皮亞傑認知發展論探討自我調整學習歷程之建構與應用

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Explaining Customer Loyalty to Retail Stores: A Moderated Explanation Chain of the Process

This chapter reassesses the process of how store customers become loyal to their stores; what are the core subprocesses generating customer store loyalty, and what contributing moderators enrich the final outcome. A new empirical research is designed to identify and test a parsimonious model of core relationships and moderators. The result is an explanation chain that incorporates relational variables, trust, and commitment to the traditional transactional one, customer satisfaction, and the moderating factors of the relational variables. The findings reveal that 1) customer commitment is the major contributor of explanation to true customer loyalty, significantly more than the contributed explanation of customer satisfaction, and 2) four cognitive attitudes and four affective attitudes significantly moderate the relational effects of trust and commitment on customer store loyalty and, thus, contribute, though in small amounts, to a stronger explanation

A Systems Dynamics Simulation Study of Network Public Opinion Evolution Mechanism

The factors that affect formation and dissemination of public opinion have been studied for a long time. However, the findings are disparate and fragmented, given the characteristics of netizens and new media in the Big Data era. To this end, this article introduces eight mechanisms working on formation and dissemination of public opinion on network. Based on system dynamics, this article further proposes a comprehensive causal relationship model to explore the factors affecting the consequence of public opinion on network. Particularly, the role of government is taken into consideration in this model. A simulation with Vensim PLE is conducted. The results of the simulation indicate that group polarization among netizens, opinion leaders, the quantity of media audience, the frequency of media report, government attention, and warning mechanism for public opinion crisis affect the consequence of public opinion on network significantly. Implications of the findings are discussed

Applicability of ARIMA Models in Wholesale Vegetable Market

To investigate the applicability of ARIMA models in wholesale vegetable market models are built taking sales data of one perishable vegetable from Ahmedabad wholesales market in India. It is found that these models can be applied to forecast the demand with Mean Absolute Percentage Error (MAPE) in the range of 20%. This error is acceptable in fresh produce market where the demand and prices are highly unstable. The model is successfully validated using sales data of another vegetable from the same market. This model can facilitate the farmers and wholesalers in effective decision making

Introduction to PANDA Data Acquisition System

AbstractDue to the anti-proton annihilation at Darmstadt (PANDA) experiment has rich physics studies with different event selection criteria and very high interaction rate of 2x107 /s and data rates of 200 GB/s and more, a high performance and high bandwidth data acquisition system without hardware trigger has been proposed. The data from various sub-detector systems are tagged by a very precise timestamp in front-end electronics. Event selection based on real time feature extraction, filtering and high level correlations will be executed on Advanced Telecommunications Computing Architecture (ATCA) compliant compute nodes. As a key element, the compute node that features high bandwidth connectivity and large memory for data buffering will be detailed. A demonstrator system for the electromagnetic calorimeter has been built to set up a basic system that can be interfaced to front-end electronics