• Journal of Distribution Science
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• v.10 no.4
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• pp.47-53
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• 2012

Businesses make use of advertising strategy using models to give consumers efficient product information. Modern advertisements often make use of models for greater reminiscence to create messages and remind viewers of the product. The purpose of this study was to examine the characteristics of each type of model. The subjects were 230 college students in their twenties or older, and the material was collected from October 20, 2011 to November 5, 2011 to examine the effects of model characteristics on buying intention as well as attitude toward a brand. A questionnaire survey was used; investigators gave one copy to each interviewee. The study investigated the characteristics of each model using a questionnaire of each 40 copies with five kinds of photographs. The characteristics of models had great influence on buying intention and attitude toward the brand: First, factor 2 (being honest and virtuous and having good credit and a good press assessment) and factor 3 (being interesting and a good communicator and creating good memories) had great influence on buying intention. Factor 2 was explained by reliability, and factor 3 by the efficiency of the model in creating a feeling. Second, factors 1 (being attractive, smart, unique, friendly, loved by others, and popular), 2, and 3 influenced attitude toward brand. Factor 1 encapsulated the outgoing characteristics of a model, factor 2 was based on reliability, and factor 3 was based on the efficiency of the model in creating a feeling. The model's positive effects on buying intention and attitudes toward brand shall be examined. For their positive influence on buying intention, reliability and efficiency shall be given attention. For their positive influence on attitude toward brand, creating a good impression, having outgoing characteristics, being reliable, and efficiency shall be given attention. The findings were as follows: Model characteristics influencing buying intention were similar to those influencing attitude toward brand. The differences were as follows. First, reliability and efficiency influenced buying intention. When customers were asked to consider the influence on buying intention of an advertisement, regardless of the strength of the buying intention, they considered these two characteristics. Customers decided to buy based not only on the credibility of the product as presented in the advertisement but also the transmission of the contents of the advertisement. Second, outgoing characteristics, reliability, and efficiency influenced attitude toward a brand. The attitude toward a brand was said to be the attitude toward the business. The attitude is produced even after buying, so businesses view it as very important. The attitude might vary depending upon the model used rather than the brand. Therefore, a model with outgoing characteristics was thought to be important. Therefore, attitude toward a brand whose model influenced buying intention as well as attitude toward brand had outgoing characteristics. The result is that an image the model was related to attitude toward the brand. As such, customers would buy the goods advertised. However, an outgoing image of a model was also important to create a positive attitude toward a business brand. For instance, talent Park Gyeong-Rim's photo was used to promote cosmetics about 10 years ago. When she worked as a model of cosmetics products, she had to make compensation for losses and damages because she made a mistake on a talk show program. At that time, customers who had bought the cosmetics product asked for refunds of several billion won. As such, models who are said to be the face of the businesses they represent can play an important role. To advertise in the most attractive and effective way, the current image of a model should be investigated by examining current activities and news articles after selecting the model, and the model's efficiency and attitude toward the brand should be examined. Factors that stimulate customers' buying decisions can be used to plan advertisement that have positive influence on a brand. This study had the limitation of investigating mainly college students and there were insufficient copies of the questionnaire. The investigation was not done widely but in detail so that a concrete investigation could not be done. Further studies shall supplement these shortcomings and discuss new directions.

• Journal of Distribution Research
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• v.17 no.1
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• pp.1-36
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• 2012

Service failure is one of the major reasons for customer defection. As the business environment gets tougher and more competitive, a single service failure might bring about fatal consequences to a service provider or a firm. Sometimes a failure won't end up with an unsatisfied customer's simple complaining but with a wide-spread animosity against the service provider or the firm, leading to a threat to the firm's survival itself in the society. Therefore, we are in need of comprehensive understandings of complainants' attitudes and behaviors toward service failures and firm's recovery efforts. Even though a failure itself couldn't be fixed completely, marketers should repair the mind and heart of unsatisfied customers, which can be regarded as an successful recovery strategy in the end. As the outcome of recovery efforts exerted by service providers or firms, recovery of the relationship between customer and service provider need to put on the top in the recovery goal list. With these motivations, the study investigates how service failure and recovery makes the changes in dynamics of fundamental elements of customer-firm relationship, such as customer affection, customer trust and loyalty intention by comparing two time points, before the service failure and after the recovery, focusing on the effects of recovery satisfaction and the failure severity. We adopted La & Choi (2012)'s framework for development of the research model that was based on the previous research stream like Yim et al. (2008) and Thomson et al. (2005). The pivotal background theories of the model are mainly from relationship marketing and social relationships of social psychology. For example, Love, Emotional attachment, Intimacy, and Equity theories regarding human relationships were reviewed. As the results, when recovery satisfaction is high, customer affection and customer trust that were established before the service failure are carried over to the future after the recovery. However, when recovery satisfaction is low, customer-firm relationship that had already established in the past are not carried over but broken up. Regardless of the degree of recovery satisfaction, once a failure occurs loyalty intention is not carried over to the future and the impact of customer trust on loyalty intention becomes stronger. Such changes imply that customers become more prudent and more risk-aversive than the time prior to service failure. The impact of severity of failure on customer affection and customer trust matters only when recovery satisfaction is low. When recovery satisfaction is high, customer affection and customer trust become severity-proof. Interestingly, regardless of the degree of recovery satisfaction, failure severity has a significant negative influence on loyalty intention. Loyalty intention is the most fragile target when a service failure occurs no matter how severe the failure criticality is. Consequently, the ultimate goal of service recovery should be the restoration of customer-firm relationship and recovery of customer trust should be the primary objective to accomplish for a successful recovery performance. Especially when failure severity is high, service recovery should be perceived highly satisfied by the complainants because failure severity matters more when recovery satisfaction is low. Marketers can implement recovery strategies to enhance emotional appeals as well as fair treatments since the both impacts of affection and trust on loyalty intention are significant. In the case of high severity of failure, recovery efforts should be exerted to overreach customer expectation, designed to directly repair customer trust and elaborately designed in the focus of customer-firm communications during the interactional recovery process to affect customer trust rebuilding indirectly. Because it is a longer and harder way to rebuild customer-firm relationship for high severity cases, low recovery satisfaction cannot guarantee customer retention. To prevent customer defection due to service failure of high severity, unexpected rewards as a recovery will be likely to be useful since those will lead to customer delight or customer gratitude toward the service firm. Based on the results of analyses, theoretical and managerial implications are presented. Limitations and future research ideas are also discussed.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Korean Society of Forest Science
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• v.34 no.1
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• pp.31-46
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• 1977

The study was made to give some helpful information for policy-making on ornamental tree cultivation by doing a survey on general situations, management analysis, and future prospects of the ornamental tree growing. The study was carried out through literature studies related to the subject, questionaire surveys, and on-the-spot investigation. The questionaire surveys could be divided into two parts: pre-questionaire survey and main-questionaire survey. In the pre-questionaire survey, the researchers intended to identify the total number of ornamental tree growers, cultivation areas in size and their locations. The questionaires were sent to each town and county administration authorities, forest cooperatives, and related organizations through-out the nation. The main-questionaires were prepared for detailed study and the questionaires were sent to 200 tree growers selected by option by taking considerations of the number of tree growers and the size of cultivating areas in regions. The main findings and some information obtained in the survey were as follows: 1. The total land for ornamental tree growing was amounted to 1,873.02 hectares and the number of cultivators was totaled to 2,717. 2. The main occupations of the ornamental tree growers were found in horticulture (41.9%), agronomy (25.9%), officialdom (11.3%), animal husbandry (6.5%), business circle(4.8%), and forestry (3.2%) in sequence. 3. The ornamental trees were cultivated mostly upperland (54.8), forest land (19.4%), rice paddy (11.3%) and others. 4. The educational training of the tree growers seemed quite high. The results of the survey indicated that a large number of tree growers was occupied by college graduates (38.7%), and then high school graduates (34.7%), middle school graduates (12.9%) in order. 5. The tree farming was undertaken as a side-job (41.9%) rather than main-job (23.4%), but a few of respondents rated as subsidiary-job (18.6%). 6. The management status classified by the rate of hired labors used was likely to belong to three categories: independant enterprise management (41.9%); half independant management (31.5%); and self-management (32.4%). 7. The majority of the tree growers sold their products to the consumers through middle-man channel (48.4%), or directly to the house-holder and detailers (13.7%), but a few of the respondents answered that they disposed of their products by bidding (11.2%) or by direct selling to the contractors (4.8%). 8. The channel cf marketing seemed somewhat complicated. The results of the survey were as: (1) producers ${\rightarrow}$consumers (22.6%) (2) producers ${\rightarrow}$field middle-men${\rightarrow}$consumers (33.1%) (3) producers ${\rightarrow}$field middle-men${\rightarrow}$first stage brokers${\rightarrow}$consumers (15.3%) (4) producers ${\rightarrow}$field middle-men${\rightarrow}$second stage middle-men${\rightarrow}$brokers${\rightarrow}$consumers (5.7%) (5) producers${\rightarrow}$field middle-men${\rightarrow}$third stage middle-men${\rightarrow}$second stage middlemen${\rightarrow}$brokers${\rightarrow}$consumers (4.8%) 9. It was responded that the margin for each stage of middle-men or brokers was assumed to be 30-50%(33.1%), 20-30%(32.3%), 50-100%(9.7%), and 100-200%(2.4%) in sequence. 10. The difference between the delivery price of consumers and field selling price of the producers seemed quite large. Majority of producers responded that they received half a price compared to the consumer's prices. 11. About two thirds of the respondents opposed to the measure of "Law on Preservation and Utilization of Agricultural Land" in which says that all the ornamental trees grown on flat agricultural lands less than 8 degrees in slope must be transplanted within three years to other places more than 8 degrees in slope. 12. The tree growers said that they have paid rather high land taxes than they ought to pay (38.7%), but come responded that land tax seemed to be appropriate (15.3%), and half of the respondents answered "not known". 13. The measures for the standardization of ornamental trees by size were backed up by a large number of respondents (57.3%), but one third of the respondents showed negative answer (29.8%). 14. About half of the respondents favored the systematic marketing through organization such as forest cooperatives (54%), but quite a few respondents opposed to organizing the systematic marketing channel (36.3%). 15. The necessary measures for permission in ornamental tree cultivation was rejected by a large number of respondents (49.2%) than those of favored (43.6%).

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.197-218
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• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.

• Journal of Distribution Research
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• v.16 no.4
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• pp.29-64
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• 2011

Overview of Research: Product availability is one of important competences of store to fulfill consumer needs. If stock-outs which means a product what consumer wants to buy is not available occurs, consumer will face decision-making uncertainty that leads to consumer's negative responses such as consumer dissatisfaction on store. Stockouts was much studied in the field of academia as well as practice in other countries. However, stock-outs has not been researched at all in Marketing and/or Distribution area in Korea. The main objectives of this study are to find out determinants of consumer responses such as Substitute, Delay, and Leave(SDL) when consumer encounters out-of-stock situation and then to examine the effects of these factors on consumer responses. Specifically, this study focuses on situational characteristics(e.g., purchase urgency and surprise), store characteristics (e.g., product assortment and store convenience), and consumer characteristics (e.g., brand loyalty and store loyalty). Then, this study empirically investigates relationships these factors with consumers behaviors such as product substitution, purchase delay, and store switching.