• Journal of the Korean Society of Food Science and Nutrition
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• v.35 no.1
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• pp.68-77
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• 2006

This study was peformed to document the association between nutrient intakes, body mass index (BMI), waist/hip ratio (WHR), and a major risk factor for chronic diseases. A three-day dietary intake survey, using a 24 hour recall method, was obtained from 187 subjects aged 46 to 84 (mean age 65.3) living in Wando island area. The average daily mean energy intakes were 1869.0 kcal for male and 1943.9 kcal for female, respectively. Daily intakes of protein for male and female were 28.0 and 30.4 g, and those of fat were 31.5 and 28.51 g, respectively Carbohydrate dependency was decreased with age. Protein dependency was increased with age. The mean intakes of energy, protein, Vit. A, Vit. D, Vit. E, Ca, Zn did not meet Korean RDA for elderly. The level of serum triglyceride was higher in males than in females and showed the tendency to increase with age in both sexes, whereas HDL-cholesterol tended to decrease with age in both sexes. The levels of serum total-cholesterol and LDL-cholesterol were significantly higher in males than in females, particularly in the age of $46\~59$ (p<0.05). The level of atherogenic index (AI) was significantly higher in females than in males, particularly in the age of 80 and over (p<0.05) Based on these results, it is evident that people in island area did not consume enough nutrient. Specially, dietary intake of protein was not adequate. This study implies that triglyceride, total-cholesterol, LDL-cholesterol, AI were increased with increasing age, BMI and WHR.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of the Korean Wood Science and Technology
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• v.10 no.1
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• pp.5-37
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• 1982

Plywood used for construction as a decorative inner material is inflammable to bring large fire accidents and burn out human life and their properties. To diminish the fire disaster, fire retardant plywood has been required indeed. In the methods of manufacturing the fire retardant plywood the soaking method is occasionally used. However after soaking plywood into fire retardant chemical solutions, redrying of soaked plywood is the most important. In this study, 3.5mm thin and 5.0mm thick plywoods were selected for fire retardant treatment. Treating solutions were prepared for 20% dilute solutions of ammonium sulfate, monoammonium phosphate, diammonium phosphate, borax-boric acid and minalith, and water solution. 1-, 3-, 6-, and 9 hour-soaking treatments were applied and after treatments hot plate drying was applied to those treated plywoods at $90^{\circ}C$, $120^{\circ}C$ and $150^{\circ}C$, of press temperature. Drying rates, drying curves, water absorption rates of fire retardant chemicals, weight per volume and fire retardant degree of plywood were investigated. The results may be summarized as follows: 1. The plywoods treated with ammonium sulfate, monoammonium phosphate and diammonium phosphate and diammonium phosphate showed increase of chemical absorption rate with proportion to increase of treating time, but not in case of the plywood treated with borax-boric acid and minalith. 2. In the treatment of definite time, the absorption rate per unit of volume of plywood showed higher in thin plywood (thickness of 3.5mm) than in thick plywood (thickness of 5.0mm). In both thin and thick plywoods, the highest absorption rate was observed in 9 hour-treatment of ammonium sulfate. The value was 1.353kg/$(30cm)^3$ in thin plywood and 1.356kg/$(30cm)^3$ in thick plywood. 3. The volume per weight of plywood after chemical treatment increased remarkably and. after hot plate drying, the values were to a little extent higher than before chemical treatment. 4. The swelling rates of thickness in chemical-treated plywoods increased similarly with that of water-treated plywood in 1- and 3 hour-treatment of both thin and thick plywoods. But in 6- and 9 hour-treatment, the greater increased value showed in water-treated ply wood than any other chemical, especially in thick plywood. 5. The shrinkage rates after hot plate drying showed the same tendency as the swelling rate, and the rate showed the increasing tendency with proportion to increase of treating time in thick plywood of both chemical and water treatments. 6. Among drying curves, the curves of water-treated plywood placed more highly than chemical-treated plywood without-relation to thickness in 6- and 9 hour-treatment except in 1- and 3 hour-treatment. 7. The drying rate related to thickness of treated plywood, was twice above in thin plywood compared with thick plywood. 8. The drying rate remarkably increased with proportion to increase of the plate temperature and, the values were respectively 1.226%/min., 6.540%/min., 25.752%/min. in hot plate temperature of $90^{\circ}C$, $120^{\circ}C$, $150^{\circ}C$ in thin plywood and 0.550%/min., 2.490%/min, 8.187%/min, in hot plate temperature of $90^{\circ}C$, $120^{\circ}C$, $150^{\circ}C$ in thick plywood. 9. In the treatment at $120^{\circ}C$ of hot plate temperature, the drying rates of chemical-treated plywood showed the highest value in monoammonium phosphate of thin plywood and in diammonium phosphate of thick plywood. But the drying rate of water-treated plywood was highest in 6- and 9 hour-treatment. 10. The fire retardant degree of chemical-treated plywood was higher than that of the untreated plywood as shown in loss of weight, burning time, flame-exhausted time and carbonized area. 11. The fire-retardant effect among fire retardant chemicals were the greatest in diammonium phosphate, the next were in monoammonium phosphate and ammonium sulfate, and the weakest were in borax-boric and minalith.

• Korean Journal of Mineralogy and Petrology
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• v.33 no.2
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• pp.99-114
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• 2020

The characteristics of the Brazilian tensile strengths(σ_t) parallel to the rock cleavages in Jurassic granite from Geochang were analysed. The evaluation for the six directions of rock cleavages was performed using the parameter values on microcrack length and the above strength. The strength values of the five test specimens belonging to each direction were classified into five groups. The strength values of these five groups increase in order of group A < B < C < D < E. The close dependence between the above microcrack and strength was derived. The analysis results of this study are summarized as follows. First, the chart showing the variation and characteristics of strength among the three rock cleavages were made. In the above chart, the strength values of six directions belonging to each group were arranged in order of rift(R1 and R2), grain(G1 and G2) and hardway(H1 and H2). The strength distribution lines of the five groups concentrate in the direction of R1. And the widths among the above five lines indicating strength difference(Δσ_t) are the most narrowest in R1 direction. From the related chart, the variation characteristics among the two directions forming each rock cleavage were derived. G2(2)-test specimen shows higher value and lower value of the difference in strength compared to the case of G1(1)-test specimen. These kinds of phenomena are the same as the case between the test specimen H2(2) and H1(1). The strength characteristics of the above test specimens (2) suggest lower microcrack density value and higher degree of uniformity in the distribution of microcracks arrayed parallel to the loading direction compared to those of test specimens (1). The six strength values belonging to each group were arranged in increasing order in the above chart. The strength values of the test specimens belonging to both group D and E appear in order of R1 < R2 < G1 < H1 < G2 < H2. Therefore, the strength values of group D and E can be indicator values for evaluating the six directions of rock cleavages. Second, the correlation chart between slope angle(θ) and strength difference(Δσ_t) were made. The values of the above two parameters were obtained from the five strength distribution lines connecting between the two directions. From the chart related to rift plane(G1-H1, R'), grain plane(R1-H2, G') and hardway plane(R2-G2, H'), the slope values of linear functions increase in order of R'(0.391) < G'(0.470) < H'(0.485). Among three planes, the charts related to hardway plane show the highest distribution density among the five groups. From the related chart for rift(R1-R2, R), grain(G1-G2, G) and hardway(H1-H2, H), the slope values of linear functions increase in order of rift(0.407) < hardway(0.453) < grain(0.460). Among three rock cleavages, the charts related to rift show the highest frequency of groups belonging to the lower region. Taken together, the width of distribution of the slope angle among the three planes and three rock cleavages increase in order of H' < G < R' < R < G' < H. Third, the correlation analysis among the parameters related to microcrack length and the tensile strengths was performed. These parameters may include frequency(N), total length(Lt), mean length(Lm), median length(Lmed) and density(ρ). The correlation charts among individual parameters on the above microcrack(X) and corresponding five levels of tensile strengths for the five groups(Y) were made. From the five kinds of correlation charts, the values of correlation coefficients(R²) increase along with the five levels of strengths. The mean values of the five correlation coefficients from each chart increase in order of 0.22(N) < 0.34(Lt) < 0.38(ρ) < 0.57(Lmed) < 0.58(Lm). Fourth, the correlation chart among the corresponding maximum strength for group E(X) and the above five parameters(Y) were made. From the related chart, the values of correlation coefficient increase in order of 0.61(N) < 0.81(Lt) < 0.87(ρ) < 0.93(Lm) < 0.96(Lmed). The two parameters that have the highest correlations are median length with maximum strength. Through the above correlation analysis between microcrack and strength, the credibility for the results from this study can be enhanced.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Asia Marketing Journal
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• v.14 no.2
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• pp.39-63
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• 2012

Previous studies of reward programs have generally focused on designing the best programs for consumers and suggested that consumers' perception of the value of reward programs can vary according to the type of reward program (e.g., hedonic vs. utilitarian and direct vs. indirect) and its timing (e.g., immediate vs. delayed). These studies have typically assumed that consumers' preference for reward programs has a positive effect on brand loyalty. However, Dowling and Uncles (1997) pointed out that this preference does not necessarily foster brand loyalty. In this regard, the present study verifies this assumption by examining the effects of consumers' perception of the value of reward programs on their brand loyalty. Although reward programs are widely used by online shopping malls, most studies have examined the conditions under which consumers are most likely to value loyalty programs in the context of offline shopping. In the context of online shopping, however, consumers' preferences may have little effect on their brand loyalty because they have more opportunities for comparing diverse reward programs offered by many online shopping malls. That is, in online shopping, finding attractive reward programs may require little effort on the part of consumers, who are likely to switch to other online shopping malls. Accordingly, this study empirically examines whether consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. Meanwhile, consumers seek utilitarian and/or hedonic value from their online shopping activity(Jones et al., 2006; Barbin et al., 1994). They visit online shopping malls to buy something necessary (utilitarian value) and/or enjoy the process of shopping itself (hedonic value). In this sense, reward programs may reinforce utilitarian as well as hedonic value, and their effect may vary according to the type of reward (utilitarian vs. hedonic). According to Chaudhuri and Holbrook (2001), consumers' perception of the value of a brand can influence their brand loyalty through brand trust and affect. Utilitarian value influences brand loyalty through brand trust, whereas hedonic value influences it through brand affect. This indicates that the effect of this perception on brand trust or affect may be moderated by the type of reward program. Specifically, this perception may have a greater effect on brand trust for utilitarian reward programs than for hedonic ones, whereas the opposite may be true for brand affect. Given the above discussion, the present study is conducted with three objectives in order to provide practical implications for online shopping malls to strategically use reward program for establishing profitable relationship with customers. First, the present study examines whether reward programs can be an effective marketing tool for increasing brand loyalty in the context of online shopping. Second, it investigates the paths through which consumers' perception of the value of reward programs influences their brand loyalty. Third, it analyzes the effects of this perception on brand trust and affect by considering the type of reward program as a moderator. This study suggests and empirically analyzes a new research model for examining how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. The model postulates the following 10 hypotheses about the structural relationships between five constructs: (H1) Consumers' perception of the value of reward programs has a positive effect on their program loyalty; (H2) Program loyalty has a positive effect on brand loyalty; (H3) Consumers' perception of the value of reward programs has a positive effect on their brand trust; (H4) Consumers' perception of the value of reward programs has a positive effect on their brand affect; (H5) Brand trust has a positive effect on program loyalty; (H6) Brand affect has a positive effect on program loyalty; (H7) Brand trust has a positive effect on brand loyalty; (H8) Brand affect has a positive effect on brand loyalty; (H9) Consumers' perception of the value of reward programs is more likely to influence their brand trust for utilitarian reward programs than for hedonic ones; and (H10) Consumers' perception of the value of reward programs is more likely to influence their brand affect for hedonic reward programs than for utilitarian ones. To test the hypotheses, we considered a sample of 220 undergraduate students in Korea (male:113). We randomly assigned these participants to one of two groups based on the type of reward program (utilitarian: transportation card, hedonic: movie ticket). We instructed the participants to imagine that they were offered these reward programs while visiting an online shopping mall. We then asked them to answer some questions about their perception of the value of the reward programs, program loyalty, brand loyalty, brand trust, and brand affect, in that order. We also asked some questions about their demographic backgrounds and then debriefed them. We employed the structural equation modeling (SEM) method with AMOS 18.0. The results provide support for some hypotheses (H1, H3, H4, H7, H8, and H9) while providing no support for others (H2, H5, H6, H10) (see Figure 1). Noteworthy is that the path proposed by previous studies, "value perception → program loyalty → brand loyalty," was not significant in the context of online shopping, whereas this study's proposed path, "value perception → brand trust/brand affect → brand loyalty," was significant. In addition, the results indicate that the type of reward program moderated the relationship between consumers' value perception and brand trust but not the relationship between their value perception and brand affect. These results have some important implications. First, this study is one of the first to examine how consumers' perception of the value of reward programs influences their brand loyalty in the context of online shopping. In particular, the results indicate that the proposed path, "value perception → brand trust/brand affect → brand loyalty," can better explain the effects of reward programs on brand loyalty than existing paths. Furthermore, these results suggest that online shopping malls should place greater emphasis on the type of reward program when devising reward programs. To foster brand loyalty, they should reinforce the type of shopping value that consumers emphasize by providing them with appropriate reward programs. If consumers prefer utilitarian value to hedonic value, then online shopping malls should offer utilitarian reward programs and vice versa.

• Asia Marketing Journal
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• v.14 no.2
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• pp.65-96
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• 2012

Brand has received much attention from considerable marketing research. When consumers consume product or services, they are exposed to a lot of brand related stimuli. These contain brand personality, brand experience, brand identity, brand communications and so on. A special kind of new crisis occasionally confronting companies' brand management today is the brand related rumor. An important influence on consumers' purchase decision making is the word-of-mouth spread by other consumers and most decisions are influenced by other's recommendations. In light of this influence, firms have reasonable reason to study and understand consumer-to-consumer communication such as brand rumor. The importance of brand rumor to marketers is increasing as the number of internet user and SNS(social network service) site grows. Due to the development of internet technology, people can spread rumors without the limitation of time, space and place. However relatively few studies have been published in marketing journals and little is known about brand rumors in the marketplace. The study of rumor has a long history in all major social science. But very few studies have dealt with the antecedents and consequences of any kind of brand rumor. Rumor has been generally described as a story or statement in general circulation without proper confirmation or certainty as to fact. And it also can be defined as an unconfirmed proposition, passed along from people to people. Rosnow(1991) claimed that rumors were transmitted because people needed to explain ambiguous and uncertain events and talking about them reduced associated anxiety. Especially negative rumors are believed to have the potential to devastate a company's reputation and relations with customers. From the perspective of marketer, negative rumors are considered harmful and extremely difficult to control in general. It is becoming a threat to a company's sustainability and sometimes leads to negative brand image and loss of customers. Thus there is a growing concern that these negative rumors can damage brands' reputations and lead them to financial disaster too. In this study we aimed to distinguish antecedents of brand rumor transmission and investigate the effects of brand rumor characteristics on rumor spread intention. We also found key components in personal acceptance of brand rumor. In contextualist perspective, we tried to unify the traditional psychological and sociological views. In this unified research approach we defined brand rumor's characteristics based on five major variables that had been found to influence the process of rumor spread intention. The five factors of usefulness, source credibility, message credibility, worry, and vividness, encompass multi level elements of brand rumor. We also selected product involvement as a control variable. To perform the empirical research, imaginary Korean 'Kimch' brand and related contamination rumor was created and proposed. Questionnaires were collected from 178 Korean samples. Data were collected from college students who have been experienced the focal product. College students were regarded as good subjects because they have a tendency to express their opinions in detail. PLS(partial least square) method was adopted to analyze the relations between variables in the equation model. The most widely adopted causal modeling method is LISREL. However it is poorly suited to deal with relatively small data samples and can yield not proper solutions in some cases. PLS has been developed to avoid some of these limitations and provide more reliable results. To test the reliability using SPSS 16 s/w, Cronbach alpha was examined and all the values were appropriate showing alpha values between .802 and .953. Subsequently, confirmatory factor analysis was conducted successfully. And structural equation modeling has been used to analyze the research model using smartPLS(ver. 2.0) s/w. Overall, R² of adoption of rumor is .476 and R² of intention of rumor transmission is .218. The overall model showed a satisfactory fit. The empirical results can be summarized as follows. According to the results, the variables of brand rumor characteristic such as source credibility, message credibility, worry, and vividness affect argument strength of rumor. And argument strength of rumor also affects rumor intention. On the other hand, the relationship between perceived usefulness and argument strength of rumor is not significant. The moderating effect of product involvement on the relations between argument strength of rumor and rumor W.O.M intention is not supported neither. Consequently this study suggests some managerial and academic implications. We consider some implications for corporate crisis management planning, PR and brand management. This results show marketers that rumor is a critical factor for managing strong brand assets. Also for researchers, brand rumor should become an important thesis of their interests to understand the relationship between consumer and brand. Recently many brand managers and marketers have focused on the short-term view. They just focused on strengthen the positive brand image. According to this study we suggested that effective brand management requires managing negative brand rumors with a long-term view of marketing decisions.

• Korean Journal of Agricultural Science
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• v.5 no.2
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• pp.93-114
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• 1978

The purpose of the study was to find out possible ways for increasing farm income through the sheep and Korean native goats farming, and to investigate meat productivity, wool productivity; woolskin utility, physiological characteristics and correlation between economical college animal farm of the Chungnam National University and sample farms in the suburbs of Dae jeon City were selected for feeding 20 heads of Corriedale wethers and another 20 heads Korean native kids as research materials for the periods of 5th May-26th November, 1977. The data such as growth rate, carcass, viscera weight, blood picture and plamsa components, hebage intake and economic traits were obtained and analysed. The result of the study are summarized as follows: 1. Meat production and quality 1) After 196days of feeding, the body weight of sheep and Korean native goats was increased by two times of those at the beginning of the trial, i.e. 20kg and 8kg respectively. 2) There was no significance of growth rates of sheep in housing and grazing. 3) The growth rate of Korean native goats were excellent at the mountainous areas of Gong ju-Gun where infectious diseases were not found 4) Accroding to the body measurements of 18-month-old sheep, percentages of hip height, body length, rump length, chest depth, chest width, hip width, chest girth and forearm circumference to the withers height were 103,%, 104%, 33%, 44%, 31%, 23%, 135% and 15% respectively, and those of hip height, body length, chest depth and chest girth of 8-month-old native goats to the withers height were 106%, 109%, 46% and 122,% respecitively. As a result, it was found that the percentage of hip height, body length and chest depth of Korean native goats were higher than those of sheep while that of the chest girth of goats was lower. 5) In the carcass data, 47, $52{\pm}2.27%$ of carcass percentage, $34.61{\pm}1.62%$ of lean meat, $26.07{\pm}2.51%$ of viscera, $9.75{\pm}1.4%$ of bone, and $20.95%{\pm}2.14%$ of woolskin for sheep, and $45.58{\pm}5.63%$ of carcass percentage, $27.62{\p}3.81%$ of meat, $34.86{\pm}4.16%$ of viscera, $11.66{\pm}1.83%$ of bone, $3.63{\pm}1.61%$ of skull and $9.26{\pm}2.41%$ of woolskin for native goats were obtained. 6) The contents of moisture, crude protein, crude fat and crude ash in native goat meat were much similar in both plots of housing and grazing. It was, however, known that the contents of moisture and protein were higher in grazinrg than in housing, while fat content was lower in grazing plots. 7) The weights of visceral organs shown similar tendency for both of sheep and native goats. For the weights of liver, heart, kidney and spleen, significance was not reconized among the treatments. Those of rumen, reticulum, small and large intestine were heavier in grazing than in housing, while the amount of visceral fat was heavier in housing. 2. Wool productivity and woolskin 1) The wool production of sheep for 7 months was $3.88{\pm}1.02kg$, and wool percentage, staple length, straighten length, wool growth per day and number of crimps were $9.27{\pm}1.48%$, 8. $47{\pm}1.00cm$, $10.63{\pm}0.99cm$, $0.40{\pm}0.04cm$ and $2.78{\pm}0.40$ respecitively. 2) The tensile strength and tear strength of woolskin treated by alum tanning were highest on the skin obtained from rump, i.e. $1,351kg/mm^2$ and $2,252kg/mm^2$ respectively, and they are in order of loin and shoulder. 3. Utilization and improvement of pasture. 1) The difference of herbage intake of native goats was not recognized between grazing and tethering, but the intake in the afternoon was s lightly higher than that in the morning. However the hervage intake of sheep was superior in grazing and in the afternoon. 2) The cultivation effect was lower in the native goat plots due to their cultivation abilities, in other words, the establishment rates of pasture by hoof cultivation were 60.25% in the goat plots and 77.35% in the sheep plots. 4. Correlation among economical traits. 1) The correlation between live weight of sheep and daily gain was higher. On the other hand, the correlation between other traits was not significant except that live weight, daily gain and lean meat percentage to the length of thoracic vertebrae. The live weight of native goats and meat production were highly correlated, and high correlation was also found between weights of carcass and meat. However, negative correlation was shown between viscera weight and live weight as well as daily gain. 2) The correlatoin between fleece weight of sheep and other traits such as live weight, daily gain and fleece percentage is very high at the 1% siginficant level, and this means that rapid-growth individuals can produce much fleece. 3) The correlation between the factors such as weights of live body, lean meat and viscera of sheep and body measurements, i. e. chest girth and body length was highest, and weights, of carcass and lean meat was highly correlated to chest width and depth. It will be therefore reasonable that the meat productivity estimates will have to be made on the basis of chest girth and body length. The meat production traits of native goats were highly correlated to the most of body measurement data, and the correlation coefficient between chest girth and weights of live body, carcass, lean meat and bone percentage was very high, i. e. 0.992-0.974 in particular. The correlations of meat production traits to chest depth, forearm circumference, body length were 0.759-0.911, 0.759-0.909 and 0.708-0.872 respectively. Therefore, the meat production of native goats will have to be estimated on the basis of chest data. 5. Blood picture and plasma components. 1) The number of erythrocyte and MCHC of native goats were $12.93{\times}10^6/mm^3$ and 36.14%, and those of sheep were $10.68{\times}10^6/mm^3$ and 36.26 respectively. The values of native goats were significantly higher than those of sheep. 2) The hemoglobin concentration, PVC, MCV and MCR of native goats were 10.92 g/100ml, $23.40{\mu}^3$ and 10.94 pg, and those of sheep were 11.73 g/100ml, 36.25 ml/100ml, $33.97{\mu}^3$ and 30.2 ml/100ml 8.43 pg respectively. The values of native goats were significantly lower those of sheep. 3) The number of leukocytes of native goats was significantly higher than that of sheep, that is, $11.64{\times}10^3/mm^3$ in native goats and $9.32{\times}10^3/mm^3$ in sheep. 4) In differential count of leukocyte, neutrophil was significantly high in native goats while lympocyte in sheep. On the other hand, the basophil, eosinophil and monocyte were not significant between native goats and sheep. 5) The amounts of total protein and glucose in the plasma of native goats were 6.2g/100ml and 53.6mg/100ml, and those of sheep were 5.6g/100ml and 45.7mg/100ml, which means that the values of native goats were significantly higher that those of sheep. The amount of total-lipid of native goats(127.6mg/100ml) was significantly than that of sheep(149.6mg/100ml). 6) The amount of non-protein nitrogen, cholesterol, Ca, P, K, Na and Cl were not different between native goats and sheep. 6. Economic analysis. 1) The gross revenue of a farm which fed native goats and sheep was 4,000won per head and the optimum size for feeding them in a farm as a subsidiary work is 5-10 heads. 2) Since there was no difference between housing and grazing, they can be fed in group for farm's subsidiary work. 3) They can be also fed by youths and house wives in the suburbs of cities, because labour requirement is estimated as only two hours per days for feeding 5 heads of native goats and sheep.