Private shopping sites （PSSs） business model is developing rapidly both in the world and in Turkey since 2000s. However, studies related consumer attitudes towards private shopping sites in Turkey are limited in the...Private shopping sites （PSSs） business model is developing rapidly both in the world and in Turkey since 2000s. However, studies related consumer attitudes towards private shopping sites in Turkey are limited in the literature. So, the main aim of this paper is revealing variables that affect attitudes of PSSs customers in Turkey and conceptualizing these variables within the scope of a model that depends on Technology Acceptance Model （TAM） In this descriptive study, research population includes all of the consumers who made at least one shopping from PSSs in Turkey and ＂snowball sampling method＂ is used. In consequence of an online survey application 409 questionnaires are analyzed. As a result of regression analysis, all of the 14 hypotheses are accepted. In conclusion all of the variables in the model （product quality, delivery service, system quality, information quality, service quality, perceived usefulness, perceived ease of use, compatibility, privacy, and security） have significant relations with consumer attitudes. The most important variables in building positive attitudes towards PSSs are perceived usefulness and compatibility. Conversely the lowest variance prediction percentage belongs to privacy variable.展开更多
A non-local denoising (NLD) algorithm for point-sampled surfaces (PSSs) is presented based on similarities, including geometry intensity and features of sample points. By using the trilateral filtering operator, the d...A non-local denoising (NLD) algorithm for point-sampled surfaces (PSSs) is presented based on similarities, including geometry intensity and features of sample points. By using the trilateral filtering operator, the differential signal of each sample point is determined and called "geometry intensity". Based on covariance analysis, a regular grid of geometry intensity of a sample point is constructed, and the geometry-intensity similarity of two points is measured according to their grids. Based on mean shift clustering, the PSSs are clustered in terms of the local geometry-features similarity. The smoothed geometry intensity, i.e., offset distance, of the sample point is estimated according to the two similarities. Using the resulting intensity, the noise component from PSSs is finally removed by adjusting the position of each sample point along its own normal direction. Ex- perimental results demonstrate that the algorithm is robust and can produce a more accurate denoising result while having better feature preservation.展开更多
文摘Private shopping sites （PSSs） business model is developing rapidly both in the world and in Turkey since 2000s. However, studies related consumer attitudes towards private shopping sites in Turkey are limited in the literature. So, the main aim of this paper is revealing variables that affect attitudes of PSSs customers in Turkey and conceptualizing these variables within the scope of a model that depends on Technology Acceptance Model （TAM） In this descriptive study, research population includes all of the consumers who made at least one shopping from PSSs in Turkey and ＂snowball sampling method＂ is used. In consequence of an online survey application 409 questionnaires are analyzed. As a result of regression analysis, all of the 14 hypotheses are accepted. In conclusion all of the variables in the model （product quality, delivery service, system quality, information quality, service quality, perceived usefulness, perceived ease of use, compatibility, privacy, and security） have significant relations with consumer attitudes. The most important variables in building positive attitudes towards PSSs are perceived usefulness and compatibility. Conversely the lowest variance prediction percentage belongs to privacy variable.
基金the Hi-Tech Research and Development Pro-gram (863) of China (Nos. 2007AA01Z311 and 2007AA04Z1A5)the Research Fund for the Doctoral Program of Higher Education of China (No. 20060335114)
文摘A non-local denoising (NLD) algorithm for point-sampled surfaces (PSSs) is presented based on similarities, including geometry intensity and features of sample points. By using the trilateral filtering operator, the differential signal of each sample point is determined and called "geometry intensity". Based on covariance analysis, a regular grid of geometry intensity of a sample point is constructed, and the geometry-intensity similarity of two points is measured according to their grids. Based on mean shift clustering, the PSSs are clustered in terms of the local geometry-features similarity. The smoothed geometry intensity, i.e., offset distance, of the sample point is estimated according to the two similarities. Using the resulting intensity, the noise component from PSSs is finally removed by adjusting the position of each sample point along its own normal direction. Ex- perimental results demonstrate that the algorithm is robust and can produce a more accurate denoising result while having better feature preservation.
