1 Relationship between BigData and the performance of two German cities: a comparing analysis of the German cities Braunschweig and Hannover From: doc. Ing. Daniela Hupková, PhD Department of Economics, FEM, Slovak University of Agriculture in Nitra, Slovakia daniela.hupkova@uniag.sk Dipl.-Verww. (FH) Dino Schubert, M.A. NSI Consult Beratungs- und Servicegesellschaft mbH, Germany d.schubert@nsi-consult.com Abstract Google Trend´s SVI is able to predict a lot of market performance indicators, e.g. stock market, carrier market or real estate market. This article has the objective to analyze if the SVI has an impact to city performance. Research objects are the two German cities Brunswick (about 250.000 inhabitants) and Hanover (about 500.000 inhabitants). City performance was measured by an index of secondary data regarding economic and attractivity indicators of the cities. The results indicate that the SVI on the search term level ´city´ is not a sufficient predictive value with regards to city performance. We found a strong and linear negative relationship from SVI to city performance. This causality, whereas, bases on continuous decreasing search terms ´city´ for both groups and a very positive development of both cities within the time recourse. If we would take another city with a continuous decreasing performance development, there could be may be also a positive causality between SVI and performance. Our conclusion is that the level of search term has to be more specific for the SVI to be able a suitable indicator. People act by more detailed searching after specific keywords instead of the whole city, before a decision of travelling, working or living there. Keywords: Big data analysis, performance measurement, public management, public sector marketing, google trends JEL Classification: H30, H68, R51, Z3 1. Introduction Big data analysis is an omnipresent topic of science and practice. Many studies handle the impact of ´Big Data´ on the performance of different life situations. Dependent variables are often economic measures, which are observed as success or performance criteria. The explanation power of Big Data measures as the Search Volume of Interest (SVI) of the Google Trends is in this context a common used independent variable (cf. Yu Xu, 2012; Perlin/Santos, 2014; Kristoufek, 2013; Penner et al., 2013; Challet/ Bel Hadj Ayed, 2013). Near many investigated studies a research design, which examines the impact of the SVI on the performance of cites, whereas, does not exist, yet. Objective of our paper is to analyze the