Abstract:To reveal the car purchase decision-making mechanism of different vehicle ownership groups during the new normal of COVID-19, a behavioral theoretical framework integrating the theory of planned behavior, the protection motivation theory and the psychological reactance theory was constructed. The partial least squares structural equation model was used to carry out decision prediction and difference analysis of heterogeneous groups of vehicle ownership. The results show that the prediction abilities of the model of the car-owning group and car-free group are 58.9% and 70.1%, respectively. There are significant differences in the car purchase decision-making mechanism of different car ownership. The purchase intention of the car-free group is significantly higher than that of the car-owning group. The car-free group comprehensively considers perceived security, perceived vulnerability, health value, cost factors and conditional value during purchasing cars, while the car-owning group mainly considers perceived vulnerability, reactance and conditional value.
YAN Y Y, ZHONG S Q, TIAN J F, et al. An empirical study on consumer automobile purchase intentions influenced by the COVID-19 outbreak[J]. Journal of Transport Geography, DOI:10.1016/j.jtrangeo.2022.103458.
[2]
DAVIS R S, STAZYK E C. Putting the methodological cart before the theoretical horse? Examining the application of sem to connect theory and method in public administration research[J]. Review of Public Personnel Administration, 2017, 37(2): 202-218.
[3]
BELGIAWAN P F, SCHMCKER J D, ABOU-ZEID M, et al. Modelling social norms: case study of students′ car purchase intentions[J]. Travel Behaviour and Society, 2017, 7: 12-25.
[4]
ROGERS R W. A protection motivation theory of fear appeals and attitude change[J]. The Journal of Psychology Interdisciplinary and Applied, 1975, 91(1): 93-114.
[5]
KIRK C P, RIFKIN L S. I′ll trade you diamonds for toilet paper: consumer reacting, coping and adapting behaviors in the COVID-19 pandemic[J]. Journal of Business Research, 2020, 117: 124-131.
[6]
CIALDINI R B. Influence: Science and Practice [M]. Boston: Allyn & Bacon, 2001.
[7]
AJZEN I. The theory of planned behavior[J]. Organizational Behavior and Human Decision Processes, 1991, 50(2): 179-211.
[8]
YANG J, ZHANG Y, LANTING C J M. Exploring the impact of QR codes in authentication protection: a study based on PMT and TPB[J]. Wireless Personal Communications, 2017, 96(4): 5315-5334.
[9]
LIANG Y H, KEE K F, HENDERSON L K. Towards an integrated model of strategic environmental communication: advancing theories of reactance and planned behavior in a water conservation context[J]. Journal of Applied Communication Research, 2018, 46(2): 135-154.
[10]
DONG X Y, ZHANG B, WANG B, et al. Urban households purchase intentions for pure electric vehicles under subsidy contexts in China: do cost factors matter?[J]. Transportation Research Part A: Policy and Practice, 2020, 135: 183-197.
[11]
ZHANG X, LIU S, WANG L, et al. Mobile health service adoption in China: integration of theory of planned behavior, protection motivation theory and personal health differences[J]. Online Information Review, 2019, 44(1): 1-23.
[12]
HARDMAN S, CHANDAN A, TAL G, et al. The effectiveness of financial purchase incentives for battery electric vehicles:a review of the evidence[J]. Rene-wable and Sustainable Energy Reviews, 2017, 80: 1100-1111.
[13]
BORHAN M N, IBRAHIM A N H, MISKEEN M A A, et al. Predicting car drivers′ intention to use low cost airlines for intercity travel in Libya[J]. Journal of Air Transport Management, 2017, 65: 88-98.
[14]
RUSSO D, STOL K J. PLS-SEM for software engineering research: an introduction and survey[J]. ACM Computing Surveys, 2021, 54(4): 1-38.
LI J X, CAI W M.The influence of tourists′ perceived value on behavior intention in historical and cultural blocks:taking Baoding west street as an example[J]. Journal of Baoding University, 2022, 35(6): 117-124.(in Chinese)