• Journal of Distribution Science
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• v.15 no.6
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• pp.77-96
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• 2017

Purpose - This study aims to develop correspondence strategies to the environment change in domestic retail store types. Recently, new types of retails have emerged in retail industries. Therefore, trade area platform has developed focusing on the speed of data, no longer trade area from district border. Besides, 'trade area smart' brings about change in retail types with the development of giga internet. Thus, context shopping is changing the way of consumers' purchase pattern through data capture, technology capability, and algorithm development. For these reasons, the sales estimation model has been shown to be flawed using the notion of former scale and time, and it is necessary to construct a new model. Research design, data, and methodology - This study focuses on measuring retail change in large multi-shopping mall for the outlook for retail industry and competition for trade area with the theoretical background understanding of retail store types and overall domestic retail conditions. The competition among retail store types are strong, whereas the borders among them are fading. There is a greater need to analyze on a new model because sales expectation can be hard to get with business area competition. For comprehensive research, therefore, the research method based on the statistical analysis was excluded, and field survey and literature investigation method were used to identify problems and propose an alternative. In research material, research fidelity has improved with complementing research data related with retail specialists' as well as department stores. Results - This study analyzed trade area survival and its pattern through sales estimation and empirical studies on trade areas. The sales estimation, based on Huff model system, counts the number of households shopping absorption expectation from trade areas. Based on the results, this paper estimated sales scale, and then deducted modified probability model. Conclusions - In times of retail store chain destruction and off-line store reorganization, modified Huff model has problems in estimating sales. Transformation probability model, supplemented by the existing problems, was analyzed to be more effective in competitiveness business condition. This study offers a viable alternative to figure out related trade areas' sale estimation by reconstructing new-modified probability model. As a result, the future task is to enlarge the borders from IT infrastructure with data and evidence based business into DT infrastructure.

• Korean Institute of Interior Design Journal
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• v.22 no.1
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• pp.283-290
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• 2013

The change of shopping environment created new emerging type of shopping center after 1990's, Urban Entertainment Center(UEC) in northen America. One hand traditional shopping center was retail-centered, the other hand UEC is entertainment-centered and offers the trinity of synergy. Each components, that are retail, dining and entertainment, play a role of drawing people, extending duration of visiting and making people revisit then the synergy makes commercial profit in shopping center. As northen America many of shopping centers with complex have been built in Korea since 2000 and some projects is planning by the change of shopping environment and regenerating urban. Although the term of "UEC" is used in Korea, it seems to be added entertainment facilities to shopping center without considering on commercial strategy. This study will take a look at mix and duration of visit which ULI stresses in UEC development and comparing with those of Yongsan Station and Chyeongrangri Station which are built recently in seoul, it will get characters and situation of these UECs. Finally, the analysis is to be used as a planning data in UEC development.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.2
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• pp.266-280
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• 2017

This study predicted consumer approach/avoidance behavior through consumer emotional experiences and examined the moderating effect of perceived surprises in the context of digital signage in store environments. A self-administered questionnaire consisted of consumer emotional experience (e.g., pleasure, arousal, and dominance), approach-avoidance behavior and perceived surprise by digital signage. A total of 278 usable responses were obtained from consumers who experienced digital signage at fashion retail stores. The findings support the Mehrabian-Russell model in the context of digital signage. Approach behavior was predicted by pleasure and arousal emotional experience, while avoidance behavior was predicted by dominance. The moderating effect of perceived surprise was also indicated in the effect of emotional experience on approach or avoidance behavior. In the high level of perceived surprise, pleasure and arousal had significant effects on approach behavior, whereas dominance had significant effect on avoidance behavior. This study discussed theoretical and managerial implications for creating emotional experiences and developing strategic store management by utilizing new digital technology within the fashion retail environments.

• The Research Journal of the Costume Culture
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• v.20 no.1
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• pp.99-110
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• 2012

The purpose of this study was to explore differences in determinants of loyalty, including years of loyalty and use of word-of-Mouth (WOM), across rural and urban apparel shoppers. The secondary data used for this study was collected by BIG research in their Consumer Intentions and Actions Study. Hierarchical multiple regression analysis was conducted, and the results showed that four store attributes (fashionability, promotion, shopping environment, and retail basics) were positively related to store loyalty. Findings of the study also revealed that the effect of fashionability and retail basics on store loyalty differed significantly across rural and urban consumers while promotion and shopping environment were not different predictors of store loyalty between rural and urban apparel shoppers. Specifically, store attributes of fashionability were stronger antecedents of loyalty for women's clothing shoppers in urban areas than rural shoppers. The retail basics had a greater influence on store loyalty among women's apparel customers in rural areas than customers in urban areas.

• Journal of Distribution Science
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• v.18 no.3
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• pp.87-97
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• 2020

Purpose: The aim of this study is to understand the internal and external responses that consumers experience when they are exposed to an innovative system in retail stores. This study considered the SST(Self-Service Technology) system in a retail setting as a type of functional environmental stimuli and selected a smart shopping cart as an example of SST system. The influences of functional environmental stimuli on consumers' emotional, cognitive, and behavioral responses were examined by applying S-O-R model. In addition, this study attempted to extend the traditional S-O-R model by (a) incorporating personal characteristics variables such as time pressure and perceived crowding and (b) considering not only emotional but also cognitive aspects of consumers' internal responses. Research Design, Data, and Methodology: This study used a video-scenario technique. Participants watched a video about grocery shopping situations using a smart shopping cart and responded to their emotional, cognitive, and behavioral responses. An online survey was conducted using Amazon's Mechanical Turk (N = 185). All participants were US consumers over 20 years old and had been shopping at the grocery store in the last month. Data were analyzed through structural equations modeling with AMOS 20. Results: Test results showed that consumers who perceived higher levels of time pressure and perceived crowding in usual shopping situations were more likely to evaluate the SST system favorably. The results showed that personal characteristics have a significant impact on consumers' evaluation of functional environmental stimuli in retail setting. As consumers evaluated the SST system favorably, they experienced more positive affect and less negative affect during their shopping behaviors. Positive affect led to good service quality inference, which further increased patronize intention. However, negative affect did not show a significant impact on service quality inference, but only on patronize intention. Conclusions: This study attempted to investigate the influence of SST system by extending the traditional S-O-R model. This study classified the SST system as functional environmental stimulus of retail stores and analyzed the effect of stimulus on consumers' internal and external responses. The results of this study showed that the introduction of innovative SST can serve as an effective store differentiation strategy in an increasingly competitive retail environment.

• Journal of Distribution Science
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• v.14 no.12
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• pp.129-139
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• 2016

Purpose - This paper examines the explanatory power of the agency theory in the determination of cash holdings for Korean retail firms. If the agency theory holds, a firm with strong corporate governance structure tends to have low cash holdings. A strong governance structure makes the CEO of this firm to behave in the interests of shareholders and thus the CEO has low incentive to stockpile cash holdings, which can be easily diverted for the CEO's own managerial purposes. We investigate this relationship between corporate governance structure and cash holdings, by using corporate governance scores as a proxy variable that captures the effectiveness of corporate governance mechanism. Research design, data, and methodology - We adopt the sample of publicly listed retail firms in KOSPI market from 2005 to 2013. Financial and accounting statements are gathered from the WISEfn database. We also use the corporate governance scores published by Korean Corporate Governance Service. The relationship between the corporate governance scores and cash holdings is cross-sectionally estimated based on the ordinary least square method. This estimation method is widely accepted in the existing literature. The sample of large conglomerates, Chebol, and the remainder firms are separately examined as well, to account for the distinctive internal financing environment in these large conglomerates. Results - We mainly contribute to the extant literature by providing empirical evidence against the agency theory of cash policy. Unlike the prediction of agency theory, we confirm statistically insignificant or even positive correlations between the set of corporate governance scores and cash-asset ratios. Almost all the major corporate governance attributes including total score, shareholder rights, board structure, and the quality of information disclosure do not show negative correlations with cash holdings, which poses a strong challenge to the validity of the agency theory in the determination of retail firms' cash holdings. Conclusions - This study presents interesting empirical results with respect to the cash policy in Korean retail firms. Consistent to prior studies, I verify that the agency theory only limitedly explains the level of cash holdings. Future studies may obtain more robust results by examining a longer sample period.

• 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 Distribution Research
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• v.2 no.1
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• pp.9-34
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• 1997

The marketing environment around the Korean retail stores is becoming increasingly voltage due to the recent changes in the marketplace. These changes are not only offering business opportunities but also posing competitive threats for many retailers these days. The key to survival and growth of these retail stores lies in developing and delivering quality services. This article reports the findings of a field survey which measured customers current perceptions of six different types of stores in terms of various service dimensions. The store types examined in the study were: traditional markets, department stores, shopping centers$.$supermarkets, convenience stores, discount stores, and membership wholesale clubs. The study also makes an attempt to determine service dimensions which have significant impact on customer perceptions across different store types. By analyzing the gap between how stores are perceived and how they should be perceived, the article discusses and suggests strategic directions for each type of retail stores.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.500-506
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• 2005

With the advent of electric power systems moving to a deregulated retail electricity market environment, calculating distribution service tariffs has become a challenging theme for distribution industries and tariff regulators. As distribution business remains as a monopoly, it is necessary to be regulated. And as multiple distribution companies compete with each other, it would be efficient to adopt competition to the determination of distribution service tariffs. This paper proposes a method to calculate distribution service tariffs using yardstick regulation, which can lead to competition among multiple distribution companies. The proposed method takes into account not only recovering revenue requirements but also the advantages of the yardstick regulation based on long-term marginal costs of distribution network expansion algorithms. A computer simulation is carried out to illustrate effectiveness of the proposed method and it is estimated that the algorithm can be applied to compute the distribution service tariffs under retail electricity markets.