi.org/10.1109/TEM.2024.3479413","url":null,"abstract":"This article explores the imperative role of responsible innovation (RI) in guiding the development and integration of emerging technologies within society. With technological progress rapidly advancing, it becomes ever more imperative to confront the complex ethical, social, economic, and environmental challenges that accompany these innovations. This research highlights the significance of RI practices, advocating for a principle-based approach to technology development. It asserts that such an approach, which is rooted in societal values and ethical standards, is crucial for nurturing long-term societal well-being and fostering trust in new technologies, as opposed to relying solely on rule-based technology development governed by regulations. Our analysis begins with the definition of each RI principle, followed by an exploration of its relevance in addressing innovation dilemmas. Our case studies involve various sectors, including artificial intelligence, connected autonomous vehicles, and quantum computing, to showcase the practical application of these principles in real-world contexts. Through these instances, we highlight the efficacy of RI in both mitigating risks and maximizing the benefits of new technologies. In addition, the article investigates the dynamics of implementing RI within organizational frameworks and innovation processes. It provides strategic recommendations for technology managers and policymakers on how to operationalize RI principles effectively, emphasizing the need for an adaptive approach that is responsive to the rapid changes in the technology landscape. The study contributes to the academic discourse by highlighting that proactive engagement with RI can lead to more sustainable and socially acceptable technology outcomes. Ultimately, this research underscores the importance of responsible practices in innovation, proposing that a principled approach to technology development is essential for fostering long-term societal well-being and trust in new technologies.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"18-28"},"PeriodicalIF":4.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938222","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 : 2024-10-30DOI: 10.1109/TEM.2024.3488325
Raghunathan Krishankumar;Dhruva Sundararajan;Muhammet Deveci;K. S. Ravichandran;Xin Wen;Bilal Bahaa Zaidan
In this article, we aim to rank barriers hindering clean energy adoption within the healthcare industry by proposing a new framework with q-rung orthopair fuzzy data (q-ROFD). Energy is paramount in health industry, and it is estimated by the World Health Organization that nearly a billion people are treated globally with limited/no electricity. United Nation strongly recommends cutting dependencies on fossil fuels, but to meet demand, clean energy is focused. Studies on clean energies reveal that direct adoption is tough, owing to diverse barriers and ranking these barriers will provide policymakers clarity on the strategic plans. Existing studies reveal gaps in uncertainty modeling by not adequately exploring orthopair variants, human intervention reduction by failing to methodically determine diverse decision parameters, consideration of subjective attitude and interactions among entities that are essential for experts and attributes, and accounting for attribute type and yielding ranks comparable with a human decision. Motivated by the gaps, in this article, a combined q-ROFD model is presented where weights of attributes are determined via criteria importance through intercriteria correlation and rank sum and experts’ weights are obtained by rank sum. A ranking algorithm is developed with CODAS formulation for determining the barriers’ grades with risk aversion trait. The significance of the study lies in rational ranking of barriers, reduced human intervention, and methodical determination of decision parameters. The usefulness of the model is testified via a case study of barrier ranking within the Indian healthcare industry and comparison/sensitivity studies reveal the pros and cons of the developed model.
{"title":"A Decision Framework With q-Rung Fuzzy Preferences for Ranking Barriers Affecting Clean Energy Utilization Within Healthcare Industry","authors":"Raghunathan Krishankumar;Dhruva Sundararajan;Muhammet Deveci;K. S. Ravichandran;Xin Wen;Bilal Bahaa Zaidan","doi":"10.1109/TEM.2024.3488325","DOIUrl":"https://doi.org/10.1109/TEM.2024.3488325","url":null,"abstract":"In this article, we aim to rank barriers hindering clean energy adoption within the healthcare industry by proposing a new framework with q-rung orthopair fuzzy data (q-ROFD). Energy is paramount in health industry, and it is estimated by the World Health Organization that nearly a billion people are treated globally with limited/no electricity. United Nation strongly recommends cutting dependencies on fossil fuels, but to meet demand, clean energy is focused. Studies on clean energies reveal that direct adoption is tough, owing to diverse barriers and ranking these barriers will provide policymakers clarity on the strategic plans. Existing studies reveal gaps in uncertainty modeling by not adequately exploring orthopair variants, human intervention reduction by failing to methodically determine diverse decision parameters, consideration of subjective attitude and interactions among entities that are essential for experts and attributes, and accounting for attribute type and yielding ranks comparable with a human decision. Motivated by the gaps, in this article, a combined q-ROFD model is presented where weights of attributes are determined via criteria importance through intercriteria correlation and rank sum and experts’ weights are obtained by rank sum. A ranking algorithm is developed with CODAS formulation for determining the barriers’ grades with risk aversion trait. The significance of the study lies in rational ranking of barriers, reduced human intervention, and methodical determination of decision parameters. The usefulness of the model is testified via a case study of barrier ranking within the Indian healthcare industry and comparison/sensitivity studies reveal the pros and cons of the developed model.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15349-15362"},"PeriodicalIF":4.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672124","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 : 2024-10-29DOI: 10.1109/TEM.2024.3487654
Xiaoyang Zhou;Jiaao Xu;Tingting Guo;Shouyang Wang;Benjamin Lev
The Internet of Things presents a significant economic value potential, where sensors play a crucial role in its infrastructure. However, the restricted electricity of sensors limits the lifespan of the entire network. The development of modern charging technology has made it possible to realize simultaneous one-to-many charging using drones. To make better use of this charging technology, this article investigates the problem of charging sensors by drones in three-dimensional (3-D) space. Differing from the existing literature, the 3-D hovering location of the drone affects the number of sensors being charged simultaneously and consequently the service time of charging. Additionally, we consider the scenario where multiple drones serve sensors in the same area, necessitating the assurance of continuous charging services. We propose a mixed integer formulation model to minimize the total cost of completing the recharging task. To solve this problem, an improved genetic algorithm is developed. Extensive experiments are conducted to show the superiority of our proposed method, the effect of one-to-many wireless charging mode, and the sensitivities of the results to the parameters including charging radius and wind scale. This article contributes to further insights into the optimization of wireless charging strategies for sensor networks and other similar problems.
{"title":"3-D Hover Location and Drone Routing Optimization for One-to-Many Continuous Wireless Charging Problem","authors":"Xiaoyang Zhou;Jiaao Xu;Tingting Guo;Shouyang Wang;Benjamin Lev","doi":"10.1109/TEM.2024.3487654","DOIUrl":"https://doi.org/10.1109/TEM.2024.3487654","url":null,"abstract":"The Internet of Things presents a significant economic value potential, where sensors play a crucial role in its infrastructure. However, the restricted electricity of sensors limits the lifespan of the entire network. The development of modern charging technology has made it possible to realize simultaneous one-to-many charging using drones. To make better use of this charging technology, this article investigates the problem of charging sensors by drones in three-dimensional (3-D) space. Differing from the existing literature, the 3-D hovering location of the drone affects the number of sensors being charged simultaneously and consequently the service time of charging. Additionally, we consider the scenario where multiple drones serve sensors in the same area, necessitating the assurance of continuous charging services. We propose a mixed integer formulation model to minimize the total cost of completing the recharging task. To solve this problem, an improved genetic algorithm is developed. Extensive experiments are conducted to show the superiority of our proposed method, the effect of one-to-many wireless charging mode, and the sensitivities of the results to the parameters including charging radius and wind scale. This article contributes to further insights into the optimization of wireless charging strategies for sensor networks and other similar problems.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"72 ","pages":"29-46"},"PeriodicalIF":4.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938293","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 : 2024-10-28DOI: 10.1109/TEM.2024.3487297
Panet Serirungsun;Yuosre F. Badir;Björn Frank
As small and medium-sized enterprises (SMEs) struggle to adopt digital technologies, open innovation could provide a solution by utilizing external and internal technological resources across organizational boundaries to help SMEs overcome digital transformation deficits. This research investigates the direct effect of two open innovation strategies, namely, external technology acquisition and external technology exploitation, on digital transformation of emerging-market SMEs. It also examines how firm age and size moderate such effect. Analyses of 320 dual-source (one general manager and one production manager), time-lagged data from 160 Thai electronics SMEs suggest that external technology acquisition and external technology exploitation positively affect digital transformation. Moreover, as SMEs age, the positive effect of external technology acquisition and external technology exploitation on digital transformation becomes weaker and stronger, respectively. Furthermore, the positive effect of external technology exploitation on digital transformation appears to be stronger for larger SMEs than for smaller ones. The findings suggest that, to achieve digital transformation, early-stage SMEs’ managers should practice external technology acquisition; once SMEs mature, they should engage more in external technology exploitation. Likewise, as firm size does not moderate the effect of external technology acquisition on digital transformation, even small SMEs can attain digital transformation by effectively acquiring external technology.
{"title":"Achieving Digital Transformation: The Effect of Open Innovation Strategies at Different Stages of SME Development in an Emerging Market","authors":"Panet Serirungsun;Yuosre F. Badir;Björn Frank","doi":"10.1109/TEM.2024.3487297","DOIUrl":"https://doi.org/10.1109/TEM.2024.3487297","url":null,"abstract":"As small and medium-sized enterprises (SMEs) struggle to adopt digital technologies, open innovation could provide a solution by utilizing external and internal technological resources across organizational boundaries to help SMEs overcome digital transformation deficits. This research investigates the direct effect of two open innovation strategies, namely, external technology acquisition and external technology exploitation, on digital transformation of emerging-market SMEs. It also examines how firm age and size moderate such effect. Analyses of 320 dual-source (one general manager and one production manager), time-lagged data from 160 Thai electronics SMEs suggest that external technology acquisition and external technology exploitation positively affect digital transformation. Moreover, as SMEs age, the positive effect of external technology acquisition and external technology exploitation on digital transformation becomes weaker and stronger, respectively. Furthermore, the positive effect of external technology exploitation on digital transformation appears to be stronger for larger SMEs than for smaller ones. The findings suggest that, to achieve digital transformation, early-stage SMEs’ managers should practice external technology acquisition; once SMEs mature, they should engage more in external technology exploitation. Likewise, as firm size does not moderate the effect of external technology acquisition on digital transformation, even small SMEs can attain digital transformation by effectively acquiring external technology.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15555-15568"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789025","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 : 2024-10-28DOI: 10.1109/TEM.2024.3487232
Yang Liu;Zuo Yuxiao
We first used text analysis methods to define and measure the level of data element input. We qualitatively demonstrated that data element input can improve total factor productivity (TFP) by constructing a new classical economic growth model by adding data elements. On this basis, we built a translog stochastic frontier model to incorporate data elements into the production function and TFP measurement model. Using data from Chinese manufacturing listed companies from 2010 to 2023, we quantitatively measured and dynamically evaluated the impact of data element input on manufacturing TFP and the role of technical efficiency and technological progress. The results revealed the following: 1) Data element input as a whole is beneficial for improving manufacturing TFP, but the main path is the improvement of technical efficiency. Additionally, data processing and application significantly improve TFP, whereas data acquisition does not. 2) The impact of digitalization on the current industrial structure has not affected technological progress, but it has restricted improvements in technical efficiency. Data elements are increasingly becoming the critical material basis for the manufacturing industry's digital transformation. In this context, this study has the following practical value: 1) It helps better identify the critical path of data elements to empower manufacturing industry TFP to implement more targeted digital transformation in practice; and 2) it contributes to a more comprehensive understanding of the impact of digitalization on the manufacturing industry structure to fully leverage the positive role of data elements in enhancing enterprise productivity.
{"title":"Can the Input of Data Elements Improve Manufacturing Productivity? Effect Measurement and Path Analysis","authors":"Yang Liu;Zuo Yuxiao","doi":"10.1109/TEM.2024.3487232","DOIUrl":"https://doi.org/10.1109/TEM.2024.3487232","url":null,"abstract":"We first used text analysis methods to define and measure the level of data element input. We qualitatively demonstrated that data element input can improve total factor productivity (TFP) by constructing a new classical economic growth model by adding data elements. On this basis, we built a translog stochastic frontier model to incorporate data elements into the production function and TFP measurement model. Using data from Chinese manufacturing listed companies from 2010 to 2023, we quantitatively measured and dynamically evaluated the impact of data element input on manufacturing TFP and the role of technical efficiency and technological progress. The results revealed the following: 1) Data element input as a whole is beneficial for improving manufacturing TFP, but the main path is the improvement of technical efficiency. Additionally, data processing and application significantly improve TFP, whereas data acquisition does not. 2) The impact of digitalization on the current industrial structure has not affected technological progress, but it has restricted improvements in technical efficiency. Data elements are increasingly becoming the critical material basis for the manufacturing industry's digital transformation. In this context, this study has the following practical value: 1) It helps better identify the critical path of data elements to empower manufacturing industry TFP to implement more targeted digital transformation in practice; and 2) it contributes to a more comprehensive understanding of the impact of digitalization on the manufacturing industry structure to fully leverage the positive role of data elements in enhancing enterprise productivity.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15320-15332"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600230","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 : 2024-10-25DOI: 10.1109/TEM.2024.3486812
João Gregório;Russell Miller;Ioannis Afxentiou;Jean Laurent-Hippolyte;Paul Morantz
A taxonomy-based data model is proposed to create a knowledge system for managing engineering skills within an organization, motivated by the need to balance organizational expertise requirements and availability. The model, adapted from the “European Skills, Competences, Qualifications, and Occupations” framework, is designed to categorize and evaluate skills relevant to the engineering department of the National Physical Laboratory. This allows extraction of quantitative data on individual staff members' skills and competency levels, and the necessary skills for specific Job Title and Job Role combinations. It distinguishes between “Job Titles,” official job designations, and “Job Roles,” unofficial designations categorizing staff according to their work areas, allowing the model to conform with inherent organizational rigiditiy. The model can cross-reference information using specific queries, such as extracting skills from specific individuals and assessing if they meet their current job functions. This model enhances existing skill management frameworks by allowing for a traceable pathway for skill allocation, allowing for future expansion by including other departments. Integrating validation procedures to assess staff skills, such as the inclusion of proof attached to skills, can also be considered. It offers operational benefits like enhanced capability planning, informed staff development, optimized resource allocation, and improved training programmes.
{"title":"A Taxonomy-Based Data Model for Assessing Engineering Skills in an Organizational Context","authors":"João Gregório;Russell Miller;Ioannis Afxentiou;Jean Laurent-Hippolyte;Paul Morantz","doi":"10.1109/TEM.2024.3486812","DOIUrl":"https://doi.org/10.1109/TEM.2024.3486812","url":null,"abstract":"A taxonomy-based data model is proposed to create a knowledge system for managing engineering skills within an organization, motivated by the need to balance organizational expertise requirements and availability. The model, adapted from the “European Skills, Competences, Qualifications, and Occupations” framework, is designed to categorize and evaluate skills relevant to the engineering department of the National Physical Laboratory. This allows extraction of quantitative data on individual staff members' skills and competency levels, and the necessary skills for specific Job Title and Job Role combinations. It distinguishes between “Job Titles,” official job designations, and “Job Roles,” unofficial designations categorizing staff according to their work areas, allowing the model to conform with inherent organizational rigiditiy. The model can cross-reference information using specific queries, such as extracting skills from specific individuals and assessing if they meet their current job functions. This model enhances existing skill management frameworks by allowing for a traceable pathway for skill allocation, allowing for future expansion by including other departments. Integrating validation procedures to assess staff skills, such as the inclusion of proof attached to skills, can also be considered. It offers operational benefits like enhanced capability planning, informed staff development, optimized resource allocation, and improved training programmes.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15363-15374"},"PeriodicalIF":4.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672166","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}
Digital management system is widely used in the business activities of enterprises. Practice has proved that the implementation of the manufacturing execution system (MES) can better monitor and manage the production process, improve the production efficiency of enterprises, and effectively realize zero defect management (ZDM). Against this background, the influencing factors of implementing MES in ZDM enterprises in digital transformation were obtained by using the literature extraction-Delphi method, and the relationship between the factors was analyzed by using the system dynamics simulation model in this study. It is found that different from the existing research works on the implementation of MES in enterprises, staff preparation and level of information sharing are the most influential factors and play an important role in the implementation of MES in ZDM enterprises. Equipment preparation and client preparation followed closely, with supplier implementation team and scale infrastructure conditions playing a key role in providing capability support. This finding provides the direction for enterprises to improve the relevant implementation measures in time to ensure the effective implementation of MES in ZDM enterprises, and also provides a breakthrough for relevant researchers to find valuable research fields.
数字化管理系统被广泛应用于企业的经营活动中。实践证明，实施制造执行系统（MES）可以更好地监控和管理生产过程，提高企业生产效率，有效实现零缺陷管理（ZDM）。在此背景下，本研究利用文献提取-德尔菲法获得了数字化转型中 ZDM 企业实施 MES 的影响因素，并利用系统动力学仿真模型分析了各因素之间的关系。研究发现，与现有的企业实施 MES 的研究著作不同，人员准备和信息共享水平是影响最大的因素，对 ZDM 企业实施 MES 起着重要作用。设备准备和客户准备紧随其后，供应商实施团队和规模基础设施条件在提供能力支持方面发挥着关键作用。这一发现为企业及时完善相关实施措施，确保在 ZDM 企业中有效实施 MES 提供了方向，也为相关研究人员找到有价值的研究领域提供了突破口。
{"title":"Modeling and Simulation Analysis of Influencing Factors of MES Implementation in Zero Defect Management Enterprises in Digital Transformation","authors":"Yu Guo;Shan Liao;Shi Yin;Giulia Bruno;Deming Zhang","doi":"10.1109/TEM.2024.3486282","DOIUrl":"https://doi.org/10.1109/TEM.2024.3486282","url":null,"abstract":"Digital management system is widely used in the business activities of enterprises. Practice has proved that the implementation of the manufacturing execution system (MES) can better monitor and manage the production process, improve the production efficiency of enterprises, and effectively realize zero defect management (ZDM). Against this background, the influencing factors of implementing MES in ZDM enterprises in digital transformation were obtained by using the literature extraction-Delphi method, and the relationship between the factors was analyzed by using the system dynamics simulation model in this study. It is found that different from the existing research works on the implementation of MES in enterprises, staff preparation and level of information sharing are the most influential factors and play an important role in the implementation of MES in ZDM enterprises. Equipment preparation and client preparation followed closely, with supplier implementation team and scale infrastructure conditions playing a key role in providing capability support. This finding provides the direction for enterprises to improve the relevant implementation measures in time to ensure the effective implementation of MES in ZDM enterprises, and also provides a breakthrough for relevant researchers to find valuable research fields.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15306-15319"},"PeriodicalIF":4.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600372","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 : 2024-10-23DOI: 10.1109/TEM.2023.3330042
Antonio Messeni Petruzzelli;Gianluca Murgia;Eva Panetti;Adele Parmentola
{"title":"Editorial: Unveiling the Digital Transformation of Organizations Across Multiple Levels of Analysis","authors":"Antonio Messeni Petruzzelli;Gianluca Murgia;Eva Panetti;Adele Parmentola","doi":"10.1109/TEM.2023.3330042","DOIUrl":"https://doi.org/10.1109/TEM.2023.3330042","url":null,"abstract":"","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14063-14070"},"PeriodicalIF":4.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10731988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540383","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 : 2024-10-23DOI: 10.1109/TEM.2024.3485235
Chia-Chi Chang;Phuong-Dung Thi Nguyen
This article anchors itself in knowledge network theory and rivalry theory to explore the antecedents of exploration by examining the possibility of technological crowding, that is, a firm position in crowded technological fields, as the enabler of firms’ exploratory innovation performance. For this purpose, we adopted regression analysis for panel data with fixed effects and considered the data of 4100 firm-year observations of Taiwanese listed companies in the electronics industry from 2000 to 2021. The findings suggest that technological crowding urges firms to pursue exploration to ease heightened competition intensity and crowd out their competitors. Recalling the literature on the competency trap phenomenon in which some dominant firms may exhibit technological superiority underperformance, we further propose that the dark side of technological superiority is more visible when firms are positioned in crowded technological areas. Specifically, firms with stronger combinatorial capabilities or greater technological prestige refrain from investing in exploration because they are tied with high couplings between their homogeneous knowledge elements and indulge themselves in existing success. Our findings remain robust after adjusting the window of research and using the negative binomial regression with fixed effects based on the alternative measure of exploratory innovation. This article contributes significantly to the literature on technological position and knowledge networks by shedding light on the critical role of technological crowding in propelling firms’ exploration efforts. Our results also offer significant implications for executives in dominant firms and policymakers to become more aware of the competency trap and seek ways to span technological boundaries.
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