{"title":"Firms’ use of predictive artificial intelligence for economic value creation and appropriation","authors":"","doi":"10.1016/j.ijinfomgt.2024.102836","DOIUrl":null,"url":null,"abstract":"<div><p>Firms are increasingly investing in the use of artificial intelligence (AI). Some succeed in creating and appropriating substantial economic value, but many fail. There is no consensus as to how a firm should use AI to create and appropriate economic value. This paper provides an answer to that question. A novel research model is advanced based on the notion of data network effects being realized within a firm’s business model. This research model is tested in a unique and natural industrial setting of two competing firms that simultaneously adopt the use of similar predictive AI. This setting is researched with two distinct empirical studies that employ mixed-methods research. The results shows that one firm fails to convert its AI use into economic value creation and appropriation while the other succeeds. Value is created and appropriated by ensuring that AI users perceive high user value that realize data network effects while being located in the firm’s business model architecture so as to activate business value drivers. These findings confirm the here proposed research model and offer novel theoretical contributions and specific managerial implications.</p></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":null,"pages":null},"PeriodicalIF":20.1000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0268401224000847/pdfft?md5=03d25f1fc6be237d329d77e3ccff8230&pid=1-s2.0-S0268401224000847-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0268401224000847","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Firms are increasingly investing in the use of artificial intelligence (AI). Some succeed in creating and appropriating substantial economic value, but many fail. There is no consensus as to how a firm should use AI to create and appropriate economic value. This paper provides an answer to that question. A novel research model is advanced based on the notion of data network effects being realized within a firm’s business model. This research model is tested in a unique and natural industrial setting of two competing firms that simultaneously adopt the use of similar predictive AI. This setting is researched with two distinct empirical studies that employ mixed-methods research. The results shows that one firm fails to convert its AI use into economic value creation and appropriation while the other succeeds. Value is created and appropriated by ensuring that AI users perceive high user value that realize data network effects while being located in the firm’s business model architecture so as to activate business value drivers. These findings confirm the here proposed research model and offer novel theoretical contributions and specific managerial implications.
期刊介绍:
The International Journal of Information Management (IJIM) is a distinguished, international, and peer-reviewed journal dedicated to providing its readers with top-notch analysis and discussions within the evolving field of information management. Key features of the journal include:
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IJIM keeps readers informed with major papers, reports, and reviews.
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The journal remains current and relevant through Viewpoint articles and regular features like Research Notes, Case Studies, and a Reviews section, ensuring readers are updated on contemporary issues.
Focus on Quality:
IJIM prioritizes high-quality papers that address contemporary issues in information management.