Multiple Channel Integration Quality Assessment Method Using NARX

Abstract

To improve the accuracy of the multiple channel integration quality (MCIQ) evaluation, this paper proposes a comprehensive evaluation method using the nonlinear autoregressive exogenous model (NARX) and constructs an index system. First, the entropy method is used to determine the objective weight of each indicator. The indicators used in this paper are process consistency, information consistency, emotional value, procedural value, service structure transparency, online result value, business relevance, and online purchase intention. Second, an improved gray relational analysis (GRA) algorithm is used to obtain the comprehensive gray relational degree between the above eight indicators' standard samples and the tested samples. Then, this study uses the dataset preprocessed with the GRA algorithm for training the NARX model. Then, this study uses the trained model to evaluate the quality of multiple channel integration comprehensively. Next, this study uses standardized methods to quantify the evaluation results to provide new ideas and theoretical guidance for teaching traditional retailers to use the advantages of multiple channels to expand their online business. This paper uses 50,000 consecutive samples of a product for 3 months as a dataset in the experimental part. Through the GRA method and the NARX model, the comprehensive gray relational degree between the test sample and the ideal sample is obtained, and the results are quantified. Experiments show that, compared with the GRA method, this paper's method has a higher degree of fit between the output value and the target value.