“后疫情时代”基于扩展UTAUT模型的共享汽车用户接受度影响因素分析*

吴文静; 杨旭; 贾洪飞

doi:10.3963/j.jssn.1674-4861.2023.02.012

“后疫情时代”基于扩展UTAUT模型的共享汽车用户接受度影响因素分析*

doi: 10.3963/j.jssn.1674-4861.2023.02.012

吉林大学交通学院长春 130022

基金项目:

吉林省教育厅科学研究项目 JJKH20211115KJ

详细信息

通讯作者:
吴文静（1980—），博士，教授. 研究方向: 运输系统规划. E-mail: wuwj@jlu.edu.cn

中图分类号: U121
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出版历程
- 收稿日期: 2022-07-12
- 网络出版日期: 2023-06-19

An Analysis of Impact Factors to the Propensity of Car Sharing in the "Post-Epidemic Era"Based on an Extended UTAUT Model

Department of Transportation, Jilin University, Changchun, 130022, China

摘要

摘要: 共享汽车具有“低碳”特征且能为出行者提供安全独立的空间，为后疫情时代出行提供了新的选择。为挖掘疫情常态化下共享汽车使用意愿的影响因素以及作用机理，以网络问卷的形式对出行者进行调查，回收有效问卷109份，并对调查结果进行分析。融合疫情感知风险和财务风险因素，构建扩展的整合型技术接受与使用理论模型（unified theory of acceptance and use of technology，UTAUT），提出11条假设并采用结构方程模型探索各潜变量影响共享汽车接受意向的途径，分析假设检验结果和模型拟合程度，对假设中不显著的路径进行中介效应分析。为探究社会经济属性变量的影响过程，构建基于结构方程的多原因多指标模型，并检验观测变量与潜变量的相关性以及潜变量与潜变量之间的相关性。研究结果表明：模型拟合程度均表现良好，潜变量中绩效期望对接受意向的正向影响最为显著，其次是促进条件和社会影响，而财务风险、努力期望对接受意愿有显著负向影响。疫情感知风险的直接影响不显著，但社会影响、绩效期望和促进条件在疫情感知风险和行为意向之间具有部分中介作用，总间接影响效应为0.240，中介效应占总效应的74.8%，间接影响显著。年龄、实际驾龄、是否持有机动车驾驶证因素对疫情下共享汽车的使用态度存在显著影响，而使用频率则直接影响疫情下的共享汽车使用意向。基于本文研究为后疫情时代共享汽车的发展提供策略和方向指引，如优化出行体验、强化安全管理、刺激消费、提升品牌价值等。
- 城市交通 /
- 共享汽车 /
- 接受度 /
- UTAUT模型 /
- 疫情感知风险
Abstract: In the post-epidemic period, car sharing offers a new choice of transportation mode because of its "low-carbon"attributes and independent space for travelers. A survey of travelers is created in the form of a web-based questionnaire. A total of 109 valid questionnaires were collected and the results are analyzed to investi-gate factors impacting people's propensity to use car sharing in the post-epidemic period. An expanded UTAUT model is developed by considering the perceived risk of epidemic and financial risk. To investigate how each latent variable influences the intention to accept car sharing, eleven hypotheses are proposed, and structural equation mod-eling is applied. Analysis of the hypothesis test findings, model degree of fit, and mediating effects are done. Mediat-ing effects are looked at for routes that are insignificant in the hypothesis. A multiple factor and multiple reasoning model based on structural equations is developed to examine the effect process of socio-economic variables. Correla-tions between observed and latent variables are examined, in addition to correlations between latent variables and la-tent variables. Findings from this study show that that all of the models have a good fit. Performance expectation had the greatest, positive influence on acceptance intention among the latent variables, followed by facilitation con-ditions and social influence. Financial risk and effort expectation have the greatest negative influence on acceptance intention. Social influence, performance expectation, and facilitation conditions partially mediated the effect be-tween perceived risk of epidemic and behavioral intention. This effect had a total indirect effect of 0.240 and a sig-nificant indirect effect of 74.8% of the total effect. However, the direct effect of perceived epidemic risk is not signif-icant. Age, actual driving age, and the possession of a driver's license all significantly influence the attitudes regard-ing the use of car sharing during the pandemic. In contrast, frequency of usage directly affectes intention to use car sharing during the epidemic. Strategies and instructions for promoting car sharing in the post-epidemic era are then offered, which include ways to improve the travel experience, strengthen safety management, encourage consump-tion, and boost brand value.
- city traffic /
- car sharing /
- acceptance /
- UTAUT model /
- epidemic perceived risk

HTML全文

图 1 扩展UTAUT模型及假设路径

Figure 1. Extend UTAUT model and hypothetical path

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图 2 结构方程模型和标准化路径系数

Figure 2. Structural equation model and standardization path coefficients

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图 3 MIMIC模型框架

矩形-观测变量，平行四边形-社会经济属性变量、椭圆形-潜变量、实线-潜变量之间的关系，虚线-观测变量与潜变量之间的关系。

Figure 3. MIMIC modeling framework

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表 1 UTAUT模型变量定义

Table 1. UTAUT model variable definition

变量	定义
行为意向	出行者愿意接受和使用共享汽车服务的主观倾向
社会影响	出行者受他人及社会环境的影响程度
努力期望	出行者认为自己使用共享汽车的难易程度
绩效期望	出行者对共享汽车能带来的效益的衡量
促进条件	共享汽车的发展收到的外界各方面条件的支持

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表 2 影响接受意向的潜变量问题项描述

Table 2. Problem items description that affects latent variable of intention to accept

变量	可观测变量	来源
社会影响	SI_1：亲戚朋友使用共享汽车我也会尝试	文献[19]
	SI_2：亲戚朋友的意见影响我是否使用
	SI_3：使用时会选择亲戚朋友推荐的品牌
绩效期望	PE_4：节约我的出行成本	文献[20]
	PE_ 5：提高我出行的舒适度
	PE_ 6：使我的出行更加便利
努力期望	EE_7：寻找一辆共享汽车对我来说很容易	文献[10]
	EE_8：租车流程简单方便
	EE_9：我的车技驾驶共享汽车完全没问题
促进条件	FC_10：提供保险服务会让我倾向使用	文献[21]
	FC_11：政府对其安全性的宣传会让我倾向使用
	FC_12：事故责任判定更加清晰会让我倾向使用
疫情感知风险	EPR_13：疫情期间使用可以减少我与他人接触	文献[2]
	EPR_14：可以降低感染新冠的风险
	EPR _15：合理的疫情防控措施会让我倾向使用
财务风险	FR_16：支付账户得不到安全保障	文献[21]
	FR_17：支付的共享汽车的费用偏高
	FR_18：押金无法退还
行为意向	BI_19：我计划尝试共享汽车出行	文献[22]
	BI_20：我计划持续使用共享汽车出行
	BI_21：如果共享汽车投放在小区、公司、学校等更方便的地方，我会尝试使用共享汽车出行

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表 3 心理潜变量信度、效度检验结果

Table 3. Reliability and validity tests Results of psychological latent variables

变量	测量变量	载荷因子	AVE	α	CR
社会影响	SI_1	0.566	0.592	0.775	0.808
	SI_2	0.894
	SI_3	0.811
绩效期望	PE_4	0.831	0.563	0.733	0.790
	PE_ 5	0.567
	PE_ 6	0.823
努力期望	EE_7	0.837	0.660	0.740	0.853
	EE_8	0.802
	EE_9	0.797
促进条件	FC_10	0.732	0.632	0.850	0.837
	FC_11	0.859
	FC_12	0.788
疫情感知风险	EPR_13	0.841	0.728	0.787	0.889
	EPR _14	0.872
	EPR _15	0.847
财务风险	FR_16	0.865	0.762	0.847	0.906
	FR_17	0.886
	FR_18	0.867
行为意向	BI_19	0.729	0.580	0.844	0.804
	BI_20	0.693
	BI_21	0.853

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表 4 区分效度检验结果

Table 4. Distinct validity test results

	AVE	社会影响	绩效期望	财务风险	疫情感知风险	促进条件	努力期望	行为意向
社会影响	0.592	0.769
绩效期望	0.563	0.453	0.750
财务风险	0.762	-0.239	-0.108	0.873
疫情感知风险	0.728	0.456	0.206	-0.109	0.853
促进条件	0.632	0.533	0.241	-0.127	0.243	0.795
努力期望	0.660	0.123	0.272	-0.029	0.056	0.066	0.812
行为意向	0.580	0.669	0.554	-0.347	0.389	0.553	-0.109	0.762

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表 5 结构方程拟合指标

Table 5. Structural equation fitting index

指标名称	卡方自由度比	RMSEA	TLI	CFI	IFI
合理区间	<0.05优秀 <3良好 <5可接受	<0.05优秀 <0.08良好	>0.90	>0.90	>0.90
模型参数	1.370	0.059	0.921	0.933	0.935
评估结果	良好	良好	接受	接受	接受

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表 6 模型假设检验结果

Table 6. Test results of model hypothesis

假设	估计	S.E.	C.R.	β	p	是否支持
H1：社会影响→行为意向	0.34	0.16	2.09	0.34	*	支持
H2：疫情感知风险→行为意向	0.12	0.10	1.17	0.12	0.24	不支持
H3：绩效期望→行为意向	0.53	0.15	3.45	0.53	***	支持
H4：促进条件→行为意向	0.40	0.15	2.69	0.40	**	支持
H5：财务风险→行为意向	0.19	0.08	2.50	0.19	*	支持
H6：努力期望→行为意向	0.30	0.10	3.03	0.30	**	支持
H7：社会影响→绩效期望	0.40	0.11	3.47	0.40	***	支持
H8：社会影响→疫情感知风险	0.50	0.15	3.42	0.50	***	支持
H9：社会影响→促进条件	0.44	0.11	4.07	0.44	***	支持
H10：绩效期望→努力期望	0.32	0.17	1.90	0.31	*	支持
H11：社会影响→财务风险	0.30	0.14	2.14	0.30	*	支持
注：- P <0.05显著水平；- P <0.01显著水平；**- P <0.001显著水平。

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表 7 中介效应检验

Table 7. Intermediary effect test

变量	路径	Effect	SE	t	p	BootLLCI	BootULCI
总效应	EPR→BI	0.321	0.083	3.876	***	0.157	0.485
直接效应	EPR→BI	0.081	0.075	1.075	0.285	-0.068	0.230
间接效应	EPR→BI	0.240	0.058			0.138	0.366
	M(SI)	0.069	0.042			0.010	0.187
	M(PE)	0.089	0.037			0.029	0.181
	M(FC)	0.083	0.040			0.023	0.187
注：M（SI）-疫情感知风险→社会影响→行为意向；M（PE）- 疫情感知风险→绩效期望→行为意向；M（FC）-疫情感知风险→促进条件→行为意向。

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表 8 MIMIC模型拟合指标

Table 8. MIMIC model fitting index

指标名称	卡方自由度比	RMSEA	TLI	CFI	IFI
合理区间	<0.05优秀 <3良好 <5可接受	<0.05优秀 <0.08良好	>0.90	>0.90	>0.90
模型参数	1.309	0.054	0.905	0.918	0.922
评估结果	良好	良好	接受	接受	接受

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表 9 MIMIC模型标准化路径系数

Table 9. MIMIC model standardization path coefficients

显著影响路径	非标准化回归系数	S.E.	C.R.	p
社会影响←性别	0.459	0.133	3.45	***
促进条件←性别	-0.317	0.114	-2.776	**
财务风险←性别	0.511	0.172	2.969	**
疫情感知风险←年龄	0.143	0.071	2.022	*
绩效期望←月收入	0.201	0.088	2.291	*
财务风险←月收入	-0.349	0.111	-3.135	**
促进条件←月收入	-0.181	0.072	-2.521	*
疫情感知风险←驾龄	-0.217	0.079	-2.765	**
疫情感知风险←车证	-0.364	0.177	-2.063	*
行为意向←使用频率	-0.170	0.089	-1.919	*

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参考文献(22)

[1]	惠英,廖佳妹,唐磊,等. 疫情期间共享单车的使用行为与意愿[J]. 城市交通,2020,18(6):100-109. https://www.cnki.com.cn/Article/CJFDTOTAL-CSJT202006015.htm HUI Y, LIAO J M, TANG L, et al. Travel behavior and prefer-ence using bike sharing during the pandemic[J]. Urban Trans-portation, 2020, 18(6): 100-109. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSJT202006015.htm
[2]	张小雨, 邵春福, 王博彬, 等. 新冠疫情影响下居民共享出行方式选择行为研究[J]. 交通运输系统工程与信息, 2022, 22(2): 186-196, 205. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202202018.htm ZHANG X Y, SHAO C F, WANG B B, et al. Travel mode choice analysis with shared mobility in the context of COV-ID-19 [J]. Transportation Systems Engineering and Informa-tion, 2022, 22(2): 186-196, 205. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202202018.htm
[3]	ZHANG X, SHAO C, WANG B, et al. Exploring the role of shared mobility in alleviating private car dependence and on-road carbon emissions in the context of COVID-19[J]. Frontiers in Environmental Science, 2022, 10: 1-9.
[4]	ALONSO-ALMEIDA M D. To use or not use car sharing mobil-ity in the ongoing COVID-19 pandemic? Identifying sharing mobility behavior in times of crisis[J]. International Journal of Environmental Research and Public Health, 2022, 19(5): 3127. doi: 10.3390/ijerph19053127
[5]	DAVIS F D, BAGOZZI R P, WARSHAW P R. User accep-tance of computer technology: A comparison of two theoreti-cal models[J]. Management Science, 1989, 35(8): 982-1003. doi: 10.1287/mnsc.35.8.982
[6]	胡晓伟, 石腾跃, 于璐, 等. 基于扩展技术接受度模型的共享自动驾驶汽车用户使用意愿研究[J]. 交通运输工程与信息学报, 2021, 19(3): 1-12. https://www.cnki.com.cn/Article/CJFDTOTAL-JTGC202103001.htm HU X W, SHI T Y, YU L, et al. Research on measuring us-ers' willingness to use shared autonomous vehicles based on an extension technology acceptance model [J]. Journal of Transportation Engineering and Information, 2021, 19(3): 1-12. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTGC202103001.htm
[7]	李继朴, 贺宜, 孙昌鑫, 等. 基于SEM的自动驾驶汽车接受度模型与风险分析[J]. 交通信息与安全, 2021, 39(1): 26-34. doi: 10.3963/j.jssn.1674-4861.2021.01.0004 LI J P, HE Y, SUN C X, et al. An acceptance model and risk analysis of automated vehicles based on SEM[J]. Journal of Transport Information and Safety, 2021, 39(1): 26-34. doi: 10.3963/j.jssn.1674-4861.2021.01.0004
[8]	袁霞, 王爱民. 基于TAM的共享汽车使用意愿影响因素研究[J]. 武汉理工大学学报(信息与管理工程版), 2018, 40(4): 434-438. https://www.cnki.com.cn/Article/CJFDTOTAL-WHQC201804014.htm YUAN X, WANG A M. Research on the influencing factors of car sharing use intention based on TAM[J]. Journal of Wu-han University of Technology(Information and Management Engineering Edition), 2018, 40(4): 434-438. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WHQC201804014.htm
[9]	FISHBEIN M, AJZEN I. Belief, attitude, intention and behav-ior: an introduction to theory and research. [J]. Philosophy & Rhetoric, 1977, 41(4): 842-844.
[10]	鲍抄抄, 王维红. 基于计划行为理论的汽车共享消费意向影响因素研究[J]. 东华大学学报(社会科学版), 2018, 18(4): 285-293. https://www.cnki.com.cn/Article/CJFDTOTAL-DHDS201804012.htm BAO C C, WANG W H. Research on the factors influencing car sharing consumption intention based on the theory of planned behavior[J]. Journal of Donghua University(Social Science Edition), 2018, 18(4): 285-293. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DHDS201804012.htm
[11]	徐豪. 基于扩展计划行为理论的拼车出行选择行为研究[D]. 重庆: 重庆交通大学, 2020. XU H. Research on car-pooling selection based on extended planned behavior theory [D]. Chongqing : Chongqing Trans-portation University, 2020. (in Chinese)
[12]	KELLER A, AGUILAR A, HANSS D. Car sharers' interest in integrated multimodal mobility platforms: a diffusion of in-novations perspective[J]. Sustainability, 2018, 10(12): 4689.
[13]	BÖSEHANS G, BELL M, THORPE N, et al. e-HUBs— Identifying the potential early and late adopters of shared electric mobility hubs[J]. International Journal of Sustainable Transportation, 2021, 17(3): 1-20.
[14]	VENKATESH V, MORRIS M G, DAVIS G B, et al. User ac-ceptance of information technology: toward a unified view[J]. MIS Quarterly, 2003, 27(3): 425-478.
[15]	CURTALE R, LIAO F, PETER V. User acceptance of elec-tric car-sharing services: The case of the Netherlands[J]. Transportation Research Part A: Policy and Practice, 2021, C(149): 266-282.
[16]	ZAFRI N M, KHAN A, JAMAL S, et al. Risk perceptions of COVID-19 transmission in different travel modes[J]. Trans-portation Research Interdisciplinary Perspectives, 2022, 13: 100548.
[17]	黄金梅. 感知风险对共享单车使用意愿的影响研究[D]. 南宁: 广西大学, 2018. HUANG J M. Research on the influence of the perceived risk to usage intention of shared bicycle[D]. Nanning: Guangxi University, 2018. (in Chinese)
[18]	徐刚. 单车智能与车路协同自动驾驶技术接受度研究[D]. 镇江: 江苏大学, 2020. XU G. Study on the acceptance of bicycle intelligence and vehicle-road cooperative autonomous driving technology[D]. Zhenjiang: Jiangsu University, 2020. (in Chinese)
[19]	MA L, ZHANG X, DING X, et al. Bike sharing and users' sub-jective well-being: an empirical study in China[J]. Transporta-tion Research Part A: Policy and Practice, 2018(118): 14-24.
[20]	景鹏, 黄芳, 徐刚, 等. 自动驾驶支付意愿及影响因素分析[J]. 长安大学学报(自然科学版), 2021, 41(1): 90-102. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL202101010.htm JING Peng, HUANG Fang, XU Gang, et al. Analysis of au-tonomous driving payment willingness and influencing fac-tors[J]. Journal of Chang'an University (Natural Science Edition), 2021, 41(1): 90-102. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL202101010.htm
[21]	YANG X H, CHENG Z, CHEN G, et al. The impact of a pub-lic bicycle-sharing system on urban public transport net-works[J]. Transportation Research Part A: Policy and Prac-tice, 2018(107): 246-256.
[22]	JING P, CHEN Y, WANG X, et al. Evaluating the effective-ness of Didi ride-hailing security measures: an integration model[J]. Transportation Research Part F: Traffic Psycholo-gy and Behaviour, 2021(76): 139-166.