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Research on predicting models of annoyance under the operation of digital hi-tech products

机译:数字高科技产品运作下的烦恼预测模型研究

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摘要

All kinds of digital hi-tech products have been incorporated into our daily lives; however, problems derived from human-computer interaction still annoy users and cause emotional dissatisfaction. In this research, a structure of factors resulting in annoyance, derived from the interaction between users and digital hi-tech products, has arisen. An open survey has been adopted for the questionnaires in order to find the products and factors that will elicit annoyance when people use digital hi-tech products in their daily lives. By means of multidimensional scaling (MDS) analysis and hierarchical cluster analysis, the factors are successfully clustered and the hierarchical structure of the annoyance is formed. The result is related to the complexity of product design. With the substantive operating experiment, quantitative data is obtained to process artificial neural network training, multiple regression analysis, and Hayashi's Qualification Theory Type I analysis. The annoyance prediction models from three recent items are formed. The accuracy and suitability of prediction of the network models is decided using the square of the correlative coefficient R (R2) value, the correlative coefficient (R) and the error index (EI). We found that the artificial neural network predicting model is the optimum predicting model from among three models.
机译:各种数字高科技产品已融入我们的日常生活;但是,人机交互产生的问题仍然困扰着用户,并引起情绪上的不满。在这项研究中,出现了导致用户烦恼的因素结构,这种结构源于用户与数字高科技产品之间的交互作用。为了使人们在日常生活中使用数字高科技产品时会感到烦恼的产品和因素,已经对问卷进行了公开调查。通过多维标度(MDS)分析和层次聚类分析,成功地对因素进行了聚类,并形成了烦恼的层次结构。结果与产品设计的复杂性有关。通过实质性的操作实验,获得了定量数据来进行人工神经网络训练,多元回归分析和林氏资格理论I类分析。从最近的三个项目中形成了烦恼预测模型。使用相关系数R(R2)值,相关系数(R)和误差指数(EI)的平方确定网络模型预测的准确性和适用性。我们发现,人工神经网络预测模型是三种模型中的最佳预测模型。

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