Google Popular Times: towards a better understanding of tourist customer patronage behavior

Abstract

Purpose This paper aims to investigate actual tourist customer visiting behavior with behavioral data from Google Popular Times to evaluate the extent that such an online source is useful to better understand, analyze and predict tourist consumer behaviors. Design/methodology/approach Following six hypotheses on tourist behavior, a purpose-built software tool was developed, pre-tested, and then used to obtain a large-scale data sample of 20,000 time periods for 198 restaurants. Both bi-variate linear regression and correlation analyzes were used for hypothesis testing. Findings Support was established for the hypotheses, through an analysis of customer reviews, timing effects, the number of pictures uploaded and price segment information provided by tourists to a given restaurant. Also, a relationship to average duration time was found to be positive. The findings demonstrate that data provided through Google Popular Times matches theoretical and logical assumptions to a high degree. Thus, the data source is potentially powerful for providing valuable information to stakeholders (e.g. researchers, managers and tourists). Originality/value This paper is the first to both conceptually and empirically demonstrate the practicality and value of Google Popular Times to better understand, analyze and predict tourist consumer behaviors. Value is thereby provided by the potential for this approach to offer insights based behavioral data. Importantly, until now such an approach to gathering and analyzing this volume of actual customer data was previously considered far less practical in terms of time and expense.