College of Applied Science and Technology, Beijing Union University, Beijing 10010, China
Taking Beijing as an example, we explore the paths and effects of several key factors on urban residents' consumption in smart retail scenarios. The study measured business identification (BI), marketing perception (MP), product experience (PE), environmental experience (EE) and purchase intention (PI). A structural equation model of the relationship between the five latent variables and their influences was constructed based on the measurement of the five influences of purchase intention (PI). In addition, the six observed variables of payment form, type, quality, logistics and delivery, shop environment and discount are very important in influencing purchase intention. Accordingly, the problems associated with smart retailing at this stage of development are identified and methodological recommendations are made. These include improving consumers' commercial acceptance of smart retail, optimising the product mix, strengthening the marketing management of smart retail and improving the quality of smart retail services.
smart retailing; consumption; influencing factors; structural equations
Di Yao. Quantitative Analysis of Consumption Influencing Factors in Smart Retail Scenarios Based on Structural Equation Modeling. Academic Journal of Business & Management (2022) Vol. 4, Issue 6: 1-10. https://doi.org/10.25236/AJBM.2022.040601.
 China Internet Network Information Center. (2022) The 49th Statistical Report on the Development Status of the Internet in China.
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