Strategic leadership and employment dynamics in European countries industries: forecasting labour force expectations through space-time analysis
Articles
Adriana Grigorescu
National University of Political Studies and Public Administration
Alina Mihaela Dima
Bucharest University of Economic Studies image/svg+xml
Cristina Lincaru
National Scientific Research Institute for Labour and Social Protection
Víctor Raúl López Ruiz
University of Castilla-La Mancha image/svg+xml
Published 2026-01-07
https://doi.org/10.15388/Tibe.2025.24.3A.11
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Keywords

strategic leadership
employment
space-time forecasting
industry
public policies

How to Cite

Grigorescu, A., Dima, A. M., Lincaru, C., & López Ruiz, V. R. (2026). Strategic leadership and employment dynamics in European countries industries: forecasting labour force expectations through space-time analysis . Transformations In Business & Economics, 24(3A (66A), 754-775. https://doi.org/10.15388/Tibe.2025.24.3A.11

Abstract

This paper analyses the evolution of employment expectations in industry (BS-IEME-BAL) for the period 1992–2025, using data from the Business and Consumer Surveys (BCS) provided by the Directorate-General for Economic and Financial Affairs (DG ECFIN) of the European Commission. The study examines cyclical fluctuations, long-term trends and the impact of macroeconomic factors on the industrial labour market. To identify recurring patterns and anticipate future changes in employment, the analysis uses spatiotemporal forecasting in GIS, applying the Exponential Smoothing Forecast (Holt-Winters) model. This model allows the decomposition of the time series into trend, seasonality and residual components, providing a robust estimate of the evolution of employment expectations. Anomalies and turning points are also assessed, contributing to the understanding of the vulnerabilities of the industrial sector in times of economic uncertainty. The results obtained provide support for strategic leadership performed by formulating public policies and strategies for adapting to the digital transition, economic fluctuations and labour market challenges. The study highlights the usefulness of spatial analysis and temporal forecasting tools for data-based decision-making in the context of structural transformations of the economy.

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References

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