Influencing factors of online furniture purchase behavior based on analytic hierarchy process

With the continuous development of the Internet economy, online furniture sales are increasingly becoming an important channel for consumers to purchase furniture. In order to investigate the influencing factors of consumers’ preference for purchasing furniture online, this paper aims to find out th...

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Bibliographic Details
Published inBioresources Vol. 18; no. 2; pp. 2857 - 2873
Main Authors Yu, Changlong, Liu, Wei, Fei, Yinan, Chen, Jiaqi, Hu, Ziyan
Format Journal Article
LanguageEnglish
Published Raleigh North Carolina State University 01.05.2023
Subjects
ahp
Analytic hierarchy process
Consumers
Consumption
Criteria
Delphi method
Electronic commerce
grey forecasting model
Grey prediction
Indicators
influencing factors
Internet
Manufacturing
online furniture
Prediction models
Product development
Purchasing
Weight analysis
weightings
United States > US
China
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Summary:With the continuous development of the Internet economy, online furniture sales are increasingly becoming an important channel for consumers to purchase furniture. In order to investigate the influencing factors of consumers’ preference for purchasing furniture online, this paper aims to find out the influencing factors of online furniture consumption and their weight relations by using the literature research method and Delphi method to clarify the influencing factors of online furniture consumption, followed by the Analytic Hierarchy Process (AHP), from the Target Level, Criterion Level, and Sub-criterion Level. Weight analysis was carried out on the influencing factors of online furniture consumption. The three primary indicators included Personal Factor, Product Factor, and Service Factor. The 16 secondary indicators included Personality Traits, Revenue, Occupation, etc. An index system model was established for online furniture consumption based on the above factors. It was concluded that consumers are most influenced by the price of furniture among Product factors when choosing online furniture. The findings were consistent with the AHP model data when tested using a grey prediction model. The data from this study can therefore provide an important reference for online furniture product development and marketing promotion.
ISSN:1930-2126
1930-2126
DOI:10.15376/biores.18.2.2857-2873