A three-fold framework for understanding HRM practices in South-Eastern European SMEs

Purpose We study particular structural and organisational factors affecting the formality of human resource management (HRM) practices in small and medium-sized enterprises (SMEs) in South-Eastern European (SEE) post-communist countries, in particular Serbia, Romania, Bulgaria and the Former Yugoslav Republic of Macedonia (FYROM) in order to understand the antecedents of formalization in such settings. Design/methodology/approach Adopting a quantitative approach, this study analyses data gathered through a survey of 168 managers of SMEs from throughout the region. Findings The results show that HRM in SMEs in the SEE region can be understood through a three-fold framework which includes: degree of internationalisation of SMEs, sector of SMEs and organisational size of SMEs. These three factors positively affect the level of HRM formalisation in SEE SMEs. These findings are further attributed to the particular political and economic context of the post-communist SEE region. Research limitations/implications Although specific criteria were set for SME selection, we do not suggest that the study reflects a representative picture of the SEE region because we used a purposive sampling methodology. Practical implications This article provides useful insights into the factors which influence HRM in SMEs in a particular context. The findings can help business owners and managers understand how HRM can be applied in smaller organisations, particularly in post-communist SEE business contexts. Originality/value HRM in SMEs in this region has hardly been studied at all despite their importance. Therefore, this exploratory research seeks to expand knowledge relating to the application of HRM in SMEs in SEE countries which have their business environments dominated by different dynamics in comparison to western European ones.

A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes

1. The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. 2. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). 3. The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. 4. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales.