{"title":"The arcanum of artificial intelligence in enterprise applications: Toward a unified framework","authors":"Heinz Herrmann","doi":"10.1016/j.jengtecman.2022.101716","DOIUrl":null,"url":null,"abstract":"<div><p>Disagreement and confusion about artificial intelligence (AI) terminology impede researchers, innovators, and practitioners when developing and implementing enterprise applications. The prevailing ambiguities and use of buzzwords are exacerbated by media and vendor marketing hype. This study identifies several ambiguities within and across AI fields and subfields. Combining a systematic review with a sequential mixed-models design, a total of 26,143 publications were reviewed and mapped, making this the largest conceptual study in the AI field. A unified framework is proposed as an Euler diagram to bring about clarity through a \"common language\" for AI researchers, innovators, and practitioners.</p></div>","PeriodicalId":50209,"journal":{"name":"Journal of Engineering and Technology Management","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0923474822000467/pdfft?md5=7c9c380a9af40425ad1acb56079d4475&pid=1-s2.0-S0923474822000467-main.pdf","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering and Technology Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0923474822000467","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 2
Abstract
Disagreement and confusion about artificial intelligence (AI) terminology impede researchers, innovators, and practitioners when developing and implementing enterprise applications. The prevailing ambiguities and use of buzzwords are exacerbated by media and vendor marketing hype. This study identifies several ambiguities within and across AI fields and subfields. Combining a systematic review with a sequential mixed-models design, a total of 26,143 publications were reviewed and mapped, making this the largest conceptual study in the AI field. A unified framework is proposed as an Euler diagram to bring about clarity through a "common language" for AI researchers, innovators, and practitioners.
期刊介绍：
The Journal of Engineering and Technology Management (JET-M) is an international scholarly refereed research journal which aims to promote the theory and practice of technology, innovation, and engineering management.
The journal links engineering, science, and management disciplines. It addresses the issues involved in the planning, development, and implementation of technological capabilities to shape and accomplish the strategic and operational objectives of an organization. It covers not only R&D management, but also the entire spectrum of managerial concerns in technology-based organizations. This includes issues relating to new product development, human resource management, innovation process management, project management, technological fusion, marketing, technological forecasting and strategic planning.
The journal provides an interface between technology and other corporate functions, such as R&D, marketing, manufacturing and administration. Its ultimate goal is to make a profound contribution to theory development, research and practice by serving as a leading forum for the publication of scholarly research on all aspects of technology, innovation, and engineering management.
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