IDEAS home Printed from https://ideas.repec.org/a/ers/journl/vxxiiiy2020i2p388-404.html
   My bibliography  Save this article

Learning and Development Tools and the Innovative Potential of Artificial Intelligence Companies

Author

Listed:
  • Zbigniew Drewniak
  • Iwona Posadzinska

Abstract

Purpose: The aim of the article is to examine the relationship between the use of learning and development tools in building the innovation potential of enterprises in the artificial intelligence sector. Design/Methodology/Approach: The study is based on a survey on companies from the artificial intelligence sector (n=127) located in Poland. The regression model defines the relationship between learning and development tools and innovations measured by the number of obtained patents. In addition, the analysis was expanded to include the results of a survey conducted among employees of the surveyed enterprises. As a result, an assessment of the usefulness of knowledge management tools was obtained. Findings: The findings indicate that modern tools of knowledge management in the form of knowledge bases and knowledge pills, or gamifications and business simulations affects the level of innovativeness. These tools are positive assessed by employees (i.e. programmers) that are directly involved in creating solutions in the field of artificial intelligence. Practical implications: The results of the analysis may indicate the directions of development of HR departments in companies of the artificial intelligence sector. It turns out that modern forms of learning stimulate the level of company innovation. Originality/Value: The artificial intelligence sector is perceived as the one that will have the greatest impact on technological progress in the coming years. Solutions in the field of artificial intelligence will have their impact on other industries, such as medicine or the IT sector. The study drew attention to factors determining the level of innovativeness of companies related to learning and development tools.

Suggested Citation

  • Zbigniew Drewniak & Iwona Posadzinska, 2020. "Learning and Development Tools and the Innovative Potential of Artificial Intelligence Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 388-404.
    • Handle: RePEc:ers:journl:v:xxiii:y:2020:i:2:p:388-404
    as

    Download full text from publisher

    File URL: https://www.ersj.eu/journal/1599/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schmiedeberg, Claudia, 2008. "Complementarities of innovation activities: An empirical analysis of the German manufacturing sector," Research Policy, Elsevier, vol. 37(9), pages 1492-1503, October.
    2. Mary M. Crossan & Marina Apaydin, 2010. "A Multi‐Dimensional Framework of Organizational Innovation: A Systematic Review of the Literature," Journal of Management Studies, Wiley Blackwell, vol. 47(6), pages 1154-1191, September.
    3. Oliveira, Pedro & von Hippel, Eric, 2011. "Users as service innovators: The case of banking services," Research Policy, Elsevier, vol. 40(6), pages 806-818, July.
    4. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    5. Kevin Boudreau, 2010. "Open Platform Strategies and Innovation: Granting Access vs. Devolving Control," Management Science, INFORMS, vol. 56(10), pages 1849-1872, October.
    6. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    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. Zhou, Yixiao & Tyers, Rod, 2019. "Automation and inequality in China," China Economic Review, Elsevier, vol. 58(C).
    2. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    3. Charles Ayoubi & Boris Thurm, 2023. "Knowledge diffusion and morality: Why do we freely share valuable information with Strangers?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(1), pages 75-99, January.
    4. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Filiou, Despoina & Kesidou, Effie & Wu, Lichao, 2023. "Are smart cities green? The role of environmental and digital policies for Eco-innovation in China," World Development, Elsevier, vol. 165(C).
    6. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    7. Vrontis, Demetris & Christofi, Michael, 2021. "R&D internationalization and innovation: A systematic review, integrative framework and future research directions," Journal of Business Research, Elsevier, vol. 128(C), pages 812-823.
    8. Bernardo S Buarque & Ronald B Davies & Ryan M Hynes & Dieter F Kogler, 2020. "OK Computer: the creation and integration of AI in Europe," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 175-192.
    9. Fernando Muñoz-Bullón & Maria J. Sanchez-Bueno & Alfredo De Massis, 2020. "Combining Internal and External R&D: The Effects on Innovation Performance in Family and Nonfamily Firms," Entrepreneurship Theory and Practice, , vol. 44(5), pages 996-1031, September.
    10. Mucha, Tomasz & Seppälä, Timo, 2020. "Artificial Intelligence Platforms – A New Research Agenda for Digital Platform Economy," ETLA Working Papers 76, The Research Institute of the Finnish Economy.
    11. Albert Bravo-Biosca, 2020. "Experimental Innovation Policy," Innovation Policy and the Economy, University of Chicago Press, vol. 20(1), pages 191-232.
    12. ZHU Chen & MOTOHASHI Kazuyuki, 2024. "The Fundraising of AI Startups: Evidence from web data," Discussion papers 24021, Research Institute of Economy, Trade and Industry (RIETI).
    13. Johnson, Prince Chacko & Laurell, Christofer & Ots, Mart & Sandström, Christian, 2022. "Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation?," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    14. Patricio Duran & Nadine Kammerlander & Marc van Essen & Thomas Zellweger, 2016. "Doing More with Less : Innovation Input and Output in Family Firms," Post-Print hal-02276703, HAL.
    15. Jabeur, Sami Ben & Ballouk, Houssein & Mefteh-Wali, Salma & Omri, Anis, 2022. "Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    17. Sean Kruger & Adriana Aletta Steyn, 2020. "Enhancing technology transfer through entrepreneurial development: practices from innovation spaces," The Journal of Technology Transfer, Springer, vol. 45(6), pages 1655-1689, December.
    18. Labhard, Vincent & Lehtimäki, Jonne, 2022. "Digitalisation, institutions and governance, and growth: mechanisms and evidence," Working Paper Series 2735, European Central Bank.
    19. Agrawal, Ajay & Gans, Joshua S. & Goldfarb, Avi, 2019. "Exploring the impact of artificial Intelligence: Prediction versus judgment," Information Economics and Policy, Elsevier, vol. 47(C), pages 1-6.
    20. Josef Åström & Wiebke Reim & Vinit Parida, 2022. "Value creation and value capture for AI business model innovation: a three-phase process framework," Review of Managerial Science, Springer, vol. 16(7), pages 2111-2133, October.

    More about this item

    Keywords

    Artificial intelligence; learning and development; knowledge management.;
    All these keywords.

    JEL classification:

    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation
    • M53 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Training
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:ers:journl:v:xxiii:y:2020:i:2:p:388-404. 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: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .

    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.