J 2022

At what lag should economic indicators be applied to predict sales in a transformed economy? Is it worthwhile when e-tailing?

BAUEROVÁ, Radka, Halina STARZYCZNÁ and Tomáš PRAŽÁK

Basic information

Original name

At what lag should economic indicators be applied to predict sales in a transformed economy? Is it worthwhile when e-tailing?

Authors

BAUEROVÁ, Radka (203 Czech Republic, guarantor, belonging to the institution), Halina STARZYCZNÁ (203 Czech Republic, belonging to the institution) and Tomáš PRAŽÁK (203 Czech Republic, belonging to the institution)

Edition

Forum Scientiae Oeconomia, Dąbrowa Górnicza, Faculty of Applied Sciences of WSB University, Dąbrowa Górnicza, 2022, 2300-5947

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

50202 Applied Economics, Econometrics

Country of publisher

Poland

Confidentiality degree

není předmětem státního či obchodního tajemství

RIV identification code

RIV/47813059:19520/22:A0000349

Organization unit

School of Business Administration in Karvina

Keywords in English

transformed economies; retail industry; retail sales; economic indicators; cointegration

Tags

Reviewed
Změněno: 30/12/2022 14:39, Ing. Radka Bauerová, Ph.D.

Abstract

V originále

Making strategic decisions in retailing is associated with an-alysing sales development, which can indicate the current trends in consumer demand. Determining retail sales indi-cators is a valuable step towards better prediction and helps to make strategic decisions more realistic. However, the ques-tion is to what extent the general assumption of profitability of using retail sales indicators can be applied in the case of a transformed economy. This paper aims to provide informa-tion on the possibility of using selected economic indicators to predict sales in the environment of a transformed economy. The paper analyses the joint time series development of retail sales in the Czech Republic. The Johansen cointegration test has been performed to determine whether retail sales show long-term cointegration with the set of selected indicators. The results establish GDP, total employment, gross wage, and consumer data as leading indicators of retail sales at different levels of delays. In contrast, in the case of online retail sales, no significant influence of the observed factors has been found. The findings suggest that if managers choose to plan their online retail processes based on retail sales estimates, they must respond to changes in these indicators much faster than managers in off line environments.