The aim of this study is to investigate the end of filaments of the different toothbrushes in the market through the stereomicroscope and to evaluate the % of rounded-end filaments considered to be acceptable. 9 brands, total 11 type toothbrushes were tested. 2 toothbrushes of each type which is marked as rounded-end filaments were tested. The toothbrushes which are not marked as rounded-end filaments were excluded. The domestic as well as foreign toothbrushes which are familiar to consumers were tested. 2 tufts of each toothbrushes were cut and examined by stereomicroscope using $40{\times}$ magnification. The procedure was carried out with blind-technique, and the digital photographs were taken. Besides the % of rounded-end filaments, total tufts number, material of the tuft, stiffness, and other special characteristics were recorded. By the classification of Silverstone and Featherstone, rounded-end filaments were examined and counted. The results shows that there are different range of rounded-end filaments according to the toothbrush types(17.7%-91.2%). Atman toothbrush has the most rounded-end filaments(91.2%) among the observed toothbrushes, and the Advantage Plus(Ora1-B) has the next(86.75%). E-Clean #411 has the least(17.70%) and E-Clean #410 of the same brand has also low % rounded-end filaments(20.60%). While G.U.M #409(Butler) has 67.90% rounded-end filaments, G.U.M #471 of the Same brand has comparative low 41.83% rounded-end filaments. 4 types of total 11 have the rounded-end filaments over 80%, however other 4 types have under even 50%. Considering that the correct brushing habit with a toothbrush which has rounded-end filaments can protect the gingival injury and tooth abrasion, it is thought that we dentists need to give the correct information about toothbrush to the patients
This research was designed to evaluate the nutritional an microbiol quality assessment of Chungmukimbab purchased from market in Tongyeoung area. Contents of calories, calcium, iron, thiamin and riboflavin in ordinary kimbab and Chungmukimbab were lower than the recommended levels of Korean adult men. So, We suggested that a fruit, beverage and ffod stuff were supplemented to maintain nutritional balance. Total aerobic bacteria and coliform group of just prepared ordinary kimbab and Chungmukimbab samples from market were not significantly different, showing approximately $5.50{\pm}0.38 log_{10} CFU/g,\;2.10{\pm}0.47log_{10}MPN/100g$ in ordinary kimbab, $5.61{\pm}0.42log_{10}CFU/g,\;1.75{\pm}0.34 log_{10} MPN/100g$ in Chungmukimbab, respectively. Total aerobic bacteria of law ingredients of chungmukimbab sample were 3 to $4 log_{10}CFU/g$ in kimbab, seasoning squid and radish roots kimchi, 4 to $5 log_{10}CFU/g$ in boiled fish paste. The coliform groups were 1 to $2 log_{10}$ MPN/100 g in kimbab, seasoning squid and radish roots kimchi, 2 to $3 log_{10}$ MPN/100g in boiled fish paste. Detection rate of E. coli and Staphylococcus aureus counts were 10.0, 12.5% in Chungmu-kimbab, 15.0, 10.0% in seasoning squid, 0, 10.0% in radish roots kimchi respectively, not detected in boiled fish paste samples. During storage at $15^{\circ}C$ for 24 hours, total aerobic counts and coliform groups in ordinary kimbab and Chungmukimbab were increased by the 1.94, $0.97log_{10}CFU/g$, 0.60, 0.72 log10 MPN/100g respectively. Total aerobic counts of Chungmukimbab ingredients increased $0.83{\sim}l.33 log_{10}CFU/g$ at different time
While e-commerce market(B2C) grows rapidly, many experts argue that EC(B2C) transactions have not reached its full potential. A notable difference between online and offline consumer markets that is suppressing the growth of EC(B2C) is the decreased presence of human and social elements in the online shopping environments. Generally online shopping lacks human warmth and sociability. In this study, social presence in online shopping mall was proposed as a substitute for face-to-face social interaction in the traditional commerce and author explored what variables affect social presence(human warmth and sociability) on online shopping malls and how human warmth and sociability can influence on online store loyalty. To achieve research objectives, we reviewed literatures related with marketing, psychology and communication research areas. Based on literature review, we proposed a research model on the online shopping mall. To examine the proposed research model, we gathered data by using a self-report questionnaire. Respondents consists of online shoppers with at least five or more times of purchase experience in online shopping malls. Because social presence is a feeling which needs frequent contacts with malls to experience, respondents must have enough purchase experiences. The empirical results are as follows : First, shopping mall's customization efforts influence perceived social presence on the mall significantly. Second, shopping mall's responsiveness influences perceived social presence significantly. Third, perceived activity of community of online shopping mall influences perceived social presence significantly. Mall managers have to activate their customer community to reinforce social presence, resulting in trust building. Finally, perceived social presence influences trust and enjoyment on the mall significantly. And then trust and enjoyment on the mall affect store loyalty significantly. From these findings it can be inferred that perceived social presence appears determinant which is critical to the formation of core variables(trust and loyalty) in existing online shopping papers.
The purpose of this study was to find out the areas and their constitute elements of new apprenticeship through the expert of vocational education to improve the growth potential in the field of industry. Through the three times Delphi research process final composing areas and elements(total 6 areas and 41 sub-elements) of new apprenticeship were extracted. Followings are specific study results of 41 sub-elements for the 6 areas. In area A(Technology Skill aspect) total nine sub-elements were deducted as follows. Technology skill's field appling ability, new technology skill's acquisition, quality assurance ability, research development ability, material management using ability, problem solving ability, core technology skill understanding ability, idea's imagery expressing ability, creative design ability. In area B(Institutional aspect) total five sub-elements were deducted as follows. Flexible human material support, precise division of works, objective result assessment, institutionalization of responsibilities and liabilities between teacher and student, institutionalization of duty invention reward. In area C(Affective aspect) total eight sub-elements were deducted as follows. Manners and cooperation between teacher & student and peer, values for job, basic attitude for technology, job ethic sense, respect of other organization, active action to organization change, attitude of technology successor, service mind. In area D(Self-improvement aspect) total nine sub-elements were deducted as follows. Self evaluation and reflection, cultivate of organization understanding, career planning and developing ability, sound philosophy of life, communication ability, decision making ability, prepare of individual competence enhance system, self-control ability improvement, reaction of unexpected situation. In area E(Knowledge aspect) total four sub-elements were deducted as follows. Basic knowledge of relevant area, knowledge of new technology & preceding technology, fusion and relocation of knowledge, practical knowledge. In area F(Environmental aspect) total six sub-elements were deducted as follows. Awareness of business environment, understanding of education and practice environment, understanding of apprenticeship's business demand, connectivity of region community, adapt ability of labor market's change, awareness of society environment change.
Green or an environmental consciousness has been a major issue for businesses and government offices, as well as consumers, worldwide. In response to this movement, the Korean government announced, in the early 2000s, the era of "Green Growth" as a way to encourage green-related business activities. The Korean fashion industry, in various levels of involvement, presents diverse eco-friendly products as a part of the green movement. These apparel products include organic products and recycled clothing. For these companies to be successful, they need information about who are the consumers who consider green issues (e.g., environmental sustainability) as part of their personal values when making a decision for product purchase, use, and disposal. These consumers can be considered as eco-sumers. Previous studies have examined consumers' purchase intention for or with eco-friendly products. In addition, studies have examined influential factors used to identify the eco-sumers or green consumers. However, limited attention was paid to eco-sumers' disposal or recycling behavior of clothes in comparison with their green product purchases. Clothing disposal behaviors are ways that consumer can get rid of unused clothing and in clue temporarily lending the item or permanently eliminating the item by "handing down" (e.g., giving it to a younger sibling), donating, exchanging, selling, or simply throwing it away. Accordingly, examining purchasing behaviors of eco-friendly fashion items in conjunction with clothing disposal behaviors should improve understanding of a consumer's clothing consumption behavior from the environmental perspective. The purpose of this exploratory study is to provide descriptive information about Korean eco-sumers who have ecologically-favorable lifestyles and behaviors when buying and disposing of clothes. The objectives of this study are to (a) categorize Koreans on the basis of clothing disposal behaviors; (b) investigate the differences in demographics, lifestyles, and clothing consumption values among segments; and (c) compare the purchase intention of eco-friendly fashion items and influential factors among segments. A self-administered questionnaire was developed based on previous studies. The questionnaire included 10 items of clothing disposal behavior, 22 items of LOHAS (Lifestyles of Health and Sustainability) characteristics, and 19 items of consumption values, measured by five-point Likert-type scales. In addition, the purchase intention of two eco-friendly fashion items and 11 attributes of each item were measured by seven-point Likert type scales. Two polyester fleece pullovers, made from fabric created from recycled bottles with the PET identification code, were selected from one Korean brand and one US imported brand among outdoor sportswear brands. A brief description of each product with a color picture was provided in the survey. Demographic variables (i.e., gender, age, marital status, education level, income, occupation) were also included. The data were collected through a professional web survey agency during May 2009. A total of 600 final usable questionnaires were analyzed. The age of respondents ranged from 20 to 49 years old with a mean age of 34 years. Fifty percent of the respondents were males and about 58% were married, and 62% reported having earned university degrees. Principal components factor analysis with varimax rotation was used to identify the underlying dimensions of the clothing disposal behavior scale, and three factors were generated (i.e., reselling behavior, donating behavior, non-recycling behavior). To categorize the respondents on the basis of clothing disposal behaviors, k-mean cluster analysis was used, and three segments were obtained. These consumer segments were labeled as 'Resale Group', 'Donation Group', and 'Non-Recycling Group.' The classification results indicated approximately 98 percent of the original cases were correctly classified. With respect to demographic characteristics among the three segments, significant differences were found in gender, marital status, occupation, and age. LOHAS characteristics were reduced into the following five factors: self-satisfaction, family orientation, health concern, environmental concern, and voluntary service. Significant differences were found in the LOHAS factors among the three clusters. Resale Group and Donation Group showed a similar predisposition to LOHAS issues while the Non-Recycling Group presented the lowest mean scores on the LOHAS factors compared to the other segments. The Resale and Donation Groups described themselves as enjoying or being satisfied with their lives and spending spare-time with family. In addition, these two groups cared about health and organic foods, and tried to conserve energy and resources. Principal components factor analysis generated clothing consumption values into the following three factors: personal values, social value, and practical value. The ANOVA test with the factors showed differences primarily between the Resale Group and the other two groups. The Resale Group was more concerned about personal value and social value than the other segments. In contrast, the Non-Recycling Group presented the higher level of social value than did Donation Group. In a comparison of the intention to purchase eco-friendly products, the Resale Group showed the highest mean score on intent to purchase Product A. On the other hand, the Donation Group presented the highest intention to purchase for Product B among segments. In addition, the mean scores indicated that the Korean product (Product B) was more preferable for purchase than the U.S. product (Product A). Stepwise regression analysis was used to identify the influence of product attributes on the purchase intention of eco product. With respect to Product A, design, price and contribution to environmental preservation were significant to predict purchase intention for the Resale Group, while price and compatibility with my image factors were significant for the Donation Group. For the Non-Recycling Group, design, price compatibility with the factors of my image, participation to eco campaign, and contribution to environmental preservation were significant. Price appropriateness was significant for each of the three clusters. With respect to Product B, design, price and compatibility with my image factors were important, but different attributes were associated significantly with purchase intention for each of the three groups. The influence of LOHAS characteristics and clothing consumption values on intention to purchase Products A and B were also examined. The LOHAS factor of health concern and the personal value factor were significant in the relationships with the purchase intention; however, the explanatory powers were low in the three segments. Findings showed that each group as classified by clothing disposal behaviors showed differences in the attributes of a product, personal values, and the LOHAS characteristics that influenced their purchase intention of eco-friendly products. Findings would enable organizations to understand eco-friendly behavior and to design appropriate strategic decisions to appeal eco-sumers.
In this study, the conditions of dry heat treatment (21 days at 65℃, 16 days at 70℃, 10 days at 75℃, and 7 days at 80℃) were investigated to inactivate Bacillus cereus ATCC 12480, Listeria monocytogenes ATCC SSA81, Staphylococcus aureus ATCC 6538, Escherichia coli O157:H7 ATCC 43894, and Salmonella Typhimurium ATCC 14028 on alfalfa seeds, without affecting the rate of germination of seeds. Alfalfa seeds were inoculated at levels of 6-7 log CFU/g and treated with dry heat at 65℃, 70℃, 75℃, and 80℃; thereafter, the rate of seed germination was determined. The rate of germination was set at 70%, according to the market standards. The bacteria were inactivated when B. cereus was treated with dry heat for 21 days at 65℃, 18 days at 70℃, 14 days at 75℃, and 4 days at 80℃; L. monocytogenes was treated for 21 days at 65℃, 18 days at 70℃, 12 days at 75℃, and 7 days at 80℃; S. aureus was treated for 18 days at 65℃, 18 days at 70℃, 11 days at 75℃, and 4 days at 80℃; E. coli O157:H7 was treated for 21 days at 65℃, 18 days at 70℃, 12 days at 75℃, and 6 days at 80℃; and Sal. Typhimurium was treated for 24 days at 65℃, 22 days at 70℃, 14 days at 75℃, and 7 days at 80℃. For all bacteria, the D-value (R2 = 0.5656-0.7957) significantly decreased when the temperature increased from 65℃ to 80℃ (P<0.05). Since dry heat treatment of alfalfa seeds at 80℃ for 7 days affects their germination rate, dry heat treatment at 75℃ for 14 days is the most effective way to ensure their safety. This study suggests a potential method of bacterial inactivation using dry heat treatment to increase the microbiological safety of sprouts.
Lee, Jung-Soo;Choi, JeeWon;Kim, Jin Se;Park, Me Hea;Choi, HyunJinn;Lee, YounSuk;Kim, Dong Eok;Hong, YuunPo;Kim, Ji-Gang
KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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v.23
no.3
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pp.163-171
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2017
Effects of different packaging methods for maintaining the shelf life and postharvest quality of iceberg lettuce (Lactuca sativa L.) were studied after harvesting in summer season. Lettuce heads were packaged in plastic crate with or without different films such as (A) Individual lettuce head sealed packaging with linear low density polyethylene (LLDPE) film; (B) Packaging lettuce head in plastic crate and wrapped with LLDPE film; (C): Individual lettuce head sealed packaging with perforated high density polyethylene (HDPE) film; (D) Packaging lettuce head in plastic crate and wrapped with perforated HDPE; and (E) Packaging lettuce head in plastic crate without any film (control), and stored at $2^{\circ}C$ for 35 days. Several quality parameters such as fresh weight loss, SPAD (soil & plant analyzer development) meter value, respiration rate, moisture content and appearance of lettuce were investigated. The lettuce wrapped with individually-sealed LLDPE film showed the lowest weight loss and the highest SPAD value rendering the best appearance index among the treataments throughout the three-week storage period at $2^{\circ}C$. Extending the freshness of iceberg lettuce during low temperature storage will definitely increase the salability potential in the domestic market even during summer season.
1. In order to investigate on the microflora and enzyme activity of mold wheat 'Nuruk' , the major source of microorganisms for the brewing of Takju (a Korean Sake), two samples of Nuruk, one prepared at the College of Agriculture, Chung Nam University (S) and the other perchased at a market (T), were taken for the study. The molds, aerobic bacteria, lactic acid bacteria, and yeasts were examined and counted. The yeasts were classified by the treatment with TTC (2, 3, 5 triphenyltetrazolium chloride) agar that yields a varied shade of color. The amylase and protease activities of Nuruk were measured. The results were as the followings. a) In the Nuruk S found were: Aspergillus oryzae group, $204{\times}10^5$; Black Aspergilli, $163{\times}10^5$; Rhizogus, $20{\times}10^5$; Penicillia, $134{\times}10^5$; Areobic bacteria, $9{\times}10^6-2{\times}10^7$; Lactic acid bacteria, $3{\times}10^4$ In the Nuruk T found were: Aspergillus oryzae group, $836{\times}10^5$; Black Aspergilli, $286{\times}10^5$; Rhizopus, $623{\times}10^5$; Penicillia, $264{\times}10^5$; Aerobic bacteria, $5{\times}10^6-9{\times}10^6$; Lactic acid bacteria, $3{\times}10^4$ b) Eighty to ninety percent of the aerobic bacteria in Nuruk S appeared to belong to Bacillus subtilis while about 70% of those in Nuruk T seemed to be spherical bacteria. In both Nuruks about 80% of lactic acid bacteria were observed as spherical ones. c) The population of yeasts in 1g. of Nuruk S was about $6{\times}10^5$, 56.5% of which were TTC pink yeasts, 16% of which were TTC red pink yeasts, 8% of which were TTC red yeasts, 19.5% of which were TTC white yeasts. In Nuruk T(1g) the number of yeasts accounted for $14{\times}10^4$ and constituted of 42% TTC pink. 21% TTC red pink 28% TTC red and 9% TTC white. d) The enzyme activity of 1g Nuruk S was: Liquefying type Amylase, $D^{40}/_{30},=256$ W.V. Saccharifying type Amylase, 43.32 A.U. Acid protease, 181 C.F.U. Alkaline protease, 240C.F.U. The enzyme activity of 1g Nuruk T was: Liquefying type Amylase $D^{40}/_{30},=32$ W.V. Saccharifying type amylase $^{30}34.92$ A.U. Acid protease, 138 C.F.U. Alkaline protease 31 C.F.U. 2. During the fermentation of 'Takju' employing the Nuruks S and T the microflora and enzyme activity throughout the brewing were observed in 12 hour intervals. TTC pink and red yeasts considered to be the major yeasts were isolated and cultured. The strains ($1{\times}10^6/ml$) were added to the mashes S and T in which pH was adjusted to 4.2 and the change of microflora was examined during the fermentation. The results were: a) The molds disappeared from each sample plot since 2 to 3 days after mashing while the population of aerobic bacteria was found to be $10{\times}10^7-35{\times}10^7/ml$ inS plots and $8.2{\times}10^7-12{\times}10^7$ in plots. Among them the coccus propagated substantially until some 30 hours elasped in the S and T plots treated with lactic acid but decreased abruptly thereafter. In the plots of SP. SR. TP. and TR the coccus had not appeared from the beginning while the bacillus showed up and down changes in number and diminished by 1/5-1/10 the original at the end stage. b) The lactic acid bacteria observed in the S plot were about $7.4{\times}10^7$ in number per ml of the mash in 24 hours and increased up to around $2{\times}10^8$ until 3-4 days since. After this period the population decreased rapidly and reached about $4{\times}10^5$ at the end, In the plot T the lactic acid becteria found were about $3{\times}10^8$ at the period of 24 fours, about $3{\times}10$ in 3 days and about $2{\times}10^5$ at the end in number. In the plots SP. SR. TP, and TR the lactic acid bacteria observed were as less as $4{\times}10^5$ at the stage of 24 hours and after this period the organisms either remained unchanged in population or ceased to exist. c) The maiority of lactic acid bacteria found in each mash were spherical and the change in number displayed a tendency in accordance with the amount of lactic acid and alcohol produced in the mash. d) The yeasts had showed a marked propagation since the period of 24 hours when the number was about $2{\times}10^8$ ㎖ mash in the plot S. $4{\times}10^8$ in 48 hours and $5-7{\times}10^8$ in the end period were observed. In the plot T the number was $4{\times}10^8$ in 24 hours and thereafter changed up and down maintaining $2-5{\times}10^8$ in the range. e) Over 90% of the yeasts found in the mashes of S and T plots were TTC pink type while both TTC red pink and TTC red types held range of $2{\times}10-3{\times}10^7$ throughout the entire fermentation. f) The population of TTC pink yeasts in the plot SP was as $5{\times}10^8$ much as that is, twice of that of S plot at the period of 24 hours. The predominance in number continued until the middle and later stages but the order of number became about the same at the end. g) Total number of the yeasts observed in the plot SR showed little difference from that of the plot SP. The TTC red yeasts added appeared considerably in the early stage but days after the change in number was about the same as that of the plot S. In the plot TR the population of TTC red yeasts was predominant over the T plot in the early stage which there was no difference between two plots there after. For this reason even in the plot w hers TTC red yeasts were added TTC pink yeasts were predominant. TTC red yeasts observed in the present experiment showed continuing growth until the later stage but the rate was low. h) In the plot TP TTC pink yeasts were found to be about $5{\times}10^8$ in number at the period of 2 days and inclined to decrease thereafter. Compared with the plot T the number of TTC pink yeasts in the plot TP was predominant until the middle stage but became at the later stage. i) The productivity of alcohol in the mash was measured. The plot where TTC pink yeasts were added showed somewhat better yield in the earely stage but at and after the middle stage the difference between the yeast-added and the intact mashes was not recognizable. And the production of alcohol was not proportional to the total number of yeasts present. j) Activity of the liquefying amylase was the highest until 12 hours after mashing, somewhat lowered once after that, and again increased around 36-48 hours after mashing. Then the activity had decreased continuously. Activity of saccharifying amylase also decreased at the period of 24 hours and then increased until 48 hours when it reached the maximum. Since, the activity had gradually decreased until 72 hours and rapidly so did thereafter. k) Activity of alkaline protease during the fermentation of mash showed a tendency to decrease continusously although somewhat irregular. Activity of acid protease increased until hours at the maximum, then decreased rapidly, and again increased, the vigor of acid protease showed better shape than that of alkaline protease throughout. 3. TTC pink yeasts that were predominant in number, two strains of TTC red pink yeasts that appeared throughout the brewing, and TTC red yeasts were identified and the physiological characters examined. The results were as described below. a) TTC pinkyeasts (B-50P) and two strains of TTC red pink yeasts (B-54 RP & B-60 RP) w ere identified as the type of Saccharomyces cerevisiae and TTC pink red yeasts CB-53 R) were as the type of Hansenula subpelliculosa. b) The fermentability of four strains above mentioned were measured as follows. Two strains of TTC red pink yeasts were the highest, TTC pink yeasts were the lowest in the fermantability. The former three strains were active in the early stage of fermentation and found to be suitable for manufacturing 'Takju' TTC red yeasts were found to play an important role in Takju brewing due to its strong ability to produce esters although its fermentability was low. c) The tolerance against nitrous acid of strains of yeast was marked. That against lactic acid was only 3% in Koji extract, and TTC red yeasts showed somewhat stronger resistance. The tolerance against alcohol of TTC pink and red pink yeasts in the Hayduck solution was 7% while that in the malt extract was 13%. However, that of TTC red yeasts was much weaker than others. Liguefying activity of gelatin by those four strains of yeast was not recognized even in 40 days. 4. Fermentability during Takju brewing was shown in the first two days as much as 70-80% of total fermentation and around 90% of fermentation proceeded in 3-4 days. The main fermentation appeared to be completed during :his period. Productivity of alcohol during Takju brewing was found to be apporximately 65% of the total amount of starch put in mashing. 5. The reason that Saccharomyces coreanuss found be Saito in the mash of Takju was not detected in the present experiment is considered due to the facts that Aspergillus oryzae has been inoculated in the mold wheat (Nuruk) since around 1930 and also that Koji has been used in Takju brewing, consequently causing they complete change in microflora in the Takju brewing. This consideration will be supported by the fact that the original flavor and taste have now been remarkably changed.
Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.
1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is
. 3.2 Hypotheses 3.2.1 The Hypothesis of On-line Service Quality and Overall Service Quality The relation between on-line service quality and overall service quality I-1. Efficiency of on-line service quality may have a significant effect on overall service quality. I-2. System availability of on-line service quality may have a significant effect on overall service quality. I-3. Fulfillment of on-line service quality may have a significant effect on overall service quality. I-4. Privacy of on-line service quality may have a significant effect on overall service quality. 3.2.2 The Hypothesis of On-line Service Recovery and Overall Service Quality The relation between on-line service recovery and overall service quality II-1. Responsiveness of on-line service recovery may have a significant effect on overall service quality. II-2. Compensation of on-line service recovery may have a significant effect on overall service quality. II-3. Contact of on-line service recovery may have a significant effect on overall service quality. 3.2.3 The Hypothesis of Overall Service Quality and Customer Satisfaction The relation between overall service quality and customer satisfaction III-1. Overall service quality may have a significant effect on customer satisfaction. 3.2.4 The Hypothesis of Customer Satisfaction and Loyalty Intention The relation between customer satisfaction and loyalty intention IV-1. Customer satisfaction may have a significant effect on loyalty intention. 3.2.5 The Hypothesis of a Mediation Variable Wolfinbarger & Gilly(2003) and Parasuraman, Zeithaml & Malhotra(2005) had made clear that each dimension of service quality has a significant effect on overall service quality. Add to this, the authors analyzed empirically that each dimension of on-line service quality has a positive effect on customer satisfaction. With that viewpoint, this study would examine if overall service quality mediates between on-line service quality and each dimension of customer satisfaction, keeping on looking into the relation between on-line service quality and overall service quality, overall service quality and customer satisfaction. And as this study understands that each dimension of on-line service recovery also has an effect on overall service quality, this would examine if overall service quality also mediates between on-line service recovery and each dimension of customer satisfaction. Therefore these hypotheses followed are set up to examine if overall service quality plays its role as the mediation variable. The relation between on-line service quality and customer satisfaction V-1. Overall service quality may mediate the effects of efficiency of on-line service quality on customer satisfaction. V-2. Overall service quality may mediate the effects of system availability of on-line service quality on customer satisfaction. V-3. Overall service quality may mediate the effects of fulfillment of on-line service quality on customer satisfaction. V-4. Overall service quality may mediate the effects of privacy of on-line service quality on customer satisfaction. The relation between on-line service recovery and customer satisfaction VI-1. Overall service quality may mediate the effects of responsiveness of on-line service recovery on customer satisfaction. VI-2. Overall service quality may mediate the effects of compensation of on-line service recovery on customer satisfaction. VI-3. Overall service quality may mediate the effects of contact of on-line service recovery on customer satisfaction. 4. Empirical Analysis 4.1 Research design and the characters of data This empirical study aimed at customers who ever purchased air ticket at the Web sites for reservation and issue. Total 430 questionnaires were distributed, and 400 were collected. After surveying with the final questionnaire, the frequency test was performed about variables of sex, age which is demographic factors for analyzing general characters of sample data. Sex of data is consist of 146 of male(42.7%) and 196 of female(57.3%), so portion of female is a little higher. Age is composed of 11 of 10s(3.2%), 199 of 20s(58.2%), 105 of 30s(30.7%), 22 of 40s(6.4%), 5 of 50s(1.5%). The reason that portions of 20s and 30s are higher can be supposed that they use the Internet frequently and purchase air ticket directly. 4.2 Assessment of measuring scales This study used the internal consistency analysis to measure reliability, and then used the Cronbach'$\alpha$ to assess this. As a result of reliability test, Cronbach'$\alpha$ value of every component shows more than 0.6, it is found that reliance of the measured variables are ensured. After reliability test, the explorative factor analysis was performed. the factor sampling was performed by the Principal Component Analysis(PCA), the factor rotation was performed by the Varimax which is good for verifying mutual independence between factors. By the result of the initial factor analysis, items blocking construct validity were removed, and the result of the final factor analysis performed for verifying construct validity is followed above. 4.3 Hypothesis Testing 4.3.1 Hypothesis Testing by the Regression Analysis(SPSS) 4.3.2 Analysis of Mediation Effect To verify mediation effect of overall service quality of and , this study used the phased analysis method proposed by Baron & Kenny(1986) generally used. As
shows, Step 1 and Step 2 are significant, and mediation variable has a significant effect on dependent variables and so does independent variables at Step 3, too. And there needs to prove the partial mediation effect, independent variable's estimate ability at Step 3(Standardized coefficient $\beta$eta : efficiency=.164, system availability=.074, fulfillment=.108, privacy=.107) is smaller than its estimate ability at Step 2(Standardized coefficient $\beta$eta : efficiency=.409, system availability=.227, fulfillment=.386, privacy=.237), so it was proved that overall service quality played a role as the partial mediation between on-line service quality and satisfaction. As
shows, Step 1 and Step 2 are significant, and mediation variable has a significant effect on dependent variables and so does independent variables at Step 3, too. And there needs to prove the partial mediation effect, independent variable's estimate ability at Step 3(Standardized coefficient $\beta$eta : responsiveness=.164, compensation=.117, contact=.113) is smaller than its estimate ability at Step 2(Standardized coefficient $\beta$eta : responsiveness=.409, compensation=.386, contact=.237), so it was proved that overall service quality played a role as the partial mediation between on-line service recovery and satisfaction. Verified results on the basis of empirical analysis are followed. First, as the result of , it shows that all were chosen, so on-line service quality has a positive effect on overall service quality. Especially fulfillment of overall service quality has the most effect, and then efficiency, system availability, privacy in order. Second, as the result of , it shows that all were chosen, so on-line service recovery has a positive effect on overall service quality. Especially responsiveness of overall service quality has the most effect, and then contact, compensation in order. Third, as the result of and , it shows that and all were chosen, so overall service quality has a positive effect on customer satisfaction, customer satisfaction has a positive effect on loyalty intention. Fourth, as the result of and , it shows that and all were chosen, so overall service quality plays a role as the partial mediation between on-line service quality and customer satisfaction, on-line service recovery and customer satisfaction. 5. Conclusion This study measured and analyzed service quality and service recovery of the Web sites that customers made a reservation and issued their air tickets, and by improving customer satisfaction through the result, this study put its final goal to grope how to keep loyalty customers. On the basis of the result of empirical analysis, suggestion points of this study are followed. First, this study regarded E-S-QUAL that measures on-line service quality and E-RecS-QUAL that measures on-line service recovery as variables, so it overcame the limit of existing studies that used modified SERVQUAL to measure service quality of the Web sites. Second, it shows that fulfillment and efficiency of on-line service quality have the most significant effect on overall service quality. Therefore the Web sites of reserving and issuing air tickets should try harder to elevate efficiency and fulfillment. Third, privacy of on-line service quality has the least significant effect on overall service quality, but this may be caused by un-assurance of customers whether the Web sites protect safely their confidential information or not. So they need to notify customers of this fact clearly. Fourth, there are many cases that customers don't recognize the importance of on-line service recovery, but if they would think that On-line service recovery has an effect on customer satisfaction and loyalty intention, as its importance is very significant they should prepare for that. Fifth, because overall service quality has a positive effect on customer satisfaction and loyalty intention, they should try harder to elevate service quality and service recovery of the Web sites of reserving and issuing air tickets to maximize customer satisfaction and to secure loyalty customers. Sixth, it is found that overall service quality plays a role as the partial mediation, but now there are rarely existing studies about this, so there need to be more studies about this.
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