• Journal of the Korean Society for information Management
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• v.20 no.2
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• pp.93-112
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• 2003

A public information system is being expanded, along with the advance of information technology, to strengthen national competitiveness and provide people with better services. And there also is a growin need for the better performance of that system, as a tremendous amount of public financial resources is invested in that. To address that need, it's required to make an evaluation of its efficiency on a regular basis to identify its problems and make it work better. The purpose of this study was, accordinglu, to examine the quality of data and services provided by the Work-Net, a Web-based public employment information system. The subjects in this study were 102 users of it, and the system was evaluated in terms of content, accuracy, timeliness, display format, ease of use and customer support. For data analysis, t-test and one-way ANOVA were implemented to find out the general characteristics of the users, and to see Whether or not their view was different according to the type of information they searched for. The findings of this study are expected to lay some foundation for intensifying the efficiency of the public and private employment information systems.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.105-130
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• 2008

Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.151-171
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• 2014

Firms today have sought management effectiveness and efficiency utilizing information technologies (IT). Numerous firms are outsourcing specific information systems functions to cope with their short of information resources or IT experts, or to reduce their capital cost. Recently, Software-as-a-Service (SaaS) as a new type of information system has become one of the powerful outsourcing alternatives. SaaS is software deployed as a hosted and accessed over the internet. It is regarded as the idea of on-demand, pay-per-use, and utility computing and is now being applied to support the core competencies of clients in areas ranging from the individual productivity area to the vertical industry and e-commerce area. In this study, therefore, we seek to quantify the value that SaaS has on business performance by examining the relationships among firm strategies, SaaS technology maturity, and business performance of SaaS providers. We begin by drawing from prior literature on SaaS, technology maturity and firm strategy. SaaS technology maturity is classified into three different phases such as application service providing (ASP), Web-native application, and Web-service application. Firm strategies are manipulated by the low-cost strategy and differentiation strategy. Finally, we considered customer acquisition as a business performance. In this sense, specific objectives of this study are as follows. First, we examine the relationships between customer acquisition performance and both low-cost strategy and differentiation strategy of SaaS providers. Secondly, we investigate the mediating and moderating effects of SaaS technology maturity on those relationships. For this purpose, study collects data from the SaaS providers, and their line of applications registered in the database in CNK (Commerce net Korea) in Korea using a questionnaire method by the professional research institution. The unit of analysis in this study is the SBUs (strategic business unit) in the software provider. A total of 199 SBUs is used for analyzing and testing our hypotheses. With regards to the measurement of firm strategy, we take three measurement items for differentiation strategy such as the application uniqueness (referring an application aims to differentiate within just one or a small number of target industry), supply channel diversification (regarding whether SaaS vendor had diversified supply chain) as well as the number of specialized expertise and take two items for low cost strategy like subscription fee and initial set-up fee. We employ a hierarchical regression analysis technique for testing moderation effects of SaaS technology maturity and follow the Baron and Kenny's procedure for determining if firm strategies affect customer acquisition through technology maturity. Empirical results revealed that, firstly, when differentiation strategy is applied to attain business performance like customer acquisition, the effects of the strategy is moderated by the technology maturity level of SaaS providers. In other words, securing higher level of SaaS technology maturity is essential for higher business performance. For instance, given that firms implement application uniqueness or a distribution channel diversification as a differentiation strategy, they can acquire more customers when their level of SaaS technology maturity is higher rather than lower. Secondly, results indicate that pursuing differentiation strategy or low cost strategy effectively works for SaaS providers' obtaining customer, which means that continuously differentiating their service from others or making their service fee (subscription fee or initial set-up fee) lower are helpful for their business success in terms of acquiring their customers. Lastly, results show that the level of SaaS technology maturity mediates the relationships between low cost strategy and customer acquisition. That is, based on our research design, customers usually perceive the real value of the low subscription fee or initial set-up fee only through the SaaS service provide by vender and, in turn, this will affect their decision making whether subscribe or not.

• 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.