E. Shaunn Mattingly, James R. Kroes, Andrew S. Manikas
{"title":"AI as an emerging trend for managing employee efficiency in the retail and services industries","authors":"E. Shaunn Mattingly, James R. Kroes, Andrew S. Manikas","doi":"10.1016/j.bushor.2025.06.005","DOIUrl":null,"url":null,"abstract":"<div><div>Generative artificial intelligence (generative AI) is transforming the nature of work, offering clear benefits in efficiency and innovation. Yet, for practitioners, adoption remains complex because of limited resources and current operational demands. This study examines how firms manage these challenges by analyzing AI adoption through the lens of the exploration-exploitation trade-off. Using data from public company filings, we analyze the drivers and barriers of adoption, links to R&D investment, and industry-specific patterns. We find that larger firms with low employee efficiency are eager to adopt AI. Adoption rates also correlate with changes in the scale and focus of R&D spending. Notably, retail and services firms—among the least efficient in terms of labor productivity—are leading in AI uptake. However, their focus remains on automating routine tasks and expanding product offerings, rather than investing in workforce development. These results provide useful guidance for leaders seeking to utilize AI in industries struggling with employee productivity.</div></div>","PeriodicalId":48347,"journal":{"name":"Business Horizons","volume":"69 4","pages":"Pages 463-475"},"PeriodicalIF":9.4000,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Business Horizons","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0007681325001089","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Generative artificial intelligence (generative AI) is transforming the nature of work, offering clear benefits in efficiency and innovation. Yet, for practitioners, adoption remains complex because of limited resources and current operational demands. This study examines how firms manage these challenges by analyzing AI adoption through the lens of the exploration-exploitation trade-off. Using data from public company filings, we analyze the drivers and barriers of adoption, links to R&D investment, and industry-specific patterns. We find that larger firms with low employee efficiency are eager to adopt AI. Adoption rates also correlate with changes in the scale and focus of R&D spending. Notably, retail and services firms—among the least efficient in terms of labor productivity—are leading in AI uptake. However, their focus remains on automating routine tasks and expanding product offerings, rather than investing in workforce development. These results provide useful guidance for leaders seeking to utilize AI in industries struggling with employee productivity.
期刊介绍:
Business Horizons, the bimonthly journal of the Kelley School of Business at Indiana University, is dedicated to publishing original articles that appeal to both business academics and practitioners. Our editorial focus is on covering a diverse array of topics within the broader field of business, with a particular emphasis on identifying critical business issues and proposing practical solutions. Our goal is to inspire readers to approach business practices from new and innovative perspectives. Business Horizons occupies a distinctive position among business publications by offering articles that strike a balance between academic rigor and practical relevance. As such, our articles are grounded in scholarly research yet presented in a clear and accessible format, making them relevant to a broad audience within the business community.