Comparison of localized and foreign restaurant brands for consumer behavior prediction

2022 ◽  
Vol 65 ◽  
pp. 102868
Author(s):  
Chih-Hsing Liu ◽  
Bernard Gan ◽  
Wen-Hwa Ko ◽  
Chih-Ching Teng
2017 ◽  
Vol 20 (2) ◽  
pp. 325
Author(s):  
Palus Palus ◽  
Jony Oktavian Haryanto

This research is conducted to examine consumer perception related with rationalization and understanding of risk consequences and their advantages by predicting desires related with digital product piracy. The motivation to conduct digital piracy is reinforced with understanding the advantages obtained and consumer behavior towards digital product piracy. Various rationalization techniques also support reinforcing their motivation to engage in piracy. Interestingly, that motivation can be hindered with perceptions about the consequences or social norm regulations and moral responsibilities. Analytical results reveal that consumer intention to use pirated digital products can continuously increase, because it is influenced by the understanding of the advantages obtained, the rational techniques used, their attitudes toward piracy, and perceptions in controlling behavior. On another side, moral responsibilities and social norms also play a role in reducing the intensity of using pirated digital products.


2014 ◽  
Author(s):  
Yanrong Zhang ◽  
Zhijie Zhao ◽  
Jing Yu ◽  
Kun Wang

The consumer behavior analysis is the technique which is applied to analyze consumer behavior. The customer behavior analysis has the three steps which are pre-processing, feature extraction and classification for prediction. In the previous work, Naïve Bayes was applied for the consumer behavior analysis. In this work, hybrid classifier is designed for the customer behavior analysis using Decision Tree and KNN. The proposed method is implemented in anaconda python and results are compared with the previously used Naïve Bayes method, for this analysis consumer reviews from Amazon website are used.


2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Lin Guo ◽  
Ben Zhang ◽  
Xin Zhao

With the rapid development of online finance and social networks, a large amount of behavioral data is stored on the Internet, which can fully reflect the shopping tendencies and habits of real users. Using big data to analyze consumer behavior is more scientific and accurate than the traditional sampling survey method. Internet consumption behavior data are time series data. Therefore, this paper proposes a method of analyzing behavioral sequence data, which learns personal consumption interests and habits, and finally predicts payment behavior. The experiments compare the execution effect of different algorithms on multiple databases and verify the feasibility and effectiveness of the proposed algorithm SeqLearn.


1987 ◽  
Vol 32 (9) ◽  
pp. 795-796
Author(s):  
Thomas K. Srull
Keyword(s):  

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