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A Case Study on High and Low Performance Areas for Family Planning (가족계획 우수.부진지역 사례연구)

  • 홍성열;김태일
    • Korea journal of population studies
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    • v.4 no.1
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    • pp.105-130
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    • 1981
  • This study was conducted to compare the characteristics of high performane areas for family planning with that of low performance areas and to find factors which strongly affected contraceptive practice behavior. For the study, eight areas were selected from 274 rural family planning canvassing areas of Korean Population Policy and Program Evaluation Study, which was an action study operated in all areas of Cheju Island from July 1, 1976 until December 31,1979. As a first step of the action study, Cheju Island was devided up 318 family planning canvasser areas Each area was consisted of 200 households in rural district and 300 households in urhan one Duriog the period of project, each canvassing area had been managed by a female family planning canvasser, selected by director of health center considering several individual conditions needed for family planning activities Basic activities of canvassers were to counsell all the eligihie couples in own charged area about family planning methods and also to distribute contraceptives such as condoms and oral pills. In case couples desire to accept sterilization including vasectomy and tubal-ligation, the canvassers played a linking role connecting potential client with family planning field workers. Canvassng areas shows significant differentce in performance for family planning, nevertheless they are supposed to have almost the same conditions regarding family planning distribution channel. Because the purpose of the Cheju project was to eliminate all the problems that existed in governmental distribution system, that is to remove geographic, economic, cognitive and administrative barriers Accumulated performances of family planning methods accepted by residents in each area were calculated by eligible women aged 14-49. And then canvassing areas were ranked according to performance score. Consequently, 4 areas in extremely high and low family planning performance areas were selected respectively. Major results were obtained by comparing characteristics of high performance area with that of low performance areas, which are as follows: 1. The mean number of living children was about the same both in high and low performance areas for family planning. But respondents' mean age (38.5) in high performance areas was higher than that (37.0) in low performance areas 2. Respondents' perception in the expectant educational level of others' children in high performance areas was higher than that in low performance areas, although respondents educational level, monthly expenditure and ratio of children in high school and above was not different. 3. Ratio of ownerships of TV and newspaper in high performance areas was highen than that in low performance areas 4. The duration of canvasser' charge in high performance areas was longer than that of low performance areas, showing the fact that canvassers didn't move cut in high performance areas 5. In high performance areas, canvassers' houses were relatively located in the center part of the village. And so villagers resided in near distances from the anvasser's house 6. 4H clubs' activities in high performance areas were more active than those in low performance areas Therefore it was assumed that cohesiveness of community in high performance areas were stronger than that in low areas. 7. Canvassers' family planning practice rate was higher than that in low performance areas, and also canvassers' human relationship was more sociable than that of canvassers in low performance areas. 8. Fourteen variables which showed relatively high significance level in $X^2$ and F test were selected as independent variables for stepwise regression analysis. According to the results of regression analysis. five of 14 variables-distributors education level ($R^2$=.4439), duration of distributor's charge ($R^2$=.6166), 4H club activities ($R^2$=.6697), canvasser's contraceptive practice ($R^2$=.7377) and location of distributions house ($R^2$=.8010) explained 80.1 percent of total variance.

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Personalized Recommendation System for IPTV using Ontology and K-medoids (IPTV환경에서 온톨로지와 k-medoids기법을 이용한 개인화 시스템)

  • Yun, Byeong-Dae;Kim, Jong-Woo;Cho, Yong-Seok;Kang, Sang-Gil
    • Journal of Intelligence and Information Systems
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    • v.16 no.3
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    • pp.147-161
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    • 2010
  • As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

Effectiveness of MDCT for the Followup of CABG Patients with LIMA to LAD and Saphenous Veins to Others (좌내흉동맥과 복재정맥편을 사용한 관상동맥우회로술 환자에서의 추적조사에서 MDCT의 유용성)

  • Kang Joon Kyu;Kim Hyung Tai;Park In Duk;Chung Young Mi;Lee Cheol Joo
    • Journal of Chest Surgery
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    • v.38 no.6 s.251
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    • pp.410-414
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    • 2005
  • There are several options for choosing a graft in CABG, we routinely chose LIMA for LAD and great saphenous vein for other target vessels. To evaluate the posoperative graft patency, we have studied the results using a 16 slices multi-detector computed tomography. Material and Method: From 1995 to 2003, 80 CABG patients who did not complain any event of MACE have been examined by 16-MDCT, mostly in an out patient clinic. Result: There were 61 men and 19 women. MDCT was used as early as 7 days to 9 years post-operatively with a median follow-up period of 6.5 years, and mean follow-up peiod of $31.5\pm25.4$ months. Mean age was $58.4\pm12.6$ years old in men and $61.5\pm17.2$ years old in women. 72180 patients received LIMA to LAD, and all other patients received vein grafts for bypass. The target vessel of vein grafts were 8 in LAD, 47 in RCA, 60 in diagonals, and 61 in obtuse marginals. Among them 42 sequential anastomoses were performed. The mean graft number was $3.1\pm1.8$ grafts. 5 year graft patency rate of each grafts was as followings; $93.1\%$ in LIMA to LAD, $94.9\%$ in vein to diagonals, $92.1\%$ in vein to obtuse marginals, and $79.2\%$ in vein to RCA. Sequential grafting showed better graft patency than the isolated grafting $(95.2\%\;vs\;78.7\~95.0\%)$. Conclusion: In this study, CABG with LIMA and saphenous veins showed satisfactory longterm results. 16-MDCT provided good images for follow-up study after CABG. Additionally, as radiologic tools (64-MDCT, MRI) improve more in the future, they can be used for diagnosing preoperative anatomical coronary disease as well as cardiac functions.

Time-Lapse Crosswell Seismic Study to Evaluate the Underground Cavity Filling (지하공동 충전효과 평가를 위한 시차 공대공 탄성파 토모그래피 연구)

  • Lee, Doo-Sung
    • Geophysics and Geophysical Exploration
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    • v.1 no.1
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    • pp.25-30
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    • 1998
  • Time-lapse crosswell seismic data, recorded before and after the cavity filling, showed that the filling increased the velocity at a known cavity zone in an old mine site in Inchon area. The seismic response depicted on the tomogram and in conjunction with the geologic data from drillings imply that the size of the cavity may be either small or filled by debris. In this study, I attempted to evaluate the filling effect by analyzing velocity measured from the time-lapse tomograms. The data acquired by a downhole airgun and 24-channel hydrophone system revealed that there exists measurable amounts of source statics. I presented a methodology to estimate the source statics. The procedure for this method is: 1) examine the source firing-time for each source, and remove the effect of irregular firing time, and 2) estimate the residual statics caused by inaccurate source positioning. This proposed multi-step inversion may reduce high frequency numerical noise and enhance the resolution at the zone of interest. The multi-step inversion with different starting models successfully shows the subtle velocity changes at the small cavity zone. The inversion procedure is: 1) conduct an inversion using regular sized cells, and generate an image of gross velocity structure by applying a 2-D median filter on the resulting tomogram, and 2) construct the starting velocity model by modifying the final velocity model from the first phase. The model was modified so that the zone of interest consists of small-sized grids. The final velocity model developed from the baseline survey was as a starting velocity model on the monitor inversion. Since we expected a velocity change only in the cavity zone, in the monitor inversion, we can significantly reduce the number of model parameters by fixing the model out-side the cavity zone equal to the baseline model.

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Spatio-temporal Changes in Macrobenthic Community Structure and Benthic Environment at an Intensive Oyster Culturing Ground in Geoje-Hansan Bay, Korea (굴 양식장 밀집해역인 거제한산만의 저서동물군집 구조와 저서환경의 시.공간적 변동)

  • Yoon, Sang-Pil;Jung, Rae-Hong;Kim, Youn-Jung;Hong, Sok-Jin;Oh, Hyun-Taik;Lee, Won-Chan
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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    • v.14 no.4
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    • pp.213-228
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    • 2009
  • This study was conducted to investigate spatio-temporal changes in macrobenthic community structure and benthic environmental conditions in Geoje-Hansa Bay, which is the greatest oyster producing site in Korea. Field survey for benthic environment and macrobenthos was seasonally carried out at 15 stations covering oyster farming sites and non-farming sites from February to November, 2008. The grain size of surface sediments was dominated by very fine silt with the mean phi of about $9\;{\Phi}$ and TOC was 1.9% on average. Mean dissolved oxygen content was 8.1 mg/L and lowest in August corresponding to the 2nd degree in seawater quality criteria. Total species number was 351 and mean density was $3,675\;ind./m^2$, both of which were dominated by polychaete worms. Spatio-temporal variation in above two biological variables was great with higher values seasonally in spring and spatially in channels rather than inner bay. Dominant species were Lumbrineris longifolia (21.3%), Aphelochaeta monilaris (17.8%) and Ericthonius pugnax(6.1%), all of which are typical species of organically enriched area. From the multivariate analyses, the whole macrobenthic community was distinguished into two groups of channel and inner bay group. Spatio-temporal changes of macrobenthic community in Geoje-Hansan Bay were related to those of TOC and acid volatile sulfide (AVS). Our results showed that Geoje-Hansan Bay should be intermediately affected by organic pollution, and that such organic enrichment was more remarkable at farming stations in the inner bay.

An Analysis of the Comparative Importance of Systematic Attributes for Developing an Intelligent Online News Recommendation System: Focusing on the PWYW Payment Model (지능형 온라인 뉴스 추천시스템 개발을 위한 체계적 속성간 상대적 중요성 분석: PWYW 지불모델을 중심으로)

  • Lee, Hyoung-Joo;Chung, Nuree;Yang, Sung-Byung
    • Journal of Intelligence and Information Systems
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    • v.24 no.1
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    • pp.75-100
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    • 2018
  • Mobile devices have become an important channel for news content usage in our daily life. However, online news content readers' resistance to online news monetization is more serious than other digital content businesses, such as webtoons, music sources, videos, and games. Since major portal sites distribute online news content free of charge to increase their traffics, customers have been accustomed to free news content; hence this makes online news providers more difficult to switch their policies on business models (i.e., monetization policy). As a result, most online news providers are highly dependent on the advertising business model, which can lead to increasing number of false, exaggerated, or sensational advertisements inside the news website to maximize their advertising revenue. To reduce this advertising dependencies, many online news providers had attempted to switch their 'free' readers to 'paid' users, but most of them failed. However, recently, some online news media have been successfully applying the Pay-What-You-Want (PWYW) payment model, which allows readers to voluntarily pay fees for their favorite news content. These successful cases shed some lights to the managers of online news content provider regarding that the PWYW model can serve as an alternative business model. In this study, therefore, we collected 379 online news articles from Ohmynews.com that has been successfully employing the PWYW model, and analyzed the comparative importance of systematic attributes of online news content on readers' voluntary payment. More specifically, we derived the six systematic attributes (i.e., Type of Article Title, Image Stimulation, Article Readability, Article Type, Dominant Emotion, and Article-Image Similarity) and three or four levels within each attribute based on previous studies. Then, we conducted content analysis to measure five attributes except Article Readability attribute, measured by Flesch readability score. Before conducting main content analysis, the face reliabilities of chosen attributes were measured by three doctoral level researchers with 37 sample articles, and inter-coder reliabilities of the three coders were verified. Then, the main content analysis was conducted for two months from March 2017 with 379 online news articles. All 379 articles were reviewed by the same three coders, and 65 articles that showed inconsistency among coders were excluded before employing conjoint analysis. Finally, we examined the comparative importance of those six systematic attributes (Study 1), and levels within each of the six attributes (Study 2) through conjoint analysis with 314 online news articles. From the results of conjoint analysis, we found that Article Readability, Article-Image Similarity, and Type of Article Title are the most significant factors affecting online news readers' voluntary payment. First, it can be interpreted that if the level of readability of an online news article is in line with the readers' level of readership, the readers will voluntarily pay more. Second, the similarity between the content of the article and the image within it enables the readers to increase the information acceptance and to transmit the message of the article more effectively. Third, readers expect that the article title would reveal the content of the article, and the expectation influences the understanding and satisfaction of the article. Therefore, it is necessary to write an article with an appropriate readability level, and use images and title well matched with the content to make readers voluntarily pay more. We also examined the comparative importance of levels within each attribute in more details. Based on findings of two studies, two major and nine minor propositions are suggested for future empirical research. This study has academic implications in that it is one of the first studies applying both content analysis and conjoint analysis together to examine readers' voluntary payment behavior, rather than their intention to pay. In addition, online news content creators, providers, and managers could find some practical insights from this research in terms of how they should produce news content to make readers voluntarily pay more for their online news content.

A Time Series Graph based Convolutional Neural Network Model for Effective Input Variable Pattern Learning : Application to the Prediction of Stock Market (효과적인 입력변수 패턴 학습을 위한 시계열 그래프 기반 합성곱 신경망 모형: 주식시장 예측에의 응용)

  • Lee, Mo-Se;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.24 no.1
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    • pp.167-181
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    • 2018
  • 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.

Intelligent VOC Analyzing System Using Opinion Mining (오피니언 마이닝을 이용한 지능형 VOC 분석시스템)

  • Kim, Yoosin;Jeong, Seung Ryul
    • Journal of Intelligence and Information Systems
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    • v.19 no.3
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    • pp.113-125
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    • 2013
  • Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

SKU recommender system for retail stores that carry identical brands using collaborative filtering and hybrid filtering (협업 필터링 및 하이브리드 필터링을 이용한 동종 브랜드 판매 매장간(間) 취급 SKU 추천 시스템)

  • Joe, Denis Yongmin;Nam, Kihwan
    • Journal of Intelligence and Information Systems
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    • v.23 no.4
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    • pp.77-110
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    • 2017
  • Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

An Exploratory Study on the Components of Visual Merchandising of Internet Shopping Mall (인터넷쇼핑몰의 VMD 구성요인에 대한 탐색적 연구)

  • Kim, Kwang-Seok;Shin, Jong-Kuk;Koo, Dong-Mo
    • Journal of Global Scholars of Marketing Science
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    • v.18 no.2
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    • pp.19-45
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    • 2008
  • This study is to empirically examine the primary dimensions of visual merchandising (VMD) of internet shopping mall, namely store design, merchandise, and merchandising cues, to be a attractive virtual store to the shoppers. The authors reviewed the literature related to the major components of VMD from the perspective of the AIDA model, which has been mainly applied to the offline store settings. The major purposes of the study are as follows; first, tries to derive the variables related with the components of visual merchandising through reviewing the existing literatures, establish the hypotheses, and test it empirically. Second, examines the relationships between the components of VMD and the attitude toward the VMD, however, putting more emphasis on finding out the component structure of the VMD. VMD needs to be examined with the perspective that an online shopping mall is a virtual self-service or clerkless store, which could reduce the number of employees, help the shoppers search, evaluate and purchase for themselves, and to be explored in terms of the in-store persuasion processes of customers. This study reviewed the literatures related to store design, merchandise, and merchandising cues which might be relevant to the store, product, and promotion respectively. VMD is a total communication tool, and AIDA model could explain the in-store consumer behavior of online shopping. Store design has to do with triggering a consumer attention to the online mall, merchandise with a product related interest, and merchandising cues with promotions such as recommendation and links that induce the desire to pruchase. These three steps might be seen as the processes for purchase actions. The theoretical rationale for the relationship between VMD and AIDA could be found in Tyagi(2005) that the three steps of consumer-oriented merchandising are a store, a product assortment, and placement, in Omar(1999) that three types of interior display are a architectural design display, commodity display, and point-of-sales(POS) display, and in Davies and Ward(2005) that the retail store interior image is related to an atmosphere, merchandise, and in-store promotion. Lee et al(2000) suggested as the web merchandising components a merchandising cues, a shopping metaphor which is an assistant tool for search, a store design, a layout(web design), and a product assortment. The store design which includes differentiation, simplicity and navigation is supposed to be related to the attention to the virtual store. Second, the merchandise dimensions comprising product assortments, visual information and product reputation have to do with the interest in the product offerings. Finally, the merchandising cues that refer to merchandiser(MD)'s recommendation of products and providing the hyperlinks to relevant goods for the shopper is concerned with attempt to induce the desire to purchase. The questionnaire survey was carried out to collect the data about the consumers who would shop at internet shopping malls frequently. To select the subject malls, the mall ranking data announced by a mall rating agency was used to differentiate the most popular and least popular five mall each. The subjects was instructed to answer the questions after navigating the designated mall for five minutes. The 300 questionnaire was distributed to the consumers, 166 samples were used in the final analysis. The empirical testing focused on identifying and confirming the dimensionality of VMD and its subdimensions using a structural equation modeling method. The confirmatory factor analysis for the endogeneous and exogeneous variables was carried out in four parts. The second-order factor analysis was done for a store design, a merchandise, and a merchandising cues, and first-order confirmatory factor analysis for the attitude toward the VMD. The model test results shows that the chi-square value of structural equation is 144.39(d.f 49), significant at 0.01 level which means the proposed model was rejected. But, judging from the ratio of chi-square value vs. degree of freedom, the ratio was 2.94 which smaller than an acceptable level of 3.0, RMR is 0.087 which is higher than a generally acceptable level of 0.08. GFI and AGFI is turned out to be 0.90 and 0.84 respectively. Both NFI and NNFI is 0.94, and CFI 0.95. The major test results are as follows; first, the second-order factor analysis and structural equational modeling reveals that the differentiation, simplicity and ease of identifying current status of the transaction are confirmed to be subdimensions of store design and to be a significant predictors of the dependent variable. This result implies that when designing an online shopping mall, it is necessary to differentiate visually from other malls to improve the effectiveness of the communications of store design. That is, the differentiated store design raise the contrast stimulus to sensory organs to promote the memory of the store and to have a favorable attitude toward the VMD of a store. The results that navigation which means the easiness of identifying current status of shopping affects the attitude to VMD could be interpreted that the navigating processes via the hyperlinks which is characteristics of an internet shopping is a complex and cognitive process and shoppers are likely to lack the sense of overall structure of the store. Consequently, shoppers are likely to be alost amid shopping not knowing where to go. The orientation tool enhance the accessibility of information to raise the perceptive power about the store environment.(Titus & Everett 1995) Second, the primary dimension of merchandise and its subdimensions was confirmed to be unidimensional respectively, have a construct validity, and nomological validity which the VMD dimensions supposed to have a positive correlation with the dependent variable. The subdimensions of product assortment, brand fame and information provision proved to have a positive effect on the attitude toward the VMD. It could be interpreted that the more plentiful the product and brand assortment of the mall is, the more likely the shoppers to favor it. Brand fame and information provision as well affect the VMD attitude, which means that the more famous the brand, the more likely the shoppers would trust and feel familiar with the mall, and the plentifully and visually presented information could have the shopper have a favorable attitude toward the store VMD. Third, it turned out to be that merchandising cue of product recommendation and hyperlinks affect the VMD attitude. This could be interpreted that recommended products could reduce the uncertainty related with the purchase decision, and the hyperlinks to relevant products would help the shopper save the cognitive effort exerted into the information search and gathering, which could lead to a favorable attitude to the VMD. This study tried to sheds some new light on the VMD of online store by reviewing the variables mentioned to be relevant with offline VMD in the existing literatures, and tried to link the VMD components from the perspective of AIDA model. The effect size of the VMD dimensions on the attitude was in the order of the merchandise, the store design and the merchandising cues.It is said that an internet has an unlimited place for display, however, the virtual store is not unlimited since the consumer has a limited amount of cognitive ability to process the external information and internal memory. Particularly, the shoppers are likely to face some difficulties in decision making on account of too many alternative and information overloads. Therefore, the internet shopping mall manager should take into consideration the cost of information search on the part of the consumer, to establish the optimal product placements and search routes. An efficient store composition would be possible by reducing the psychological burdens and cognitive efforts exerted to information search and alternatives evaluation. The store image is in most part determined by the product category and its brand it deals in. The results of this study support this proposition that the merchandise is most important to the VMD attitude than other components, the manager is required to take a strategic approach to VMD. The internet users are getting more accustomed and more knowledgeable about the internet media and more likely to accept the internet as a shopping channel as the period of time during which they use the internet to shop become longer. The web merchandiser should be aware that the product introduction using a moving pictures and a bulletin board become more important in order to present the interactive product information visually and communicate with customers more actively, therefore leading to making the quantity and quality of product information more rich.

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