{"title":"The use of generative AI in research: a production management case study from the aviation industry","authors":"R. O. Walton, D. V. Watkins","doi":"10.1057/s41270-024-00317-y","DOIUrl":null,"url":null,"abstract":"<p>Generative Artificial Intelligence (GAI) marks a groundbreaking shift in research. Unlike traditional AI, GAI can generate novel insights and content using natural language processing. Using case study methodology, this paper explored GAI's application in identifying research gaps in aviation's use of Additive Manufacturing (AM), focusing on Design Optimization. Recent advances, such as ChatGPT-4, enable GAI to process extensive data and recognize complex patterns. The research method includes paper selection, GAI-driven gap analysis, and thematic extraction. Generative AI uncovered research domains but has limitations in content attribution and accuracy. Nevertheless, GAI promises to revolutionize knowledge discovery and problem-solving across various fields.</p>","PeriodicalId":43041,"journal":{"name":"Journal of Marketing Analytics","volume":"136 12 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Marketing Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1057/s41270-024-00317-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Generative Artificial Intelligence (GAI) marks a groundbreaking shift in research. Unlike traditional AI, GAI can generate novel insights and content using natural language processing. Using case study methodology, this paper explored GAI's application in identifying research gaps in aviation's use of Additive Manufacturing (AM), focusing on Design Optimization. Recent advances, such as ChatGPT-4, enable GAI to process extensive data and recognize complex patterns. The research method includes paper selection, GAI-driven gap analysis, and thematic extraction. Generative AI uncovered research domains but has limitations in content attribution and accuracy. Nevertheless, GAI promises to revolutionize knowledge discovery and problem-solving across various fields.
生成式人工智能（GAI）标志着研究领域的突破性转变。与传统的人工智能不同，GAI 可以利用自然语言处理生成新颖的见解和内容。本文采用案例研究方法，探讨了 GAI 在确定航空业使用增材制造 (AM) 的研究差距方面的应用，重点是设计优化。ChatGPT-4 等最新进展使 GAI 能够处理大量数据并识别复杂模式。研究方法包括论文选择、GAI 驱动的差距分析和主题提取。生成式人工智能揭示了研究领域，但在内容归属和准确性方面存在局限性。不过，GAI有望彻底改变各领域的知识发现和问题解决方式。
期刊介绍：
Data has become the new ore in today’s knowledge economy. However, merely storing and reporting are not enough to thrive in today’s increasingly competitive markets. What is called for is the ability to make sense of all these oceans of data, and to apply those insights to the way companies approach their markets, adjust to changing market conditions, and respond to new competitors.
Marketing analytics lies at the heart of this contemporary wave of data driven decision-making. Companies can no longer survive when they rely on gut instinct to make decisions. Strategic leverage of data is one of the few remaining sources of sustainable competitive advantage. New products can be copied faster than ever before. Staff are becoming less loyal as well as more mobile, and business centers themselves are moving across the globe in a world that is getting flatter and flatter.
The Journal of Marketing Analytics brings together applied research and practice papers in this blossoming field. A unique blend of applied academic research, combined with insights from commercial best practices makes the Journal of Marketing Analytics a perfect companion for academics and practitioners alike. Academics can stay in touch with the latest developments in this field. Marketing analytics professionals can read about the latest trends, and cutting edge academic research in this discipline.
The Journal of Marketing Analytics will feature applied research papers on topics like targeting, segmentation, big data, customer loyalty and lifecycle management, cross-selling, CRM, data quality management, multi-channel marketing, and marketing strategy.
The Journal of Marketing Analytics aims to combine the rigor of carefully controlled scientific research methods with applicability of real world case studies. Our double blind review process ensures that papers are selected on their content and merits alone, selecting the best possible papers in this field.
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