• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1063-1070
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• 2021

The study aims to investigate the role of consumer's social risk perceptions in retailing private label brands. Since private label brands are exclusively available at retail stores, consumers make their purchase decisions regarding them based on the image of that retail outlet. While buying them, risk perceptions are influenced by the retail store's image. The study identifies various retail store dimensions. For this purpose, primary data was collected using a survey questionnaire that was administered to a representative sample of retail store consumers in Riyadh. The data was analyzed and exploratory factor analysis was applied using SPSS 25 version to extract store image dimensions. The results showed six significant dimensions of retail store image namely 'Sales Staff', 'Promotion', 'Store Environment', 'Store Services', 'Product Assortment', and 'Customer Convenience'. Regression Analysis was performed and the effect of these retail store image dimensions was tested on social risk perceptions of consumers. Results indicate that store image dimensions significantly influence consumer's perceived social risk perceptions. However, the relationship is not consistent across all the six identified store image dimensions. The study brings forth several valuable consumer insights and the findings of the study have some very interesting and practical implications for retailers.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.215-220
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• 2020

The purpose of this study is to analyze the structural relationship among expectation value(relational, hedonistic), brand image, brand attitude, continuance viewing intention. The survey subjects to achieve the purpose of this study were selected the 521 YouTube sports contents Viewer in the metropolitan area. Data processing was done with SPSS 23 for frequency analysis, Cronbach's α analysis. Also, AMOS 21 was used for confirmatory factor analysis and structural equation model analysis. The results of the analysis are as follows: First, both relational value and,, hedonistic value had a positive effect on the brand image. Second, both relational value and,, hedonistic value had a positive effect on the brand attitude. Third, both brand image and brand attitudee had a positive effect on the brand attitude.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.1
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• pp.79-87
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• 1999

The most part of the faculty drop a trouble and damage of machine equipment even if whatever cause they break out take place at local and trifling place and the factor dominating their trouble is due to wear debris occurred in the lubricated machine moving surface. This study has been car-ried out to identify morphology of wear debris on the lubricated machine moving system by means of computer image analysis. Namely the wear debris contained in lubricating oil extracted from movable machine equipment will be filtered through membrane filter(void diameter 0.45${\mu}m$) and will be analyzed with its data information such as 50% volume diameter aspect roundness and reflectivity. Morphological characteristic of wear debris is easily distinguished by four shape parameters it is necessary to divide small class of every 100 wear debris in total wear particles in order to distinguish morphological characteristic of wear debris more easily by computer image analysis. We are sure that operation condition diagnosis of the lubricated machine moving surfaces is possible by computer image analysis.

• Fashion & Textile Research Journal
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• v.3 no.3
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• pp.204-210
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• 2001

The purpose of the study is to clarify red image in the modem fashion. 40 kinds of costume samples being visual power in red have been selected from photographs in fashion magazines and divided into tones: pale (Vp, Lgr, L), bright (P, B), vivid (S, B, Dp), dark (Gr, Dl, Dgr, Dk). The study was measured by using Semantic Differential method. The subjects were 50 students majoring in clothing and textile. The data were analyzed by factor analysis, ANOVA, discrimminant analysis, MDS and regression analysis. The results of analysis are as follow; 1. Factor analysis has extracted 5 factors of red image in the fashion. These factor are Attractiveness, Hardness and Softness, Emotion, Attention, Simplicity. 2. There were significant difference in visual evaluation of red tones. 3. The discrimination among 4 red tones was related to attention and weight of red. 4. Evaluative dimensions of red was classified as Soft-Hard, Lively-Decent. 5. The image effect on Preference, Buying needs, Pleasant and Riches was consist of complicated sensibility.

• Korean Management Science Review
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• v.27 no.2
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• pp.97-108
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• 2010

Previous studies have shown that country image affects consumers' valuation of products. Based on a literature review this paper identifies five dimensions (economic, political, relational, people and cultural image) and purifies them with Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The data was gathered by using a structured questionnaire from 252 Korean consumers. Among the five dimensions of country image, this paper derives the three factors of country image for four countries (The United States, Japan, Australia, and China) - economic, relational, and cultural image. Then it examines the impacts of the three dimensions of country image on consumers' purchase intention of two industry types : industrial products vs. agricultural products. The result shows there is no difference between both of the two types in the impacts of country image on purchase intention. This implies that for managing the country image it not necessary to develop a communication strategy which is differentiated by industry types.

• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.173-177
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• 2018

Masks are generated by adding different fonts of learning data characters in pixel unit, and pixel values belonging to each of the masks are divided into 3 groups. Using the directional expansion operators, we expand the text area of the test data character into 4 diagonal directions in order to create the boundary areas to distinguish it from the background area. A mask with a minimum average discordance is selected as the final recognition result by calculating the degree of discordance between the expanded test data and the masks. Image comparison using directional expansion operations more accurately recognizes test data through 4 subdivided recognition processes. It is also possible to expand the ranges of 3 groups of pixel values of masks more evenly such that new fonts can easily be added to the given learning data.

• Korean Journal of Remote Sensing
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• v.7 no.1
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• pp.29-43
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• 1991

The study was performed to extraction the ground water potential area using LANDSAT TM data. The image processing techniques developed for the study are contrast transformation, differential filtering and pseudo stereoscopic image methods. These were examined for lineament extraction, lineament interpretation and the integration of vertor data with LANDSAT data. The differential filtering method is much usefull for lineament extraction, and all direction lineaments are clearly shown on the band 5 image of LANDSAT TM. The pseudo stereoscopic image are made in which color differential method is adopted, the pair images are usefull for the lineament interpretation. The results of the analysis are as follows. 1) there is a close correlation between lineament and cased well in the study area, because 33 wells of the developed 45 cased wells coincide with the lineaments. 2) 21 sites in the study area were selected for pumping test, and as a result 11 sites of them produces over than 200 ton/day.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.1-12
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• 2004

Nowadays, a satellite image is widely utilized in identifying and predicting urban spatial growth. It provides essential informations on horizontal expansion of urbanized areas. However, its usefulness becomes very limited in analyzing density of urban development. On the contrary, zonal data, typically census data, provides various density information such as population, number of houses, floor information within a given zone. The problem of the zonal data in analyzing urban growth is that the size of the zone is too big. The minimum administration unit, Dong, is too big to match the satellite images. This study tries to derive synergy effects by matching the merits of the two information sources-- image data and zonal data. For this purpose, basic statistical unit (census block size) is utilized as a zonal unit. By comparing the image and zonal data of 1985 and 2000 of Daegu metropolitan area, this study concludes that urban growth pattern is better explained when the two types of data are properly used.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.1
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• pp.3-14
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• 2000

The purpose of this study were 1) to segment 20's female apparel market using consumer's fashion image preference in formal wear, and 2) to identify the group differences in seasonal color (hue and tone) and color image (image associate with lightness and chroma) preference as well as in demographic variables. The subjects were 253 females in their late twenties living in Seoul, Korea. The data were collected using self-administred questionnaires and analyzed by factor analysis. Cluster analysis, $\chi$2 -test, one-way ANOVA, and Duncan test. The results of this study were as follows: 1) Four fashion image groups were identified through cluster analysis using consumer's fashion image preference: Elegant image group, Sexy image group, Lively image group, and Romantic image group. 2) There were significant differences among fashion image groups in hue preference for spring clothes, tone preferences for spring and fall clothes. Color images are associated with lightness for spring and summer, and are associated with chroma for spring, summer, and fall. Group differences in demographic variables were found in socio-economic status and average expenditure for formal jacket.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.831-842
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• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.