Earticle

The brand-based online community recently plays an important roles for consumers to facilitates searching and sharing information among them. Firms often find such a brand community as a critical channel to gain collective intelligence for developing new ideas and products. As a new web platform such as web 2.0 has been introduced, consumers could more easily participate in the new social networks created by sharing mutual value and belief among themselves. Accordingly firms began to recognize potentials of online brand assets and pay attention to the importance of online brand community loyalty. Previous research related to online community tends to focus on identifying the antecedents of community loyalty and their subsequent impacts on brand. They, however, tend to neglect the importance of individual characteristics of online community users. As integrating the fragmented variables with an individual charac-teristics, therefore, this study reexamined the impacts of interactivity, information, reward, and personalization services provided by an online brand community on the sense of community, community loyalty, and brand attitude. Also, this study investigated how users' individual characteristics(need for cognition: NFC) can play moderating roles among the variables identified in the previous research. A field survey was administrated and 671 valid samples were collected. In order to test the hypothesis we conducted the multi-sample structural equation modeling(MSEM) between two groups(a group with high vs. a group with low level of NFC). Results show that previously identified variables such as interactivity, information, reward, and personalization services have significant effects on the sense of community as previous research demonstrated. Subsequently, the sense of community positively influences the community loyalty and brand attitude. However, when considering the NFC as a moderating variable, we found that the effect of interactivity and reward service on the sense of community was stronger for a group with a lower level of NFC compared to a group with a higher level, while the effect of information providing service on the sense of community was stronger for a group with a higher level of NFC compared to a group with a lower level. This research revealed that NFC can affect the degree of individual perception on the sense of community which has been considered as an important indicator for the community loyalty and brand attitude. Hence, when firms developing customer relation strategy through building an online brand community, they need to reflect customers' NFC and accordingly provide varying degree of interactivity, information, reward, and personalization services.

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources.Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google’s PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high.Kleinberg’s algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg’s algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages.As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web (WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework (RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a ‘refers to’ property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web.Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg’s algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg’s, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community (TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it.In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predi-cate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC (Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

Small-and-medium sized enterprises (SMEs) represent quite a large proportion of the industry as a whole in terms of the number of enterprises or employees. However researches on information system so far have focused on large companies, probably because SMEs were not so active in introducing information systems as larger enterprises. SMEs are now increasingly bringing in information systems such as ERP (Enterprise Resource Planning Systems) and some of the companies already entered the stage of ongoing use. Accordingly, researches should deal with the use of information systems by SMEs operating under different conditions from large companies. This study examined factors and mechanism inducing faithful appropriation of information systems, in particular integrative systems such as ERP, in view of individuals’ active feedback-seeking behavior. There are three factors expected to affect end users’ feedback-seeking behavior for faithful appropriation of information systems. They are management support, peer IT champ support, and IT staff support. The main focus of the study is on how these factors affect feedback-seeking behavior and whether the feedback-seeking behavior plays the role of mediator for realizing faithful appropriation of information systems by end users.To examine the research model and the hypotheses, this study employed an empirical method based on a field survey. The survey used measurements mostly employed and verified by previous researches, while some of the measurements had gone through minor modifications for the purpose of the study. The survey respondents are individual employees of SMEs that have been using ERP for one year or longer. To prevent common method bias, Task-Technology Fit items used as the control variable were made to be answered by different respondents. In total, 127 pairs of valid questionnaires were collected and used for the analysis. The PLS (Partial Least Squares) approach to structural equation modeling (PLS-Graph v. 3.0) was used as our data analysis strategy because of its ability to model both formative and reflective latent constructs under small-and medium-size samples. The analysis shows Reliability, Construct Validity and Discriminant Validity are appropriate. The path analysis results are as follows; first, the more there is peer IT champ support, the more the end user is likely to show feedback-seeking behavior (path-coefficient=0.230, t=2.28, p<0.05). In other words, if colleagues proficient in information system use recognize the importance of their help, pass on what they have found to be an effective way of using the system or correct others’ misuse, ordinary end users will be able to seek feedback on the faithfulness of their appropriation of information system without hesitation, because they know the convenience of getting help. Second, management support encourages ordinary end users to seek more feedback (path-coefficient=0.271, t=3.06, p<0.01) by affecting the end users’ perceived value of feedback (path-coefficient=0.401, t=6.01, p<0.01). Management support is far more influential than other factors that when the management of an SME well understands the benefit of ERP, promotes its faithful appropriation and pays attention to employees’ satisfaction with the system, employees will make deliberate efforts for faithful appropriation of the system. However, the third factor, IT staff support was found not to be conducive to feedback-seeking behavior from end users (path-coefficient=0.174, t=1.83). This is partly attributable to the fundamental reason that there is little support for end users from IT staff in SMEs. Even when IT staff provides support, end users may find it less important than that from coworkers more familiar with the end users’ job. Meanwhile, the more end users seek feedback and attempt to find ways of faithful appropriation of information systems, the more likely the users will be able to deploy the system according to the purpose the system was originally meant for (path-coefficient=0.35, t=2.88, p<0.01). Finally, the mediation effect analysis confirmed the mediation effect of feedback-seeking behavior. By confirming the mediation effect of feedback-seeking behavior, this study draws attention to the im-portance of feedback-seeking behavior that has long been overlooked in research about information system use. This study also explores the factors that promote feedback-seeking behavior which in result could affect end user’s faithful appropriation of information systems. In addition, this study provides insight about which inducements or resources SMEs should offer to promote individual users’feedback-seeking behavior when formal and sufficient support from IT staff or an outside information system provider is hardly expected. As the study results show, under the business environment of SMEs, help from skilled colleagues and the management plays a critical role. Therefore, SMEs should seriously consider how to utilize skilled peer in-formation system users, while the management should pay keen attention to end users and support them to make the most of information systems.

Most traditional newspaper publishers provide online editions to counter the competition of online news providers. However, the relationship between the online and print editions of the same newspaper has not been clearly defined. Some see the online newspaper as a substitute, while others consider it a complement. A 2002 NAA online newspaper consumer survey indicated that one-third of its respondents said they were now using the print newspaper less. Others have argued that the online edition will not wipe out print consumption, and may even complement it. While the print edition offers particular advantages such as portability, less eye strain, and the tactile experience of a printed page, the online edition also offers specific advantages such as access to breaking news, continually updated information, access to old archives, etc. All these factors would tend to lower the degree of interchangeability between the products. However, recent empirical studies show that the online edition is a substitute for rather than a complement of the print edition. Still, to some print readers, the online edition provides additional value. In this paper, by capturing the two different aspects of online editions–the substitute aspect and the addi-tional value added aspect–as well as other available online alternatives, we develop an analytical model to derive the optimal production and distribution strategies of both online and print editions. Confronting the ”free versus fee” issue, we show that it is optimal to provide an online version of the print newspaper for free to non-print subscribers. However, the amount of free news content that the publishers need to put on the Web depends on the available alternatives on the online market. The ”fee” and ”free” options both have merits and demerits as well. If the publisher charges for the online version of the print newspaper, she can generate revenue from the fee charged to online readers. However, doing so will limit the size of the online audience and further reduce online advertising revenue. At the same time, by providing a high-quality online version and charging for it, the price of the print newspaper must stay low in order to lure high valued readers. On the contrary, if the publisher provides an online version of the print newspaper for free, she can obtain a larger audience for the online version. At the same time, by providing a low-quality online newspaper, the publisher can increase the print newspaper price to get more revenue from high valued offline readers, although no revenue is incoming from online version readers. Through systematic measuring of all the pros and cons, our analysis shows that the optimal option is not ”fee” but ”free.”

Price rigidity involves prices that do not change with the regularity predicted by standard economic theory. It is of long-standing interest for firms, industries and the economy as a whole. However, due to the difficulty of measuring price rigidity and price adjustments directly, only a few studies have attempted to provide empirical evidence for explanatory theories from Economics and Marketing.This paper proposes and validates a research model to examine different theories of price rigidity and to predict what variables can explain the observed empirical regularities and variations in price adjustment patterns of Internet-based retailers. I specify and test a model using more than 3 million daily observations on 385 books, 118 DVDs and 154 CDs, sold by 22 Internet-based retailers that were collected over a 676-day period from March 2003 to February 2005. I obtained a number of interesting findings from the estimation of our logit model. First, quality seems to play a role-I find that both price levels as proxies for store quality, and information on the quality of a product consumers have, affect online price rigidity. Second, greater competition (i.e., less industry concentration) leads to less price rigidity (i.e., more price changes) on the Internet. I also find that Internet-based sellers more frequently change the prices of popular products, and the sellers with broader product coverage change prices less frequently, which seem due to economic forces faced by these Internet-based sellers. To the best of my knowledge, this research is the first to empirically assess price rigidity patterns for multiple industries in Internet-based retailing, and attempt to explain the variation in these patterns. I found that price changes are more likely to be driven by quality, competitive and economic considerations. These results speak to both the IS and economics literatures. To the IS literature these results suggest we take economic considerations into account in more sophisticated ways. The existence and variation in price rigidity argue that simplistic assumptions about frictionless and completely flexible digital prices do not capture the richness of pricing behavior on the Internet. The quality, competitive and economic forces identified in this model suggest promising directions for future theoretical and empirical work on their role in these technologically changing markets. To the economics literature these results offer new evidence on the sources of price rigidity, which can then be incorporated into the development of models of pricing at the firm, industry and even macro-economic level of analysis. It also suggests that there is much to be learned through interdisciplinary research between the IS, economics and related business disciplines.

Growth in the outsourcing market signals that firms of all sizes believe outsourcing will ultimately deliver many benefits and conveniences. But there are not many firms satisfied with the results of outsourcing. What is more, previous researches were fragmentary analyses focused on specific variables of outsourcing such as sourcing decisions, partnership rather than comprehensive analysis. Thus, they could not propose general systematic methodologies applicable to the real situation. To solve these problems, we developed an integrated theoretical framework that considered both contract with the hard side and trust with the soft side from a contingency viewpoint and tested this model using 143 data of Korean companies executing outsourcing. In addition, we examined how situational factors (outsourcing task complexity and outsourcing management competence) affects each path in the research model. The results of this study are as follows. First, it was proved the theory that trust is not a substitute for contract but its complement. Previous empirical studies on outsourcing success factors were focused on the establishment of successful partnership on the assumption that trust can replace contract in many situations. According to the results of our empirical analysis, however, contract and trust were in a mutually complementary relation with each other and their emphasis was different. Furthermore, different from previous researches, it was found effective to use trust as a supplementary tool and contract as a main means in outsourcing management strategy. Second, this study provided an integrated view that sees both contract and trust from a contingency view-point in theoretically reestablishing the relationship between contract and trust. Previous researches leaned to specific variables or theory-centered fragmentary analysis, but this study proposed a more practical and integrated research model and tested its effectiveness. Based on the results, with the model, decision makers are expected to scrutinize outsourcing situation more closely and to have a practical insight to the situation.Third, it was found that contract mechanism and trust building do not have a direct effect on outsourcing performance but relationship management intensity mediates the effect of contract mechanism and trust building. This is considered significantly meaningful to outsourcing partners who have believed that out-sourcing would be successful if a contract is made properly or trust is built.Lastly, the path from trust building to relationship management intensity was moderated by informed buying, as the path coefficients from trust building to relationship management intensity varied by the degree of informed buying competence.

With the advent of a wide variety of Internet shopping malls, consumers can choose a best appealing shopping mall from among the Bricks-and-Clicks and Pure-Player malls. Pure-Players launched their operation grandiosely with the early stage of Internet use in 1995. However, after the burst of Dot-com company bubbles in 1997, Pure-Players introduce various types of business models to meet potential needs of consumers. While Pure-Players suffer skeptical views from market analysts as well as consumers, traditional offline compa-nies learned important lessons from Dot-com companies collapse phenomena, and expanded their business channels into online in the name of Bricks-and-Clicks. Nowadays, Bricks-and-Clicks successfully establish in the market as one of reliable business partners among consumers. Therefore, it is no surprise that recent competitions between Bricks-and Clicks and Pure-Players become fiercer than ever to attract potential cus-tomers to their websites. In this situation, consumers can choose a shopping mall to their best satisfaction. Consumers can enjoy both offline and online options for shopping because Bricks-and Clicks provide both offline and online chan-nels to consumers, which is compared with Pure-Players offering only online channel. Offline channel is unique in providing consumers with chances to touch and feel target products and services. Meanwhile, online channel is considered very viable and convenient shopping options for consumers. In this respect, it is easily assumed that consumers will show different online shopping behavior when they have to choose either Bricks-and-Clicks mall or Pure-Player mall for the sake of shopping. Remaining research issue in this case is how much consumers’ schema would influence online shopping behavior between Bricks-and-Clicks and Pure-Players. Basically, schema is a framework for synthetic information recognition that individual consumers have and is very characteristic in that it focuses not on fragmentary facts but on the combination of various causes affecting results. Consumers’ schema is closely represented by trust, structural assurance, and perceived relative advantage towards a specific type of shopping mall. In literature, there exist a lot of studies comparing Bricks-and-Clicks and Pure-Players. However, there is no study to pursue the analysis of consumer behaviors comparing Bricks-and Clicks and Pure-Players from the schema perspective. Therefore, this study aims to investigate this research gap. Empirical analysis is adopted by garnering valid questionnaires from 514 Internet shopping mall users. 237 were mainly using Bricks-and-Clicks for shopping, while 277 were found to visit Pure-Players for shopping. PLS was applied to analyze the survey data to verify the proposed research hypotheses. Findings from the empirical test results are as follows.First, consumers perceive more trust and relative advantage in Pure-Players, comparing with Bricks-and-Clicks. This result is against widely-accepted perception that Bricks-and-Clicks would be perceived by consumers as more trustworthy and relatively advantageous because they have offline reputation and stores. Therefore, it becomes more obvious that Internet is becoming daily necessaries, and consumers increasingly feel very comfortable in using the Internet for their own personal purposes. Second, consumers have firm faith in transaction safety, regardless Bricks-and-Clicks and Pure-Players. This seems due to the fact that most of shopping malls showing dubious transaction safety have no place in the market. In a nutshell, empirical results tell us that Pure-Players will grow very much in the future, to the extent that consumers perceive no difference in comparison with Bricks-and-Clicks. Besides, consumers’ schema accumulated through trust and perceived relative advantage plays crucial role in determining consumer behavior.

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload.Recommender systems can be defined as systems for suggesting some Items (goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the pre-diction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process.In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error.Research hypothesisTwo research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows.Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference.To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different.Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences.To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of 　　 goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

Measuring Information Technology(IT) organizations’ activities have been limited to mainly measure financial indicators for a long time. However, according to the multifarious functions of Information System, a number of researches have been done for the new trends on measurement methodologies that come with financial measurement as well as new measurement methods. Especially, the researches on IT Balanced Scorecard (BSC), concept from BSC measuring IT activities have been done as well in recent years. BSC provides more advantages than only integration of non-financial measures in a performance measure-ment system. The core of BSC rests on the cause-and-effect relationships between measures to allow pre-diction of value chain performance measures to allow prediction of value chain performance measures, communication, and realization of the corporate strategy and incentive controlled actions. More recently, BSC proponents have focused on the need to tie measures together into a causal chain of performance, and to test the validity of these hypothesized effects to guide the development of strategy. Kaplan and Norton[2001] argue that one of the primary benefits of the balanced scorecard is its use in gauging the success of strategy. Norreklit[2000] insist that the cause-and-effect chain is central to the bal-anced scorecard.The cause-and-effect chain is also central to the IT BSC. However, prior researches on relationship be-tween information system and enterprise strategies as well as connection between various IT performance measurement indicators are not so much studied. Ittner et al.[2003] report that 77% of all surveyed companies with an implemented BSC place no or only little interest on soundly modeled cause-and-effect relationships despite of the importance of cause-and- effect chains as an integral part of BSC. This shortcoming can be explained with one theoretical and one practical reason[Blumenberg and Hinz, 2006]. From a theoretical point of view, causalities within the BSC method and their application are only vaguely described by Kaplan and Norton. From a practical consideration, modeling corporate causalities is a complex task due to tedious data acquisition and following reliability maintenance.However, cause-and effect relationships are an essential part of BSCs because they differentiate performance measurement systems like BSCs from simple key performance indicator(KPI) lists. KPI lists present an ad-hoc collection of measures to managers but do not allow for a comprehensive view on corporate performance. Instead, performance measurement system like BSCs tries to model the relationships of the underlying value chain in cause-and-effect relationships.Therefore, to overcome the deficiencies of causal modeling in IT BSC, sound and robust causal modeling approaches are required in theory as well as in practice for offering a solution.The propose of this study is to suggest critical success factors(CSFs) and KPIs for measuring performance for IT organizations and empirically validate the casual relationships between those CSFs.For this purpose, we define four perspectives of BSC for IT organizations according to Van Grembergen’s study[2000] as follows. The Future Orientation perspective represents the human and technology resources needed by IT to deliver its services. The Operational Excellence perspective represents the IT processes employed to develop and deliver the applications. The User Orientation perspective represents the user evaluation of IT. The Business Contribution perspective captures the business value of the IT investments.Each of these perspectives has to be translated into corresponding metrics and measures that assess the current situations. This study suggests 12 CSFs for IT BSC based on the previous IT BSC’s studies and COBIT 4.1. These CSFs consist of 51 KPIs. We defines the cause-and-effect relationships among BSC CSFs for IT Organizations as follows.The Future Orientation perspective will have positive effects on the Operational Excellence perspective. Then the Operational Excellence perspective will have positive effects on the User Orientation perspective. Finally, the User Orientation perspective will have positive effects on the Business Contribution perspective.This research tests the validity of these hypothesized casual effects and the sub-hypothesized causal relationships. For the purpose, we used the Partial Least Squares approach to Structural Equation Modeling (or PLS Path Modeling) for analyzing multiple IT BSC CSFs. The PLS path modeling has special abilities that make it more appropriate than other techniques, such as multiple regression and LISREL, when analyzing small sample sizes. Recently the use of PLS path modeling has been gaining interests and use among IS researchers in recent years because of its ability to model latent constructs under conditions of nonormality and with small to medium sample sizes(Chin et al., 2003).The empirical results of our study using PLS path modeling show that the casual effects in IT BSC significantly exist partially in our hypotheses.