Assessing food retail competitors with a multi-criteria GIS-based method

Roig-Tierno, Norat; Baviera-Puig, Amparo; Buitrago-Vera, Juan; Escriba-Perez, Carmen

doi:10.7201/earn.2018.01.01

Cited by 4 publications

(17 citation statements)

References 34 publications

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“…The close relationship between price and value makes consumers easily associate with value when they are exposed to product prices, thinking about their core attributes, quality, and dedication. Individuals tend to characterize values at high levels of interpretation 7–9 …”

Section: Introductionmentioning

confidence: 99%

Retracted: Influence of promotion mode on purchase decision based on multilevel psychological distance dimension of visual attention model and data mining

Tiantian

2019

Concurrency and Computation

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In the era of rapid development of information technology, e-commerce has opened up a different cross-distance trade model from traditional trade and has changed the traditional market competition. With the rapid development of network information technology and the rapid spread of online shopping, online shopping will gradually become one of the main ways people buy. It is a decision-making process for a customer to purchase a product to characterize the product, evaluate the characteristics of the product, and select a product that matches its needs. For online merchants, it is especially important to make a profit in online sales and to be in a competitive position. This paper studies the impact of promotional methods based on visual attention models and data mining on purchasing decisions. The revised Itti visual saliency model is designed considering the visual features; the machine learning algorithm is integrated to construct the comprehensive structure of the model. The experimental results show the robustness and efficiency of the designed framework.

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Section: Introductionmentioning

confidence: 99%

Retracted: Influence of promotion mode on purchase decision based on multilevel psychological distance dimension of visual attention model and data mining

Tiantian

2019

Concurrency and Computation

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“…Figure 3 shows the framework of the proposed method. Given the availability of influencing factors and related literature research results [11,18,39,40], we evaluated the market potential demand of retail stores from four dimensions, namely, consumer groups, urban infrastructure, road network, and commercial competition. In this study, consumer groups were subdivided into local permanent residents and passenger flow.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, consumer groups were subdivided into local permanent residents and passenger flow. On the basis of the characteristics of the original data, the population data of WorldPop were used as the parameter to measure the local permanent residents, and the Sina Weibo check-in data were used as the Given the availability of influencing factors and related literature research results [11,18,39,40], we evaluated the market potential demand of retail stores from four dimensions, namely, consumer groups, urban infrastructure, road network, and commercial competition. In this study, consumer groups were subdivided into local permanent residents and passenger flow.…”

Section: Methodsmentioning

confidence: 99%

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Site Selection Improvement of Retailers Based on Spatial Competition Strategy and a Double-Channel Convolutional Neural Network

Ouyang

Fan

Wang

et al. 2020

IJGI

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The issue of site selection has become a critical challenge in the development of the retail industry with the growth of the Chinese economy and the improvement in the level of household consumption. Previous studies have considered the area of stores as the main factor of retail competition; however, the actual business performance of different stores in these studies was ignored. In addition, few studies have considered the differences in the spatial distribution of the factors of site selection. In this study, we discuss the improvement of site selection of small retail shops. A spatial competition index model was proposed as one of the features in estimating region market potential, and a market demand regression model of a double-channel convolutional neural network (CNN) was constructed based on the spatial correlation range of features. The study area was Guiyang, China. The experiments were based on the monthly sales data of fast-moving consumer goods retail stores in Guiyang. On the basis of the estimated results of the model, 18 sites with high potential for market demand were recommended. The performance of the proposed model was the best among well-known regression methods. Moreover, in comparison with a single-channel CNN, the proposed model decreased the root mean square error by 22.61%. Evaluation results showed that the proposed method could provide effective decision support for the issue of retail site selection.

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“…Nevertheless, in the case of supermarkets, an efficiency evaluation tool such as DEA could leave out a crucial aspect for sales: the establishment's location (Li & Liu, 2012). Population density and the distance from competing supermarkets may influence the efficiency score, as the sector's financial results strongly depend on demand (Roig-Tierno et al, 2018). Consequently, the benchmark provided by DEA in this type of business could lead to poorlyfocused decisions, as it fails to sufficiently consider these factors.…”

Section: Introductionmentioning

confidence: 99%

Internal Benchmarking in Retailing With Dea and Gis: The Case of a Loyalty-Oriented Supermarket Chain

Baviera‐Puig

Baviera

Buitrago‐Vera

2020

Journal of Business Economics and Management

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Data Envelopment Analysis (DEA) is a relative measure of efficiency applied to a set of decision units and is being used more and more frequently in the supermarket sector. Nonetheless, given how strongly the sector’s financials depend on demand, companies need to combine this measurement with trade area information to best manage corporate efficiency. In this paper, the proposal consists of integrating DEA with a clearly articulated, structural typology so that supermarkets, based on their particular characteristics, can determine which variables are most critical for improving their efficiency. This methodology has been validated in the case of one of Spain’s five largest supermarket chains. A principal component analysis and a classification analysis were carried out on a series of internal management variables from 61 locations for which DEA had been used to calculate efficiency and to which multiple trade area variables were added using GIS. Some of them are related to the loyalty scheme membership programme. These latter variables described the implantation of the loyalty scheme member programme and were revealed as key elements for the efficiency of the supermarket. This methodology provides marketing profiles that are more adapted to local circumstances, thus allowing companies to set better internal benchmarking objectives.

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Assessing food retail competitors with a multi-criteria GIS-based method

Cited by 4 publications

References 34 publications

Retracted: Influence of promotion mode on purchase decision based on multilevel psychological distance dimension of visual attention model and data mining

Retracted: Influence of promotion mode on purchase decision based on multilevel psychological distance dimension of visual attention model and data mining

Site Selection Improvement of Retailers Based on Spatial Competition Strategy and a Double-Channel Convolutional Neural Network

Internal Benchmarking in Retailing With Dea and Gis: The Case of a Loyalty-Oriented Supermarket Chain

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