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Research Article | Open Access

How do users view time-sharing car: Research of user feedback based on customer journey

Yan Xu( ), Xu Ji, Xiaobo Tao
Department of Management, North China University of Technology, Beijing 100144, China
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Abstract

A deficit in comprehensive academic research on user feedback for time-sharing cars exists. This study addresses this shortfall by examining 123.8 thousand user posts from Baidu Post Bar’s social media. Within a customer journey framework, it gathers and assesses user feedback to chart the progression, repercussions, and triggers of negative experiences. The analysis shows that feedback clusters around car usage, return, settlement, and after-sales stages. Key issues, such as inadequate vehicle conditions, filthy interiors, and post-use violation misassessments, frequently lead to dissatisfaction and distrust, spurring negative word-of-mouth. The study further pinpoints the obsolescence of user supervision technology as a primary cause of user liability risks, including unruly customer behavior and hygiene concerns. This technological lag not only compounds these issues but also triggers a broken window effect through negative word-of-mouth, creating a vicious cycle of deteriorating service quality. Finally, the paper evaluates the efficacy of current service strategies in the time-sharing car industry and suggests strategic directions for future improvement.

Keywords

Transport economicsTime-sharing carCustomer journeyUsers’ emotional attitudeWord-of-mouth communicationService improvement strategy

References

[1]
Firnkorn J, Müller M. Free-floating electric carsharing-fleets in smart cities: the dawning of a post-private car era in urban environments?. Environmental Science & Policy, 2015, 45: 30−40.
Crossref
[2]
Schmidt P. The effect of car sharing on car sales. International Journal of Industrial Organization, 2020, 71: 102622. https://doi.org/10.1016/j.ijindorg.2020.102622.
Crossref
[3]
Mitchell WJ, Borroni-bird C, Burns LD. Reinventing the automobile: personal urban mobility for the 21st century. Cambridge: MIT Press, 2010.
Crossref
[4]
Kato H, Suzuki S, Kayama S, et al. Perspectives on the feasibility of their cooperation in the carsharing market: evidence from Japan. Asian Transport Studies, 2014−2015, 3(4): 416−429. Doi: 10.11175/eastsats.3.416,
[5]
Lindloff K , Pieper N, Badelow NC, et al. Drivers of carsharing diffusion in Germany: An actor-centred approach. International Journal of Automotive Technology and Management, 2014, 14(3/4):217−245. https://doi.org 10.1504/ijatm.2014.065291.
Crossref
[6]
Chun YY, Matsumoto M, Tahara K, et al. Exploring factors affecting car sharing use intention in the Southeast-Asia Region: a case study in Java, Indonesia. Sustainability, 2019, 11(18): 5103. https://doi.org/10.3390/su11185103.
Crossref
[7]
Zhou YF, Li YY, Hao MY, et al. A System of shared autonomous vehicles combined with park-and-ride in residential Areas. Sustainability, 2019, 11(11): 3113. https://doi.org/10.3390/su11113113.
Crossref
[8]
Magnani G, Re B. Lived experiences about car sharing in young adults: emerging paradoxes. Italian Journal of Marketing, 2020, 2020(2): 207−229.
Crossref
[9]
Xu Y, Ji XH, Ye M, et al. Travel scenes comparison of time-sharing and car-hailing based on traveling spatiotemporal data. Journal of Geo-information Science, 2021, 23 (8) : 1461−1472. DOI: 10.12082/dqxxkx.2021.200602.
[10]
De Luca S, Di Pace R. Modelling Users’ behaviour in inter-urban carsharing program: a stated preference approach. Transportation Research Part A: Policy and Practice, 2015, 71: 59−76.
Crossref
[11]
Morency C, Trepanier M, Agard B, et al. Car sharing system: what transaction datasets reveal on users’ behaviors. 2007 IEEE Intelligent Transportation Systems Conference. Bellevue: IEEE, 2007: 284−289.
Crossref
[12]

Müller J, Correia G, Bogenberger K. An explanatory model approach for the spatial distribution of free-floating carsharing bookings: a case-study of German cities. Sustainability, 2017, 9(7): 1290.
Crossref Google Scholar
[13]
De Lorimier A, El-Geneidy AM. Understanding the factors affecting vehicle usage and availability in carsharing networks: a case study of communauto carsharing system from Montréal, Canada [J]. International Journal of Sustainable Transportation, 2013, 7(1): 35−51.10.1080/15568318.2012.660104.
Crossref
[14]

Schmöller S, Weikl S, Müller J, et al. Empirical analysis of free-floating carsharing usage: The munich and Berlin case. Transportation Research Part C: Emerging Technologies, 2015, 56: 34–51.
Crossref Google Scholar
[15]
Kim J, Rasouli S, Timmermans H. Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: application to intended purchase of electric cars. Transportation Research Part A: Policy and Practice, 2014, 69: 71−85.
Crossref
[16]
Coll MH, Vandersmissen MH, Thériault M. Modeling spatio-temporal diffusion of carsharing membership in Québec city. Journal of Transport Geography, 2014, 38: 22−37.
Crossref
[17]
Khan M.; Machemehl RB. The impact of land-use variables on free-floating carsharing vehicle rental choice and parking duration. Seeing Cities Through Big Data. Cham, Switzerland: Springer, 2017:331–347.
Crossref
[18]
Sai QY, Bi J, Xie DF, et al. Identifying and predicting the expenditure level characteristics of car-sharing users based on the empirical data. Sustainability, 2019, 11(23): 6689. https://doi.org/10.3390/su11236689.
Crossref
[19]
Rosenbaum MS, Otalora ML, Ramírez GC . How to create a realistic customer journey map. Business Horizons, 2016, 60(1):143−150.
Crossref
[20]

Bitner MJ, Ostrom AL, Morgan FN. Service blueprinting: a practical technique for service innovation. California Management Review, 2008, 50(3): 66–94.
Crossref Google Scholar
[21]
Li HS, Kannan PK. Attributing conversions in a multichannel online marketing environment: an empirical model and a field experiment. Journal of Marketing Research, 2014, 51(1): 40–56.
Crossref
[22]
Xu LZ, Duan JA, Whinston A. Path to purchase: a mutually exciting point process model for online advertising and conversion. Management Science, 2014, 60(6): 1392−1412.
Crossref
[23]
Wang S. Marketing 4.0: transitioning from traditional to digital - the evolution and constants in marketing: a special interview with Philip Kotler, the father of modern marketing. Tsinghua Business Review, 2017(3): 59−64.
[24]
Shankar V, Kalyanam K, Setia P, et al. How technology is changing retail. Journal of Retailing, 2021, 97(1): 13−27.
Crossref
[25]
Grewal D, Gauri DK, Das G, et al. Retailing and emergent technologies. Journal of Business Research, 2021, 134: 198−202.
Crossref
[26]
De Bellis E, Venkataramani Johar G. Autonomous shopping systems: identifying and overcoming barriers to consumer adoption. Journal of Retailing, 2020, 96(1): 74−87.
Crossref
[27]

Davenport T, Guha A, Grewal D, et al. How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 2020, 48(1): 24–42.
Crossref Google Scholar
[28]
Novak TP, Hoffman DL. Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects. Journal of the Academy of Marketing Science, 2019, 47(2): 216−237.
Crossref
[29]

PUNTONI S, RECZEK RW, GIESLER M, et al. Consumers and Artificial Intelligence: an Experiential Perspective[J]. Journal of Marketing, 2021, 85(1): 131–151.
Crossref Google Scholar
[30]
Schweidel DA, Bart Y, Inman JJ, et al. How consumer digital signals are reshaping the customer journey. Journal of the Academy of Marketing Science, 2022, 50(6): 1257−1276.
Crossref
[31]
Yang Y, Zhang KP, Kannan PK. Identifying market structure: a deep network representation learning of social engagement. Journal of Marketing, 2022, 86(4): 37−56.
Crossref
[32]
Liu Y, Li K, Ren H. Analysis of bike-sharing system from the perspective of service design. Packaging Engineering, 2017, 38(10):11−18.
[33]

Wu CM, Chen L, Li P. User experience map model in sharing product service design. Packaging Engineering, 2017, 38(18): 62–66.
Google Scholar
[34]
Wang Y, Yu DJ, Zhang F, et al. Design of “mobility as a service (MaaS)” product service system in the post-epidemic era. Hunan Packaging, 2023. 38(3): 108−115.
[35]
Lu YL,Guo WS,Zhao Y. A Empirical study on online-complaining intention. Economic Management Journal, 2009(2): 144−149.
[36]
Grob LM, Meyers RA, Schuh R. Powerful/powerless language use in group interactions: sex differences or similarities?. Communication Quarterly, 1997, 45(3): 282−303. 10.1080/01463379709370066.
Crossref
[37]
Yao YN, Guo GQ, Lian Y, et al. Creation and experiential value under the CGM environment: content analysis based on the interview from micro-blog users. Management Review, 2017, 29(4): 98−107.
[38]
Tang ZJ, Cui JF, Tang XW, et al. A demand feature pattern mining method for short life cycle experiential product by integrating content analysis and association analysis. Chinese Journal of Management Science, 2019, 27(11): 166−175.
[39]
Krippendroff K. Content Analysis: An Introduction to Its Methodology. CA: Sage Publications, 1980.
[40]
Alkhalisi AF. Creating a qualitative typology of electric vehicle driving: EV journey-making mapped in a chronological framework. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 69: 159−186.
Crossref
[41]
Ferdrick FR. Learning from customer defections. Harvard Business Review, 1996, 74(2) : 56−63.
[42]
Hofmann E, Hartl B, Penz E. Power versus trust – what matters more in collaborative consumption?. Journal of Services Marketing, 2017, 31(6): 589−603.
Crossref
[43]
Curtale R, Liao FX, Van Der Waerden P. User acceptance of electric car-sharing services: the case of the netherlands. Transportation Research Part A: Policy and Practice, 2021, 149: 266−282.
Crossref
[44]
Xu W. On the developing deviation and classified correction of sharing platform. Journal of Guangdong University of Finance & Economics, 2020, 35(1): 67−79.
[45]
Ranaweera C, Prabhu J. The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. International. Journal of Service Industry Management, 2003, 14(4): 374−395.
Crossref
[46]
Cyr D. Modeling Web site design across cultures: relationships to trust, satisfaction, and e-loyalty. Journal of Management Information Systems, 2008, 24(4): 47−72. 10.2753/MIS0742-1222240402.
Crossref
[47]
Jansen BJ, Zhang MM, Sobel K, et al. Twitter power: tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 2009, 60(11): 2169−2188.
Crossref
[48]
Chu SC, Kim Y. Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 2011, 30(1): 47−75.10.2501/IJA-30-1-047−075
Crossref
[49]
King RA, Racherla P, Bush VD. What we know and don’t know about online word-of-mouth: a review and synthesis of the literature. Journal of Interactive Marketing, 2014, 28(3): 167−183.
Crossref
[50]
Yang XC, Lan B, Sun F, et al. How brand microblogging attract fans interaction? An empirical study based on CMC theory. Management Review, 2015(1):11.
[51]
Ji X, Xian W. Research on profitable mode of carsharing leasing of electric vehicles. Automob.Parts 2015, 50, 39–41
[52]
Bolton RN, Lemon KN, Verhoef PC. The theoretical underpinnings of customer asset management: a framework and propositions for future research. Journal of the Academy of Marketing Science, 2004, 32(3): 271−292.
Crossref
[53]
Kang GD, James J. Service quality dimensions: an examination of Grönroos’s service quality model. Managing Service Quality, 2004, 14(4): 266−277.
Crossref
[54]
Rowley J. The four Cs of customer loyalty. Marketing Intelligence & Planning, 2005, 23(6): 574−581.
Crossref
[55]
Hays JM, Hill AV. A preliminary investigation of the relationships between employee motivation/vision, service learning, and perceived service quality. Journal of Operations Management, 2001, 19(3): 335−349.
Crossref
[56]
Oracle. Oracle Helps Brands Eliminate Customer Blind Spots: Oracle CX Unity provides comprehensive view into customer interactions across channels and applications. San Francisco, Calif: Oracle OpenWorld. https://www.oracle.com/emea/corporate/pressrelease/oow18−oracle-cx-unity-2018−10−22.html.
[57]
Hashem TN. The impact of quality of services in the car rental companies on customer satisfaction. Economy & Business, 2015, 9(1): 494−502.
[58]
Pantouvakis A, Lymperopoulos K. Customer satisfaction and loyalty in the eyes of new and repeat customers. Managing Service Quality, 2008, 18(6): 623−643.
Crossref
[59]
Frechette M, Arnold M, Kaikati A, et al. Collaborative consumption, social distance and the extended self. Journal of Consumer Marketing, 2020, 37(4): 413−422.
Crossref
[60]
Gutman J. A means-end chain model based on consumer categorization processes. Journal of Marketing, 1982, 46(2): 60−72.
Crossref
Journal of Highway and Transportation Research and Development (English Edition)
Volume 19 Issue 1,
March 2025
Pages 61-71
DOI: 10.26599/HTRD.2025.9480053
Cite this article:
Xu Y, Ji X, Tao X. How do users view time-sharing car: Research of user feedback based on customer journey. Journal of Highway and Transportation Research and Development (English Edition), 2025, 19(1): 61-71. https://doi.org/10.26599/HTRD.2025.9480053

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Received: 09 January 2024
Revised: 27 July 2024
Accepted: 18 August 2024
Published: 01 April 2025
© The Author(s) 2025.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/).
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