IDEAS home Printed from https://ideas.repec.org/a/cub/journl/v26y2023i2p123-134.html
   My bibliography  Save this article

Unlocking the Path to AI Adoption: Antecedents to Behavioral Intentions in Utilizing AI for Effective Job (Re)Design

Author

Listed:
  • Ljupcho EFTIMOV

    (Ss. Cyril and Methodius University in Skopje)

  • Bojan KITANOVIKJ

    (Ss. Cyril and Methodius University in Skopje)

Abstract

Purpose – The study attempts to shed light on the level of adoption of artificial intelligence (AI) in the human resource (HR) departments for the purposes of designing jobs through assessment of the willingness and utilization of the employees in the said departments. Aim(s) – The objective is to identify the primary antecedents that influence the behavioral intentions of employees in HR departments to use AI specifically for the HR function of job design. Design/methodology/approach – The study uses a multiple linear regression method grounded in a survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The purposive sample consisted of 107 HR professionals working in companies in the Republic of North Macedonia. Findings – The results from the regression analysis revealed that performance expectancy, social influence, and facilitating conditions positively influence the behavioral intentions of HR professionals toward AI adoption in job design activities. Limitations of the study – Future studies could address the limitations of our research endeavor by broadening the sample size, assessing the opportunity for adopting AI in other HR functions, and including more countries in the sampling and analysis. Practical implications – The study points out several recommendations to HR managers, business leaders, and policy-makers regarding the effective and ethical utilization of AI for designing and redesigning jobs in the contemporary business environment to make the workforce more satisfied, engaged, and productive. Originality/value – This study represents one of the first research endeavors on the application of AI for the particular HR function of job design, considering its previous wider adoption in HR functions like recruitment and payroll, which is heavily researched. It further contributes to the discussion of if and to what extent HR professionals tend to use AI.

Suggested Citation

  • Ljupcho EFTIMOV & Bojan KITANOVIKJ, 2023. "Unlocking the Path to AI Adoption: Antecedents to Behavioral Intentions in Utilizing AI for Effective Job (Re)Design," Journal of Human Resource Management, Comenius University in Bratislava, Faculty of Management, vol. 26(2), pages 123-134.
  • Handle: RePEc:cub:journl:v:26:y:2023:i:2:p:123-134
    as

    Download full text from publisher

    File URL: https://www.jhrm.eu/123-unlocking-the-path-to-ai-adoption-antecedents-to-behavioral-intentions-in-utilizing-ai-for-effective-job-redesign/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    artificial intelligence; human resource management; job design; UTAUT; industry 4.0;
    All these keywords.

    JEL classification:

    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation

    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:cub:journl:v:26:y:2023:i:2:p:123-134. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Anna Lasakova (email available below). General contact details of provider: https://edirc.repec.org/data/fmkomsk.html .

    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.