Economics & Sociology

ISSN: 2071-789X eISSN: 2306-3459 DOI: 10.14254/2071-789X
Index PUBMS: f5512f57-a601-11e7-8f0e-080027f4daa0
Article information
Title: EVA and Key Performance Indicators: The Case of Automotive Sector in Pre-Crisis, Crisis and Post-Crisis Periods
Issue: Vol. 11, No 3, 2018
Published date: 09-2018 (print) / 09-2018 (online)
Journal: Economics & Sociology
ISSN: 2071-789X, eISSN: 2306-3459
Authors: Drahomíra Pavelková
Tomas Bata University in Zlín

Lubor Homolka
Tomas Bata University in Zlín

Adriana Knápková
Tomas Bata University in Zlín

Karel Kolman
Tomas Bata University in Zlín

Ha Pham
HCM City Open University
Keywords: economic value added, key performance indicators, sensitivity analysis, stochastic frontier analysis, business cycle, automotive industry
DOI: 10.14254/2071-789X.2018/11-3/5
Index PUBMS: 0757d987-cdf5-11e8-92b1-901b0efa6e97
Language: English
Pages: 78-95 (18)
JEL classification: M20, G30, L60
Website: http://www.economics-sociology.eu/?600,en_eva-and-key-performance-indicators-the-case-of-automotive-sector-in-pre-crisis-crisis-and-post-crisis-periods
Licenses:
Abstract

The choice of a suitable measure for company's performance and identification of key performance indicators are among the most frequently discussed topics in the field of corporate management strategizing. This paper shows how the value-based measure represented by Economic Value Added (EVA) and its pyramidal breakdown could act as facilitators in revealing value drivers. The univariate sensitivity analysis and the Stochastic Frontier Analysis are employed to identify the key performance indicators. The analysis is based on the samples of original equipment manufacturers and suppliers in Czech automotive sector. The automotive industry, in general, is sensitive to the business cycle. Therefore, KPIs of the multiple EVA/Sales distinguished for the samples in the Pre-crisis, Crisis and Post-crisis periods are identified. The detailed sensitivity analysis reveals several differences in these periods in both samples and across companies of different sizes. Some of the results are further confirmed by the Stochastic Frontier Analysis. Besides other indicators, value added is demonstrated as the key driver with the highest positive impact and personnel cost with the highest negative impact on EVA in all periods although the magnitude of these effects is changing. Analysis of the technical efficiency scores reveals that companies in the crisis periods are more similar to each other and are closer to the best-performing companies than in other periods.

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