{"title":"Why AI Projects Fail: Lessons From New Product Development","authors":"Robert G. Cooper","doi":"10.1109/EMR.2024.3419268","DOIUrl":"https://doi.org/10.1109/EMR.2024.3419268","url":null,"abstract":"The majority of artificial intelligence (AI) projects in business do not succeed. Despite numerous opinion pieces on the causes of these failures, there is a notable scarcity of solid research on the subject. Analysis of the available data shows that the reasons for AI failures are strikingly similar to those identified in the extensively studied field of new product development (NPD). Furthermore, effective strategies from NPD can be adapted to address AI project failures. This article identifies seven primary reasons for AI failures in business, primarily stemming from poor business practices, and offers corresponding recommendations for improvement.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 4","pages":"15-21"},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761471","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}
{"title":"Exploring New Frontiers in Multimaterial Additive Manufacturing","authors":"Andreas Wagner;Helen Rogers;Alina Le","doi":"10.1109/EMR.2024.3412403","DOIUrl":"https://doi.org/10.1109/EMR.2024.3412403","url":null,"abstract":"Multimaterial additive manufacturing (MMAM) introduces a new dimension to production technologies by seamlessly integrating multiple materials into a single object, enabling unparalleled design possibilities. This article explores MMAM value chains and methods and showcases real-world applications in the aerospace, biomedical, and industrial sectors. While MMAM holds promise for application in high-tech industries, its widespread adoption is contingent on quantity, utilization, and technical requirements. Continued research into developing material properties and reducing unit costs is needed. Only once these two key challenges have been addressed is mainstream adoption likely.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"53 2","pages":"122-133"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10564007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925081","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}
{"title":"Toward a Standardized Metaverse Definition: Empirical Evidence From the ITU Metaverse Focus Group","authors":"Elli Kontogianni;Leonidas Anthopoulos","doi":"10.1109/EMR.2024.3416331","DOIUrl":"https://doi.org/10.1109/EMR.2024.3416331","url":null,"abstract":"Although the term metaverse was first coined in 1992, discussions surrounding its definition began to emerge in the literature only around 2010 and have not been standardized yet. Recent literature provides numerous definitions of the term, which appear to lack conceptual clarity and cause an ambiguous understanding of the metaverse and its accompanying terminology, strengthening the importance of a commonly agreed definition. This article follows a multimethod research methodology consisting of a bibliometric analysis, a literature review, and an international focus group dedicated to the standardization of the metaverse. The bibliometric analysis that was conducted using the keywords “metaverse” and “definition” to the scientific repositories ScienceDirect, Web of Science, and Scopus, revealed, among others, the most cited articles. Definitions extracted from the top ten cited articles were compiled, and their associated keywords and their weaknesses were identified. As these findings needed to be confirmed, they were circulated with the focus group experts, leading to an exchange of various insights and information concerning the definition of the metaverse. The outcomes of this study are highly interesting since they delineate the pivotal terms of the metaverse, culminating in the establishment of a universally accepted definition.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"53 3","pages":"110-127"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299370","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}
Anne M. M. Turner;Sara S. Grobbelaar;Faatiema Salie;Martin Nieuwoudt
{"title":"From Idea to Market in the Local Medical Device Value Chain: A Conceptual Framework","authors":"Anne M. M. Turner;Sara S. Grobbelaar;Faatiema Salie;Martin Nieuwoudt","doi":"10.1109/EMR.2024.3409943","DOIUrl":"https://doi.org/10.1109/EMR.2024.3409943","url":null,"abstract":"While the medical device value chain has been extensively studied, this article enhances understanding by offering a refined approach to analyze and explore its bottlenecks. Leveraging a design science research methodology, our article introduces a medical device value chain framework that advances beyond traditional mappings. Initially, two systematic literature reviews were employed to construct a preliminary map, pinpointing 74 value-adding activities across seven distinct categories: Idea Generation, Research and Development, Production/ Manufacturing, Market, Distribution and Use, Waste Management, and Systemic aspects. The framework was utilized with structured interviews with 17 subject matter experts from the Western Cape province in South Africa. These experts evaluated each value-adding activity on a 3-D scale—importance, effort required, and difficulty. After these evaluations were looked at more closely, the most important bottlenecks in the medical device value chain could be identified. The results were then visually combined using a linking grid. This matrix-based tool helps delineate the connections between medical device value chain components, pinpointing where bottlenecks align with undesirable effects. It forms a basis for formulating strategic interventions to achieve desirable outcomes.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"53 2","pages":"63-84"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925170","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}
Barimwotubiri Ruyobeza;Sara S. Grobbelaar;Adele Botha
{"title":"Toward a Framework for Interweaving Adoption and Scaling Requirements Into Healthcare System Development and Project Management Processes","authors":"Barimwotubiri Ruyobeza;Sara S. Grobbelaar;Adele Botha","doi":"10.1109/EMR.2024.3412795","DOIUrl":"https://doi.org/10.1109/EMR.2024.3412795","url":null,"abstract":"In order to enhance their impact on traditional healthcare systems and improve health outcomes, digital health interventions must undergo widespread adoption, scaling, and consistent use. However, achieving the adoption and scaling of digital tools in healthcare remains a challenging goal globally. This challenge is compounded by an inconsistent and fragmented approach to adoption and scaling, as well as a limited reliance on theoretical tools throughout a digital health intervention's lifecycle phase. This article argues that adoption and scaling requirements should be prominently integrated into each phase of traditional healthcare system development and project management processes to facilitate these systems’ widespread adoption and use. Addressing this necessitates the operationalization of multiple theoretical tools across a digital health intervention's lifecycle. To tackle the identified weakness of limited reliance on theoretical tools, we conducted a qualitative evidence synthesis, culminating in a reusable framework for theory operationalization. To illustrate how the above framework can be applied in practice, we provide and discuss a use case scenario, involving a hypothetical design, development, and implementation of a remote patient management application. This work expands the existing literature on technology implementation in healthcare by introducing a framework that assists practitioners to connect theoretical concepts to traditional system development and project management activities and phases.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"53 2","pages":"96-109"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925171","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}
{"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}
{"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}
{"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}
{"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}
{"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}