824 resultados para trust graph


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The effects of e-commerce institutional mechanisms on trust and online purchase have traditionally been understood in the initial online purchase context. This study extends this literature by exploring the role of e-commerce institutional mechanisms in the online repurchase context. In doing so, it responds to the emerging call for understanding the institutional context under which customer trust operates in an e-commerce environment. Specifically, this study introduces a key moderator, perceived effectiveness of e-commerce institutional mechanisms (PEEIM), to the relationships between trust, satisfaction, and repurchase intention. Drawing on the theory of organizational trust, and based on a survey of 362 returning online customers, we find that PEEIM negatively moderates the relationship between trust in an online vendor and online customer repurchase intention, as it decreases the importance of trust to promoting repurchase behavior. We also find that PEEIM positively moderates the relationship between customer satisfaction and trust as it enhances the customer’s reliance on past transaction experience with the vendor to reevaluate trust in the vendor. Consistent with the predictions made in the literature, PEEIM does not directly affect trust or repurchase intention. Academic and practical implications and future research directions are discussed.

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Special Joint Issue - Irish Marketing Review and Journal of the Korean Academy of Marketing Science

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Thomas Würtenberger, Dieter K. Tscheulin, Jean-Claude Usunier, Dominique Jeannerod, Eric Davoine (Hrsg.):
Wahrnehmungs- und Betätigungsformen des Vertrauens im deutsch-französischen Vergleich
Berlin, Berlin GmbH, 2002

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In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.

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Civic participation is important for peacebuilding and democratic development; however, the role of mental health has been largely overlooked by policymakers aiming to stimulate engagement in civil society. This study investigated antecedents of civic participation in Colombia, a setting of protracted political conflict, using bootstrapped mediation in path analysis. Past exposure to violence, experience with community antisocial behavior, and perceived social trust were all significantly related to civic participation. In addition, depression mediated the impact of past exposure to political violence and perceived social trust, but not community antisocial behavior, on civic participation. In this context, findings challenged depictions of helpless victims and instead suggested that when facing greater risk (past violence exposure and community antisocial behavior), individuals responded in constructive ways, taking on agency in their communities. Social trust in one’s neighbors and community also facilitated deeper engagement in civic life. Relevant to the mediation test, interventions aiming to increase civic participation should take mental health into account. Limitations and possible future research are discussed.

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The importance of inter-organizational trust to project success has been increasingly highlighted in the construction industry. This study aims to explore the role of trust between project parties. It adopts a combination of quantitative and qualitative methodologies. Based on the analysis of the responses of a questionnaire survey, trust is demonstrated to have a significant contribution to the development of cooperative or collaborative relationships; fostering trust proves to have a major influence on the improvement of project performance; and some relationship and performance indicators are found to have closer associations with trust than others so that trust is more important to
the development of relationship and the improvement of performance in these aspects. The analysis of questionnaire responses also provides significant evidence for the reduction in monitoring and control following the increase of mutual trust. The questionnaire survey is followed by a series of expert interviews, both of which contribute to the establishment of a model that links trust with relationship and performance and distinguishes the new approach that is based on trust from the traditional mechanism that relies on monitoring and control.

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The UK government introduced the Private Finance Initiative (PFI) and, latterly, the Local Improvement Finance Trust (LIFT) in an attempt to improve public service provision. As a variant of PFI, LIFT seeks to create a framework for the effective provision of primary care facilities. Like conventional PFI procurement, LIFT projects involve long-term contracts, complex multi-party interactions and thus create various risks to public sector clients. This paper investigates the advantages and disadvantages of LIFT with a focus on how this approach facilitates or impedes risk management from the public sector client perspective. Our paper concludes that LIFT has a potential for creating additional problems, including the further reduction of public sector control, conflicts of interest, the inappropriate use of enabling funds, and higher than market rental costs affecting the uptake of space in the buildings by local health care providers. However, there is also evidence that LIFT has facilitated new investment and that Primary Care Trusts (PCTs) have themselves started addressing some of the weaknesses of this procurement format through the bundling of projects and other forms of regional co-operation.

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In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

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Realising memory intensive applications such as image and video processing on FPGA requires creation of complex, multi-level memory hierarchies to achieve real-time performance; however commerical High Level Synthesis tools are unable to automatically derive such structures and hence are unable to meet the demanding bandwidth and capacity constraints of these applications. Current approaches to solving this problem can only derive either single-level memory structures or very deep, highly inefficient hierarchies, leading in either case to one or more of high implementation cost and low performance. This paper presents an enhancement to an existing MC-HLS synthesis approach which solves this problem; it exploits and eliminates data duplication at multiple levels levels of the generated hierarchy, leading to a reduction in the number of levels and ultimately higher performance, lower cost implementations. When applied to synthesis of C-based Motion Estimation, Matrix Multiplication and Sobel Edge Detection applications, this enables reductions in Block RAM and Look Up Table (LUT) cost of up to 25%, whilst simultaneously increasing throughput.