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A service innovation evaluation framework for tourism e-commerce in China based on BP neural network

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Abstract

With the rapid development of tourism e-commerce in China, how to evaluate the effectiveness of tourism e-commerce service innovation in the e-commerce field has become an important and critical issue. Drawing on pertaining literature, this paper chooses the back propagation (BP) neural network model to evaluate the effectiveness of tourism e-commerce service innovation. The study first establishes the evaluation index system that is consistent with the characteristics of the tourism e-commerce service industry and selects ten tourism e-commerce service providers to conduct an empirical analysis. Then Matlab7.0 is employed to simulate this evaluation model and to draw the corresponding conclusions. Finally, this paper summarizes the limitations of the study and proposes future research avenues. The insightful results of this study can mirror the development situation of tourism e-commerce and provide an effective evaluation framework for the tourism e-commerce service innovation performance in China.

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Acknowledgements

This research was supported by the NCET Foundation of Fujian Province of China 2007, and the Fundamental Research Funds for the Central Universities under Grant 2013221028. In addition, we heartily thank the three anonymous reviewers and the responsible editor for their insightful comments which have significantly helped improve the previous versions of this article.

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Authors and Affiliations

  1. Management School, Xiamen University, No.422, South of Siming Road, Siming District, 361005, Xiamen, Fujian Province, People’s Republic of China

    Lifang Peng

  2. Department of Business, Xiamen City University, No.1263, South of Qianpu Road, Siming District, 361008, Xiamen, Fujian Province, People’s Republic of China

    Lingling Lai

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  1. Lifang Peng
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Correspondence to Lifang Peng.

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Responsible Editor: Xin Luo

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Peng, L., Lai, L. A service innovation evaluation framework for tourism e-commerce in China based on BP neural network. Electron Markets 24, 37–46 (2014). https://doi.org/10.1007/s12525-013-0148-0

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