• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.169-185
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• 2017

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

• Journal of Information Technology Services
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• v.11 no.2
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• pp.319-337
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• 2012

To stress the importance of privacy in social networking, I presented an analysis on how information control and information management vulnerability influence trust and privacy concerns in social networking, and how trust and privacy concerns influence the sustainable usage intention of social network services. I also analyzed the factors affecting privacy concerns to present the method to alleviate social network users' concerns about privacy. Information collection control, information processing control and information management vulnerability were chosen and analyzed as the factors affecting privacy concerns. The results showed that information collection control and information management vulnerability significantly affected trust and privacy concerns; and information processing control did not significantly affect privacy concerns. The relationship between trust and privacy concerns, and sustainable usage intention was statistically significant; and the relationship between trust and expansion of communication channels was also statistically significant.

• 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.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.509-513
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• 2020

Network marketing in Indonesia, especially in a time of crisis, is massively rising and has become a popular opportunity among other industries. Network marketing organizations, consists of partnership and trust, a community of connections and contact between individuals. This article has tried to examine the potential strategy to mitigate low trust in network marketing organizations specifically in the Indonesia market, where no studies on the subject has been done before. In doing this research assignment employed a secondary research methodology by reviewing previous academic literatures, by exploration and evaluation. For the purpose, 6 main articles and 25 relevant supporting articles were selected, there is an interesting and prominent research in an effort to repair trust in the perspective of the organization's efforts to build trust and control trust framework in strategy trust repair. The result of this analysis showed that the application of trust-building activities studies reveals how trust-building behavior is related to controls and how the efforts towards fostering subordinate cooperation are motivated by different types of controls and display of trustworthiness. It can be concluded that by implementing this trust repair model consistently and with a full commitment, it can gradually restore people's trust in the network marketing industry, sustain industry existence and exalted purpose of this industry can be achieved.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.149-160
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• 2016

The purpose of this paper is to examine the relationship between the social interactions on the social network and the purchase intention. And the trust propensity of a trustor, the ability of a trustee, and the sincerity of a trustee are selected as the antecedents of social interactions. This paper also examines the effect of type of product as a moderating variable. The result of this paper reveals that social interactions(in terms of closeness, familiarity, and interpersonal trust) have a positive(+) effect on the purchase intention. The more social interactions, the more trustors have intentions to purchase the recommended products by trustees. In addition, the study reveals that the trust propensity of a trustor and the ability of a trustee directly and indirectly influenced on the intention to purchase the recommended product. The findings also suggest that the trust propensity of a trustor and the ability of a trustee have an effect on the closeness, familiarity, and interpersonal trust resulting from social interactions.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.361-364
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• 2008

A aim of this study is to show that trust formed among actors in clusters effects on the innovation for firms. This paper finds that trust is not enough formed. firms feel trust in relationship between firms and universities and between firms and research institutes. However, trust not to be formed in relationship between firm and public agencies and between firms and other firms. By mean of the finding, the various network types which can facilitate trust must be made by policy support.

• Korean System Dynamics Review
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• v.1 no.2
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• pp.61-89
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• 2000

The purpose of this study is first, to find out what factors affect the cooperation and trust within the functions in the policy implementation network and in what mechanism these factors interact, second to investigate the whys to manage trust and cooperation successfully in the dynamic situation such as the network setting. For these purpose, this study reviews the concept and characteristics of policy implementation organizations, second, extracts the various factors affecting trust and cooperation in the network situation, third applies and analyzes the relationship among factors to system dynamics model based on the game theory. The results of this study could be summarized as follows: It was found that the utility change within the participants by persuasion ＆ mutual understanding and change of rule would be leading to success in policy implementation network. Also bureaucratic management such as power enforcement does not have any good impact in the managing network. In this study, system simulation method tried to analyze the hypothesis. Quantitative and case analyses were not accompanied and analysis was limited to two-person game theory. So there is some doubt this results could be generalized to actual situation which is N-person game.

• Journal of Korea Trade
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• v.25 no.5
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• pp.19-38
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• 2021

Purpose - This study aims to verify the direct causal relationship between cost competitiveness and global network competitiveness, which are the tangible service quality factors determined by the shipping company, which in turn affect the shipper's customer satisfaction. Additionally, we empirically investigate the intangible, related service qualities determined by shipping companies, such as operational competitiveness and customer relationship quality, and how these then positively affect customer satisfaction through the formation of trust. Therefore, we examine the mediating effect of trust formation among different contractors for shipping services. Design/methodology - In order to examine the shipping company's tangible and intangible service-qualities perceived by the shipper on customer satisfaction and the process of trust formation between contractors, we collected valid data from 114 respondents out of 200 distributed questionnaires. The respondents consisted of domestic freight forwarders who engage with domestic and international shipping and logistics agencies. Descriptive statistics, confirmatory factor analysis, reliability, convergent and discriminant validities, common method bias, and PLS-SEM (partial least square-structural equation model) were analyzed using the program STATA 16. Findings - The findings of this study are as follows. First, our results showed that all hypotheses assumed in this study had statistically significant supporting evidence. Second, it was found that the mediating effect of trust was significant in affecting the quality of intangible service- qualities for customer satisfaction. Third, through supplementary analysis, we found that the global network competitiveness of domestic shipping companies will increase in importance in the future. In conclusion, the theoretical and practical implications of these findings are presented. Originality/value - This study reaffirmed the traditional causal relationship between customer satisfaction and tangible service quality. Additionally, we also contribute to the literature on the understanding of the causal relationship between trust formation and customer satisfaction through intangible interactions from a long-term perspective.

• Journal of Distribution Science
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• v.20 no.10
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• pp.51-60
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• 2022

Purpose: The aim of this research is to study 1) the effects of internal factors (technological capability and entrepreneurial orientation) that affect Thailand's hotel business network capability. 2) the effects of external factors (government policy and trust relationship) that affect Thailand's hotel business network capability. 3) the impact of network capability on the firm performance. 4) the moderating effect of absorptive capacity between network capability and firm performance. Research design, data and methodology: The test model collected data from a mail survey of 164 hotel businesses in Thailand. The correlation and multiple regression were adopted to analyze and test the proposed hypotheses. Results: Interestingly, technological capability, entrepreneurship orientation, and trust relationship have a direct impact on network capability. However, network capability still does not have a significant relationship with firm performance in all dimensions. Surprisingly, the absorptive capacity does not have a moderating effect on the relationship of network capability on firm performance of hotel businesses in Thailand. Conclusions: This research found that the hotel business should focus on analyzing the external and internal environment as it affects network building, which will guide the creation of strategies for further increasing hotel distribution channels and competitive advantage.