Pub Date : 2024-06-12DOI: 10.1109/EMR.2024.3411550
Qian Lingxiao;Yin Ximing;Wang Yi;Chen Jin
Breakthroughs in artificial intelligence (AI) have spawned numerous AI companies. Yet, AI's role in facilitating corporate innovation and competence remains understudied. Based on innovation theories, the resource-based view, and the organizational change theory, we develop a holistic framework that integrates organizational AI resources, AI innovation capability, and corporate performance to depict how AI empowers corporate innovation and competence. We propose that corporate AI resources, consisting of data, human, and strategic resources, enhance their corporate performance by improving their AI innovation capability. Furthermore, we propose that a greater extent of human–machine collaboration, the ability of humans to utilize algorithms, and computational power effectively in various contexts improves corporate performance. Finally, we outline the key topics for future research, suggesting areas where further investigation could yield valuable insights into AI's role in corporate innovation. Our article offers actionable insights into companies’ AI resource allocation and new capability building for competing in the AI era. Firms should prioritize a balanced approach to manage their AI data resources, human resources, strategic resources, and human–machine collaboration to effectively enhance AI innovation capability and improve corporate performance. Our article answers how AI resources could empower corporate competence and contribute to AI innovation, organizational change theory, and resource-based view.
{"title":"Deciphering AI's Role in Corporate Innovation: A Holistic Framework of AI Resources, Capability, and Performance","authors":"Qian Lingxiao;Yin Ximing;Wang Yi;Chen Jin","doi":"10.1109/EMR.2024.3411550","DOIUrl":"https://doi.org/10.1109/EMR.2024.3411550","url":null,"abstract":"Breakthroughs in artificial intelligence (AI) have spawned numerous AI companies. Yet, AI's role in facilitating corporate innovation and competence remains understudied. Based on innovation theories, the resource-based view, and the organizational change theory, we develop a holistic framework that integrates organizational AI resources, AI innovation capability, and corporate performance to depict how AI empowers corporate innovation and competence. We propose that corporate AI resources, consisting of data, human, and strategic resources, enhance their corporate performance by improving their AI innovation capability. Furthermore, we propose that a greater extent of human–machine collaboration, the ability of humans to utilize algorithms, and computational power effectively in various contexts improves corporate performance. Finally, we outline the key topics for future research, suggesting areas where further investigation could yield valuable insights into AI's role in corporate innovation. Our article offers actionable insights into companies’ AI resource allocation and new capability building for competing in the AI era. Firms should prioritize a balanced approach to manage their AI data resources, human resources, strategic resources, and human–machine collaboration to effectively enhance AI innovation capability and improve corporate performance. Our article answers how AI resources could empower corporate competence and contribute to AI innovation, organizational change theory, and resource-based view.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"53 2","pages":"85-95"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.1109/EMR.2024.3412116
Vikram Singh;Somesh Kumar Sharma
High-tech products (HTPs) are key drivers that help strengthen the economy of any nation. Literature advocates that most of the research focused on the marketing and exports of HTPs. However, little attention has been paid to examining the variables of HTPs that affect their development in international market competitiveness, posing a challenge for high-tech industries (HTIs). In this context, this research is a unique contribution in this domain, which aims to analyze the key variables of HTPs that influence the performance of HTIs. A theoretical framework of 8 key variables and 30 HTP variables has been developed. The fuzzy multicriteria decision-making technique is applied to prioritize, rank, and measure the interrelationships between variables. This application evolved HTP features as the most prioritized and influential key variable, followed by others, all of which are interrelated. In contrast, nearer to technological development, technical convergence trends, closely related to science, quality of design, and object clarity are the top-globally ranked variables for measuring the performance of key HTP variables. These findings provide a roadmap for designers to maintain the feature of HTP, manufacturers in quality management, marker analysts to select the potential market, and management to make the best decision.
{"title":"Key Variables of High-Tech Products Influencing High-Tech Industries: A Hybrid Multicriteria Decision-Making Analysis","authors":"Vikram Singh;Somesh Kumar Sharma","doi":"10.1109/EMR.2024.3412116","DOIUrl":"https://doi.org/10.1109/EMR.2024.3412116","url":null,"abstract":"High-tech products (HTPs) are key drivers that help strengthen the economy of any nation. Literature advocates that most of the research focused on the marketing and exports of HTPs. However, little attention has been paid to examining the variables of HTPs that affect their development in international market competitiveness, posing a challenge for high-tech industries (HTIs). In this context, this research is a unique contribution in this domain, which aims to analyze the key variables of HTPs that influence the performance of HTIs. A theoretical framework of 8 key variables and 30 HTP variables has been developed. The fuzzy multicriteria decision-making technique is applied to prioritize, rank, and measure the interrelationships between variables. This application evolved HTP features as the most prioritized and influential key variable, followed by others, all of which are interrelated. In contrast, <italic>nearer to technological development, technical convergence trends, closely related to science</i>, quality of design, and object clarity are the top-globally ranked variables for measuring the performance of key HTP variables. These findings provide a roadmap for designers to maintain the feature of HTP, manufacturers in quality management, marker analysts to select the potential market, and management to make the best decision.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"53 1","pages":"33-53"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.1109/EMR.2024.3411882
Xuan Wang;Yaojie Li;Scott Smith;Helmut Schneider
As data-driven decision making becomes prevalent, research needs to provide more evidence to direct user decision making, particularly concerning transportation systems. In concurrence, ridesharing has been touted to reduce driving-while-intoxicated fatalities, albeit prior studies have provided inconsistent findings. A limitation of prior research on this topic is lacking adequate experimental controls while addressing the impact of potential confounds. This issue may affect potential assumptions and conclusions on whether the deployment of ridesharing services has led to a considerable reduction in driving-while-intoxicated fatalities. The present article leverages statistical modeling to control age, education, vehicle miles traveled, and metropolitan size. It reveals that ridesharing represented a 13.8% decline in driving-while-intoxicated fatalities among youths’ ages 17–34, but without significantly affecting drivers’ ages 35–65. Also, the results suggest that city population, vehicle miles traveled, and educational attainment can affect younger adults, whereas the same features were not significant for older adults. Furthermore, the article suggests that the initiation of UberX can serve as a ride-planning option to reduce driving-while-intoxicated fatalities among younger rather than older drivers. Based on the analysis results, multiple implications for transportation platform and software engineering managers are provided, especially in the areas of dispatch algorithms, requirement analysis, and ridesharing security and safety.
{"title":"Data-Driven Decision Making: The Case of Ridesharing With Implications for Engineering Managers","authors":"Xuan Wang;Yaojie Li;Scott Smith;Helmut Schneider","doi":"10.1109/EMR.2024.3411882","DOIUrl":"https://doi.org/10.1109/EMR.2024.3411882","url":null,"abstract":"As data-driven decision making becomes prevalent, research needs to provide more evidence to direct user decision making, particularly concerning transportation systems. In concurrence, ridesharing has been touted to reduce driving-while-intoxicated fatalities, albeit prior studies have provided inconsistent findings. A limitation of prior research on this topic is lacking adequate experimental controls while addressing the impact of potential confounds. This issue may affect potential assumptions and conclusions on whether the deployment of ridesharing services has led to a considerable reduction in driving-while-intoxicated fatalities. The present article leverages statistical modeling to control age, education, vehicle miles traveled, and metropolitan size. It reveals that ridesharing represented a 13.8% decline in driving-while-intoxicated fatalities among youths’ ages 17–34, but without significantly affecting drivers’ ages 35–65. Also, the results suggest that city population, vehicle miles traveled, and educational attainment can affect younger adults, whereas the same features were not significant for older adults. Furthermore, the article suggests that the initiation of UberX can serve as a ride-planning option to reduce driving-while-intoxicated fatalities among younger rather than older drivers. Based on the analysis results, multiple implications for transportation platform and software engineering managers are provided, especially in the areas of dispatch algorithms, requirement analysis, and ridesharing security and safety.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"53 1","pages":"17-32"},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1109/EMR.2024.3417055
Eugenia Rosca;Alexander Brem
In 2023, several progress reports were published to take stock of the mid-way progress toward the 2030 United Nations Sustainable Development Goals (SDGs) agenda. The results outlined in these reports are distressing: the assessment of the 140 targets under the SDGs shows that “only about 12% are on track; close to half, though showing progress, are moderately or severely off track, and some 30% have either seen no movement or regressed below the 2015 baseline” [United Nations, 2023]. This raises important questions: Are we doing enough to address the societal challenges we face? Have we adopted the suitable approaches, methods, and tools? Are there new approaches we can follow?
{"title":"Editorial Embracing Complexity and Tensions to Advance Sustainable Managerial Practice","authors":"Eugenia Rosca;Alexander Brem","doi":"10.1109/EMR.2024.3417055","DOIUrl":"https://doi.org/10.1109/EMR.2024.3417055","url":null,"abstract":"In 2023, several progress reports were published to take stock of the mid-way progress toward the 2030 United Nations Sustainable Development Goals (SDGs) agenda. The results outlined in these reports are distressing: the assessment of the 140 targets under the SDGs shows that “only about 12% are on track; close to half, though showing progress, are moderately or severely off track, and some 30% have either seen no movement or regressed below the 2015 baseline” [United Nations, 2023]. This raises important questions: Are we doing enough to address the societal challenges we face? Have we adopted the suitable approaches, methods, and tools? Are there new approaches we can follow?","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 3","pages":"6-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1109/EMR.2024.3399157
{"title":"Call for Papers: Special Issue on Supply Chain Digitalization in the Age of (R)Evolution","authors":"","doi":"10.1109/EMR.2024.3399157","DOIUrl":"https://doi.org/10.1109/EMR.2024.3399157","url":null,"abstract":"","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 3","pages":"251-251"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1109/EMR.2024.3381176
Jonas Benedikt Arnold;Dominik Hörauf
This article investigates the summarization capabilities of ChatGPT, a language model seen as effectively shortening texts, employing a hypothesis-generating and explorative approach. Using a specific prompt, the study examines the expected lengths of generated summaries across various input word counts (IWC). A shortening ratio is introduced to describe these relationships, with identified dependencies on IWCs between 100 and 400 words. The study also explores coherence comparisons, highlighting that the ChatGPT-generated text is often evaluated as more coherent than the original. The article introduces a multimetric approach for the evaluation and discusses dependencies of best case summaries on different input word counts, providing insights into the model's performance characteristics.
{"title":"A Multimetric Approach for Evaluation of ChatGPT-Generated Text Summaries","authors":"Jonas Benedikt Arnold;Dominik Hörauf","doi":"10.1109/EMR.2024.3381176","DOIUrl":"https://doi.org/10.1109/EMR.2024.3381176","url":null,"abstract":"This article investigates the summarization capabilities of ChatGPT, a language model seen as effectively shortening texts, employing a hypothesis-generating and explorative approach. Using a specific prompt, the study examines the expected lengths of generated summaries across various input word counts (IWC). A shortening ratio is introduced to describe these relationships, with identified dependencies on IWCs between 100 and 400 words. The study also explores coherence comparisons, highlighting that the ChatGPT-generated text is often evaluated as more coherent than the original. The article introduces a multimetric approach for the evaluation and discusses dependencies of best case summaries on different input word counts, providing insights into the model's performance characteristics.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 3","pages":"43-53"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1109/EMR.2024.3429056
{"title":"Proceedings of the IEEE","authors":"","doi":"10.1109/EMR.2024.3429056","DOIUrl":"https://doi.org/10.1109/EMR.2024.3429056","url":null,"abstract":"","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 3","pages":"252-252"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1109/EMR.2024.3428509
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/EMR.2024.3428509","DOIUrl":"https://doi.org/10.1109/EMR.2024.3428509","url":null,"abstract":"","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 3","pages":"254-254"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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