IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v63y2020i2p171-181.html

Innovation analytics: Leveraging artificial intelligence in the innovation process

Author

Listed:
  • Kakatkar, Chinmay
  • Bilgram, Volker
  • Füller, Johann

Abstract

Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature—coupled with our experiences as practitioners—suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.

Suggested Citation

  • Kakatkar, Chinmay & Bilgram, Volker & Füller, Johann, 2020. "Innovation analytics: Leveraging artificial intelligence in the innovation process," Business Horizons, Elsevier, vol. 63(2), pages 171-181.
    • Handle: RePEc:eee:bushor:v:63:y:2020:i:2:p:171-181
    DOI: 10.1016/j.bushor.2019.10.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0007681319301533
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bushor.2019.10.006?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Kaminski, Jermain & Hopp, Christian & Tykvová, Tereza, 2019. "New technology assessment in entrepreneurial financing – Does crowdfunding predict venture capital investments?," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 287-302.
    2. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
    5. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
    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. Yin Bai & Yan Wang & Xingyao Ren, 2025. "Big data or big brother: When is recontacting the digital customer OK?," Journal of the Academy of Marketing Science, Springer, vol. 53(6), pages 1624-1643, December.
    2. Adrian Micu & Angela-Eliza Micu & Marius Geru & Alexandru Capatina & Mihaela-Carmen Muntean, 2021. "The Impact of Artificial Intelligence Use on the E-Commerce in Romania," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 137-137, February.
    3. Miller, Klaus M. & Skiera, Bernd, 2024. "Economic consequences of online tracking restrictions: Evidence from cookies," International Journal of Research in Marketing, Elsevier, vol. 41(2), pages 241-264.
    4. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.
    5. Pizzi, Gabriele & Scarpi, Daniele, 2020. "Privacy threats with retail technologies: A consumer perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    6. Donthu, Naveen & Reinartz, Werner & Kumar, Satish & Pattnaik, Debidutta, 2021. "A retrospective review of the first 35 years of the International Journal of Research in Marketing," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 232-269.
    7. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    8. Valeria Stourm & Scott A. Neslin & Eric T. Bradlow & Els Breugelmans & So Yeon Chun & Pedro Gardete & P. K. Kannan & Praveen Kopalle & Young-Hoon Park & David Restrepo Amariles & Raphael Thomadsen & Y, 2020. "Refocusing loyalty programs in the era of big data: a societal lens paradigm," Marketing Letters, Springer, vol. 31(4), pages 405-418, December.
    9. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    10. Gavoille, Nicolas & Zasova, Anna, 2023. "What we pay in the shadows: Labor tax evasion, minimum wage hike and employment," Journal of Public Economics, Elsevier, vol. 228(C).
    11. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    12. Peng, Qiao & McKillop, Donal & Quinn, Barry & Liu, Kailong, 2025. "Modeling and predicting failure in US credit unions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1237-1259.
    13. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    14. Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2025. "A Multi-Stage Financial Distress Early Warning System: Analyzing Corporate Insolvency with Random Forest," JRFM, MDPI, vol. 18(4), pages 1-16, April.
    15. 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.
    16. David, Benjamin, 2017. "Computer technology and probable job destructions in Japan: An evaluation," Journal of the Japanese and International Economies, Elsevier, vol. 43(C), pages 77-87.
    17. Ignaciuk, Ada & Ilicic, Joanna & Asprooth, Lauren & Sitko, Nicholas J. & Bernard, Angela & Maggio, Giuseppe & Tubiello, Francesco N. & Mueller, Marc, "undated". "Progress towards sustainable agriculture – Drivers of change," FAO Agricultural Development Economics Technical Study 319833, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    18. Axun Xing & Haiyan Wang & Bei Bian & Xinxin Guo, 2024. "Contract Theory-Based Collection, Updating, and Packaging Combination Strategies in Data Trading," Service Science, INFORMS, vol. 16(4), pages 297-318, December.
    19. Akachi, Yoko & Canning, David, 2015. "Inferring the economic standard of living and health from cohort height: Evidence from modern populations in developing countries," Economics & Human Biology, Elsevier, vol. 19(C), pages 114-128.
    20. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:171-181. 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.