• Journal of Information Technology Applications and Management
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• v.19 no.2
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• pp.197-215
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• 2012

SNS has been recognized as a means of expanding social capital by promoting interaction and efficient communication among users. On the other hand, there are serious concerns on negative side of social network which is often called epidemics. Trust plays a critical role in controlling the spread of distorted information and vicious rumor as well as reducing uncertainties and risk from unreliable users in social network. This study focuses on what the antecedents of interpersonal trust are in social network. We performed online survey from 252 Twitter users and tested candidate antecedents which are chosen from previous literature. As a result, propensity to trust of trustor, ability and sincerity of trustee, intimacy between trustor and trustee significantly affected to the interpersonal trust in Twitter.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4892-4911
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• 2015

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

• Korean Management Science Review
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• v.29 no.2
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• pp.1-20
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• 2012

While feeling anxious about the risk of exposure of personal information and privacy, users of microblogs and social network services are continuously using them. This study aims to develop a model to investigate this phenomenon. Specifically, this study explores the relationship between personal characteristics (represented by privacy concern and self-presentation) and an individual's self-disclosure. An individual's personal belief (represented by perceived risk and perceived trust) is also tested as an mediator between the relationship. Through a questionnaire survey to 183 twitter users in Korea, the results indicate that self-presentation has a direct influence on self-disclosure as well as an indirect influence through perceived trust. In contrast, privacy concern has not a direct but an indirect negative influence on self-disclosure through perceived risk. In conclusion, self-presentation has a stronger influence on self-disclosure then privacy concern to Twitter users. An individual who has a higher propensity for self-presentation will form a stronger perceived trust on Twitter, which in turn, affects the individual's self-disclosure. On the other hand, an individual who is more concerned with personal privacy will feel more serious about perceived risk, which in turn, negatively influences one's perception of the trust in Twitter as well as his desire for self-disclosure.

• Asia pacific journal of information systems
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• v.21 no.3
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• pp.1-18
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• 2011

Blog have become very popular with Internet users as one of the latest forms of online communication as well as knowledge sharing tools. However, blogs generate growing concerns regarding information privacy issues. This study, based on social exchange theory, presents results about bloggers' information sharing behavior. The 157 surveys are collected from a large university in the eastern U.S. The survey results indicate that trust which has four second order factors: economy based trust, trust in reciprocity, trust in other bloggers and trust in social interaction positively affects bloggers' information sharing behavior. However, online information privacy concerns have a negative impact on the relationship between trust and bloggers' information sharing behavior.

• Knowledge Management Research
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• v.9 no.4
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• pp.91-112
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• 2008

Recently, the virtual world where avatars are playing crucial roles in connecting users with each other receives much attention from practioners as well as academicians. Especially, Second Life is one of the most prevailing website where 3-D avatars can be easily designed in line with for users' personal tastes. However, there are no studies showing how the selling agents designed in 3-D avatars can affect customer trust and purchase intention in the context of virtual world. This study is concerned with suggesting the empirical results to fill in the research void like this. Results reveal that the proposed hypotheses are proven with statistical significance.

• Journal of Distribution Science
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• v.20 no.9
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• pp.127-139
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• 2022

Purpose: As sharing economy is becoming increasingly relevant to people's lives, we want to understand why people participate in the sharing economy. We propose and validate three factors that are likely to influence consumers' choice of participating in the sharing economy at distribution market. Also, we found antecedents that affect these variables. These antecedents include appointment and return convenient, extended operating time, variety-seeking need, usage irregularity, other's clean usage, and feeling of membership between users. Research design, data, and methodology: This research collected 341 questionnaire data from participants in China. These participants were asked about the usage of DiDi, the most popular shareware in China. The data analysis and hypothesis testing were conducted using SPSS and Amos. Results: Usage convenience, usefulness of short-term usage, and trust in other users were found to have a positive impact on consumers' intention to participate in the sharing economy. In addition, we found that all the antecedents affect these variables positively. Conclusions: This research provides new driving factors for consumer participation in the sharing economy. Moreover, these findings will help managers develop marketing strategies for inducing the consumers to participate in the sharing economy.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.27-55
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• 2008

Due to the increasing and intensifying competition in the Internet shopping market, it has been recognized as very important to develop an effective policy and strategy for acquiring loyal customers. For this reason, web site designers need to know if a new Internet shopping mall(ISM) will be accepted. Researchers have been working on identifying factors for explaining and predicting user acceptance of an ISM. Some studies, however, revealed inconsistent findings on the antecedents of user acceptance of a website. Lack of consideration for individual differences in user ability is believed to be one of the key reasons for the mixed findings. The elaboration likelihood model (ELM) and several studies have suggested that individual differences in ability plays an moderating role on the relationship between the antecedents and user acceptance. Despite the critical role of user ability, little research has examined the role of user ability in the Internet shopping mall context. The purpose of this study is to develop a user acceptance model that consider the moderating role of user ability in the context of Internet shopping. This study was initiated to see the ability of the technology acceptance model(TAM) to explain the acceptance of a specific ISM. According to TAM. which is one of the most influential models for explaining user acceptance of IT, an intention to use IT is determined by usefulness and ease of use. Given that interaction between user and website takes place through web interface, the decisions to accept and continue using an ISM depend on these beliefs. However, TAM neglects to consider the fact that many users would not stick to an ISM until they trust it although they may think it useful and easy to use. The importance of trust for user acceptance of ISM has been raised by the relational views. The relational view emphasizes the trust-building process between the user and ISM, and user's trust on the website is a major determinant of user acceptance. The proposed model extends and integrates the TAM and relational views on user acceptance of ISM by incorporating usefulness, ease of use, and trust. User acceptance is defined as a user's intention to reuse a specific ISM. And user ability is introduced into the model as moderating variable. Here, the user ability is defined as a degree of experiences, knowledge and skills regarding Internet shopping sites. The research model proposes that the ease of use, usefulness and trust of ISM are key determinants of user acceptance. In addition, this paper hypothesizes that the effects of the antecedents(i.e., ease of use, usefulness, and trust) on user acceptance may differ among users. In particular, this paper proposes a moderating effect of a user's ability on the relationship between antecedents with user's intention to reuse. The research model with eleven hypotheses was derived and tested through a survey that involved 470 university students. For each research variable, this paper used measurement items recognized for reliability and widely used in previous research. We slightly modified some items proper to the research context. The reliability and validity of the research variables were tested using the Crobnach's alpha and internal consistency reliability (ICR) values, standard factor loadings of the confirmative factor analysis, and average variance extracted (AVE) values. A LISREL method was used to test the suitability of the research model and its relating six hypotheses. Key findings of the results are summarized in the following. First, TAM's two constructs, ease of use and usefulness directly affect user acceptance. In addition, ease of use indirectly influences user acceptance by affecting trust. This implies that users tend to trust a shopping site and visit repeatedly when they perceive a specific ISM easy to use. Accordingly, designing a shopping site that allows users to navigate with heuristic and minimal clicks for finding information and products within the site is important for improving the site's trust and acceptance. Usefulness, however, was not found to influence trust. Second, among the three belief constructs(ease of use, usefulness, and trust), trust was empirically supported as the most important determinants of user acceptance. This implies that users require trustworthiness from an Internet shopping site to be repeat visitors of an ISM. Providing a sense of safety and eliminating the anxiety of online shoppers in relation to privacy, security, delivery, and product returns are critically important conditions for acquiring repeat visitors. Hence, in addition to usefulness and ease of use as in TAM, trust should be a fundamental determinants of user acceptance in the context of internet shopping. Third, the user's ability on using an Internet shopping site played a moderating role. For users with low ability, ease of use was found to be a more important factors in deciding to reuse the shopping mall, whereas usefulness and trust had more effects on users with high ability. Applying the EML theory to these findings, we can suggest that experienced and knowledgeable ISM users tend to elaborate on such usefulness aspects as efficient and effective shopping performance and trust factors as ability, benevolence, integrity, and predictability of a shopping site before they become repeat visitors of the site. In contrast, novice users tend to rely on the low elaborating features, such as the perceived ease of use. The existence of moderating effects suggests the fact that different individuals evaluate an ISM from different perspectives. The expert users are more interested in the outcome of the visit(usefulness) and trustworthiness(trust) than those novice visitors. The latter evaluate the ISM in a more superficial manner focusing on the novelty of the site and on other instrumental beliefs(ease of use). This is consistent with the insights proposed by the Heuristic-Systematic model. According to the Heuristic-Systematic model. a users act on the principle of minimum effort. Thus, the user considers an ISM heuristically, focusing on those aspects that are easy to process and evaluate(ease of use). When the user has sufficient experience and skills, the user will change to systematic processing, where they will evaluate more complex aspects of the site(its usefulness and trustworthiness). This implies that an ISM has to provide a minimum level of ease of use to make it possible for a user to evaluate its usefulness and trustworthiness. Ease of use is a necessary but not sufficient condition for the acceptance and use of an ISM. Overall, the empirical results generally support the proposed model and identify the moderating effect of the effects of user ability. More detailed interpretations and implications of the findings are discussed. The limitations of this study are also discussed to provide directions for future research.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1281-1292
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• 2020

More and more cloud computing services are being applied in various fields; however, it is difficult for users and cloud computing service platforms to establish trust among each other. The trust value cannot be measured accurately or effectively. To solve this problem, we design a service-oriented cloud trust assessment model using a cloud model. We also design a subjective preference weight allocation (SPWA) algorithm. A flexible weight model is advanced by combining SPWA with the entropy method. Aiming at the fuzziness and subjectivity of trust, the cloud model is used to measure the trust value of various cloud computing services. The SPWA algorithm is used to integrate each evaluation result to obtain the trust evaluation value of the entire cloud service provider.

• Journal of Information Technology Applications and Management
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• v.22 no.2
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• pp.95-112
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• 2015

Spread of internet service had a huge effect on usage and spread of SNS world widely. Variety of the service has increased from human network managing to commerce, education, payment method and so on. Also, it is expanding its service territory to not only PC area but also that of mobile equipment with development of information technology. Spread of use on SNS provides existing corporations the expansion of service area. Late SNS starter companies are advancing in diverse way for attracting users by serving shopping, games and other services with association with SNS. Therefore, analysis on consumers' thought and pattern which analyze users' demand, service's function and contents and other factors in order to reflect on plans and operations of SNS is needed. On this perspective, this research is suggesting properties that are crucially related to sustained usage of SNS and study factors of SNS's characteristics that influence trust and satisfaction on SNS and continuous usage intent of SNS.