A Micro‐Econometric Store Choice Model Incorporating Multi‐ and Omni‐Channel Shopping: The Case of Furniture Retailing in Germany

Abstract

Online retailing and multi-/omni-channel shopping are gaining in importance. However, there is a significant lack of research focused on incorporating online shopping into models of spatial shopping behavior. The present study aims (1) to construct a store choice model which includes both physical and online stores as well as the opportunity for omnichannel shopping, and ( 2) to identify the main drivers of spatial shopping behavior given the availability of both channels. Based on a representative survey, this study employs a revealed-preference approach toward store choice and expenditures in furniture retailing. The statistical analysis is performed using a hurdle model approach, with the expenditures of individual consumers at (online or physical) furniture stores serving as the dependent variable. Results show that channel choice (online vs. offline) is mainly influenced by psychographic characteristics, place of residence, and age of the consumers. Store choice and expenditures are primarily explained by store features such as assortment size, omni-channel integration, and accessibility. This study demonstrates that e-shopping can be integrated into a store choice model and that both the modeling approach and the subsequent findings are of significance for retail companies and spatial planning.

Influencing factors of online furniture purchase behavior based on analytic hierarchy process

“…Then the Delphi method was used to collect experts' opinions and determine the basic indicators (Brady 2015) to understand the basic situation of online purchasing platforms and the constituent factors of online item purchase (Wang 2014;Chen et al 2022). A further goal was to understand how online and offline consumption will be influenced by its related factors, leading to different choice results (Wieland 2021). The research results were eventually summarized and adjusted according to the literature on the online furniture market (Zhao 2016;Li 2022;Wang and Yang 2022) and the current state of buying by replanning the influencing factors and using the indicator system of the existing literature as a reference.…”

Spatial shopping behavior during the Corona pandemic: insights from a micro-econometric store choice model for consumer electronics and furniture retailing in Germany

“…With respect to store choice, store size increases and travel time (driving, public transport, or walking) reduces the likelihood of a store being chosen, which is in line with previous store choice studies. Using a store choice and expenditure model, Wieland ( 2021a , b , 2023 ) has combined the aforementioned approaches of channel and store choice. Results have shown that channel choice is mainly influenced by shopping attitudes, place of residence, and age of the consumers.…”

“…The econometric strategy in this study is based on the modeling approach outlined in Wieland ( 2021a , b , 2023 ), which incorporates an adaptation of the hurdle model (Mullahy 1986 ) applied to (spatial) shopping behavior. The hurdle model is a special kind of count data model which was designed for excess of zeros in the dependent variable.…”

“…(3) Have fear of infection and the obligation to wear a face mask in retail stores influenced channel choice in favor of online retailing? In order to answer these questions, Wieland’s ( 2021a , b , 2023 ) study design on micro-econometric modeling of spatial shopping behavior in a multi-channel context is here repeated for the purposes of comparison. The empirical analysis focuses on consumer electronics retailing and furniture retailing.…”

“…Empirical data on shopping behavior was collected via a representative consumer survey conducted in three German regions. The econometric modeling approach follows on from Wieland’s ( 2021a , 2023 ) previous studies on consumer electronics and furniture shopping; it is based on the hurdle model, which is used frequently to answer questions about individual consumption decisions and with which it is possible to split human behavior into an “if” and a “how” decision. Since the model is designed for micro data, it is possible to include individual consumer characteristics of both an objective and subjective nature.…”