Recently internet shopping malls provide newer and more varied goods and services to meet the demand of their customers. And as new companies enter the on-line shopping mall business, competition in this specific market is getting stiffer. Therefore, to keep a more sustained relationship with their customers, internet shopping malls need to satisfy their customers with their goods and services and to make them loyal customers. Unlike bricks-and-mortar stores in real life, This study also aims to propose an on-line physical environment model, and to develop the existing on-line research into a physical environment.The physical environment is measured on the four dimensions and 4 measurement units and the on-line physical environment, trust, and purchasing decisions are also explained.This study makes contributions as follows. First, online physical environments contain theoretical syntheses and operational definition. Second, this study finds that trust is an important part of an on-line transaction. Third, this study provides an insight into some of the factors preceding purchasing decisions. Lastly, this study finds that the consumer's comprehension of the on-line environment has a considerable influence on trust and purchasing decisions.

The satisfaction of customers, who are in a servicescape, will be subject to many environmental stimulations. In particularly, the servicescapes of the store and the customer characteristics are important factors that affecting the emotional commitment of consumers. In this study, the servicescape that consumers evaluated can be divided into physical and social factors. Furthermore, how these factors can affect the emotional commitment through perceived service quality and social interaction will be studied as well. In addition, the moderating effects of the individual characteristics at the perception of servicescape, such as optimum stimulation level and social appropriateness will be studied together.The results show that the physical factor of servicescape is one of the most important elements that affect "emotional commitment" and under this process; the perceived service quality is performed as a parameter. Besides, the social factor of servicescape, such as the other customer and companion affect can also affect emotional commitment through social interaction. On the other hand, through the analysis of optimum stimulation level and social appropriateness, we found that the higher the social appropriateness, the greater the effect of servicescape.Overall, in order to enhance the customer's emotional commitment, we must build high quality servicescape, meanwhile; the management of other customers and the companion are needed at the same time.■ keyword :|Servicescape|Perceived Service Quality|Social Interaction|Optimum Stimulation|Social appropriateness| Emotional Commitment|

Background/Objectives: In this paper, we propose a Deep Learning Mechanism (DLMBN) mechanism (Deep Learning Mechanism on Blockchain) that optimizes the load balance that can occur in the network by deep learning some important information related to the load balance after connecting the information of multiple distributed controllers into the blockchain. Methods/Statistical analysis: The proposed mechanism binds and manages the load of each controller distributed over the network with a blockchain, thus reducing load time while dynamically balancing the load balance. In particular, deep learning technology was used to ensure that each controller classified as a group would not be biased to one side and would maintain a balanced load balance across the entire network. Findings: As a result of the experiment, the proposed mechanism improved the load balance retention time by 14.6% on average compared to the mechanism previously studied, and the efficiency of SDNs processed in multiple groups by 17.3% on average. In addition, the overhead of SDNs for each group was lowered by 7.9%. Improvements/Applications: Based on the results of this study, future studies plan to apply the results to the actual network and test whether the performance analysis results are applicable to heterogeneous networks consisting of heterogeneous devices.

No abstract

No abstract