“Third space” or “infinite occasion”: Location choice and influencing factors of the new retail industry

Wang, Fan; LIN, Yuexi; Wang, Mingfeng

doi:10.18306/dlkxjz.2020.09.009

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…They use a sophisticated recommendation system to suggest products to customers based on purchase history, searches, and browsing behavior. The system uses machine learning algorithms to continuously refine its recommendations, making the shopping experience more personalized and increasing sales (Cai et al, 2021;Ermolina & Tiberius, 2021;Poushneh, 2021) (Carvalho et al, 2019;WANG et al, 2020;Wayesa et al, 2023) 4. Spotify: Spotify uses AI to offer highly personalized music recommendations.…”

Section: Case Studies or Real Facts: Provide Real-life Examples Of Co...mentioning

confidence: 99%

Using Artificial Intelligence (AI) in Developing Marketing Strategies

Mirwan,

Ginny,

Darwin

et al. 2023

IJARSS

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This research explores the application of artificial intelligence (AI) in contemporary marketing strategies. Through case analysis of leading companies, the study uncovers how AI contributes to market data analysis, marketing personalization, trend forecasting, marketing process automation, recommendation systems, and chatbot development. Data was collected by studying existing literature and analyzing case studies of companies such as Amazon, Netflix, Starbucks, Spotify, Alibaba, and Sephora. The results show that using AI in marketing improves operational efficiency, enriches the customer experience, and increases engagement and loyalty

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Section: Case Studies or Real Facts: Provide Real-life Examples Of Co...mentioning

confidence: 99%

Using Artificial Intelligence (AI) in Developing Marketing Strategies

Mirwan,

Ginny,

Darwin

et al. 2023

IJARSS

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“…Chain stores are largely influenced by spatial demand and uneven economic development when they undergo spatial expansion. The location choice of chain stores belongs to the theoretical study of fine-grained scale, so this paper chooses the distribution of street population and shopping areas as the measure of market demand and uses the distribution of bus stations and subway stations to measure the traffic conditions around the roast duck stores [42]. Figure 8 shows the kernel density estimates for the traffic and commercial areas of Beijing and analyzes the population density in terms of streets.…”

Section: Intrinsic Driving Mechanisms Of Spatial Patternsmentioning

confidence: 99%

Famous Chinese Traditional Dishes: Spatial Diffusion of Roast Duck in Mainland China and Spatial Association Characteristics of Chain Stores

Zhang

Qiu

et al. 2022

Sustainability

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The spatial pattern and geographical diffusion of Chinese traditional food culture are important manifestations of population migration and cultural chain remodeling. Taking the national roast duck stores and Beijing Quanjude and Bianyifang brand chain roast duck stores as the research objects, the spatial distribution characteristics and geographic diffusion patterns of roast duck stores, and the spatial association characteristics of the chain stores are analyzed by using spatial analysis methods and mathematical statistics. The results of the study showed that: (1) The roast duck stores in the mainland show an overall northeast-southwest direction, and the spatial distribution is extremely uneven. The eastern coast of China shows a high-value continuous distribution, from the Bohai Bay Economic Circle and the Yangtze River Delta Economic Circle, gradually radiating westward to the middle and showing the clustering characteristics of “point + surface”. (2) Using the point cluster analysis method, the diffusion pattern of roast duck stores in the three major economic zones of China is explored, and roast duck stores in the western region show the characteristics of contact diffusion combined with hierarchical diffusion. Contact diffusion is the main diffusion mode of roast duck stores in the east. The central region shows the diffusion characteristics of contact diffusion combined with hierarchical diffusion. Overall, the roast duck stores in mainland China show a composite diffusion pattern. (3) Quanjude and Bianyifang stores have spatial agglomeration characteristics, Quanjude chain stores have a slightly stronger central pointing, while Bianyifang roast duck chain stores have slightly wider spatial diffusion. Both brands significantly show spatial orientation close to transportation facilities and high consumption markets. The street population has a slightly weaker influence on the spatial distribution of the two brands. (4) Through the multivariate spatial analysis method, it is found that the spatial correlation of mutual attraction between Quanjude and Bianyifang roast duck chain stores is presented, but there are differences in the formation mechanism and weak asymmetry in the attraction intensity, which is related to the consumer population and corporate positioning of Quanjude and Bianyifang. With the advent of the big data era, it is possible to obtain and use big data analysis methods to reshape the deep information under the surface logic. Attention should be paid to the location choice of traditional restaurant chains in the new era, to explore the possibilities of enterprise development, and to improve the efficiency of urban space.

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“…For example, by incorporating the spatial lag model (SLM) and ordinary least squares (OLS) models, Wang et al employed buffer zone analysis and spatial autocorrelation to explore the spatial layout features and the affecting factors for of Guangzhou commercial gyms, it is concluded that population density and education level were the most significant factors 4 . Wang et al 5 used various spatial analysis methods to analyze the characteristics of spatial location differences between traditional retail and new retail in Shanghai and used spatial econometric models to explore the differences at the level of location factors. Wang et al 6 used a multi-objective optimization algorithm to solve the problem based on data such as the coverage rate of the firefighting responsibility area to provide a reference for fire station locations.…”

Section: Introductionmentioning

confidence: 99%

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

Niu,

Wang,

Liu

et al. 2023

Sci Rep

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Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness. According to the study's findings: (1) K-fold cross-validation was applied to test the random forest model that was built using the built metro stations and particular factors. The average accuracy of the tests and out-of-bag data (OOB) of tenfold cross-validation were 89.62% and 91.285%, respectively. Additionally, the AUC area under the ROC curve was 0.9823, indicating that this time, from the perspective of the natural environment, traffic location, and social factors The 19 elements selected from the views of the urban function structure, social economics, and natural environment are closely associated to the locations of the metro station in the research region, and the prediction the findings are more reliable; (2) It becomes apparent that more than half of the built station sites display excellent agreement with the predicted sites in terms of geographical location by superimposing the built metro station sites with the prediction results and tally up their cumulative prediction probability values within the 300 m buffering zone; (3) Based on the contribution rate of each indicator to the model, transport facilities, companies, population density, night lighting, science, education and culture, residential communities, and road network density are identified as the primary influential factors, each accounting for over 6.6%. Subsequently, land use, elevation, and slope are found to have relatively lower contributions. The results of the research provided important information for the local metro's best location selection and planning.

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“Third space” or “infinite occasion”: Location choice and influencing factors of the new retail industry

Cited by 3 publications

References 12 publications

Using Artificial Intelligence (AI) in Developing Marketing Strategies

Using Artificial Intelligence (AI) in Developing Marketing Strategies

Famous Chinese Traditional Dishes: Spatial Diffusion of Roast Duck in Mainland China and Spatial Association Characteristics of Chain Stores

Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China

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