基于多元动机理论的共享电动汽车出行意愿影响因素研究

doi:10.1007/s12204-023-2563-5

摘要/Abstract

摘要： 分析城市居民对共享电动汽车的使用意愿对于促进城市交通低碳发展意义重大。考虑到目前共享电动汽车仍存在空闲率高、吸引力不足等问题，本研究提出了一种基于多元动机理论的结构方程模型。首先利用多元动机理论将影响消费者是否选择共享出行方式归为消费者驱动动机、方案驱动动机以及企业驱动动机三类，并对各变量的影响因素进行了细分；随后通过调查问卷得到上海市居民对电动汽车共享出行的意愿数据；最后构建结构方程模型定量化分析了各变量对于出行意愿的影响。研究结果表明：消费者驱动动机（影响权重范围为0.14~0.63）对于共享电动汽车出行意愿的影响最大，远大于方案驱动动机（-0.14~0.15）和企业驱动动机（0.02~0.06）的影响，其中在消费者驱动变量中，绿色出行意识影响最大。基于以上分析，本文分别从消费者、企业及政府层面提出了具体的相关政策建议。

关键词: 多元动机理论, 共享电动汽车, 出行意愿, 结构方程模型, 城市治理

Abstract: Determining the travel intention of residents with shared electric vehicles (EVs) is significant for promoting the development of low-carbon transportation, considering that common problems such as high idle rate and lack of attractiveness still exist. To this end, a structural equation model (SEM) based on the theory of multiple motivations is proposed in this paper. First, the influencing motivations for EV sharing are divided into three categories: consumer-driven, program-driven, and enterprise-driven motivations. Then, the intentions of residents in Shanghai to travel with shared EVs are obtained through a survey questionnaire. Finally, an SEM is constructed to analyze quantitatively the impact of different motivations on the travel intention. The results show that consumer-driven motivations with impact weights from 0.14 to 0.63 have the overwhelming impact on travel intention, compared to program-driven motivations with impact weights from ?0.14 to 0.15 and enterprise-driven motivations with impact weights from 0.02 to 0.06. In terms of consumer-driven motivations, the weight of green travel awareness is the highest. The implications of these results on the policy to enable large-scale implementation of shared EVs are discussed from the perspectives of the resident, enterprise, and government.

中图分类号:

. 基于多元动机理论的共享电动汽车出行意愿影响因素研究[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 1-9.

BAO Lewen (鲍乐雯), MIAO Rui, ∗ (苗瑞), CHEN Zhihua (陈志华), ZHANG Bo (张博), GUO Peng (郭鹏), MA Yuze (马宇泽). Travel Intention of Electric Vehicle Sharing based on Theory of Multiple Motivations[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 1-9.

参考文献 29

[1]	ZHANG J, ZHAO S W, WANG Y Q, et al. Improvedsocial emotion optimization algorithm for short-term traffic flow forecasting based on back-propagation neural network [J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(2): 209-219.
[2]	STOKKINK P, GEROLIMINIS N. Predictive userbased relocation through incentives in one-way carsharing systems [J]. Transportation Research Part B:Methodological, 2021, 149: 230-249.
[3]	HU C Y, WU X, ZHOU F. Green certificate transaction of electric vehicle based on blockchain technology[J]. Journal of Shanghai Jiao Tong University, 2021,55(Sup 2): 64-71 (in Chinese).
[4]	WANG L Y, WANG L F, LIAO C L, et al. Research on multi-parameter evaluation of electric vehicle power battery consistency based on principal component analysis [J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(5): 711-720.
[5]	LONG Z, AXSEN J. Who will use new mobility technologies? Exploring demand for shared, electric, and automated vehicles in three Canadian metropolitan regions [J]. Energy Research & Social Science, 2022, 88: 102506.
[6]	BECKER H, CIARI F, AXHAUSEN K W. Comparing car-sharing schemes in Switzerland: User groups and usage patterns [J]. Transportation Research Part A: Policy and Practice, 2017, 97: 17-29.
[7]	PRIETO M, BALTAS G, STAN V. Car sharing adoption intention in urban areas: What are the key sociodemographic drivers? [J]. Transportation Research Part A: Policy and Practice, 2017, 101: 218-227.
[8]	EFTHYMIOU D, ANTONIOU C, WADDELL P. Factors affecting the adoption of vehicle sharing systems by young drivers [J]. Transport Policy, 2013, 29: 64-73.
[9]	COSTAIN C, ARDRON C, HABIB K N. Synopsis of users’ behaviour of a carsharing program: A case study in Toronto [J]. Transportation Research Part A: Policy and Practice, 2012, 46(3): 421-434.
[10]	HU S H, CHEN P, LIN H F, et al. Promoting carsharing attractiveness and efficiency: An exploratory analysis [J]. Transportation Research Part D: Transport and Environment, 2018, 65: 229-243.
[11]	CHENG J Q, CHEN X H, YE J H, et al. Flow-based unit is better: Exploring factors affecting mid-term OD demand of station-based one-way electric carsharing [J]. Transportation Research Part D: Transport and Environment, 2021, 98: 102954.
[12]	JU P, ZHOU J, CHEN X G, et al. Evolutionary game analysis of travel mode selection considering car sharing [J]. Modernization of Management, 2017, 37(1): 70-72 (in Chinese).
[13]	WANG B Q, SHAO Z Y. Research on users’ willingness of electric vehicle car-sharing market based on the modified UTAUT model [J]. Soft Science, 2018, 32(11): 130-133 (in Chinese).
[14]	PRIETO M, STAN V, BALTAS G. New insights in peer-to-peer carsharing and ridesharing participation intentions: Evidence from the “provider-user” perspective [J]. Journal of Retailing and Consumer Services, 2022, 64: 102795.
[15]	ZHANG K, GUO H W, YAO G Z, et al. Modeling acceptance of electric vehicle sharing based on theory of planned behavior [J]. Sustainability, 2018, 10(12): 4686.
[16]	TURO′N K, KUBIK A, CHEN F. Operational aspects of electric vehicles from car-sharing systems [J]. Energies, 2019, 12(24): 4614.
[17]	MOUSAVI R, CHEN R, KIM D J, et al. Effectiveness of privacy assurance mechanisms in users’ privacy protection on social networking sites from the perspective of protection motivation theory [J]. Decision Support Systems, 2020, 135: 113323.
[18]	DIN?ER S, DOGANAY A. The effects of multiplepedagogical agents on learners’ academic success, motivation, and cognitive load [J]. Computers & Education, 2017, 111: 74-100.
[19]	NIKULINA A, WYNSTRA F. Understanding supplier motivation to engage in multiparty performance-based contracts: The lens of Expectancy theory [J]. Journal of Purchasing and Supply Management, 2022, 28(2): 100746.
[20]	DECI E L, CONNELL J P, RYAN R M. Selfdetermination in a work organization [J]. Journal of Applied Psychology, 1989, 74(4): 580-590.
[21]	LINDENBERG S, STEG L. Normative, gain and hedonic goal frames guiding environmental behavior [J]. Journal of Social Issues, 2007, 63(1): 117-137.
[22]	PATANAKUL P, PINTO J K, PINTO M B. Motivation to perform in a multiple-project environment: The impact of autonomy, support, goal clarity, and opportunities for learning [J]. Journal of Engineering and Technology Management, 2016, 39: 65-80.
[23]	LINNENBRINK-GARCIA L, WORMINGTON S V, SNYDER K E, et al. Multiple pathways to success: An examination of integrative motivational profiles among upper elementary and college students [J]. Journal of Educational Psychology, 2018, 110(7): 1026-1048.
[24]	ANDERMAN E M. Achievement motivation theory: Balancing precision and utility [J]. Contemporary Educational Psychology, 2020, 61: 101864.
[25]	GRAHAM S. An attributional theory of motivation [J]. Contemporary Educational Psychology, 2020, 61: 101861.
[26]	SCHUNK D H, DIBENEDETTO M K. Motivation and social cognitive theory [J]. Contemporary Educational Psychology, 2020, 60: 101832.
[27]	PAUNDRA J, ROOK L, VAN DALEN J, et al. Preferences for car sharing services: Effects of instrumental attributes and psychological ownership [J]. Journal of Environmental Psychology, 2017, 53: 121-130.
[28]	SONG H N, YIN G F, WAN X H, et al. Increasing bike-sharing users’ willingness to pay - A study of China based on perceived value theory and structural equation model [J]. Frontiers in Psychology, 2022, 12: 747462.
[29]	BHATTACHARYA T, SENGUPTA A. Large-sample tests for comparing Likert-type scale data [J]. Communications in Statistics-Theory and Methods, 2022, 51(5): 1179-1196.