Ohbyung Kwon

Intimacy, familiarity and continuance intention: An extended expectation-confirmation model in web-based services

To date, plenty of theories, such as the expectation-confirmation model (ECM), have been proposed to explain why and how consumers are motivated to continue to use web-based services. In particular, various affective factors have been proposed to explain user satisfaction and continued use of web-based services recently in the IS community. In IS continuance research, several affective factors, such as perceived playfulness, perceived enjoyment and pleasure, have been examined. Affective factors discussed in the existing continuance intention-related studies are mostly short-term emotional factors like this. However, if a users continued usage of a web-based service can be interpreted as a long-term relationship between a user and the service, then the factors such as familiarity and intimacy which are the emotions created accumulatively over time based on an established relationship with the user can be helpful for better explaining the users continuance intention. Also, if relationships between consumers and web-based services have been built up due to repetitive usage, then we can assume that both affective and cognitive factors may explain consumers continuance intention. Hence, the purpose of this paper is to propose an extended ECM. We focus on two new constructs, familiarity and intimacy, as persistent affective factors. To investigate how cognitive and affective factors are interrelated in continuance intention, we conducted surveys focusing on users continued intention to use web-based services. The results indicate that continuance intention is affected conjointly by cognitive factors, such as perceived usefulness, and affective factors, such as familiarity and intimacy. However, the effects of affective factors such as intimacy were larger than those of cognitive factors such as perceived usefulness. In addition, the results indicate that intimacy, a purer affective concept than familiarity, affects users continuance intention more than familiarity.

A Galois lattice approach to a context-aware privacy negotiation service

Online privacy has consistently been a major concern for customers that has grown commensurately with the growth of e-services. Service providers have responded by making their privacy policies clearer for customers; however, most providers use legacy systems that are unable to actually change and adapt to users concerns, which can lead to fewer customers using the system. Hence, the purpose of this paper is to propose a context-aware privacy policy negotiation service. To do so, we adopt a Galois lattice theory to generate policy concepts embedded in e-services. Based on the Galois lattice, we develop a process for generating privacy policy rules. To show the feasibility of the ideas proposed in this paper, we perform a simulation test with two different online auction sites as an illustrative case in terms of two metrics: the number of rules generated and success throughput. Desirable features in applying the Galois lattice approach to context-aware privacy policy negotiation service are discussed.

A new ensemble method for gold mining problems: Predicting technology transfer

Of the many available innovative e-commerce technologies, only a small number have been successful in practice. Choosing and purchasing the right e-commerce technology is similar to finding gold in the mountains: there is a low frequency of a desirable state and a high frequency of an undesirable state. Thus, such scenarios are called gold mining problems. In such cases, the goal is to increase the probability of accurately predicting the desirable state. However, few prediction methods are sophisticated enough to predict gold mining problem results accurately. Hence, the purpose of this paper is to propose a novel ensemble method dedicated to increasing the probability of accurately predicting desirable states. We develop the vertical boosting with rewarded vote strategy, which generates classifiers for each attribute in a sample. Each classifier then generates individual rules with the assistance of a sensitivity level, to find desirable states. The individual rule sets are generated with adjustment by the multiplier, and then used in the ensemble method to generate combined rules. To show the methods soundness, we perform an experiment with a representative gold mining problem: prediction of transferability of the intellectual properties of e-transaction technology.

A relationship‐aware methodology for context‐aware service selection

: A future smart space will include many intelligent objects that can operate using ubiquitous computing technology. These intelligent objects should provide personalized, ad hoc application services by automatically recognizing relationships with users. To do so, there should be a method of establishing awareness of the relationship between a user and an object. However, while several recent approaches have suggested simple tools to improve the relationships themselves, very few methods are proposed for recognizing those relationships. Hence, this paper proposes a methodology for reasoning the relationship context between a user and an object. To do so, the Case-Based Reasoning method is adopted and amended. We also implement a prototype system and analyse significance via a laboratory experiment to demonstrate the feasibility of the ideas proposed in this paper.

Rule selection for knowledge-based product design

Knowledge-intensive and collaborative environment becomes more significant in the modern product development. To realize a true knowledge-based product design environment, however, the complexity of design constraint is still cumbersome issue to tackle. Typically, product design information comes from various sources and rapidly changes; design is evolutionary. Thus, a minimal set of rules is required to make an appropriate design decision. This paper aims to present a rule reduct based approach to select systematically minimal set of rules. Rough set theory synthesizes approximation of concepts, analyzes data by discovering patterns, and classifies into certain decision classes, which can be extracted from data by means of methods based on Boolean reasoning and discernibility. In this paper, this rule reduct based approach is compared with the absorption theorem based approach.