• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.171-196
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• 2018

With the development of the web, two-way communication and evaluation became possible and marketing paradigms shifted. In order to meet the needs of consumers, web design trends are continuously responding to consumer feedback. As the web becomes more and more important, both academics and businesses are studying consumer emotions and satisfaction on the web. However, some consumer characteristics are not well considered. Demographic characteristics such as age and sex have been studied extensively, but few studies consider psychological characteristics such as regulatory focus (i.e., emotional regulation). In this study, we analyze the effect of web style on consumer emotion. Many studies analyze the relationship between the web and regulatory focus, but most concentrate on the purpose of web use, particularly motivation and information search, rather than on web style and design. The web communicates with users through visual elements. Because the human brain is influenced by all five senses, both design factors and emotional responses are important in the web environment. Therefore, in this study, we examine the relationship between consumer emotion and satisfaction and web style and design. Previous studies have considered the effects of web layout, structure, and color on emotions. In this study, however, we excluded these web components, in contrast to earlier studies, and analyzed the relationship between consumer satisfaction and emotional indexes of web-style only. To perform this analysis, we collected consumer surveys presenting 40 web style themes to 204 consumers. Each consumer evaluated four themes. The emotional adjectives evaluated by consumers were composed of 18 contrast pairs, and the upper emotional indexes were extracted through factor analysis. The emotional indexes were 'softness,' 'modernity,' 'clearness,' and 'jam.' Hypotheses were established based on the assumption that emotional indexes have different effects on consumer satisfaction. After the analysis, hypotheses 1, 2, and 3 were accepted and hypothesis 4 was rejected. While hypothesis 4 was rejected, its effect on consumer satisfaction was negative, not positive. This means that emotional indexes such as 'softness,' 'modernity,' and 'clearness' have a positive effect on consumer satisfaction. In other words, consumers prefer emotions that are soft, emotional, natural, rounded, dynamic, modern, elaborate, unique, bright, pure, and clear. 'Jam' has a negative effect on consumer satisfaction. It means, consumer prefer the emotion which is empty, plain, and simple. Regulatory focus shows differences in motivation and propensity in various domains. It is important to consider organizational behavior and decision making according to the regulatory focus tendency, and it affects not only political, cultural, ethical judgments and behavior but also broad psychological problems. Regulatory focus also differs from emotional response. Promotion focus responds more strongly to positive emotional responses. On the other hand, prevention focus has a strong response to negative emotions. Web style is a type of service, and consumer satisfaction is affected not only by cognitive evaluation but also by emotion. This emotional response depends on whether the consumer will benefit or harm himself. Therefore, it is necessary to confirm the difference of the consumer's emotional response according to the regulatory focus which is one of the characteristics and viewpoint of the consumers about the web style. After MMR analysis result, hypothesis 5.3 was accepted, and hypothesis 5.4 was rejected. But hypothesis 5.4 supported in the opposite direction to the hypothesis. After validation, we confirmed the mechanism of emotional response according to the tendency of regulatory focus. Using the results, we developed the structure of web-style recommendation system and recommend methods through regulatory focus. We classified the regulatory focus group in to three categories that promotion, grey, prevention. Then, we suggest web-style recommend method along the group. If we further develop this study, we expect that the existing regulatory focus theory can be extended not only to the motivational part but also to the emotional behavioral response according to the regulatory focus tendency. Moreover, we believe that it is possible to recommend web-style according to regulatory focus and emotional desire which consumers most prefer.

• Korean Journal of Heritage: History & Science
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• v.45 no.3
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• pp.60-79
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• 2012

Environment of ancient Chinese Buddhist temple can be classified to three types such as regional society(鄕村), famous mountain(名山), and urban areas(都市). This made differences in environment where a temple existed and in turn, affected development of Buddhism. And this made another type in relationship between Buddhist temple and a society. This study explains influences which regional society gave on not only Buddhist temple and a monk but also existence and development of Buddhism. When temples are placed in different environmental position, that is, urban areas and regional society, among a social structure, they eventually should adapt to a different society externally and internally. As told in above, ancient Chinese Buddhist temple was located in regional society, famous mountain, and urban areas. Since Eastern Jin and Sixteen Kingdoms, as number of temple much increased, and temples and monks were concentrated on famous mountain, temples in famous mountains and urban areas had developed showing similar aspects each other. But because temples in regional society were influenced a little differently, this study focused on the point. There are four kinds of influences between temples and monks in regional areas. Monks in regional areas had a comparatively close relationship with a society because they came from same area or surrounding areas. Therefore,powers of regional areas restrict influences made by monk group in temple. Second, temples in regional areas shared their joys and sorrows depending on regional economy. Temples in regional areas became a public place for the society and often a market place. In fact, construction and existence of a temple originally became a driving force in regional economy. This is because construction of temple needs artisans and materials and some temples had visitors and included market economy like consumption of incense and candles, though the economic size was large or small. And when regional areas experienced natural disaster or man-made disaster or had poor harvest or economy was in depression, monks left temples and then, temples themselves could not exist. Third, the relationship between temples in regional areas and Buddhists was distinguished from the temples in urban areas and famous mountains. This is because temples in China were places where monks practiced and at the same time, places where general Buddhists worshipped. So there were always a number of Buddhists around the temples. Forth, Buddhism in resional areas was connected to regional Folk beliefs. As a result, Buddhism was spread across the nation, worship with local color often was changed to Buddhist belief or was tinged with Buddhism. While temples in regional areas maintained a close relationship with regional society.they were influenced by the region or gave influences. As a representative example, temples in regional areas showed model behaviors instead of roles of facilities related to various cultures with comparatively advanced level - for example, school, hospital etc. The temples highly affected funerary rites in regional areas. Chinese tombs were mainlymade in regional areas. After death,people living in urban areas were buried in hometown or at least, they were buried in suburbs not urban areas. Temples in regional areas generally participated in funerary rites. Above shows that though most of famous Buddhist temples were located in urban areas not in famous mountains,majority of temples were located in vast regional areas. Through mutual interaction between temples and regional society, the temples in the regional areas were related to Chinese people of over 90% and regional areas became the most important foundation for Buddhism in China. Mutual influences between temples in regional areas and the general public in regions were omnidirectional and spreaded to every aspects of social life in small or large degree. Thus Tombs in temple were widely spreaded across regional areas over time and space. This is enough to explain a close relationship between Buddhist temples and rural society in ancient China.

• Korean Journal of Animal Reproduction
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• v.24 no.1
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• pp.1-18
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• 2000

Eighty dairy farms in Palmers ton North area in New Zealand were surveyed on 1) general characteristics (10 Questions), 2) milk yield and feed supplementary (7 questions), 3) reproductive efficiencies (12 questions) and 4) reproductive disorders (12 questions) by mail questions from February to July, 1998. Among those 4 items from 38 dairy farms (47.5%), especially in items 1) and 2), overall dairy farming situation, supplementary feeding and milk yields were surveyed and analyzed for Korean dairy farmers (especially in Cheju island) to have better understanding or higher economical gains. The results were as follows. 1. In dairy experience, 21 (45%) among 38 dairy farms surveyed were answered that farming less than 15 years, 15~19 year, 20~25 years and over 26 years experience were 3 (7.9%), 7 (18.4%), 6 (15.8%) and 5 (13.2%) which generally showed longer experience compare to Korean dairy farming situation. In survey of labour input and business goal of dairy farming, self-managing farms, sharemilkers, unpaid family manpowering farms, manager running farms, farms with hired worker, farms with part time helper and other type was 21 (55.3%), 10 (26.3%), 2 (3.5%), 3 (5.3%), 18 (31.6%), 2 (3.5%), and 1 (1.8%), respectively. 2. Analyzing pasture and tillable land, pasture according to feeding scale (200, 300 and 400 heads) were 56, 90 and 165.3 ha, and tillable lands were 51, 78 and 165 ha which showed some differences among feeding scale. In recording methods in 38 farms replied, 36 (95%) dairy handbook and 23 (70%) dual methods taking farms were higher than that of 10 (26.3%) computer and 15(39.5%) well-recorder methods. 3. Dairy waste processing facilities in environmental field were almost perfect except of metropolitan area, and so no problem was developed in its control so far. Hence, 26 farm (68.4%) of pond system was higher rather than those in 8 (21.2%) of using as organic manure after storing feces of dairy cattle, 1(2.6%) bunker system and 3 (7.9%) other type farms. 4. In milking facilities, 33 farms (86.9%) of Harringbone types were higher than those in 3 (7.9%) of Walkthrough types, 1 (2.6%) of Rotary system and other types. Although the construction facilities was not enough, this system show the world-leveled dairy country to attempted to elevate economic gains using the advantage of climatic condition. 5. In milking day and yearly yield per head, average 275 milking days and 87 drying days were longer than that of 228 average milking days in New Zealand. Annual total milk yield per head and milk solid (ms) was 3,990 kg and approximately 319 kg. Dairy milk solid (ms) per head, milk yield, fat percentage was 1.2 kg, 15.5 kg and average 4.83% which was much higher than in other country, and milk protein was average 3.75%. 6. In coclusion, Palmerstone North has been a center of dairy farming in New Zealand for the last 21 years. Their dairy farming history is 6~9 year longer than ours and the average number of milking cows per farm is 355, which is much greater than that (35) of Korea. They do not have dairy barn, but only milking parlors. Cows are taken care of by family 0.5 persons), are on a planned calving schedule in spring (93%) and milked for 240~280 days a year, avoiding winter. Cows are dried according to milk yield and body condition score. This management system is quite different from that of Korean dairy farms. Cows are not fed concentrates, relying entirely on pasture forages and the average milk yield per cow is 3,500 kg, which is about 1/2 milk yield of Korean dairy farms. They were bred to produce high fat milk with an average of 4.5%. Their milk production cost is the lowest in the world and the country's economy relies heavily on milk production. We Korean farmers may try to increase farming size, decreasing labor and management costs.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.