IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v63y2020i2p157-170.html
   My bibliography  Save this article

Machine learning for enterprises: Applications, algorithm selection, and challenges

Author

Listed:
  • Lee, In
  • Shin, Yong Jae

Abstract

Machine learning holds great promise for lowering product and service costs, speeding up business processes, and serving customers better. It is recognized as one of the most important application areas in this era of unprecedented technological development, and its adoption is gaining momentum across almost all industries. In view of this, we offer a brief discussion of categories of machine learning and then present three types of machine-learning usage at enterprises. We then discuss the trade-off between the accuracy and interpretability of machine-learning algorithms, a crucial consideration in selecting the right algorithm for the task at hand. We next outline three cases of machine-learning development in financial services. Finally, we discuss challenges all managers must confront in deploying machine-learning applications.

Suggested Citation

  • Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
    • Handle: RePEc:eee:bushor:v:63:y:2020:i:2:p:157-170
    DOI: 10.1016/j.bushor.2019.10.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681319301521
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bushor.2019.10.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lee, In & Lee, Kyoochun, 2015. "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, Elsevier, vol. 58(4), pages 431-440.
    2. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    3. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    4. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
    5. Wright, Scott A. & Schultz, Ainslie E., 2018. "The rising tide of artificial intelligence and business automation: Developing an ethical framework," Business Horizons, Elsevier, vol. 61(6), pages 823-832.
    6. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Black, J. Stewart & van Esch, Patrick, 2020. "AI-enabled recruiting: What is it and how should a manager use it?," Business Horizons, Elsevier, vol. 63(2), pages 215-226.
    2. Ayat Sami ODEIBAT, 2021. "The Effect Of Technology Evolution On The Future Of Jobs," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 17, pages 57-67, June.
    3. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    4. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    5. Saarikko, Ted & Westergren, Ulrika H. & Blomquist, Tomas, 2020. "Digital transformation: Five recommendations for the digitally conscious firm," Business Horizons, Elsevier, vol. 63(6), pages 825-839.
    6. Jarrahi, Mohammad Hossein & Askay, David & Eshraghi, Ali & Smith, Preston, 2023. "Artificial intelligence and knowledge management: A partnership between human and AI," Business Horizons, Elsevier, vol. 66(1), pages 87-99.
    7. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    8. Nataša Đurđević & Aleksandra Labus & Dušan Barać & Miloš Radenković & Marijana Despotović-Zrakić, 2022. "An Approach to Assessing Shopper Acceptance of Beacon Triggered Promotions in Smart Retail," Sustainability, MDPI, vol. 14(6), pages 1-25, March.
    9. Eric Forcael & Isabella Ferrari & Alexander Opazo-Vega & Jesús Alberto Pulido-Arcas, 2020. "Construction 4.0: A Literature Review," Sustainability, MDPI, vol. 12(22), pages 1-28, November.
    10. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.
    11. Hamilton, R.H. & Sodeman, William A., 2020. "The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources," Business Horizons, Elsevier, vol. 63(1), pages 85-95.
    12. Jake B. Telkamp & Marc H. Anderson, 2022. "The Implications of Diverse Human Moral Foundations for Assessing the Ethicality of Artificial Intelligence," Journal of Business Ethics, Springer, vol. 178(4), pages 961-976, July.
    13. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    14. Canhoto, Ana Isabel, 2021. "Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective," Journal of Business Research, Elsevier, vol. 131(C), pages 441-452.
    15. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    16. Kinkel, Steffen & Capestro, Mauro & Di Maria, Eleonora & Bettiol, Marco, 2023. "Artificial intelligence and relocation of production activities: An empirical cross-national study," International Journal of Production Economics, Elsevier, vol. 261(C).
    17. Huo, Dongyang & Malik, Asad Waqar & Ravana, Sri Devi & Rahman, Anis Ur & Ahmedy, Ismail, 2024. "Mapping smart farming: Addressing agricultural challenges in data-driven era," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    18. Rosa Lombardi & Raffaele Trequattrini & Federico Schimperna & Myriam Cano-Rubio, 2021. "The Impact of Smart Technologies on theManagement and Strategic Control: A Structured Literature Review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(suppl. 1), pages 11-30.
    19. Leonel Jorge Ribeiro Nunes & Radu Godina & João Carlos de Oliveira Matias, 2019. "Technological Innovation in Biomass Energy for the Sustainable Growth of Textile Industry," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    20. Fehmi Krasniqi & Hysni Terziu, 2021. "Challenges of Kosovo Micro Businesses," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 7, ejes_v7_i.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:bushor:v:63:y:2020:i:2:p:157-170. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/bushor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.