• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.759-776
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• 2009

This paper aims at identifying trust factors to be managed in UCC environment, and how those trust factors impact on trust in UCC site. Also this paper examines the moderating effect of level of UCC participation(UCC retrieve, UCC production) on the relation between trust in UCC site and intention to use. For this purpose, we identified information, system, service, and site trust through intensive review of prior researches and some expert interviews. A total 338 surveys were used to analyze research hypothesis. The findings show that information, system, and service trust is positively and significantly related to site trust, and then site trust has impact on UCC usage. But, It is proven that there is no difference in effect of site trust on UCC usage according to level of UCC participation.

• The Journal of Information Systems
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• v.19 no.4
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• pp.65-85
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• 2010

The importance of communities of practice(CoP) as an organizational informal unit for fostering knowledge transfer and sharing gains a lot of attention from KM researchers and practitioners. Since most of CoPs are formulated online these days, the credibility or trustworthiness of knowledge contents circulated within a certain CoP should be considered thoroughly for them to be fully utilized safely. Here comes the need for an appropriate trust measuring methodology to determine the true value of knowledge given by unknown people through an online channel. In this paper, an improved trust measuring method is proposed using new trust variables such as level of degrees derived from the relationships among community users. In addition, activeness, relevance, and usefulness of the knowledge contents themselves, which are calculated automatically using a text categorization technique, are also used for trust measurement. The proposed framework incorporates fuzzy set and calculation concepts to help build trust matrices and models, which are used to measure the level of trust involved in specific knowledge artifacts concerned.

• Proceedings of the Korea Database Society Conference
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• 2010.06a
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• pp.277-287
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• 2010

We are sometimes interacting with people who we know nothing and facing with the difficult task of making decisions involving risk in social network. To reduce risk, the topic of building Web of trust is receiving considerable attention in social network. The easiest approach to build Web of trust will be to ask users to represent level of trust explicitly toward another users. However, there exists sparsity issue in Web of trust which is represented explicitly by users as well as it is difficult to urge users to express their level of trustworthiness. We propose a fuzzy-based inference model for Web of trust using user behavior information in social network. According to the experiment result which is applied in Epinions.com, the proposed model show improved connectivity in resulting Web of trust as well as reduced prediction error of trustworthiness compared to existing computational model.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.235-250
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• 2021

Purpose - This study analyzed the correlation and influence between social support, trust (seller, brand), and continuous purchase intention in C2C social commerce in China. Design/methodology/approach - To test the hypothesis, SPSS and Smart PLS 3.0 statistical packages were used based on the collected data. Findings - First, it was confirmed that social support (emotional support, informational support) had a positive effect on trust in sellers. Second, it was found that trust in sellers had a positive effect on brand trust. Third, both seller trust and brand trust have a positive effect on consumers' continuous purchase intention. Research implications or Originality - When consumers gain emotional and informational support from sellers, trust in sellers will be effectively improved. Companies wishing to improve brand credibility of their products will have to outsource the sale of their products to trusted sellers. The C2C social commerce platform should build its own trust rating system, recommend sellers with high reliability ratings, and encourage sellers to provide consumers with a lot of information about their brand.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.87-95
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• 2020

Highly competitive environment has been forcing e-commerce industries to seek strategies to achieve competitive advantages. Mobile payment is a kind of service that allows mobile phone user to easily and conveniently initiate payments and transfer funds using their mobile phone anytime, and anywhere. This study is designed to identify factors that affect the intention of continued use of mobile payment services between users in Korea and Laos. As a result, first, in the case of Korean consumers, system quality, information quality and service quality were shown to have a positive effect on trust and satisfaction. In addition, trust and satisfaction were shown to have a positive effect on continuance intention. Second, in the case of Laotian consumers, system quality and service quality were shown to have a positive effect on trust, and system quality and information quality were shown to have a positive effect on satisfaction. In addition, trust and satisfaction were shown to have a positive effect on continuance intention. The study has its implications by analyzing factors affecting the continuance intention with the comparison of the customers from a developed nation and a developing nation, providing a direction of development for developing competitive advantages for those in development. For the developed, the study provides a guideline of what to modify and supplement in cases of entering the markets of developing nations.

• Safety and Health at Work
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• v.10 no.4
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• pp.461-469
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• 2019

Background: Collisions involving workers and mobile machines continue to be a major concern in underground coal mines. Over the last 30 years, these collisions have resulted in numerous injuries and fatalities. Recently, the Mine Safety and Health Administration (MSHA) proposed a rule that would require mines to equip mobile machines with proximity detection systems (PDSs) (systems designed for automated collision avoidance). Even though this regulation has not been enacted, some mines have installed PDSs on their scoops and hauling machines. However, early implementation of PDSs has introduced a variety of safety concerns. Past findings show that workers' trust can affect technology integration and influence unsafe use of automated technologies. Methods: Using a mixed-methods approach, the present study explores the effect that factors such as mine of employment, age, experience, and system type have on workers' trust in PDSs for mobile machines. The study also explores how workers are trained on PDSs and how this training influences trust. Results: The study resulted in three major findings. First, the mine of employment had a significant influence on workers' trust in mobile PDSs. Second, hands-on and classroom training was the most common types of training. Finally, over 70% of workers are trained on the system by the mine compared with 36% trained by the system manufacturer. Conclusion: The influence of workers' mine of employment on trust in PDSs may indicate that practitioners and researchers may need to give the organizational and physical characteristics of each mine careful consideration to ensure safe integration of automated systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1898-1903
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• 2021

Recently, the need to be wary of internal access such as internal access as well as external attackers' access to work has increased due to network expansion, cloud infrastructure expansion, and changes in working patterns due to COVID-19 situations. For this reason, a new network security model called Zero Trust is drawing attention. Zero Trust has a key principle that a trusted network does not exist, and in order to be allowed access, it must be authenticated first, and data resources can only be accessed by authenticated users and authenticated devices. In this paper, we will explain these zero trust and zero trust architectures and examine new security application strategies applicable to various companies using zero trust and the process of building a new security system based on the zero trust architecture model.

• Proceedings of the Technology Innovation Conference
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• 2002.06a
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• pp.141-156
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• 2002

This study tries to testify that how much knowledge sharing behavior affects organizational effectiveness and is affected by the supervisory trust empirically. The results show that the higher the supervisory trust was the higher the level of knowledge sharing behavior and ultimately, organizational effectiveness is improved. Specially, the supervisory trust affects job-related knowledge sharing behavior and knowledge sharing system behavior. The knowledge sharing culture, however, is no relation with the supervisory trust. Job-related knowledge sharing behavior influenced both job satisfaction and organizational commitment. But knowledge sharing system influenced only job satisfaction and knowledge sharing culture influenced only organization at commitment. The implication from this paper is that knowledge sharing improves the organizational effectiveness and the supervisory trust is important for knowledge sharing in R&D organization.

• Journal of Smart Tourism
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• v.3 no.2
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• pp.5-13
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• 2023

Because of increasing anxiety about infectious diseases and the demand for contactless service caused by the COVID-19 pandemic, it has become crucial for the tourism and hospitality sector to understand customers' psychological mechanism of contactless service during and post COVID-19. Thus, this paper proposes a conceptual model by integrating trust in the framework of the behavioral immune system. Interestingly, our study found that anxiety about infectious diseases during the COVID-19 pandemic has not only increased hotel customers' desire for contactless service and changed their behavioral intentions, but it has also impacted customers' trust in hotel service robots. Therefore, irrespective of how the hotel service environment changes, trust in technology has become the most fundamental factor for hotel customers' attitudes toward adopting technology. Based on the results, this paper provides salient theoretical and practical implications.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.