• The Korean Journal of Franchise Management
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• v.12 no.1
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• pp.45-55
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• 2021

Purpose: How to build the positive emotion of customer is very important, because it affects the positive attitude. Brand evidence has a significant impact on consumer behavior in terms of reinforcing consumers' perception of food service companies and differentiating them from competing brands. Thus, this study examines the effect of brand evidence on emotion (positive emotion and negative emotion), and attitude in restaurant industry. Research design, data, and methodology: This study examines the structural relationship among brand evidence, emotion, and attitude. Brand evidence divide into three sub-dimensions such as physical evidence, core service, and employee service. In order to test the purposes of this study, research model and hypotheses were developed. The questionnaire items were modified and used according to the content of this study based on previous studies. All constructs were measured by multiple items tested and developed in the previous research. The data were collected from 439 restaurant users from Seoul area were analyzed using SPSS 22.0 and SmartPLS 3.0 program. A total of 460 questionnaires were distributed and a survey was conducted for 4 weeks, and a total of 439 were used for analysis, excluding non-response data and 21 unusable response data among the collected questionnaires. Frequency analysis was conducted to identify the general characteristics of the survey subjects. To measure the reliability and validity of the measurement tools, confirmatory factor analysis was conducted. Structural model analysis was conducted to verify the research model. Result: The findings demonstrate that physical evidence, core service, employee service had positive effects on positive emotion. And core service and employee service had negative effects on negative emotion while physical evidence did not have. Also, positive emotion had positive effect on attitude and negative emotion had negative effect on attitude. Conclusions: The findings of this study provide guidelines on how to enhance competitiveness in restaurant industry through understanding brand evidence's effects on raising perceived consumer's emotion and attitude. Therefore, food service companies should establish a marketing strategy that can stimulate positive emotions through brand evidence, which is all factors related to service brands that influence consumers' evaluation of service products and purchase decision-making process.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Archives of design research
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• v.14 no.1
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• pp.17-26
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• 2001

A shopping mall sie is the area which males use of the Internee activity as a medium in business transactions, But their growth rate of revenues does not keep up with the growth rate in the number of internet users. One of the reasons why they fall behind of the expected earning proportionate to the growing number of internet user is that the shopping sites do not satisfy various need of customers on the web. This comes from that there has been not enough efforts on a shopping site design satisfying their needs. This paper is to find the elements of the shopping site design to affect the my customers purchase goods on the internet on the premise that a design being closely related with a marketing strategy makes a good design. This paper consists of three parts. The first chapter is about related works inducing e-commerce, marketing as wet as design theory. In the second chapter, data are analyzed to find the elements of a good design. The data are categorized by criteria of general properties, elements in design theory, demographic characteristics and hypothesis tests are done. The third chapter is the conclusion.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.

• Journal of Service Research and Studies
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• v.12 no.2
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• pp.80-93
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• 2022

This study is a study on a methodology that can extract various factors that affect purchase and use of products/services from the consumer's point of view through previous studies, and analyze the types and tendencies of consumers according to age and gender. To this end, we quantify factors in terms of general personal propensity, consumption influence, consumption decision, etc. to check the consistency of data, and based on these studies, we conduct research to suggest and prove data analysis methodologies of consumer types that are meaningful from the perspectives of startups and SMEs. did As a result, it was confirmed through cross-validation that there is a correlation between the three main factors assumed for data analysis from the consumer's point of view, the general tendency, the general consumption tendency, and the factors influencing the consumption decision. verified. This study presented a data analysis methodology and a framework for consumer data analysis from the consumer's point of view. In the current data analysis trend, where digital infrastructure develops exponentially and seeks ways to project individual preferences, this data analysis perspective can be a valid insight.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.3 no.4
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• pp.89-116
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• 2008

RFID (Radio Frequency Identification) is regarded as a core technology of ubiquitous computing. Although it has some technical limitations such as technological standardization of RFID tags as well as economical limitations, many companies around the world have already accepted RFID to improve their management efficiency. In this regard, this study is to meet with results that the adoption of RFID technology willbring opportunities that companies' operational process are improved and customer satisfaction is highly strengthened. This research focuses on providing more understanding for building RFID marketing strategy to suppliers who want to sell their RFID products to customers through analyzing purchasing process. The findings are as follows; First, the study shows that buying center members usually take product reliability and precision of technical specification in the case of new-task buying situation while they put their first purchasing priority on prices in the straight rebuy. Second, the finding presents that in new-task buying situation and the straight rebuy purchasing personnel get information about new products through product performance test, organizational engineers, opinions from other companies' purchasing personnel, and checking out samples. Third, this research demonstrates when it comes to purchasing risk in their first purchasing, the persons who are in charge of material purchasing are inclined to be aware of the risk most in technical problems, followed by financial problems and time delay problems in order. And in addition to those risks are mentioned above, once-again-purchasers take the risk like an opportunity loss for better products into consideration. Fourth, the study shows that the role of concerning departments makes no difference in each purchasing stage. Accordingly marketers need to beef up the differentiated strategy to persuade their customers Fifth, the findings of this study demonstrate that purchasing decision making is much influenced by the final users. So suppliers are supposed to perform the most active marketing strategy at the first stage of purchasing through various resources. Finally, the study presents that the suppliers who will have had close relationships with their customers need to give consistent information to them so that their customers can have lower motive in purchasing products from competitors.

• 한국벤처창업학회:학술대회논문집
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• 2008.11a
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• pp.257-282
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• 2008

RFID (Radio Frequency Identification) is regarded as a core technology of ubiquitous computing. Although it has some technical limitations such as technological standardization of RFID tags as well as economical limitations, many companies around the world have already accepted RFID to improve their management efficiency. In this regard, this study is to meet with results that the adoption of RFID technology willbring opportunities that companies' operational process are improved and customer satisfaction is highly strengthened. This research focuses on providing more understanding for building RFID marketing strategy to suppliers who want to sell their RFID products to customers through analyzing purchasing process. The findings are as follows; First, the study shows that buying center members usually take product reliability and precision of technical specification in the case of new-task buying situation while they put their first purchasing priority on prices in the straight rebuy. Second, the finding presents that in new-task buying situation and the straight rebuy purchasing personnel get information about new products through product performance test, organizational engineers, opinions from other companies' purchasing personnel, and checking out samples. Third, this research demonstrates when it comes to purchasing risk in their first purchasing, the persons who are in charge of material purchasing are inclined to be aware of the risk most in technical problems, followed by financial problems and time delay problems in order. And in addition to those risks are mentioned above, once-again-purchasers take the risk like an opportunity loss for better products into consideration. Fourth, the study shows that the role of concerning departments makes no difference in each purchasing stage. Accordingly marketers need to beef up the differentiated strategy to persuade their customers. Fifth, the findings of this study demonstrate that purchasing decision making is much influenced by the final users. So suppliers are supposed to perform the most active marketing strategy at the first stage of purchasing through various resources. Finally, the study presents that the suppliers who will have had close relationships with their customers need to give consistent information to them so that their customers can have lower motive in purchasing products from competitors.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.