Earticle

According to technology acceptance model(TAM) which is one of the most important research models for explaining IT users' behavior, an intention of using IT is determined by usefulness and ease of use of it. However, TAM wouldn't explain the performance of using IT while it has been considered as a very good model for prediction of the intention. Many people would not be confirmed in the performance of using IT until they can control it at their will, although they think it useful and easy to use. In other words, in addition to usefulness and ease of use as in TAM, controllability is also should be a factor to determine acceptance of IT. Especially, there is a very close relationship between controllability and ease of use, both of which explain the other sides of control over the performance of using IT, so called perceived behavioral control(PBC) in social psychology. The objective of this study is to identify the relationship between ease of use and controllability. and analyse the effects of both two beliefs over performance and intention in using IT. For this purpose, we review the issues related with PBC in information systems studies as well as social psychology. Based on a review of PBC, we suggest a research model which includes the relationship between control and performance in using IT, and prove its validity empirically. Since it was introduced as a variable for explaining volitional control for actions in theory of planned behavior(TPB), there have been confusion about concept of PBC in spite of its important role in predicting so many kinds of actions. Some studies define PBC as self-efficacy that means actor's perception of difficulty or ease of actions, while others as controllability. However, this confusion does not imply conceptual contradiction but a double-faced feature of PBC since the performance of actions is related with both self-efficacy and controllability. In other words, these two concepts are discriminated and correlated with each other. Therefore, PBC should be considered as a composite concept consisting of self-efficacy and controllability. Use of IT has been also one of important areas for predictions by PBC. Most of them have been studied by analysis of comparison in prediction power between TAM and TPB or modification of TAM by inclusion of PBC as another belief as like usefulness and ease of use. Interestingly, unlike the other applications in social psychology, it is hard to find such confusion in the concept of PBC in the studies for use of IT. In most of studies, controllability is adapted as PBC since the concept of self-efficacy is included in ease of use explicitly. Based on these discussions, we can suggest perceived use control (PUC) which is defined as perception of control over the performance of using IT and composed of controllability and ease of use as sub-concepts. We also suggest a research model explaining acceptance of IT which includes the relationships of PUC with attitude and performance of using IT. For empirical test of our research model, two user groups are selected for surveying questionnaires. In the first group, there are freshmen who take a basic course for Microsoft Excel, and the second group consists of senior students who take a course for analysis of management information by Excel. Most of measurements are adapted ones that have been validated in the other studies, while performance is real score of mid-term in each class. In result, four hypotheses related with PUC are supported statistically with very low significance level. Main contribution of this study is suggestion of PUC through theoretical review of PBC. Specifically, a hierarchical model of PUC are derived from very rigorous studies in the relationship between self-efficacy and controllability with a view of PBC in social psychology. The relationship between PUC and performance is another main contribution.

With the explosive growth of the Internet, Internet shopping malls have become recognized as one of the major purchasing channels for consumers, as well as one of the competitive distribution channels for companies that allow them to contact with customers without intermediaries. It has motivated information systems (IS) researchers to examine the factors influencing consumer behavior and the purchase decision process in the context of Internet shopping malls. Despite the extensive research that has been conducted on the purchase decision process of consumers in online shopping malls, the results have demonstrated a need for further understanding of consumer behavior due to the unique features of virtual space and the characteristics of online consumers. Previous studies from marketing and consumer behavior domains have suggested that the concept of involvement plays an important role in explaining consumers’ purchase behavior. Despite the critical role of involvement and the explosive growth of e-commerce, little research has examined the role of involvement in the Internet shopping mall context. With this motivation, this study has two research objectives. First, it introduces and tests a theoretical model capable of better explaining consumers’ intention to purchase in the Internet shopping mall context. The proposed model extends and integrates existing models on purchase intention by incorporating purchase experience, innovativeness, and perceived self-control as the consumer factors, along with perceived risk, information provision, and perceived price as the Internet shopping mall factors. Second, this study examines how involvement differences may affect consumers’ intention to purchase. For this purpose, two factors from involvement theory, involvement type and involvement level, are introduced into the research model as moderating variables. In order to test the proposed model, the overall approach employed was a field study using the structural equation model. We developed our data collection instrument by adopting existing validated questions wherever possible. All question items were measured with a seven-point, Likert-type scale, with anchors ranging from ‘strongly disagree’ to ‘strongly agree.’ Two IS researchers reviewed the instrument and checked its face validity. We collected empirical data for this study over a period of two weeks from subjects who had purchase experiences through Internet shopping malls. A total of 473 complete and valid responses were obtained. We carried out data analysis using a two-step methodology with AMOS 4.0. The first step in the data analysis was to establish the convergent and discriminant validity of the constructs. In the second step, we examined the structural model based on the cleansed measurement model. The empirical results partly support the proposed model and identify the moderating effect of involvement differences. Theoretical and practical implications of the study are discussed, along with its limitations.

The purpose of this study is the investigation of the effects of user participation, network effect, social influence, and usefulness on stickiness and continued use on Internet communities. In this research, stickiness refers to repeat visit and visit duration to an Internet community. Continued use means the willingness to continue to use an Internet community in the future. Internet community-based companies can earn money through selling the digital contents such as game, music, and avatar, advertizing on Internet site, or offering an affiliate marketing. For such money making, stickiness and continued use of Internet users is much more important than the number of Internet users. We tried to answer following three questions. First, what is the effects of user participation on stickiness and continued use on Internet communities? Second, by what is user participation formed? Third, are network effect, social influence, and usefulness that was significant at prior research about technology acceptance model (TAM) still significant on Internet communities? In this study, user participation, network effect, social influence, and usefulness are independent variables, stickiness is mediating variable, and continued use is dependent variable. Among independent variables, we are focused on user participation. User participation means that Internet user participates in the development of Internet community site (called mini-hompy or blog in Korea). User participation was studied from 1970 to 1997 at the research area of information system. But since 1997 when Internet started to spread to the public, user participation has hardly been studied. Given the importance of user participation at the success of Internet-based companies, it is very meaningful to study the research topic of user participation. To test the proposed model, we used a data set generated from the survey. The survey instrument was designed on the basis of a comprehensive literature review and interviews of experts, and was refined through several rounds of pretests, revisions, and pilot tests. The respondents of survey were the undergraduates and the graduate students who mainly used Internet communities. Data analysis was conducted using 217 respondents (response rate, 97.7 percent). We used structural equation modeling (SEM) implemented in partial least square (PLS). We chose PLS for two reason. First, our model has formative constructs. PLS uses components-based algorithm and can estimate formative constructs. Second, PLS is more appropriate when the research model is in an early stage of development. A review of the literature suggests that empirical tests of user participation is still sparse. The test of model was executed in the order of three research questions. First, user participation had the direct effects on stickiness (β = 0.150, p < 0.01) and continued use (β = 0.119, p < 0.05). And user participation, as a partial mediation model, had a indirect effect on continued use mediated through stickiness (β = 0.070, p < 0.05). Second, optional participation and prosuming participation significantly formed user participation. Optional participation, with a path magnitude as high as 0.986 (p < 0.001), is a key determinant for the strength of user participation. Third, Network effect (β = 0.236, p < 0.001), social influence (β = 0.135, p < 0.05), and usefulness (β = 0.343, p < 0.001) had directly significant impacts on stickiness. But network effect and social influence, as a full mediation model, had both indirectly significant impacts on continued use mediated through stickiness (β = 0.11, p < 0.001; and β = 0.063, p < 0.05, respectively). Compared with this result, usefulness, as a partial mediation model, had a direct impact on continued use and a indirect impact on continued use mediated through stickiness. This study has three contributions. First, this is the first empirical study showing that user participation is the significant driver of continued use. The researchers of information system have hardly studied user participation since late 1990s. And the researchers of marketing have studied a few lately. Second, this study enhanced the understanding of user participation. Up to recently, user participation has been studied from the bipolar viewpoint of participation v.s. non-participation. Also, even the study on participation has been studied from the point of limited optional participation. But, this study proved the existence of prosuming participation to design and produce products or services, besides optional participation. And this study empirically proved that optional participation and prosuming participation were the key determinant for user participation. Third, our study compliments traditional studies of TAM. According prior literature about of TAM, the constructs of network effect, social influence, and usefulness had effects on the technology adoption. This study proved that these constructs still are significant on Internet communities.

When individuals collaborate in virtual settings, communication is mediated through a variety of communication technologies, and is associated not only with communication effectiveness but also with socio-emotional interactions among group members. In this regards, scholars have examined how technology-mediated communication systems can be designed and used to facilitate communication interaction. However, the empirical results of the previous studies have revealed inconsistencies in the effects of communication media on users’ behavioral or attitudinal responses, and on their viable effectiveness in organizations. Some studies claim that computer-mediated communication (CMC) is task-oriented but not suitable for emotional expression since it hinders close interpersonal interaction. On the other hand, some studies argue that individuals are able to develop interpersonal relationships more effectively in a CMC environment than in an FtF environment. Due to the different perspectives, a theoretical gap exists, and it leads to the inconsistent research findings. The purpose of this paper is to combine the two different perspectives into a single unified model, thereby providing a more realistic and comprehensive understanding about virtual collaboration. The present study here sought to answers the following questions with organizational communication perspective: What are the major components of virtual collaboration? What factors affect the performance of virtual collaboration? And what kind of managerial efforts should organization make in order to facilitate CMC media effectiveness in virtual collaboration? Although there is a certain belief that new media, namely technology-mediated communication support would create new opportunities, the problem of “how” or “why” has been an important question that is still not fully addressed. In this regards, we collectively reexamined previous literatures with major issues which are still controversial and integrated various theoretical activity within computer-mediated communication domain: task-oriented approach, socio-emotional approach, and evolutionary psychological approach. Our first contribution is to develop a framework for virtual collaboration by combining two different perspectives into a single unified model, providing a more realistic and comprehensive understanding. The second main contribution is the joint modeling of both social presence and cognitive effort, and the effects on two distinct but important communication outcomes (i.e., task performance and relational development). We tested the research hypotheses which were developed based on the various CMC theories using data gathered through a self-administered mail survey of 127 individuals of 69 virtual workgroups. The proposed model was supported, providing preliminary evidence that the tension between two opposite view should be integrated. The results show that the individual’s psychological processes (social presence and cognitive effort) in a virtual environment significantly mediated the effect of CMC inputs (media richness, user adaptation, and shared context) on the CMC outputs (task performance and relational development). Furthermore, this study shows that the lack of perceived media richness of CMC media can be complemented by user adaptation and shared context. Based on the results, we discuss how communication system should be designed and implemented so as to promote virtual interaction as well as how a virtual workgroup should be composed to complement the lack of media richness. A virtual collaboration using CMC media may create new value by overcoming the logistical constraints. On the other hand, it may also generate various managerial risks such as communicational depersonalization, process dissatisfaction, and low cohesion. Therefore, this study suggests that organization managers should carefully choose the CMC mediums and monitor individual member’s cognitive and affective psychological processes during virtual collaboration to reduce potential risks in virtual collaboration.

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the too many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket (i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.