From the beginning of the chosun dynasty, an agriculture-first policy was imposed by being written farming books, for instance, Nongsajiksul, matched with real conditions of local agriculture, which provided the grounds of new, intensive farming technologies. This farming book was the collection of good fanning technologies that were experienced in rural farm areas at that time. According to Nongsajiksul, rice culture systems were divided into "Musarmi"(Water-Seeded rice), /"Kunsarmi"(dry-seeded rice), /transplanted rice and mountainous rice (upland rice) culture. The characteristics of these rice cultures with high technologies were based of scientific weeding methods, improved fertilization, and cultivation works using cattle power and manpower tools systematically. Reclamation of coastal swampy and barren land was possible in virtue of fire cultivation farming(火耕) and a weeding tool called "Yoonmok"(輪木). Also, there was an improved hoe to do weeding works as well as thinning and heaping-up of soil at seeding stages of rice. Direct-seeded rice culture in flat paddy fields were expanded by constructing the irrigation reservoirs and ponds, and the valley paddy fields was reclaimed by constructing "Boh(洑)". These were possible due to weed control by irrigation waters, keeping soil fertility by inorganic fertilization during irrigation, and increased productivity of rice fields by supplying good physiological conditions for rice. Also, labor-saving culture of rice was feasible by transplanting but in national-wide, rice should not basically be transplanted because of the restriction of water use. Thus, direct-seeded rice in dry soils was established, in which rice was direct-seeded and grown in dry soils by seedling stages and was grown in flooded fields when rained, as in the book "Nongsajiksul". During the middle of the dynasty(AD 1495-1725), the excellent labor-saving farmings include check-rowing transplanting because of weeding efficiency and availability in rice("Hanjongrok"), and, nurserybed techniques (early transplanting of rice) were emphasized on the basis of rice transplanting ["Nongajibsung"]. The techniques for deep plowing with cattle powers and for putting more fertilizers were to improve the productivity of labor and land, The matters advanced in "Sanlimkyungje" more than in "Nongajibsung" were, development of "drybed of rice nursery stock", like "upland rice nursery" today, transplanting, establishment of "winter barly on drained paddy field, and improvement of labor and land-productivity in rice". This resulted in the community of large-scale farming by changing the pattern of small-farming into the production system of rice management. Woo-hayoung(1741-1812) in his book "Chonilrok" tried to reform from large-scale farmings into intensive farmings, of which as eminent view was to divide the land use into transplanting (paddy) and groove-seeding methods(dry field). Especially as insisted by Seo-yugo ("Sanlimkyungjeji"), the advantages of transplanting were curtailment of weeding labors, good growth of rice because of soil fertility of both nurserybed and paddy field, and newly active growth because rice plants were pulled out and replanted. Of course, there were reestimation of transplanting, limitation of two croppings a year, restriction of "paddy-upland alternation", and a ban for large-scale farming. At that period, Lee-jiyum had written on rice farming technologies in dry upland with consider of the land, water physiology of rice, and convenience for weeding, and it was a creative cropping system to secure the farm income most safely. As a integrated considerations, the followings must be introduced to practice the improved farming methods ; namely, improvement of farming tools, putting more fertilizers, introduction of cultural technologies more rational and efficient, management of labor power, improvement of cropping system to enhance use of irrigation water and land, introduction of new crops and new varieties.
Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.
Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as