Engineering projects are vulnerable to opportunistic behavior due to their one-off and uncertain nature. Contractual governance is the crucial mechanism for matching the two key project characteristics, i.e., asset specificity and uncertainty, to curtail opportunism. However, the existing studies failed to agree on the above matching principle. In this article, we divide contractual governance into control, coordination, and adaptation from the functional perspective and employ machine learning to code actual contract texts. Based on the paired data from the text-mining results and survey, this study uses qualitative comparative analysis to investigate the aligning (or misaligning) combinations that lead to low (or high) opportunism. The results show that, for projects with low uncertainty, detailed contractual coordination is essential and it should be complemented by less detailed adaptation or detailed control. In such projects, low asset specificity reinforces the significance of contractual coordination alone. This study also finds the limitations of contractual governance in projects with high uncertainty, especially combined with low asset specificity, which necessitates other governance mechanisms. This study helps to resolve previous contradictory matching principles from the view of contract dimensions, contract measurement, and data analysis methods. Project managers can benefit from this study to effectively reduce opportunism and avoid disputes.
{"title":"The Opportunism-Inhibiting Effects of the Alignment Between Engineering Project Characteristics and Contractual Governance: Paired Data From Contract Text Mining and Survey","authors":"Chenglong Xu;Yongqiang Chen;Hongjiang Yao;Lihan Zhang","doi":"10.1109/TEM.2024.3480254","DOIUrl":"https://doi.org/10.1109/TEM.2024.3480254","url":null,"abstract":"Engineering projects are vulnerable to opportunistic behavior due to their one-off and uncertain nature. Contractual governance is the crucial mechanism for matching the two key project characteristics, i.e., asset specificity and uncertainty, to curtail opportunism. However, the existing studies failed to agree on the above matching principle. In this article, we divide contractual governance into control, coordination, and adaptation from the functional perspective and employ machine learning to code actual contract texts. Based on the paired data from the text-mining results and survey, this study uses qualitative comparative analysis to investigate the aligning (or misaligning) combinations that lead to low (or high) opportunism. The results show that, for projects with low uncertainty, detailed contractual coordination is essential and it should be complemented by less detailed adaptation or detailed control. In such projects, low asset specificity reinforces the significance of contractual coordination alone. This study also finds the limitations of contractual governance in projects with high uncertainty, especially combined with low asset specificity, which necessitates other governance mechanisms. This study helps to resolve previous contradictory matching principles from the view of contract dimensions, contract measurement, and data analysis methods. Project managers can benefit from this study to effectively reduce opportunism and avoid disputes.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15110-15124"},"PeriodicalIF":4.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524210","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 : 2024-10-18DOI: 10.1109/TEM.2024.3457520
Hong Zhang
This study investigates the transformative impact of artificial intelligence (AI) on human resource management (HRM) practices through a quantitative descriptive approach. Data were collected from 285 employees and 144 HR professionals across seven organizations using purposive sampling to explore AI's influence on recruitment, performance assessment, job satisfaction, and workforce planning. A key novelty of this research lies in its comprehensive analysis of AI's holistic influence on HRM dynamics, going beyond isolated aspects of AI implementation. Findings reveal that organizations leveraging AI in HR processes experience significantly higher recruitment efficiency and employee productivity compared to those without AI integration. Moreover, successful adaptation to AI in HR correlates with increased levels of employee job satisfaction and reduced turnover rates, highlighting AI's potential to enhance organizational performance and employee well-being. Additionally, positive perceptions of AI in HR positively correlate with elevated levels of organizational trust and employee engagement. These insights contribute to a nuanced understanding of AI's role in reshaping HRM strategies and fostering a supportive workplace environment conducive to sustainable organizational success. Practical implications are discussed to assist HR professionals and organizational leaders in effectively harnessing AI to optimize HR practices and adapt to evolving workforce dynamics.
{"title":"Exploring the Impact of AI on Human Resource Management: A Case Study of Organizational Adaptation and Employee Dynamics","authors":"Hong Zhang","doi":"10.1109/TEM.2024.3457520","DOIUrl":"https://doi.org/10.1109/TEM.2024.3457520","url":null,"abstract":"This study investigates the transformative impact of artificial intelligence (AI) on human resource management (HRM) practices through a quantitative descriptive approach. Data were collected from 285 employees and 144 HR professionals across seven organizations using purposive sampling to explore AI's influence on recruitment, performance assessment, job satisfaction, and workforce planning. A key novelty of this research lies in its comprehensive analysis of AI's holistic influence on HRM dynamics, going beyond isolated aspects of AI implementation. Findings reveal that organizations leveraging AI in HR processes experience significantly higher recruitment efficiency and employee productivity compared to those without AI integration. Moreover, successful adaptation to AI in HR correlates with increased levels of employee job satisfaction and reduced turnover rates, highlighting AI's potential to enhance organizational performance and employee well-being. Additionally, positive perceptions of AI in HR positively correlate with elevated levels of organizational trust and employee engagement. These insights contribute to a nuanced understanding of AI's role in reshaping HRM strategies and fostering a supportive workplace environment conducive to sustainable organizational success. Practical implications are discussed to assist HR professionals and organizational leaders in effectively harnessing AI to optimize HR practices and adapt to evolving workforce dynamics.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14991-15004"},"PeriodicalIF":4.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524094","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 : 2024-10-17DOI: 10.1109/TEM.2024.3479775
R. Castagnoli;M. Cugno;S. Maroncelli;A. Cugno
The European Union recognizes Industry 5.0 as a cultural revolution that complements the fourth industrial revolution by requiring companies to implement a sustainable, resilient, and human-centric organization. This article critically analyzes the role of the human-centric approach in Industry 4.0 and 5.0 through a systematic literature review of 69 studies published between 2011 and December 2023. The results show that the human-centric approach 1) is underinvestigated in management and mainly ergonomically approached in engineering. Furthermore, it 2) enables response to the challenges of an aging population and increasing working age and improves response to acute events exogenous and endogenous to the firm. The human-centric approach also 3) positively impacts economic and social sustainability and 4) should be investigated through a transdisciplinary approach.
{"title":"A New Research Agenda for Human-Centric Manufacturing: A Systematic Literature Review","authors":"R. Castagnoli;M. Cugno;S. Maroncelli;A. Cugno","doi":"10.1109/TEM.2024.3479775","DOIUrl":"https://doi.org/10.1109/TEM.2024.3479775","url":null,"abstract":"The European Union recognizes Industry 5.0 as a cultural revolution that complements the fourth industrial revolution by requiring companies to implement a sustainable, resilient, and human-centric organization. This article critically analyzes the role of the human-centric approach in Industry 4.0 and 5.0 through a systematic literature review of 69 studies published between 2011 and December 2023. The results show that the human-centric approach 1) is underinvestigated in management and mainly ergonomically approached in engineering. Furthermore, it 2) enables response to the challenges of an aging population and increasing working age and improves response to acute events exogenous and endogenous to the firm. The human-centric approach also 3) positively impacts economic and social sustainability and 4) should be investigated through a transdisciplinary approach.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15236-15253"},"PeriodicalIF":4.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10721234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594970","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 : 2024-10-16DOI: 10.1109/TEM.2024.3481439
Jiaming Ding;Anning Wang;Kenneth Guang-Lih Huang;Qiang Zhang;Shanlin Yang
Professional technical documents (PTDs) offer a wealth of information for R&D personnel and innovation management scholars. Recently, the increase in the categories and volume of PTDs has introduced new challenges for their automatic and accurate classification. Existing studies have focused on leveraging the semantic information of documents (i.e., titles and abstracts) for classification tasks. However, the standard label hierarchy of classification systems and the rich label semantic information have been generally ignored. In this paper, we propose a supervised learning-based classification model, designed to Make Full Use of Label Information (MFULI) for hierarchical multi-label PTD classification. Firstly, we deploy a Label-aware Supervised Contrastive Learning Module (LSCLM), which introduces the definition of label set similarity with the aim of improving document representation. Then, we propose a Hierarchy-aware Label Embedding Attentive Module (HLEAM) that dynamically incorporates label hierarchy information into the classification model. We evaluate our proposed model on two public patent datasets, namely USPTO-1 and WIPO-alpha. Experimental results show that our model outperforms other state-of-the-art classification models. Furthermore, we perform a series of ablation studies and analyses to demonstrate the necessity of each component of our model. This paper provides important theoretical contributions and practical implications for innovation and technology management. <p><i>Managerial Relevance Statement</i>—This study helps advance the field of R&D, innovation and technology management by introducing a novel supervised learning-based classification model for professional technical documents (PTDs). Our proposed approach, termed Making Full Use of Label Information (MFULI), is specifically designed for hierarchical multi-label PTD classification, addressing the challenges posed by the growing diversity and volume of PTDs. By integrating innovative components such as the Label-aware Supervised Contrastive Learning Module (LSCLM) and the Hierarchy-aware Label Embedding Attentive Module (HLEAM), MFULI significantly enhances document representation and classification accuracy. The experimental validation of the model on public patent datasets underscores its practical utility and superiority over other existing state-of-the-art models. For managers and practitioners in R&D, innovation and technology management, the implications of this research are profound. Our study provides significant contributions to the fields of technology and innovation management, engineering management, and automated document classification, yielding both theoretical insights and practical implications. The model's ability to effectively categorize large-scale PTDs aids in streamlining knowledge management processes, enhancing decision-making, and fostering more efficient innovation strategies. In summary, this research offers a robust and innovati
{"title":"Improving Large-Scale Classification in Technology Management: Making Full Use of Label Information for Professional Technical Documents","authors":"Jiaming Ding;Anning Wang;Kenneth Guang-Lih Huang;Qiang Zhang;Shanlin Yang","doi":"10.1109/TEM.2024.3481439","DOIUrl":"https://doi.org/10.1109/TEM.2024.3481439","url":null,"abstract":"Professional technical documents (PTDs) offer a wealth of information for R&D personnel and innovation management scholars. Recently, the increase in the categories and volume of PTDs has introduced new challenges for their automatic and accurate classification. Existing studies have focused on leveraging the semantic information of documents (i.e., titles and abstracts) for classification tasks. However, the standard label hierarchy of classification systems and the rich label semantic information have been generally ignored. In this paper, we propose a supervised learning-based classification model, designed to Make Full Use of Label Information (MFULI) for hierarchical multi-label PTD classification. Firstly, we deploy a Label-aware Supervised Contrastive Learning Module (LSCLM), which introduces the definition of label set similarity with the aim of improving document representation. Then, we propose a Hierarchy-aware Label Embedding Attentive Module (HLEAM) that dynamically incorporates label hierarchy information into the classification model. We evaluate our proposed model on two public patent datasets, namely USPTO-1 and WIPO-alpha. Experimental results show that our model outperforms other state-of-the-art classification models. Furthermore, we perform a series of ablation studies and analyses to demonstrate the necessity of each component of our model. This paper provides important theoretical contributions and practical implications for innovation and technology management. \u0000<p><i>Managerial Relevance Statement</i>—This study helps advance the field of R&D, innovation and technology management by introducing a novel supervised learning-based classification model for professional technical documents (PTDs). Our proposed approach, termed Making Full Use of Label Information (MFULI), is specifically designed for hierarchical multi-label PTD classification, addressing the challenges posed by the growing diversity and volume of PTDs. By integrating innovative components such as the Label-aware Supervised Contrastive Learning Module (LSCLM) and the Hierarchy-aware Label Embedding Attentive Module (HLEAM), MFULI significantly enhances document representation and classification accuracy. The experimental validation of the model on public patent datasets underscores its practical utility and superiority over other existing state-of-the-art models. For managers and practitioners in R&D, innovation and technology management, the implications of this research are profound. Our study provides significant contributions to the fields of technology and innovation management, engineering management, and automated document classification, yielding both theoretical insights and practical implications. The model's ability to effectively categorize large-scale PTDs aids in streamlining knowledge management processes, enhancing decision-making, and fostering more efficient innovation strategies. In summary, this research offers a robust and innovati","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15188-15208"},"PeriodicalIF":4.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595099","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 : 2024-10-16DOI: 10.1109/TEM.2024.3481670
Robin Weidlich;Tobias Albrecht;Patrick Derr;Maximilian Röglinger
Digital twins have gained significant attention in recent years as a means to represent physical objects digitally. It is now applied for planning, monitoring, and decision-making across various domains. While extensively leveraged in manufacturing, digital twins also present promising opportunities in other process-intensive sectors, such as the testing, inspection, and calibration industries. Industrial testing laboratories face challenges such as cost pressures and efficiency demands, operating within a complex socio-technical and highly regulated environment. Current digital solutions, such as laboratory information management systems, fall short of providing a comprehensive data and process management perspective and do not fully comply with the ISO/IEC 17025 standard, which ensures trust in laboratory operations and results. This article aims to address these gaps by proposing a set of design principles and a software architecture for a process-oriented digital lab twin developed through a design science research approach. The artifact is evaluated through expert interviews, a prototypical implementation, and a field study in an industrial laboratory setting. The findings offer valuable insights for designing digital twins in laboratory process management, guiding future research and practical applications.
{"title":"Designing a Process-Oriented Digital Twin for Industrial Testing Laboratories","authors":"Robin Weidlich;Tobias Albrecht;Patrick Derr;Maximilian Röglinger","doi":"10.1109/TEM.2024.3481670","DOIUrl":"https://doi.org/10.1109/TEM.2024.3481670","url":null,"abstract":"Digital twins have gained significant attention in recent years as a means to represent physical objects digitally. It is now applied for planning, monitoring, and decision-making across various domains. While extensively leveraged in manufacturing, digital twins also present promising opportunities in other process-intensive sectors, such as the testing, inspection, and calibration industries. Industrial testing laboratories face challenges such as cost pressures and efficiency demands, operating within a complex socio-technical and highly regulated environment. Current digital solutions, such as laboratory information management systems, fall short of providing a comprehensive data and process management perspective and do not fully comply with the ISO/IEC 17025 standard, which ensures trust in laboratory operations and results. This article aims to address these gaps by proposing a set of design principles and a software architecture for a process-oriented digital lab twin developed through a design science research approach. The artifact is evaluated through expert interviews, a prototypical implementation, and a field study in an industrial laboratory setting. The findings offer valuable insights for designing digital twins in laboratory process management, guiding future research and practical applications.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15174-15187"},"PeriodicalIF":4.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565581","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 : 2024-10-15DOI: 10.1109/TEM.2024.3468928
Irem Dikmen;Joseph H. M. Tah;Guzide Atasoy
{"title":"Editorial Managing Risk and Complexity in Construction Projects With Digital Technologies","authors":"Irem Dikmen;Joseph H. M. Tah;Guzide Atasoy","doi":"10.1109/TEM.2024.3468928","DOIUrl":"https://doi.org/10.1109/TEM.2024.3468928","url":null,"abstract":"","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"14878-14881"},"PeriodicalIF":4.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10719022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443025","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 : 2024-10-11DOI: 10.1109/TEM.2024.3478355
Emilia Lavi;Yoram Reich
The main goal of design is to create value. Recently, there has been a growing understanding that value extends beyond purely economic and technical factors and should embrace the multifaceted sociotechnical ecosystem. Studies discussing system value acknowledge its imperative role in design while observing the lack of a comprehensive value notion complying with the need. In this article, the primary objective is to propose a holistic multidomain system value model (SVM), targeted to be general and field agnostic, to be utilized as a decision-support tool. The generation of the model includes the synthesis of multiple, transcending engineering, data sources, system value ontology formulation, and design of a concise SVM. Aiming to be practical and generally applicable, the SVM is validated by focus groups and case studies, analyzing engineered systems along with policy-related decisions. The findings indicate that the suggested model enables systematic and time-efficient system value analysis, assisting in evaluating alternatives and unveiling differing attitudes. Used as a decision-making support tool, the SVM can transform the scope of discussions, expand the range of factors considered during alternatives comparison, and guide the way toward higher value outcomes.
{"title":"Supporting Decision Making in Value-Oriented Design: A Multidomain System Value Model","authors":"Emilia Lavi;Yoram Reich","doi":"10.1109/TEM.2024.3478355","DOIUrl":"https://doi.org/10.1109/TEM.2024.3478355","url":null,"abstract":"The main goal of design is to create value. Recently, there has been a growing understanding that value extends beyond purely economic and technical factors and should embrace the multifaceted sociotechnical ecosystem. Studies discussing system value acknowledge its imperative role in design while observing the lack of a comprehensive value notion complying with the need. In this article, the primary objective is to propose a holistic multidomain system value model (SVM), targeted to be general and field agnostic, to be utilized as a decision-support tool. The generation of the model includes the synthesis of multiple, transcending engineering, data sources, system value ontology formulation, and design of a concise SVM. Aiming to be practical and generally applicable, the SVM is validated by focus groups and case studies, analyzing engineered systems along with policy-related decisions. The findings indicate that the suggested model enables systematic and time-efficient system value analysis, assisting in evaluating alternatives and unveiling differing attitudes. Used as a decision-making support tool, the SVM can transform the scope of discussions, expand the range of factors considered during alternatives comparison, and guide the way toward higher value outcomes.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15084-15095"},"PeriodicalIF":4.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524159","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 : 2024-10-10DOI: 10.1109/TEM.2024.3477629
Mohsin Ali Soomro;Ali Nawaz Khan
This article investigates visionary leadership in the context of economic crises coupled with political strife. This article examines the visionary leaders’ influence through mediating and moderating role of digital transformation and organizational strategic flexibility, respectively, in developing organizational resilience. We test this model on data from construction sector small- and medium-sized enterprises (SMEs) in the developing economy's context. We have employed the lens of contingency theory to understand and explain the results. The collected data were analyzed on SPSS-23 and AMOS 23. Findings reveal that visionary leader would lead the organizational shift to digital transformation in economic crises in order to preserve resources and increase the efficiency of business operations. Results have further shown that digital transformation alone may not foster organizational resilience. Nevertheless, the relationship of digital transformation with resilience becomes more robust when an organization is endowed with high levels of strategic flexibility. Results also have shown that visionary leadership's influence over organizational resilience through digital transformation becomes stronger if organizations uphold strategic flexibility. This article includes valuable recommendations for SMEs to survive during economic crises and political instability and emphasizes the systematic approach when overcoming such issues to survive.
{"title":"Reimagining Resilience: Visionary Leadership, Digital Transformation, and Strategic Flexibility in Small and Medium Enterprises in Construction Sector","authors":"Mohsin Ali Soomro;Ali Nawaz Khan","doi":"10.1109/TEM.2024.3477629","DOIUrl":"https://doi.org/10.1109/TEM.2024.3477629","url":null,"abstract":"This article investigates visionary leadership in the context of economic crises coupled with political strife. This article examines the visionary leaders’ influence through mediating and moderating role of digital transformation and organizational strategic flexibility, respectively, in developing organizational resilience. We test this model on data from construction sector small- and medium-sized enterprises (SMEs) in the developing economy's context. We have employed the lens of contingency theory to understand and explain the results. The collected data were analyzed on SPSS-23 and AMOS 23. Findings reveal that visionary leader would lead the organizational shift to digital transformation in economic crises in order to preserve resources and increase the efficiency of business operations. Results have further shown that digital transformation alone may not foster organizational resilience. Nevertheless, the relationship of digital transformation with resilience becomes more robust when an organization is endowed with high levels of strategic flexibility. Results also have shown that visionary leadership's influence over organizational resilience through digital transformation becomes stronger if organizations uphold strategic flexibility. This article includes valuable recommendations for SMEs to survive during economic crises and political instability and emphasizes the systematic approach when overcoming such issues to survive.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15070-15083"},"PeriodicalIF":4.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to the dynamic and ever-changing landscape of supply chains, which are continually challenged by internal and external factors, there is a critical need for continuous adaptation, learning, and improvement. Historically, scholars have argued that traditional information systems lack the capacity to effectively support resilience strategies within supply chains. However, advancements in Industry 4.0 technologies may have shifted this paradigm. This article explores how enabling technologies (ET) can support the development of resilient operations at the supply chain level. To that end, a systematic literature review is combined with a multiple case study to understand how these technologies can support the development of elements of resilience. Three distinct sectors from different geographical locations were chosen for this study: an agri-food company in Brazil, a manufacturing firm in the food industry in Canada, and a logistics service provider in Italy. Integrating both theoretical insights and empirical findings leads to the formulation of a research framework, the primary contribution of this study, which serves as a resource for scholars and practitioners aiming to leverage ET to increase supply chain resilience. The article concludes with key findings and suggests avenues for future research.
{"title":"Enabling Technologies as a Support to Achieve Resilience in Supply Chain Operations","authors":"Enzo Domingos;Carla Pereira;Fabiano Armellini;Christophe Danjou;Francesco Facchini","doi":"10.1109/TEM.2024.3477946","DOIUrl":"https://doi.org/10.1109/TEM.2024.3477946","url":null,"abstract":"In response to the dynamic and ever-changing landscape of supply chains, which are continually challenged by internal and external factors, there is a critical need for continuous adaptation, learning, and improvement. Historically, scholars have argued that traditional information systems lack the capacity to effectively support resilience strategies within supply chains. However, advancements in Industry 4.0 technologies may have shifted this paradigm. This article explores how enabling technologies (ET) can support the development of resilient operations at the supply chain level. To that end, a systematic literature review is combined with a multiple case study to understand how these technologies can support the development of elements of resilience. Three distinct sectors from different geographical locations were chosen for this study: an agri-food company in Brazil, a manufacturing firm in the food industry in Canada, and a logistics service provider in Italy. Integrating both theoretical insights and empirical findings leads to the formulation of a research framework, the primary contribution of this study, which serves as a resource for scholars and practitioners aiming to leverage ET to increase supply chain resilience. The article concludes with key findings and suggests avenues for future research.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15292-15305"},"PeriodicalIF":4.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600229","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 : 2024-10-09DOI: 10.1109/TEM.2024.3477508
Jinhong Kim;Youngjung Geum
With drastic changes in technology and its converging power in new product development, technology convergence has long been considered imperative in the innovation literature. Despite these efforts, previous articles neglected the importance of technology convergence in identifying promising technologies. To address this limitation, this article assumes that patents with high mediating power for subsequent technology convergence are likely to be promising. For this purpose, this article proposes the concept of convergence distance, which is measured by the differences in IPCs in backward and forward citations of patents, and defines it as the mediating power of technology convergence. Three indicators are defined: convergence distance, convergence intensity, and convergence diversity. Using these convergence-related indicators, we developed a machine-learning model to predict promising technologies. Consequently, the models with new evolution indicators outperformed the original models. Moreover, our suggested indicators turned out to be very important for predicting promising technologies, implying that the mediating power of technology convergence is very important for predicting future promising technologies and should be considered very significant for technology opportunity discovery.
{"title":"Identifying Promising Technologies Considering Technology Convergence: A Patent-Based Machine-Learning Approach","authors":"Jinhong Kim;Youngjung Geum","doi":"10.1109/TEM.2024.3477508","DOIUrl":"https://doi.org/10.1109/TEM.2024.3477508","url":null,"abstract":"With drastic changes in technology and its converging power in new product development, technology convergence has long been considered imperative in the innovation literature. Despite these efforts, previous articles neglected the importance of technology convergence in identifying promising technologies. To address this limitation, this article assumes that patents with high mediating power for subsequent technology convergence are likely to be promising. For this purpose, this article proposes the concept of convergence distance, which is measured by the differences in IPCs in backward and forward citations of patents, and defines it as the mediating power of technology convergence. Three indicators are defined: convergence distance, convergence intensity, and convergence diversity. Using these convergence-related indicators, we developed a machine-learning model to predict promising technologies. Consequently, the models with new evolution indicators outperformed the original models. Moreover, our suggested indicators turned out to be very important for predicting promising technologies, implying that the mediating power of technology convergence is very important for predicting future promising technologies and should be considered very significant for technology opportunity discovery.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15096-15109"},"PeriodicalIF":4.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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