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.2024.3488325
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 : 2024-10-28DOI: 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.2024.3485751
Tao Wang;Chao Yu;Jun Huang;Hsin-Ning Su
In a world of swiftly changing technology and external challenges, predicting the role of core patents in technology systems' strength and power is vital. This research presents a method that combines robustness analysis of patent citation networks with core patent identification, assessing their global industrial technology innovation significance. It aims to identify patents key to network stability and external change adaptation, understanding their impact in dynamic tech environments. Using network robustness, the study examines connectivity, efficiency, and clustering in patent citation networks, assessing patent node importance based on structural feature changes postremoval. The study employs patents from five technological domains as case studies, ranking the importance of nodes and exploring how patent attributes affect these rankings. This research contributes by merging patent network robustness with valuation, supporting IP strategies and tech management policies, and offering insights into tech system complexity and dynamism.
{"title":"Robust Networks, Pivotal Patents: Identifying and Assessing Key Technological Influencers","authors":"Tao Wang;Chao Yu;Jun Huang;Hsin-Ning Su","doi":"10.1109/TEM.2024.3485751","DOIUrl":"https://doi.org/10.1109/TEM.2024.3485751","url":null,"abstract":"In a world of swiftly changing technology and external challenges, predicting the role of core patents in technology systems' strength and power is vital. This research presents a method that combines robustness analysis of patent citation networks with core patent identification, assessing their global industrial technology innovation significance. It aims to identify patents key to network stability and external change adaptation, understanding their impact in dynamic tech environments. Using network robustness, the study examines connectivity, efficiency, and clustering in patent citation networks, assessing patent node importance based on structural feature changes postremoval. The study employs patents from five technological domains as case studies, ranking the importance of nodes and exploring how patent attributes affect these rankings. This research contributes by merging patent network robustness with valuation, supporting IP strategies and tech management policies, and offering insights into tech system complexity and dynamism.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15254-15277"},"PeriodicalIF":4.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594978","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 : 2024-10-22DOI: 10.1109/TEM.2024.3484664
Peng He;Zhen-Song Chen;Abbas Mardani;Henry Xu
Given the increasing societal emphasis on corporate social responsibility (CSR), a critical strategic decision facing retailers is whether to introduce green products when distributing the manufacturer's nongreen ones. To address this issue, this article mainly considers two scenarios: the regular or green-supportive manufacturer. Furthermore, we develop four mathematical models to determine the retailer's green item introduction decisions. The research findings indicate that the manufacturer's CSR would significantly impact both firms’ pricing decisions and may change market distribution. Additionally, the retailer introducing green items will reduce the wholesale/retail prices of nongreen items but may foster an augmentation in overall demand. It is noteworthy that the retailer introducing green items does not necessarily cannibalize the sales of nongreen items; instead, it may even enhance their sales. Significantly, this strategy can benefit the green-supportive manufacturer if the greening cost is relatively low. These implications underscore that incentivizing manufacturers to care more about environmentally conscious consumers can serve as a viable strategy for promoting sustainable development initiatives.
{"title":"Should a Retailer Introduce Green Items in Socially Responsible Supply Chains? A Game-Theoretic Analysis","authors":"Peng He;Zhen-Song Chen;Abbas Mardani;Henry Xu","doi":"10.1109/TEM.2024.3484664","DOIUrl":"https://doi.org/10.1109/TEM.2024.3484664","url":null,"abstract":"Given the increasing societal emphasis on corporate social responsibility (CSR), a critical strategic decision facing retailers is whether to introduce green products when distributing the manufacturer's nongreen ones. To address this issue, this article mainly considers two scenarios: the regular or green-supportive manufacturer. Furthermore, we develop four mathematical models to determine the retailer's green item introduction decisions. The research findings indicate that the manufacturer's CSR would significantly impact both firms’ pricing decisions and may change market distribution. Additionally, the retailer introducing green items will reduce the wholesale/retail prices of nongreen items but may foster an augmentation in overall demand. It is noteworthy that the retailer introducing green items does not necessarily cannibalize the sales of nongreen items; instead, it may even enhance their sales. Significantly, this strategy can benefit the green-supportive manufacturer if the greening cost is relatively low. These implications underscore that incentivizing manufacturers to care more about environmentally conscious consumers can serve as a viable strategy for promoting sustainable development initiatives.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15224-15235"},"PeriodicalIF":4.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595098","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 : 2024-10-22DOI: 10.1109/TEM.2024.3485088
Baoqiang Zhan;Yongli Li;Chuang Wei;Chong Wu;Eric W. T. Ngai
Firms increasingly rely on the third-party platforms to manage customer complaints. In this article, we explore the impact of complaints on firms’ idiosyncratic risk by considering both the valence and channel characteristics of complaints. Through textual analysis, complaints are categorized into four types based on the intersection of two dimensions: the valence (mild or severe) of the complaint and the channel (firm or platform) through which the complaint is lodged. Empirical analysis is conducted to test the hypotheses. The results reveal that firms’ idiosyncratic risk rises after severe complaints but decreases after mild complaints. Moreover, while mild platform-channel complaints increase risk, severe platform-channel complaints reduce risk. This study highlights the disparate impacts of the channel and valence of complaints on manufacturing and service firms. This research contributes to the literature on online complaints and the marketing–finance interface, offering valuable insights for firms seeking to mitigate their idiosyncratic risk through complaint management strategies.
{"title":"Evaluating the Impact of Complaints on Firms’ Idiosyncratic Risk: The Roles of Valance and Channel","authors":"Baoqiang Zhan;Yongli Li;Chuang Wei;Chong Wu;Eric W. T. Ngai","doi":"10.1109/TEM.2024.3485088","DOIUrl":"https://doi.org/10.1109/TEM.2024.3485088","url":null,"abstract":"Firms increasingly rely on the third-party platforms to manage customer complaints. In this article, we explore the impact of complaints on firms’ idiosyncratic risk by considering both the valence and channel characteristics of complaints. Through textual analysis, complaints are categorized into four types based on the intersection of two dimensions: the valence (mild or severe) of the complaint and the channel (firm or platform) through which the complaint is lodged. Empirical analysis is conducted to test the hypotheses. The results reveal that firms’ idiosyncratic risk rises after severe complaints but decreases after mild complaints. Moreover, while mild platform-channel complaints increase risk, severe platform-channel complaints reduce risk. This study highlights the disparate impacts of the channel and valence of complaints on manufacturing and service firms. This research contributes to the literature on online complaints and the marketing–finance interface, offering valuable insights for firms seeking to mitigate their idiosyncratic risk through complaint management strategies.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15333-15348"},"PeriodicalIF":4.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600373","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}
Entrepreneurs traditionally use “learning-by-doing” and “learning-by-thinking” as alternative approaches to iteratively build business models for their new ventures. However, both approaches face criticism in how they address novelty and uncertainty, which are crucial to successful entrepreneurship. While generative AI (GenAI) is increasingly used in entrepreneurial tasks, the practices through which it becomes a learning resource for entrepreneurs remain unexplored. Based on a qualitative study, we present a process model that illustrates how entrepreneurs incorporate GenAI into business model design through five resourcing practices. These practices transform GenAI into a valuable resource for facilitating learning during the design process. This approach, which we term “learning-by-conversing,” introduces a generative startup methodology to complement the lean startup model. We distinguish two modes of learning by conversing—reflexive learning and confirmatory learning—based on how novice and experienced entrepreneurs engage with it. By proposing a learning approach that integrates GenAI with entrepreneurial efforts, we bridge the “thinking” versus “doing” debate in business model generation and deepen our understanding of GenAI's role in entrepreneurship.
{"title":"Too Much AI Hype, Too Little Emphasis on Learning? Entrepreneurs Designing Business Models Through Learning-by-Conversing With Generative AI","authors":"Angelos Kostis;Johan Lidström;Sujith Nair;Jonny Holmström","doi":"10.1109/TEM.2024.3484750","DOIUrl":"https://doi.org/10.1109/TEM.2024.3484750","url":null,"abstract":"Entrepreneurs traditionally use “learning-by-doing” and “learning-by-thinking” as alternative approaches to iteratively build business models for their new ventures. However, both approaches face criticism in how they address novelty and uncertainty, which are crucial to successful entrepreneurship. While generative AI (GenAI) is increasingly used in entrepreneurial tasks, the practices through which it becomes a learning resource for entrepreneurs remain unexplored. Based on a qualitative study, we present a process model that illustrates how entrepreneurs incorporate GenAI into business model design through five resourcing practices. These practices transform GenAI into a valuable resource for facilitating learning during the design process. This approach, which we term “learning-by-conversing,” introduces a generative startup methodology to complement the lean startup model. We distinguish two modes of learning by conversing—reflexive learning and confirmatory learning—based on how novice and experienced entrepreneurs engage with it. By proposing a learning approach that integrates GenAI with entrepreneurial efforts, we bridge the “thinking” versus “doing” debate in business model generation and deepen our understanding of GenAI's role in entrepreneurship.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15278-15291"},"PeriodicalIF":4.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595091","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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