Purpose - Generally, the consumer's decision-making process for restaurant selection is simple and familiar. However, online information becomes important because the decision-making process of consumers becomes complicated in new restaurants and special situations. Because consumers can search for information online, information retrieval is now possible in selecting new restaurants. In particular, consumers often make decisions based on online information when making restaurant reservations. Research design, data, and methodology - All items were measured based on previous studies. The data were collected from customers who had visited the store by visiting the web page of the food service franchise within the last 3 months for the panel of the online survey institute. The questionnaires were surveyed from July 2 to July 11, 2018 for about 10 days. A mail and a message were sent to 2,000 people, and 310 people were asked to complete the questionnaire. Total of 303 data were used in the questionnaire, excluding 7 insufficient data. Results - The findings of this study are as follows: Consensus, vividness, and neutrality have positive effects on enjoyment. Consensus have positive effect on anxiety, but vividness and neutrality did not have significant effect on anxiety. Also, enjoyment has a positive influence on intention to visit, and anxiety has a negative influence on visit intention. Conclusions - First, franchise companies online advertising in a variety of ways, but they are mixed with other customers' WOM and offered to consumers. In this case, the information provided by the company may be distorted. Therefore, a restaurant franchise company needs to operate an official online channel to provide accurate information to its customers. Second, it is necessary to make contents so that other customers can participate in online channels of food service franchise companies. Third, food service franchise company should produce enough online contents to experience indirectly even if the customer does not visit the store directly. Fourth, food service franchise company needs to prepare a way for many customers to participate in many official contents.
The Journal of the Korea institute of electronic communication sciences
/
v.16
no.6
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pp.1231-1238
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2021
Recently, large online shopping malls are providing image search services as well as text or category searches. However, in the case of an image search service, there is a problem in that the search service cannot be used in the absence of an image. This paper describes the development of a system that allows users to find the clothes they want through hand-drawn images of the style of clothes when they search for clothes in an online clothing shopping mall. The hand-drawing data drawn by the user increases the accuracy of matching through neural network learning, and enables matching of clothes using various object detection algorithms. This is expected to increase customer satisfaction with online shopping by allowing users to quickly search for clothing they are looking for.
Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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v.8
no.10
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pp.71-79
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2018
The Internet of Things is a technology that makes it possible for objects made up of hardware and software to exchange information with one another via the Internet, thereby facilitating the servitization of the objects. An IoT service, which is composed of an IoT device and a web service, has recently been applied to the marketing field and is being used as a means to meet customer needs. However, applying appropriate marketing elements to IoT services is not easy. Therefore, analysis tools are needed to properly apply marketing elements to IoT services. This study aims to construct an analysis model for marketing evaluation of IoT services. In this study, the technical elements and marketing elements of IoT were derived through a literature review, and the analysis model for marketing evaluation of IoT services was established by exploring the relationship between the elements based on a service blueprint. We also applied real cases to verify the analytical model. This study is expected to contribute to the development of tools for evaluating IoT services.
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.
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.
Nowadays, it is common that most consumers are purchasing goods in e-stores. The e-stores eager to attract, revisit, retain, and finally convert them into loyal customers. The e-store marketers have planned and executed numerous marketing efforts. As one of the marketing activities, e-store managers attempt to build web sites that meet customers' functional and psychological needs. A wide array of studies has been done to identify factors that could affect customers' response of web sites. Majority of studies were conducted to verify technology-related and functional variables of the website which facilitate transactions and enhance customer responses such as purchase intention and website loyalty. However, there has been little research on the external cues of website and psychological variables of consumer that could have positive influences on customer response. The purpose of this study is to investigate the influence of e-store personality on e-store loyalty through mediating variables such as e-store identification, e-store trust, and e-store engagement. The authors of this study develop the model and set up the six main hypotheses and a set of sub-hypotheses based on a literature review, shown in
. This model is composed of four paths such as dimensions of e-store personality${\rightarrow}$e-store identification, e-store identification${\rightarrow}$e-store loyalty, e-store identification ${\rightarrow}$e-store trust${\rightarrow}$e-store loyalty, and e-store identification${\rightarrow}$e-store engagement${\rightarrow}$e-store loyalty. II. Research Method Ladies under 30s were the respondents of this survey. Data were collected from January 20th to February 26th in 2010. A total of 200 questionnaires were distributed and 169 respondents were analysed finally to test hypotheses because 31 questionnaires had incorrect or missing responses. SPSS 12.0 and LISREL 7.0 program were used to test frequency, reliability, factor, and structural equation modeling analysis. III. Result and Conclusion According to results from factor analysis, eigen value was over 1.0 and items which were below 0.6 were deleted. Consequently, 9 factors(% of total variance is 72.011%) were searched. All Cronbach's ${\alpha}$ values are over the recommended level(${\alpha}$ > 0.7). The overall fit indices are acceptable such as ${\chi}^2$=2028.36(p=0.00), GFI=0.87, AGFI=0.82, CFI=0.81, IFI=0.92, RMR=0.075. All factor loadings were over the recommended level. As the result of discriminant validity check with chi-square difference test between paired constructs, each construct has good discriminant validity. The overall fit indices of final model are acceptable such as ${\chi}^2$=340.73(df=36, p=0.00), GFI=0.92, AGFI=0.81, CFI=0.91, IFI=0.91, RMR=0.085. As test results, 5 out of 6 hypotheses are supported because there are statistically significant casual relationships in structural equation model, shown in
. First of all, hypothesis 1 is partially supported because sub-hypothesis 1-1 and 1-2 are supported, whereas sub-hypothesis 1-3, 1-4, and 1-5 are rejected. Specifically, it reveals that warmth and sophistication dimensions in e-store personality have positive influence on e-store identification, however, activity, progressiveness, and strictness does not have any significant relationship on e-store identification. Secondly, hypothesis 2 was supported. Therefore, it can be said that e-store identification has a positive impact on e-store trust. Thirdly, hypothesis 3 is also supported. Hence, there is a positive relationship between e-store identification and e-store engagement. Fourthly, hypothesis 4 is supported too. e-store identification has a positive influence on e-store loyalty. Fifthly, hypothesis 5 is also accepted. This indicates that e-store trust is a precedent variable which positively affects e-store loyalty. Lastly, it reveals that e-store engagement has a positive impact on e-store loyalty. Therefore, hypothesis 6 is supported. The findings of the study imply that some dimensions of e-store personality have a positive influence on e-store identification, and that e-store identification has direct and indirect influence on e-store loyalty through e-store trust and e-store engagement positively. These results also suggest that the e-store identification in e-store personality is a precedent variable which positively affects e-store loyalty directly and indirectly through e-store trust and engagement as a mediating variable. Therefore, e-store marketers need to implement website strategy based on e-store personality, e-store identification, e-store trust, and e-store engagement to meet customers' psychological needs and enhance e-store loyalty. Finally, the limitations and future study directions based on this study are discussed.
This paper suggests a new retrieval method using weighted vector sum to resolve a problem of traditional location-based retrieval method, nearest neighbor (NN) query, and NN query using direction. The proposed method filters out data with the radius, and then the remained retrieval area is filtered by a direction information compounded of a user's moving direction, a pre-fixed interesting direction, and a pre-fixed retrieval angle. The moving direction is computed from a vector or a weighted vector sum of several vectors using a weight to adopt several cases. The retrieval angle can be set from traditional $360^{\circ}$ to any degree you want. The retrieval data for this method can be a still and moving image recorded shooting location, and also several type of media like text, web, picture offering to customer with location of company or resort. The suggested method guarantees more accurate retrieval than traditional location-based retrieval methods because that the method selects data within the radius and then removes data of useless areas like passed areas or an area of different direction. Moreover, this method is more flexible and includes the direction based NN.
In response to the changing demands of ever competitive market, SK Telecom has built a new marketing system that can support dynamic marketing campaigns and, at the same time, scale up to the large volumes of data and transactions for the next decade. The system which employs Unix-based client-server (using Web browser interfaces) architecture will replace the current mainframe-based COIS system. The project, named NGM (Next Generation Marketing ), is unprecedentedly large in scale. However, both managerial and technical problems led the project into a crisis. The application framework that depended on a software solution from a major global vendor could not support the dynamic functionalities required for the new system. In March 2005, SK telecom declared the suspension of the NGM project. The second phase of the project started in May 2005 following a comprehensive replanning. It was decided that no single existing solution could cope with the complexity of the new system and hence the new system would be custom-built. As such. a number of technical challenges emerged. In this paper, we report on the three key dimensions of technical challenges - middleware and application framework, database architecture and tuning, and system performance. The processes and approaches, adopted in building NGM system, may be viewed as "best practices" in the telecom industry. The completed NGM system, now called "U.key System," successfully came into operation on the ninth of October, 2006. This new infrastructure is expected to give birth to a series of innovative, fruitful, and customer-oriented applications in the near future.
BACKGROUND/OBJECTIVES: The expansion of menu labeling to restaurants has created a need to study customers' behavior toward nutrition information. Therefore, the purpose of this research was to compare college students' behavior toward nutrition information communication between Korea and the US. This study consisted of three objectives: 1) to compare the frequency of usage as well as degree of trust regarding smartphone-based communication channels in the acquisition of nutrition information among college students between Korea and the US, 2) to compare knowledge-sharing behavior related to nutrition information among college students between Korea and the US, and 3) to identify the role of country in the process of knowledge-sharing behavior. SUBJECTS/METHODS: A survey was distributed via the web to college students in Korea and the US. Data were collected in the 2nd week of March 2017. Completed responses were collected from 423 Koreans and 280 Americans. Differences between Koreans and Americans were evaluated for statistical significance using a t-test. In order to verify the effects of knowledge self-efficacy and transactive memory capability on knowledge-sharing behavior related to nutrition information, a regression analysis was performed. RESULTS: Significant differences were found in the frequency of usage as well as degree of trust in communication channels related to nutrition information between Korean and American college students. While knowledge self-efficacy and tractive memory capability had positive effects on knowledge-sharing behavior related to nutrition information, country had a significant effect on the process. CONCLUSIONS: This study is the first to compare customer behavior toward nutrition information acquisition and sharing between Korea and the US. Comparative research on nutrition information revealed differences among the different countries. Therefore, this study contributes to the body of knowledge on the nutrition information research, in particular, by providing a comparison study between countries.
In the ASP(Application Service Provider) business, applications using database sometimes require some data from clients' databases. These days such data are extracted from client database using manual database operations as an EXCEL file and the ASP, once receiving this file, transfers it into the application's database using manual database operations. This paper describes how to deal with data transmitting between the client database and ASP database on the web without using database manual operations for data extraction and insertion. We propose a framework which enables to transmit client data in a systematical way, to match different attribute names of each database for sharing same attribute values, and to avoid exposing information about the network path of client database to the ASP. This approach consists of two steps of data processing. The first is extracting data from client database as XML format by using a downloaded client program from ASP site, the second is uploading and storing the XML file into the ASP database. The implemented prototype system shows the suggested data integration paradigm is valid and ASP business needing integration of client database can be activated using it.
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