• Fashion & Textile Research Journal
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• v.7 no.5
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• pp.509-518
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• 2005

In recent years retail competition has intensified, generally as a consequence of technologies, more sophisticated management practices and industry consolidation. An understanding of current customers' loyalty intentions and their determinants is an important basis for the identification of optimal retailer actions. The focus of this study is to examine the links between patronage intention and the effects of various antecedents of current customers' store loyalty intentions in fashion store. 340 female universities students living in Seoul were analyzed by utilizing multiple regressions to investigate the predictability of each of the 4 different sets of variables(consumer value, source of information, clothing benefits, importance of store attributes) on four patronage intentions of apparel shopping(discount store, speciality store, conventional market, Fashion shopping mall). Four factors were significant in predicting conventional market patronage intention. Brand had a negative coefficient, while price, social affiliation, store fashion service/promotion had positive coefficients. Fashion shopping mall were predicted by five factors:brand had a negative coefficient, while media, social affiliation, price, uniqueness had positive coefficients. For specialty store, four factors were significant: brand had a negative coefficient, while store fashion service/promotion, personal sources, uniqueness had positive coefficients. Four factors were significant in predicting discount store patronage intention :price, store fashion service/promotion, social affiliation, variety of price & product had positive coefficients. Despite the relatively low $r^2s$, all four variables appeared to have, to some degree, predictability of choosing among four different types of store for apparel shopping. Based on the results, patronage intention profiles for four retail stores were developed. Marketing implications are discussed.

• Journal of the Korean Society of Tobacco Science
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• v.29 no.1
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• pp.41-51
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• 2007

This study was implemented to examine the effect of tobacco on operation of a retail store and propose a relevant direction to development of a retail store. In terms of an examination method, the questionnaire was conducted in respect of supermarkets and convenience stores(CVS) around the nation. As a result of analysis based on such questionnaire, although there was a little difference in between a supermarket and a convenience store, a similar result could be found in general. In other words, retailers selling tobacco regarded the tobacco as a medium to solicit customers. Since the significance of tobacco in terms of total sales volume was so high, without the tobacco, they might have suspend the business. Consequently, the effect of tobacco was enormous. In addition, under the premise that many regular customers should be acquired to develop such retail stores, it was found that it would be necessary to increase $15{\sim}20%$ in terms of a margin in tobacco, preserve the profit ratio by the government, make efforts to enhance a quality and design by a tobacco company, actively recommend a particular brand, and actively display a tobacco publicity booklet, etc. Moreover, the proper number of tobacco stores in comparison with habitual smokers was one per 200 persons. In respect of the most unreasonable tobacco policy, a minor-related system and a policy of increasing a tobacco price were named. Thus, under the premise that a medium such as tobacco is highly important with respect to an operation of retail store, it is necessary to pay a more careful attention to an improvement of a reasonable and equitable system to further develop a retail store.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.177-192
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• 2014

The factors that affect on the ratio of monthly rent to total rents in commercial real estate lease contract was empirically investigated. The theoretical basis for the research was location theory, retail trade-area analysis, bid rent, agglomeration theory, and demand externality theory. The data used in this study included information on goodwills per 3.3 square meters, deposit money per 3.3 square meters, retail rents per 3.3 square meters, and passing pedestrians' characteristics in 96 retail trade areas in South korea. As the results, using the hedonic price functions and multi-regression analysis, the independent variables does affect the ratio of monthly rent to total rents in the each retail trade area were goodwills per 3.3 square meters, deposit money per 3.3 square meters, retail rents per 3.3 square meters, and the number of Small Wholesale Retail Trade Firms at the level of nation. also, the results show goodwills per 3.3 square meters and the number of Small Wholesale Retail Trade Firms are important factors in determining the ratio of monthly rent to total rents in commercial real estate lease contract in seoul. In summary, not only the economic conditions in the retail trade area but also the passing pedestrian count should be considered to determine the ratio of monthly rent to total rents in commercial real estate lease contract.

• International Area Studies Review
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• v.18 no.3
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• pp.231-248
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• 2014

The relationship between market structure and social welfare outcomes has received considerable critical attention in the field of competition policy research. In particular, it is necessary to study in greater depth the impact of market power on social welfare in the telecommunications industry, which is highly likely to form a monopolistic market structure. This is because, when market powers are concentrated on few upper carriers, there are negative effects on social welfare due to an excess of profits. Against this background, the present study investigates the relationship between the market structure of the mobile communications industry (the level of market power) and social welfare outcomes (the retail rate cut) through an international comparison. The results demonstrate that both the market structure and competition status of the Korean market have had significant gaps in global trends. It also points out that the monopolistic market structure (when the leading provider has more than 50% of the market share) has significantly negative effects on consumer welfare (the retail price cut). In addition, the findings of this study suggest that the direction of competition policy should focus on not only improving market concentration(HHI), but also on mitigating the monopoly of power of a dominant operator.

• Environmental and Resource Economics Review
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• v.21 no.3
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• pp.493-517
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• 2012

This paper analyzes a price discovery process for gasoline among branded and independent stations in Korea using a vector error correction model (VECM) and directed acyclic graphs (DAG). Two data sets for daily prices of medium level gasoline running from April 15, 2008 to May 31, 2009 and from January 1, 2011 to December 31, 2011 are used for empirical analysis. Empirical results show that S-OIL has an exogeneity and played a important role in the flow of price information in the market in the first period. In the second period, SK energy played a key role in price discovery process in the market. The price of NH-OIL stations do not cause the price of any other stations, which implies that the entrance of new branded stations with lower gasoline price to market has no influence on gasoline prices of retail markets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1201-1206
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• 2014

Retail stores are mostly relying their product management on handwriting. This handwriting management is limited and poor at managing the status of stocking and releasing. Also, to confirm real-time product status, additional statistic system is required. POS system developed to overcome foresaid problems is operating only in big and special stores because of expensive price and limited space. Therefore, new automated management system which has low installation price needs to be developed and adopted instead of handwriting management system. In this paper, we developed real-time product management system which can manage the products in retail stores by utilizing NFC function in mobile device.

• Korean Management Science Review
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• v.34 no.2
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• pp.15-33
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• 2017

This paper considers a Bayesian Network analysis for understanding the heterogeneous cross-category effects of different promotion activities and developing an efficient overall promotion strategy for a large retail store. More specifically we differentiate price reduction promotion and floor promotion and study their heterogeneous effect on consumer purchase behavior under a market basket setting. We then utilize Bayesian networks in identifying complex association structure in market basket dataset by analyzing the effects of different promotional activities and also include the effects of time, family income and size. We find from our Bayesian network analysis that the dominant cross-category promotion effect of price promotion is the indirect effect whereas the dominant cross-category promotion effect of floor promotion is the direct effect. Also, among the demographic variables we find that family size of the household is linked with more product categories compared to income and see that there are differences in the extent of the effects by product category. Finally, we also show the existence of products acting as a network hub and how they can be utilized by retailers faced with a limited marketing budget and suggest a more efficient promotion strategy.

• The Journal of Fisheries Business Administration
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• v.46 no.3
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• pp.143-161
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• 2015

This study presents a comparative analysis on mackerel distribution process and price formation process, and investigation of price and margin between traditional markets and Large-scale discount store distribution channel. Through this, the study investigated distribution efficiency of each channel, and examined whether a difference of distribution efficiency leads to a difference of performance through the investigation of a difference of function and role between members of a wholesale market and vendor of Large scale discount store. The following are the results of this study. As a consequence of investigating supply and sum by distribution channel of mackerel, it appeared that mackerels shipped from port market are distributed into 9 consumption sites(Wholesale market, Large scale discount store, Institutional Food Service, etc.). In the comparison of distribution efficiency between traditional retail store and Large scale discount store 52.0% margin is formed in traditional retail store distribution channel and 43.1% margin is formed in Large scale discount store, and a distribution cost rate consists of 19.4% cost in a traditional retail store for fishery products and 18.1% cost in a Large-scale discount store. To analyze a difference of performance, the study examine a difference of role and function between vendor and Wholesale market company, wholesaler and middleman. Wholesale market company and middleman of wholesale market for consumer have slightly high or similar score in collection function, sorting function, evaluation function and financial function which are traditional and original. However, it was confirmed that vendor has a better score in other functions, that is, newly-demanded functions(ex : market frontier function, product development function, Integral Distribution Function, etc.).