Journal of Scientific Papers

ECONOMICS & SOCIOLOGY


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EVA and Key Performance Indicators: The Case of Automotive Sector in Pre-Crisis, Crisis and Post-Crisis Periods

Vol. 11, No 3, 2018

Drahomíra Pavelková,

 

Tomas Bata University in Zlín,  Zlín, Czech Republic, 

E-mail: pavelkova@utb.cz

EVA and Key Performance Indicators: The Case of Automotive Sector in Pre-Crisis, Crisis and Post-Crisis Periods

 

Lubor Homolka,

 

Tomas Bata University in Zlín, Zlín, Czech Republic, 

E-mail: homolka@utb.cz


Adriana Knápková,

 

Tomas Bata University in Zlín, Zlín, Czech Republic, 

E-mail: knapkova@utb.cz


Karel Kolman,

 

Tomas Bata University in Zlín, Zlín, Czech Republic, 

E-mail: kolman@utb.cz


Ha Pham,

 

HCM City Open University, 

Ho Chi Minh City, Vietnam,

E-mail: ha.p@ou.edu.vn


 


 


 

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.

 

Received: November, 2017

1st Revision: April, 2018

Accepted: July, 2018

 

DOI: 10.14254/2071-789X.2018/11-3/5

JEL ClassificationM2, G3, L6

Keywords: economic value added, key performance indicators, sensitivity analysis, stochastic frontier analysis, business cycle, automotive industry