• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.515-523
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• 2012

Most companies make efforts to maximize their profitability by improving loyalty to existing customers through customer relationship management (CRM). According to the Wikipedia, CRM is a widely implemented strategy for managing a company's interactions with customers, clients and sales prospects. And RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail. In general, one considers recency, frequency, and monetary for customer segmentation in RFM method. In this paper, we apply RFMP method added to the purchase period of advertising items in the traditional RFM model for real estate customer segmentation. We will be able to establish the differentiated marketing strategy by RFMP method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.615-621
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• 2016

The selection of a flagship industry is based on whether the industry's developmental impact is great and whether the industry can be the center of the national economy. Here, a ripple effect may be derived by analyzing the forward and backward linkage effects, but in the case of industries that are the centerpieces of the national economy, each researcher reported different results. Consequently, they could not agree on their flagship industry despite belonging to the same time. This study presents a prestige centrality of network analysis as a way of analyzing an industry, which was the center of the national economy, and performed empirical analysis utilizing the 2013 I-O Table. The analysis showed that the industries classified as those with high centrality include the energy industry, which is essential for economic development, can create a synergy effect with other industries, such as the transportation industry, industries with a high level of export and employment, such as electronics and chemicals, and industries for domestic demand, such as wholesale and retail, food services and accommodation.

• Horticultural Science & Technology
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• v.29 no.1
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• pp.43-47
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• 2011

This study was conducted to determine the effects of silver thiosulfate (STS) and 1-methylcyclopropene (1-MCP) on flower opening and lifespan of potted Kalanchoe blossfeldiana 'Oriba' for exportation. Ethylene inhibitors, STS and 1-MCP were applied to the kalanchoe plants prior to their export to Japan. STS 0.5 mM with 1% Tween 20 surfactant was directly sprayed (20 mL per plant) to leaves, buds, and flowers and 1-MCP 100 $nL{\cdot}L^{-1}$ was injected into sealed glass chambers containing kalanchoe plants, which were placed on the chambers for 6 hours. After transport to Japan, the plants were immediately transferred to a simulated retail condition room (80 ${\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ for 12 hours of photoperiod at $22^{\circ}C$ and 64% RH) at Toyko University. The numbers of buds, open florets, and wilted florets in the middle inflorescence for each plant were counted right after export, 1 week after export, and 6 weeks after export. The percentages of open florets and wilted florets were calculated from the numbers. STS treatment resulted in 35% more open florets than the control and only 11% of wilted florets at 6 weeks after export to Japan which indicate the extension of lifespan of potted kalanchoe plants. Meanwhile, the plants exposed to 1-MCP before export did not show any significant differences in the numbers of buds and open florets and the percentages of open and wilted florets compared to control plants. In conclusion, STS 0.5 mM treatment strikingly induced better opening florets and lifespan of kalanchoe plants from 1 week to 6 weeks after export than control.

• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.109-114
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• 2018

In general, the simple items we need to live in are purchased through retail stores such as supermarkets near our home. In the store, not only the items but also the management personnel and the payment instruments for the store management are located in one space called the store. Such a general store environment is gradually changing into an 'unmanned market' as a result of the development and fusion of information and communication technology (ICT). An unmanned market is an environment in which no one runs a market as the word has. An example of a typical change is Amazon's Unofficial Amazon Store. In addition, the usage and prospects of unmanned market in China are growing very meaningfully. In this study, the present situation of the unmanned market is examined in the US and China markets, and the development prospects are described. It also describes the key milestones necessary for the unmanned market.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.469-479
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• 2021

This case study investigates fashion brands that have entered the pet fashion market. A total of 25 cases were identified and analyzed from three perspectives: 1) product types, size systems, prices, 2) design aspects, fabrics, patterns, styles, and 3) marketing strategies. The study results are as follows. First, the product types of pet fashion are not diverse, and only the sizes of small dogs can be found. However, there is a significant price difference between brands. Second, knitted fabrics with good elasticity are mainly used for pet fashion products, and patterns incorporating their brands are extremely common. The style is casual and sporty. Third, marketing strategies should include a new line within a brand or launch a single specialized brand as a one-shot test for consumer reaction. Additionally, it has been expanded and presented as a family look to meet the needs of the petfam. Further, existing fashion brands and retail-based brands select diverse small-scale dog fashion product brands and expand their operation as a dog lifestyle total selectional shop. Therefore, brands entering the future should consider strategies such as size segmentation, product diversification, and premium price of high-quality materials that help market products such as the expansion or promotion of existing brands.

• The Korean Journal of Franchise Management
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• v.12 no.1
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• pp.7-19
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• 2021

Purpose: According to the growth of technology in the service industry, the interaction service between customer and employee has recently been transformed into between customer and technology by Self Service Technology (SST) requiring direct interaction with customers. In this context, self service technology such as unmanned ordering system installed at the store is actively introduced at the work place to reduce labor costs by food and retail company and the research for self-service technology which is rapidly replacing existing face-to-face service is needed. As the growth speed of SST is rapid, many researchers have studied the characteristics of SST, in every sector of business worldwide. Among the characteristics, attributes, Self Service Technology Quality (SSTQUAL) to evaluate SST is important because it may cause the customer's behavior. Thus, this research focuses on the effects of SSTQUAL on SST Satisfaction and SST continuance usage intention. This research suggests the guidelines for how Restaurant Company should prepare SST and build their customer satisfaction and continuance usage that increase the sales. Research design, data and methodology: This study tests the structural relationship between SSTQUAL of unmanned ordering system, SST satisfaction and SST continuance usage. SSTQUAL divided into four sub-dimensions and two categories, cognitive service attributes (Convenience, Functionality) and affective service attributes (Enjoyment, Assurance). In order to achieve the purposes of this research, research model and hypotheses were developed based on previous researches. All constructs were measured with multiple items developed and tested in the previous studies. The data were collected from 524 customers experiencing SST and were analyzed through SPSS 25.0 and SmartPLS 3.0 statistical package program. Results: The findings of this research are as follows. First, all SSTQUAL have significant positive impacts on SST satisfaction. Second, SST satisfaction has significant positive impact on SST continuance intention. Third, cognitive service attributes and affective service attributes had wealth of explanation of service attribute more than a single dimension. Conclusions: The implications of this study are as follows. Overall, Restaurant Company should manage SSTQUAL consisting of not only cognitive service attributes (Convenience, Functionality) but also affective service attributes (Enjoyment, Assurance) to satisfy customers basically regardless of the type of restaurant.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.80-98
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• 2022

Artificial intelligent chatbot services have recently become common in fashion e-retailing and are expected to improve online shopping by making it easy to recommend products. This study examines whether the perceived quality of a fashion chatbot affects consumers' trust and perception of usefulness, which in turn influences satisfaction and intention to use, in accordance with the information system success model. The study also investigates differences in perceived quality and consumer response variables between high and low groups of self-efficacy. A total of 341 consumers participated in an online survey. The results revealed that information quality and system quality had a significant impact on perceived usefulness and trust, and that service quality significantly impacted trust. Perceived usefulness and trust had a positive effect on consumer satisfaction, which in turn had a positive effect on intention to use. In addition, the findings revealed that people who had higher self-efficacy showed higher scores on perceived usefulness, trust, satisfaction, and intention to use chatbots as compared to people who had lower self-efficacy. This study suggested theoretical implications by applying the information system success model theory to fashion chatbot studies. It also suggested practical implications for e-commerce marketers developing retail strategies.

• Asia Marketing Journal
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• v.12 no.1
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• pp.81-101
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• 2010

Lotte Shopping Co. started its business from Lotte Department Store. From the beginning, Lotte Shopping Co. opened the largest department store and, since then, has expanded its business size through the processes of active developments of its own stores and merges and acquisitions of other companies. Currently, it operates a variety of retail shops such as department stores, discount stores, movies theaters, shopping malls, and supermarkets along with a TV home-shopping station and an online shopping mall. Lotte group, a business conglomerate Lotte Shopping belongs to, has an objective of becoming TOP 10 business group in Asia by 2018. Lotte group declared its vision statements in 2009 and has spent its effort to accomplish the goal. Lotte Shopping is implementing the group-level growth strategy through merges and acquisitions and diverse marketing programs. We will briefly investigate the current situation of Lotte Shopping and will then analyze its 1) entertainment shopping center and multi-channel strategy, 2) global market entry, and 3) education system.

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.207-222
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• 2024

Purpose - This study investigates whether the lagged price difference ratio between preferred and common stocks is related to the return and closing price of the preferred stock using three panel models. Design/methodology/approach - As a first step, we use a two-way fixed effect panel model with stationary preferred stock returns as a dependent variable. For robustness, we then apply the autoregressive distributed lag model (ARDL) and error correction model (ECM) with nonstationary closing prices of the preferred stocks as a dependent variable and compare the results of each model. The ARDL and ECM models provide an advantage of estimating a long-run equilibrium equation together if a long-run relationship exists between the two time-series variables compared to the fixed effect model. Findings - Our sample consists of 107 preferred stocks with at least four years of daily observations as of the end of December 2023. The coefficients of the error correction terms in the ARDL and ECM models are highly statistically significant, approximately -0.08. This indicates that the disequilibrium between the closing prices of common and preferred stocks adjusts by about 8% per day toward equilibrium. In all three models, the price difference ratio on day t-1 was statistically significant in explaining the preferred stock returns or closing prices on day t, implying that trading based on the previous day's price difference ratio is effective for one day. Research implications or Originality - Furthermore, the returns on preferred stocks are higher for firms with a lower proportion of foreign investors or a lower foreign market capitalization of preferred stocks. This suggests that foreign investors with informational advantages do not actively engage in profit-taking by trading preferred stocks, thus not narrowing the price difference. In summary, the recent surge in preferred stock prices is likely driven mainly by the irrational behavior of retail investors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.