Agric. Econ. - Czech, 2025, 71(1):46-57 | DOI: 10.17221/118/2024-AGRICECON

Approach to the differentiated motivation of employees in agricultureOriginal Paper

Miloš Hitka ORCID...¹, Lenka Ližbetinová ORCID...², Jozef Ďurian ORCID...³, Agota Giedré Raišiené ORCID...⁴

¹ Faculty of Wood Sciences and Technology, Technical University in Zvolen, Zvolen, Slovakia

² Faculty of Technology, The Institute of Technology and Business in České Budějovice, České Budějovice, Czech Republic

³ Faculty of Economics, Matej Bel University in Banská Bystrica, Banská Bystrica, Slovakia

⁴ Faculty of Public Governance and Business, Mykolas Romeris University, Vilnius, Lithuania

The capacity for flexibility, personal development, and intrinsic motivation is a crucial aspect needed by employees in the process of transforming agricultural organisations. This paper aims to determine the typically oriented groups of employees in terms of their motivation and verify whether the identified groups are invariant even under changing conditions over time. The data from 2019–2023 (from 374 respondents employed in agricultural enterprises in south-central Slovakia) were evaluated using descriptive and inferential (K-means cluster) statistics. According to preferences and constancy of attitudes, three clusters of employees called ‘Materialists’, ‘Team progressives’, and ‘Preferences settled’ were determined. The clusters were also solved in terms of stability over time. Despite the differences in the preferences of these clusters, relational factors indeed played one of the most critical roles, even in the case of materialistically oriented employees. The practical result of the research is the possibility of designing a differentiated motivation program specifically targeted to individual groups of employees with similar motivational preferences based on a competent statistical analysis of motivation. The results are used by agricultural businesses to design motivation programs for similarly motivationally oriented employees sophisticatedly.

Keywords: age; clusters; human resource management; K-means; staff; work preferences

Received: March 27, 2024; Revised: November 20, 2024; Accepted: November 27, 2024; Published: January 30, 2025  Show citation