Abstract
The utilization rate of cold chain logistics in China is far lower than that in developed countries. The reasons are its high cost and low social willingness to pay. However, there is little research on the willingness of Chinese consumers to pay for cold chain logistics. Here, based on the theory of planned behavior, according to the survey data of 711 consumers in 9 cities, the improved CVM estimation method and double hurdle model are used to analyze factors influencing Chinese consumer willingness to pay for cold chain logistics and the degree of the premium payment willingness. The results show that the household income and education level of consumers, government supervision of cold chain logistics and support subsidies are the most significant impacts. Family monthly income of 15,000 yuan and bachelor degree is the dividing line of consumers’ willingness from weak to strong; the maximum premium payment of consumers is 41.7% of the original price. Therefore, with the increase of income and education level in China, consumers will more and more accept to pay the cost of cold-chain logistics, and the premium of payment will be relatively high. At the same time, the government should further improve industry standards, strengthen supervision and subsidies, and ultimately increase the utilization rate of cold chain logistics in China.
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Abbreviations
- CVM:
-
Contingent valuation method
- WTP:
-
Willingness to pay
- TPB:
-
Theory of planned behavior
- WTA:
-
Willingness to accept
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Acknowledgements
The authors acknowledge the Natural Science Foundation of Heilongjiang Province (Grant: G201403), the National Natural Science Foundation of China (Grant: 71403047), the Young Talent Project of Northeast Agricultural University (Grant: 14QC56), the China Postdoctoral Science Foundation Funded Project (Grant: 2014M561322), Postdoctoral Science Foundation Funded Project of Heilongjiang Province (Grant: LBH-Z14037).
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Rong, F., Zhang, Y., Wang, Z. et al. Influencing factors of consumer willingness to pay for cold chain logistics: an empirical analysis in China. J Ambient Intell Human Comput 10, 3279–3285 (2019). https://doi.org/10.1007/s12652-018-1056-0
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DOI: https://doi.org/10.1007/s12652-018-1056-0