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

Zbigniew Drewniak and Iwona Posadzinska

European Research Studies Journal, 2020, vol. XXIII, issue 2, 388-404

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.

Keywords: Artificial intelligence; learning and development; knowledge management. (search for similar items in EconPapers)
JEL-codes: D83 M12 M53 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.ersj.eu/journal/1599/download (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxiii:y:2020:i:2:p:388-404

Access Statistics for this article

More articles in European Research Studies Journal from European Research Studies Journal
Bibliographic data for series maintained by Marios Agiomavritis ().