• Journal of Distribution Science
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• v.9 no.4
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• pp.5-13
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• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Distribution Research
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• v.17 no.2
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• pp.1-28
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• 2012

I. Introduction : The purpose of this study is to test empirically hypothetical causality among constructs used in previous studies to build the model of relational market behavior on customers' participation and consumer-brand relationship after introducing theories of relationship marketing, loyalty program, consumer-brand relationship, customers' participation in service marketing as previous studies with regard to relational market behavior, which Bagozzi(1995) and Peterson(1995) commented on constructs and definition suggested by Sheth and Parvatiyar (1995). For this purpose, loyalty program by the service provider, customers' participation and consumer-brand relationship as preceding variables explain relational market behavior defined by Sheth and Parvatiyar(1995). This study proposes that loyalty program as a tool of relationship marketing will be effective in that consumers' participation in marketing relationship results in a narrow range of choice(Sheth and Parvatiyar, 1995) because consumers think that their participation motive result in benefits(Peterson, 1995). Also, it is proposed that the quality of consumer-brand relationship explain the performance of relationship as well as the intermediary effect because the loyalty program could be evaluated based on relationship with customers. We reviewed the variables with regard to performance of relationship based on relation maintain in marketing literature, and then tested our hypotheses related to several performance variables including loyalty and intention of relation maintain based on the previous studies and constructs(Bendapudi and Berry, 1997 ; Bettencourt, 1997 ; Palmatier, Dant, Grewal and Evans, 2006 ; You Jae Yi and Soo Jin Lee, 2006). II. Study Model : Analyses about hypothetical causality were proceeded. The marketing effect of loyalty program on relational market behavior was empirically tested in study regarding a service provider. The research model in according to the path hypotheses (loyalty program ${\rightarrow}$ customers' participation ${\rightarrow}$ consumer-brand relationship ${\rightarrow}$ relational market behavior and loyalty program ${\rightarrow}$ consumer-brand relationship, and loyalty program ${\rightarrow}$ relational market behavior and customers' participation ${\rightarrow}$ consumer-brand relationship, and customers' participation ${\rightarrow}$ relational market behavior) proceeded as an activity for customer relation management was suggested. The main purpose of study is to see if relational market behavior could be brought as a result of developing relationship between consumers and a corporate into being stronger and more valuable when a corporate or a service provider try aggressively to build the relationship with customers (Bettencourt, 1997; Palmatier, Dant, Grewal and Evans, 2006; Sheth and Parvatiyar, 1995). III. Conclusion : The results of research into the membership fitness club, one of service areas with high level of customer participation (Bitner, Faranda, Hubbert and Zeithaml, 1997; Chase, 1978; Kelley, Donnelly, Jr. and Skinner, 1990) are as follows: First, causalities in according to path hypotheses were tested, after the preceding variables affecting relational market behavior and conceptual frame were suggested. In study, all hypotheses were supported as expected. This result confirms the proposition suggested by Sheth and Parvatiyar(1995), who claimed that intention of consumer and corporate to participate in marketing relationship brings high level of marketing productivity. Also, as a corporate or a service provider try aggressively to build relationship with customers, the relationship between consumers and a corporate can be developed into stronger and more valuable one (Bettencourt, 1997; Palmatier, Dant, Grewal and Evans, 2006). This finding supports the logic of relationship marketing. Second, because the question regarding the path hypothesis of consumer-brand relationship ${\rightarrow}$ relational market behavior are still at issue, the further analyses were conducted. In particular, there existed the mediating effects of consumer-brand relationship toward relational market behavior. Also, multiple regressions were conducted to see if which one strongly influences relational market behavior among specific question items with regard to consumer-brand relationship. As a result, the influence between items composing consumer-brand relationship and ones composing relational market behavior was different. Among items composing consumer-brand relationship, intimacy was an influence of sustaining relationship, word of mouth, and recommendation, intimacy and interdependence were influences of loyalty, intimacy and self-connection were influences of tolerance and advice. Notably, commitment among items measuring consumer-brand relationship had the negative influence with relational market behavior. This means that bringing relational market behavior is not consumer-brand relationship without personal commitment, but effort to build customer relationship like intimacy, interdependence, and self-connection. This finding confirms the results of Breivik and Thorbjornsen(2008). They reported that six variables composing the quality of consumer-brand relationship have higher explanation in regression model directly affecting performance of consumer-brand relationship. As a result of empirical analysis, among the constructs with regard to consumer-brand relationship, intimacy(B=0.512), interdependence(B=0.196), and quality of partner(B=0.153) had the effects on relation maintain. On the contrary, self-connection, love and passion, and commitment had little effect and did not show the statistical significance(p<0.05). On the other hand, intimacy(B=0.668) and interdependence(B=0.181) had the high regression estimates on word of mouth and recommendation. Regarding the effect on loyalty, explanation level of the model was high(R2=0.515), intimacy(0.538), interdependence(0.223), and quality of partner(0.177) showed the statistical significance(p<0.05). Furthermore, intimacy(0.441) had the strong effect as well as self-connection(0.201) and interdependence (0.163) had the effect on tolerance and forgive. And these three variables showed effects even on advice and suggestion, intimacy(0.373), self-connection(0.270), interdependence (0.155) respectively. Third, in study with regard to the positive effect(loyalty program ${\rightarrow}$ customers' participation, loyalty program ${\rightarrow}$ consumer-brand relationship, loyalty program ${\rightarrow}$ relational market behavior, customers' participation ${\rightarrow}$ consumer-brand relationship, customers' participation ${\rightarrow}$ relational market behavior, consumer-brand relationship ${\rightarrow}$ relational market behavior), the path hypothesis of customers' participation ${\rightarrow}$ consumer-brand relationship, was supported. The fact that path hypothesis of customers' participation ${\rightarrow}$ consumer-brand relationship was supported confirms assertion by Bitner(1995), Fournier(1994), Sheth and Parvatiyar(1995) about consumer relationship to participate in marketing relationship.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.309-314
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• 2017

In general, there are many difficulties in collecting, storing, and analyzing SNS (social network service) data, since those data have big data characteristics, which occurs very fast with the mixture form of structured and unstructured data. In this paper, we propose a new data visualization framework that works on Apache Storm, and it can be useful for real-time and dynamic analysis of SNS data. Apache Storm is a representative big data software platform that processes and analyzes real-time streaming data in the distributed environment. Using Storm, in this paper we collect and aggregate the real-time Twitter data and dynamically visualize the aggregated results through the tag cloud. In addition to Storm-based collection and aggregation functionalities, we also design and implement a Web interface that a user gives his/her interesting keywords and confirms the visualization result of tag cloud related to the given keywords. We finally empirically show that this study makes users be able to intuitively figure out the change of the interested subject on SNS data and the visualized results be applied to many other services such as thematic trend analysis, product recommendation, and customer needs identification.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.85-96
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• 2023

With advance of cloud computing technology, Blockchain as a Service of Cloud Service Provider has been utilized in various areas such as e-Commerce and financial companies to manage customer history and distribution history. However, if users' search history, purchase history, etc. are to be utilized in a BaaS in areas such as recommendation algorithms and search engine development, the users' search queries will be exposed to the company operating the BaaS, and privacy issues will be occured. Z. Guan et al. ensure the unlinkability between users' search query and search result using searchable encryption, and based on the inner product similarity, they select Top-k results that are highly relevant to the users' search query. However, there is a problem that the Top-k results selection may be not possible due to ties of inner product similarity, and BaaS over cloud is not considered. Therefore, this paper solve the problem of Z. Guan et al. using cosine similarity, so we improve accuracy of search result. And based on this, we design a BaaS with secure Top-k search that improved accuracy. Furthermore, we design a smart contracts that preserve privacy of users' search and obtain Top-k search results that are highly relevant to the users' search.

• Culinary science and hospitality research
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• v.17 no.3
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• pp.20-32
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• 2011

The purpose of this study is to analyze customers' behavior in using word-of-mouth(WOM) information about Korean local food restaurants. The questionnaire developed for this study was distributed to 500 customers living in Jeonbuk area and a total of 455 copies (91.0%) were used for analysis. The statistical analysis was conducted using SPSS Win(12.0). The results were summarized as follows. The recommendation of people experienced'($M=3.57{\pm}1.24$) and 'word-of-mouth through people around'($M=3.52{\pm}1.20$) were major word-of-mouth information sources of Korean local foods; 'taste of food'($M=4.16{\pm}1.15$) and 'service quality'($M=3.79{\pm}1.11$) were important attributes in word-of-mouth information. In addition, to the question about the reasons for recommending the restaurant to the people around, the most people replied that 'flavor, nutrition and quality of local foods can be kept only in the specific location' ($3.53{\pm}1.08$), followed by 'to keep the memory of the visit to the areas in mind through local foods'($3.51{\pm}1.03$). These results showed that people usually recommend a restaurant based on the quality of the food itself or local characteristics. As a result, it is deemed that word-of-mouth effect is an important factor for the spread of Korean local foods.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.147-187
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• 2008

Online game researchers focus on the flow and factors influencing flow. Flow is conceptualized as an optimal experience state and useful explaining game experience in online. Many game studies focused on the customer loyalty and flow in playing online game, In showing specific game experience, however, it doesn't examine multidimensional experience process. Flow is not construct which show absorbing process, but construct which show absorbing result. Hence, Flow is not adequate to examine multidimensional experience of games. Online game is included in hedonic consumption. Hedonic consumption is a relatively new field of study in consumer research and it explores the consumption experience as a experiential view(Hirschman and Holbrook 1982). Hedonic consumption explores the consumption experience not as an information processing event but from a phenomenological of experiential view, which is a primarily subjective state. It includes various playful leisure activities, sensory pleasures, daydreams, esthetic enjoyment, and emotional responses. In online game experience, therefore, it is right to access through a experiential view of hedonic consumption. The objective of this paper was to make up for lacks in our understanding of online game experience by developing a framework for better insight into the hedonic experience of online game. We developed this framework by integrating and extending existing research in marketing, online game and hedonic responses. We then discussed several expectations for this framework. We concluded by discussing the results of this study, providing general recommendation and directions for future research. In hedonic response research, Lacher's research(1994)and Jongho lee and Yunhee Jung' research (2005;2006) has served as a fundamental starting point of our research. A common element in this extended research is the repeated identification of the four hedonic responses: sensory response, imaginal response, emotional response, analytic response. The validity of these four constructs finds in research of music(Lacher 1994) and movie(Jongho lee and Yunhee Jung' research 2005;2006). But, previous research on hedonic response didn't show that constructs of hedonic response have cause-effect relation. Also, although hedonic response enable to different by stimulus properties. effects of stimulus properties is not showed. To fill this gap, while largely based on Lacher(1994)' research and Jongho Lee and Yunhee Jung(2005, 2006)' research, we made several important adaptation with the primary goal of bringing the model into online game and compensating lacks of previous research. We maintained the same construct proposed by Lacher et al.(1994), with four constructs of hedonic response:sensory response, imaginal response, emotional response, analytical response. In this study, the sensory response is typified by some physical movement(Yingling 1962), the imaginal response is typified by images, memories, or situations that game evokes(Myers 1914), and the emotional response represents the feelings one experiences when playing game, such as pleasure, arousal, dominance, finally, the analytical response is that game player engaged in cognition seeking while playing game(Myers 1912). However, this paper has several important differences. We attempted to suggest multi-dimensional experience process in online game and cause-effect relation among hedonic responses. Also, We investigated moderate effects of perceived complexity. Previous studies about hedonic responses didn't show influences of stimulus properties. According to Berlyne's theory(1960, 1974) of aesthetic response, perceived complexity is a important construct because it effects pleasure. Pleasure in response to an object will increase with increased complexity, to an optimal level. After that, with increased complexity, pleasure begins with a linearly increasing line for complexity. Therefore, We expected this perceived complexity will influence hedonic response in game experience. We discussed the rationale for these suggested changes, the assumptions of the resulting framework, and developed some expectations based on its application in Online game context. In the first stage of methodology, questions were developed to measure the constructs. We constructed a survey measuring our theoretical constructs based on a combination of sources, including Yingling(1962), Hargreaves(1962), Lacher (1994), Jongho Lee and Yunhee Jung(2005, 2006), Mehrabian and Russell(1974), Pucely et al(1987). Based on comments received in the pretest, we made several revisions to arrive at our final survey. We investigated the proposed framework through a convenience sample, where participation in a self-report survey was solicited from various respondents having different knowledges. All respondents participated to different degrees, in these habitually practiced activities and received no compensation for their participation. Questionnaires were distributed to graduates and we used 381 completed questionnaires to analysis. The sample consisted of more men(n=225) than women(n=156). In measure, the study used multi-item scales based previous study. We analyze the data using structural equation modeling(LISREL-VIII; Joreskog and Sorbom 1993). First, we used the entire sample(n=381) to refine the measures and test their convergent and discriminant validity. The evidence from both the factor analysis and the analysis of reliability provides support that the scales exhibit internal consistency and construct validity. Second, we test the hypothesized structural model. And, we divided the sample into two different complexity group and analyze the hypothesized structural model of each group. The analysis suggest that hedonic response plays different roles from hypothesized in our study. The results indicate that hedonic response-sensory response, imaginal response, emotional response, analytical response- are related positively to respondents' level of game satisfaction. And game satisfaction is related to higher levels of game loyalty. Additionally, we found that perceived complexity is important to online game experience. Our results suggest that importance of each hedonic response different by perceived game complexity. Understanding the role of perceived complexity in hedonic response enables to have a better understanding of underlying mechanisms at game experience. If game has high complexity, analytical response become important response. So game producers or marketers have to consider more cognitive stimulus. Controversy, if game has low complexity, sensorial response respectively become important. Finally, we discussed several limitations of our study and suggested directions for future research. we concluded with a discussion of managerial implications. Our study provides managers with a basis for game strategies.