In 2006, the share of fisheries distribution in Busan amounted to 1.9 million ton, which was 41 percent of the whole country. In details, coastal fishery 334 thousand ton(14% of the whole country), deep sea fishery 452 thousand ton(82%), import fishery 964 thousand ton(70%), export fishery 157 thousand ton(43%) were distributed in Busan region, respectively. Recently, the share of fisheries sale through e-commerce is increasing owing to the growth of IT and competitive price of its products. and the sale share of large discount store is also on the 10% more increase owing to the effectiveness of distribution structure. Hereafter these structure changes of fisheries distribution in Busan will be more intensified. Therefore, after reflecting the change in distribution policy of Busan Fisheries, the directions of distribution policy should be established, as follows. Distribution policy to prepare for increasing of sales ratio in large discount store
The innovative development of museum cultural products is an important way for museums to play the function of cultural communication with their collections. In the context of consumer upgrading, traditional cultural product design and sales methods gradually fail to meet the diverse needs of consumers. This study aims to propose the construction of a customized interactive experience platform for museum cultural products, promote the development of museum cultural products, and facilitate the inheritance and preservation of museum culture. The research methodology analyzes the model and characteristics of existing cultural product customization platforms by collating existing literature studies, and distributes 159 questionnaires to investigate the needs of cultural product consumers, and finally combines the customization experience with existing e-tailing platform systems according to user needs, proposes a theoretical framework and conducts design practice and usability testing using the Shenyang Palace Museum as an example. The findings show that users have a high acceptance of the customized platform for cultural products and that the design of the customized platform can be used to promote the dissemination of the cultural connotations of museums, optimize the personalized user experience of cultural products, and provide new ideas for the development, design, and retailing of museum cultural products. Based on the above findings, this paper suggests that museums' cultural product development can utilize the design model of customized platforms to further enhance consumers' personalized service experience.
Online reviews attract much attention because they play an essential role in consumer decision-making. Therefore, it is necessary to investigate the review attributes that affect the perceived helpfulness of consumers. However, most previous studies on the helpfulness of online reviews mainly focus on quantitative factors such as review volume and reviewer attributes. Recently, some studies have investigated the impact of review content and linguistic style matching on consumers' purchase decision-making. Those studies show that consumers consider additional review attributes when evaluating reviews in decision-making. To fill the research gap with existing literature, we investigated the impact of review content and linguistic style matching on review helpfulness. Moreover, this study investigated how the reviewers' expertise moderates the effect of the review content and linguistic style matching on the review helpfulness. The empirical results show that positive affective content has a negative effect on the review helpfulness. The negative affective content and linguistic style matching positively affect review helpfulness. Review expertise relieved the impact of negative affective content and linguistic style matching on review helpfulness. According to the mechanism confirmed in this study, online e-commerce companies can achieve corporate sales growth by identifying factors affecting review helpfulness and reflecting them in their marketing strategies.
Purpose - This study empirically investigates the effects of Official Development Assistance (ODA) on the economic activities of private actors in recipient countries. As a proxy for the economic activities of private actors, we utilize the job creation activities of foreign subsidiaries in recipient countries. The foreign subsidiaries provide a foundation for economic development by creating paying jobs. That is, if ODA has been successfully transferred to foreign subsidiaries, then these foreign subsidiaries should help economic growth and help create a boom in the local market by providing jobs. These jobs eventually lead to the achievement of the primary aims of foreign aid, including poverty reduction. Thus, this study empirically examines the relationship between ODA and the number of jobs created by foreign subsidiaries in recipient countries. Design/methodology - This is the first study to examine the effects of the ODA on the job creation of foreign subsidiaries because it has been hard to obtain internal information related to the employment status of foreign subsidiaries. Fortunately, we have a unique panel dataset provided by the Export-Import Bank of Korea (KEXIM) for 2006 to 2013. In terms of the empirical specification, we use the generalized least squares (GLS) method. The panel GLS estimator allows us to have an efficient estimation that overcomes the limitations of the panel data. It employs assumptions about the heteroscedasticity between the panels and makes an autocorrelation of the error term within each panel. Findings - We find that ODA influences job creation in foreign subsidiaries. In particular, we found that ODA creates more jobs in sales than in managerial or production positions. This study also shows that the effect of the ODA on the foreign subsidiaries' job creation activities depend on the purpose of the ODA. By examining ODA effects on the foreign subsidiaries' economic activities (e.g., job creation), this study fills a gap in the current literature. Originality/value - Existing studies that focus on the ODA effect have either a macroeconomic point or a microeconomic point of view. However, both approaches do not explain how well foreign aid has influenced private economic actors of recipient countries. In essence, previous researchers found it difficult to obtain the necessary data for internal employment status from foreign subsidiaries. However, thanks to the Korea Export-Import Bank, this study shows that ODA indeed influences the job creation activities of foreign subsidiaries even after controlling for other factors such as FDI, GDP growth rate, employment rate, household expenditure, mother firms' share, etc. By doing so, we can examine how ODA influences the job creation of foreign subsidiaries, which might help economic development and reduce the amount of poverty in recipient countries.
Since the global financial crisis, major countries have been executing policies related to two top-priority goals to create more jobs: revitalization of entrepreneur activity and the cultivation of small and medium-sized companies. In South Korea, the interest of policy makers is increasingly focusing on the role of SMEs that have a technological competitive edge in the realization of a "job-centered creative economy." Due to the nature of the field, the health and medical industry requires a particularly long time until the achievement of industrialization, Also, because of the complex distribution structure, it is essential for related government ministries and institutions to jointly devise strategies. A lack of policy supports for the industry has thus far resulted in its development being relegated for the most part of small and medium-sized companies, which consequently means low global competitiveness. Now is the time for the South Korean government to provide the revolutionary supported options and strategies. This study aims to propose a general policy direction and policy areas for the cultivation of Korea's small and medium-sized companies in the healthcare industry into global small giant companies through an exploration of the German case. It is crucial to first cultivate the international competitiveness of Korean small and medium-sized companies (as in the case of Germany) so that they can grow into global small giant companies. Another important task is the creation of an environment that expedites the qualitative growth of promising SMEs as well as technological development. After securing competitiveness in terms of both product quality and technology in the global health market, substantive policy supports will be necessary to cultivate global small giant companies that are export-based (e.g. job creation effect, sales value added).
This case illustrates a story of passionate and innovative new market development by Pulmuone, a fresh food provider in Korea. The company has been successfully developing a new market by introducing a (non-frying) fresh ramen, 'Jayeonun Masitda - The Nature Is Tasty' in the packaged ramen market dominated by fried ones. In this case, a detailed new market development process by Pulmuone will be investigated including; company overview, a new product development process, marketing strategy formulation, marketing mix implementation, market performance, and future directions. Pulmuone has been making efforts to create a new product category by marketing non-frying ramens since 1995, but with a modest success. In 2011, Pulmuone finally succeeded to develop an innovative product, 'Jayeonun Masitda' that brought more health and nutrition conscious consumers' attention in the ramen market. The company intended to change the current competitive structure in the ramen market, i.e., from the strength of taste and the amount of ingredient to fried/non-fried and the freshness of ingredient. By this new positioning, Pulmuone aimed to reshaping the ramen market into competition between healthy and unhealthy ramens. Pulmuone has been successful in developing a new market. Sales revenue of 'Jayeonun Masitda' has been continuously increasing, and customers are found to be highly satisfied with the product resulting in a high repeat purchase rate. The company's successful new market development can be attributed to a faithful new product development process, innovative technology, an appropriate positioning strategy, and consistent marketing communication. In addition, Pulmuone's eco-friendly corporate image and the organization's passion to grow are also important factors for success of this new market development.
Dhones Rodrigues de Andrade;Flavia Adriane de Sales Silva;Jardeson de Souza Pinheiro;Julia Travassos da Silva;Nathalia Veloso Tropia;Leticia Artuzo Godoi;Rizielly Saraiva Reis Vilela;Fernando Alerrandro Andrade Cidrini;Luciana Navajas Renno;Diego Zanetti;Tiago Sabella Acedo;Sebastiao de Campos Valadares Filho
Animal Bioscience
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v.36
no.12
/
pp.1831-1841
/
2023
Objective: This study was realized to evaluate the effects of supplementation with blends of water and fat-soluble vitamins on animal performance and carcass traits of young Nellore bulls. Methods: Forty-three Nellore bulls, with an initial weight of 261±27.3 kg and a mean age of 8±1.0 months, were used. Five animals were slaughtered at the beginning of the experiment (reference group), to determine the initial empty body weight of the bulls that remained in the experiment. The remaining 38 bulls were fed ad libitum and distributed in a completely randomized design in a 2×2 factorial scheme, with or without supplementation of water-soluble (B-blend+ or B-blend-) and fat-soluble (ADE+ or ADE-) vitamin blends. Diets were isonitrogenous (120 g of crude protein/kg dry matter [DM] of total mixed ration) and consisted of a roughage:concentrate rate of 30:70 based on total DM of diet. The experiment lasted 170 days, with 30 days of adaptation and 140 days for data collection. At the beginning and end of the experimental period, the bulls were weighed to determine the average daily gain. To estimate the apparent digestibility of nutrients and microbial efficiency, spot collections of feces and urine were performed for five consecutive days. Results: DM, ashes, organic matter, crude protein, ethereal extract, neutral detergent fiber corrected for residual ash and residual nitrogenous, and N intake and apparent digestibility were not influenced by vitamin supplementation, but total digestible nutrients intake and non-fibrous carbohydrates digestibility were influenced by B complex vitamin supplementation. Nitrogen balance, microbial efficiency, and performance data were not influenced (p>0.05) by vitamin supplementation. Conclusion: Vitamin supplementation (a blend of water-soluble and fat-soluble vitamins or their combinations) does not influence the animal performance and carcass traits of young Nellore bulls.
Internet commerce has been growing at a rapid pace for the last decade. Many firms try to reach wider consumer markets by adding the Internet channel to the existing traditional channels. Despite the various benefits of the Internet channel, a significant number of firms failed in managing the new type of channel. Previous studies could not cleary explain these conflicting results associated with the Internet channel. One of the major reasons is most of the previous studies conducted analyses under a specific market condition and claimed that as the impact of Internet channel introduction. Therefore, their results are strongly influenced by the specific market settings. However, firms face various market conditions in the real worlddensity and disutility of using the Internet. The purpose of this study is to investigate the impact of various market environments on a firm's optimal channel strategy by employing a flexible game theory model. We capture various market conditions with consumer density and disutility of using the Internet.
shows the channel structures analyzed in this study. Before the Internet channel is introduced, a monopoly manufacturer sells its products through an independent physical store. From this structure, the manufacturer could introduce its own Internet channel (MI). The independent physical store could also introduce its own Internet channel and coordinate it with the existing physical store (RI). An independent Internet retailer such as Amazon could enter this market (II). In this case, two types of independent retailers compete with each other. In this model, consumers are uniformly distributed on the two dimensional space. Consumer heterogeneity is captured by a consumer's geographical location (ci) and his disutility of using the Internet channel (${\delta}_{N_i}$).
shows various market conditions captured by the two consumer heterogeneities.
(a) illustrates a market with symmetric consumer distributions. The model captures explicitly the asymmetric distributions of consumer disutility in a market as well. In a market like that is represented in
(c), the average consumer disutility of using an Internet store is relatively smaller than that of using a physical store. For example, this case represents the market in which 1) the product is suitable for Internet transactions (e.g., books) or 2) the level of E-Commerce readiness is high such as in Denmark or Finland. On the other hand, the average consumer disutility when using an Internet store is relatively greater than that of using a physical store in a market like (b). Countries like Ukraine and Bulgaria, or the market for "experience goods" such as shoes, could be examples of this market condition.
summarizes the various scenarios of consumer distributions analyzed in this study. The range for disutility of using the Internet (${\delta}_{N_i}$) is held constant, while the range of consumer distribution (${\chi}_i$) varies from -25 to 25, from -50 to 50, from -100 to 100, from -150 to 150, and from -200 to 200.
summarizes the analysis results. As the average travel cost in a market decreases while the average disutility of Internet use remains the same, average retail price, total quantity sold, physical store profit, monopoly manufacturer profit, and thus, total channel profit increase. On the other hand, the quantity sold through the Internet and the profit of the Internet store decrease with a decreasing average travel cost relative to the average disutility of Internet use. We find that a channel that has an advantage over the other kind of channel serves a larger portion of the market. In a market with a high average travel cost, in which the Internet store has a relative advantage over the physical store, for example, the Internet store becomes a mass-retailer serving a larger portion of the market. This result implies that the Internet becomes a more significant distribution channel in those markets characterized by greater geographical dispersion of buyers, or as consumers become more proficient in Internet usage. The results indicate that the degree of price discrimination also varies depending on the distribution of consumer disutility in a market. The manufacturer in a market in which the average travel cost is higher than the average disutility of using the Internet has a stronger incentive for price discrimination than the manufacturer in a market where the average travel cost is relatively lower. We also find that the manufacturer has a stronger incentive to maintain a high price level when the average travel cost in a market is relatively low. Additionally, the retail competition effect due to Internet channel introduction strengthens as average travel cost in a market decreases. This result indicates that a manufacturer's channel power relative to that of the independent physical retailer becomes stronger with a decreasing average travel cost. This implication is counter-intuitive, because it is widely believed that the negative impact of Internet channel introduction on a competing physical retailer is more significant in a market like Russia, where consumers are more geographically dispersed, than in a market like Hong Kong, that has a condensed geographic distribution of consumers. illustrates how this happens. When mangers consider the overall impact of the Internet channel, however, they should consider not only channel power, but also sales volume. When both are considered, the introduction of the Internet channel is revealed as more harmful to a physical retailer in Russia than one in Hong Kong, because the sales volume decrease for a physical store due to Internet channel competition is much greater in Russia than in Hong Kong. The results show that manufacturer is always better off with any type of Internet store introduction. The independent physical store benefits from opening its own Internet store when the average travel cost is higher relative to the disutility of using the Internet. Under an opposite market condition, however, the independent physical retailer could be worse off when it opens its own Internet outlet and coordinates both outlets (RI). This is because the low average travel cost significantly reduces the channel power of the independent physical retailer, further aggravating the already weak channel power caused by myopic inter-channel price coordination. The results implies that channel members and policy makers should explicitly consider the factors determining the relative distributions of both kinds of consumer disutility, when they make a channel decision involving an Internet channel. These factors include the suitability of a product for Internet shopping, the level of E-Commerce readiness of a market, and the degree of geographic dispersion of consumers in a market. Despite the academic contributions and managerial implications, this study is limited in the following ways. First, a series of numerical analyses were conducted to derive equilibrium solutions due to the complex forms of demand functions. In the process, we set up V=100, ${\lambda}$=1, and ${\beta}$=0.01. Future research may change this parameter value set to check the generalizability of this study. Second, the five different scenarios for market conditions were analyzed. Future research could try different sets of parameter ranges. Finally, the model setting allows only one monopoly manufacturer in the market. Accommodating competing multiple manufacturers (brands) would generate more realistic results.
Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.
To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.
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