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RFID-based intelligent shopping environment: a comprehensive evaluation framework with neural computing approach

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Abstract

This research proposes a radio frequency identification (RFID)-based intelligent shopping environment and its distributed reading capability to raise quality of service through improving the automation of product presentation, inventory monitor, billing procedures, manpower logistics, and customer lifetime value prediction. This research also uses RFID to successfully create a smart-shelf-enabled system as an advanced decision-making mechanism for managers. A case study based on a well-known fashion retailing company is used to demonstrate how the proposed system can significantly improve daily business operations. In addition, this research also used artificial neural network to predict the VIP member classification and customer retention rate. The experimental results figure out that the artificial intelligence approach would be outperformed the statistical and decision tree methods. Finally, a questionnaire was administered to 120 customers and investigated their degree of RFID usage willingness and purchase intention based on the Unified Theory of Acceptance and Use of Technology model. The empirical results of our study present the easy-to-use and social influence factors that would be most influenced the customers’ usage willing and purchase intention with RFID technology.

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Acknowledgments

The author is grateful for the support of the National Scientific Council (NSC) of the Republic of China (ROC) under Grant No. NSC-100-2622-E-029-003-CC3 and NSC-100-2410-H-029-043. The author also gratefully acknowledges the Editor and anonymous reviewers for their valuable comments and constructive suggestions.

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  1. Department of Management Information Systems, National Chung Hsing University, No. 250, Kuo Kuang Rd., Taichung City, 402, Taiwan, ROC

    Chia-Chen Chen

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  1. Chia-Chen Chen
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Correspondence to Chia-Chen Chen.

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Chen, CC. RFID-based intelligent shopping environment: a comprehensive evaluation framework with neural computing approach. Neural Comput & Applic 25, 1685–1697 (2014). https://doi.org/10.1007/s00521-014-1652-7

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  • DOI: https://doi.org/10.1007/s00521-014-1652-7

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