The paper reports the evolution of scientific research on usage of large datasets in online consumer behaviour between 2000 and 2018. Thus, it affords information regarding the evolution of the field in terms of identifying key publications and authors as well as how certain topics have evolved over time. In addition, by utilising topic modelling and text analytic techniques, it is identified certain research themes from the papers within the published articles included in the dataset. This offers a guide to those who want to contribute to the field. In addition, paper contributes to the methodology related to literature surveys and bibliometric analyses by conducted topic modelling to extract the latent topics from the collected literature by utilising Structural Topic Modelling in order to gain more elaborated results.

Introduction

In the past two decades, advances in information technology as well as the Internet and digitalisation have transformed our daily lives and shifted the focus of commerce towards online digital environments. This change has also altered the ways consumers behave, for including how they shop and buy products and services (Darley, Blankson, & Luethge, 2010; Kim & Lennon, 2008). In the online environment, consumers’ decisions to purchase certain products and services are influenced by variables beyond the actual product or service, such as the design of the website or electronic word-of-mouth (including online reviews and recommendations) (see e.g. Cantallops & Salvi, 2014). Thus, online consumer behaviour has been one of the major research areas in marketing science, and it is the subject of a vast number of studies. For example, information system and marketing scholars have examined environmental site features (Manganari, Siomkos, Rigopoulou, & Vrechopoulous, 2011; Richard & Habibi, 2016), global search features (Hausman & Skiepe, 2009), language options (Hausman & Skiepe, 2009), site design (Ha, Kwon, & Lennon, 2007; Mummalaneni, 2005), site security (Hausman & Skiepe, 2009) and culture (Richard & Habibi, 2016). In recent years, there has been an increasing trend emphasising the importance of data analytics as well as the use of larger data sets. Data analytics is not new. Indeed, various kinds of data have been collected in one form or another for a very long time. However, technological improvements (e.g. in storing and transmitting information) have enabled the continuous and ubiquitous collection of data.