Location effects: Geo-spatial and socio-demographic determinants of sales dynamics in brick-and-mortar retail stores

Formánek, Tomáš; Sokol, Ondřej

doi:10.1016/j.jretconser.2021.102902

Cited by 17 publications

(7 citation statements)

References 34 publications

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“…This provides an important basis for predicting its commercial potential, especially catering and retail facilities. The general process of location selection for commercial facilities includes two aspects: macro-location selection and micro-location determination [40,41]. In order to determine the best location for commercial facilities, scholars measure the degree of relative advantage of a location-that is, the potential spatial benefit or economic value-based on theoretical frameworks from different perspectives.…”

Section: Multiple Dimensions Of Locations Evaluations and Decision-ma...mentioning

confidence: 99%

How Information and Communications Technology Affects the Micro-Location Choices of Stores on On-Demand Food Delivery Platforms: Evidence from Xinjiekou’s Central Business District in Nanjing

Hu,

Zhang,

Shi

et al. 2024

IJGI

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The digitization of consumption, led by information and communications technology (ICT), has reshaped the urban commercial spatial structure (UCSS) of restaurants and retailers. However, the impacts of ICT on UCSS and location selection remain unclear. In this study, based on on-demand food delivery data and real-time traffic data, we used two types of machine learning algorithms, random forest regression (RFR) and the density-based spatial clustering of applications with noise (DBSCAN), to study the spatial distribution patterns, driving factors, and new geographical location phenomena of ‘brick-and-click’ (B&C) stores in Xinjiekou’s central business district (CBD) in Nanjing, China. The results show that the UCSS in the CBD is being decentralized, but the degree of influence is related to the business type. Additionally, the scale of demand and the distance from core commercial nodes greatly affect the scales of B&C stores. Moreover, the agglomeration of high-sales B&C stores seems to indicate a micro-location advantage, characterized by the concentration of delivery riders, which is usually located in the commercial hinterland with dense traffic. This makes stores situated in traditionally advantageous locations more attractive for online sales. Thus, ICT enhances the Matthew effect in business competition. These findings deepen our understanding of urban digital planning management and business systems.

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Section: Multiple Dimensions Of Locations Evaluations and Decision-ma...mentioning

confidence: 99%

How Information and Communications Technology Affects the Micro-Location Choices of Stores on On-Demand Food Delivery Platforms: Evidence from Xinjiekou’s Central Business District in Nanjing

Hu,

Zhang,

Shi

et al. 2024

IJGI

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“…Badorf et al discovered that the weather can have an impact on daily sales in the retail industry and that weather forecast information has a positive effect on the prediction of retail sales [19]. Tomáš Formánek established a correlation between store location and its distance from the city center, the presence of competitor stores, and population density, employing these variables as controlling factors to evaluate the sales dynamics within the retail industry [20]. Xiao et al studied the spatial and temporal distribution pattern of pharmacies in China, revealing that pharmacies are unevenly distributed between cities and regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Distribution Characteristics and Influencing Factors on the Retail Industry in the Central Urban Area of Lanzhou City at the Scale of Daily Living Circles

Lu,

Yu,

Xin

et al. 2023

IJGI

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Using a people-centered approach to new urbanization, China has committed to building high-quality living environments through improving urban livability and promoting a stronger sense of belonging among residents. Retail stores serve as one of the most immediate and accessible destinations for residents’ consumption, and their spatial configuration has a direct impact on residents’ satisfaction and happiness in their daily lives. In this context, for the present study we selected the central urban area of Lanzhou City as the case study area. Based on POI data and using the daily life circle as the basic unit, we applied methods such as kernel density analysis, hotspot analysis, and the Shannon–Weaver index to analyze spatial distribution patterns of the retail industry. Furthermore, we applied Geodetector to analyze the impacts of four factors that are closely related to the retail industry: economic level, convenience level, market demand, and location. The conclusions are as follows: In the central urban area of Lanzhou, the retail industry exhibits a belt distribution pattern along the Yellow River. The density of distribution gradually decreases from the city center toward the outskirts, forming four prominent agglomeration centers. Overall, within the central urban area of Lanzhou, the spatial distribution of the retail industry at the scale of daily living circles shows that only a small proportion of the industry demonstrates noticeable clustering effects. In terms of spatial patterns, the retail industry at the scale of the daily living circles demonstrates similar characteristics in terms of diversity and agglomeration distribution. It exhibits a decreasing trend from the urban core toward the peripheral areas. The agglomeration distribution pattern of the retail industry in the central urban area of Lanzhou is considerably influenced by market demand, economic level, convenience, and location. The spatial distribution of the retail industry in the central urban area is primarily influenced by economic factors and convenience, while market demand plays a major role and location has a relatively minimal impact.

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“…Spatial density, including in-store promotions, store design, temperature, music, odour and the arrangement of racks, merchandise and displays, also plays a crucial role (Mehta, 2013). Apart from these internal moderators, external moderators such as neighbourhood characteristics, population and competition density (Verhetsel, 2005), location (Formánek and Sokol, 2022), industry type Katona et al. (2018) and availability of public transport and car ownership density in the geographical area warrant attention as well.…”

Section: Conclusion Limitations and Future Researchmentioning

confidence: 99%

Satellite imagery and machine learning for channel member selection

Brei,

Rech,

Bozkaya

et al. 2023

IJRDM

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PurposeThis study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.Design/methodology/approachThe authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.FindingsThe relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.Research limitations/implicationsThe proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.Originality/valueThis research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.

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Location effects: Geo-spatial and socio-demographic determinants of sales dynamics in brick-and-mortar retail stores

Cited by 17 publications

References 34 publications

How Information and Communications Technology Affects the Micro-Location Choices of Stores on On-Demand Food Delivery Platforms: Evidence from Xinjiekou’s Central Business District in Nanjing

How Information and Communications Technology Affects the Micro-Location Choices of Stores on On-Demand Food Delivery Platforms: Evidence from Xinjiekou’s Central Business District in Nanjing

Spatial Distribution Characteristics and Influencing Factors on the Retail Industry in the Central Urban Area of Lanzhou City at the Scale of Daily Living Circles

Satellite imagery and machine learning for channel member selection

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