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ISSN : 1738-3110
The Factors Influencing the Use of Shared Economy-Based Mobility Services
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Abstract
Purpose: Shared mobility services are the most notable in the shared economy; however, they have yet to be activated in Korea due to various regulations and conflicts amongst stakeholders. Nevertheless, shared mobility has become an irresistible trend of the times, as it can cause a great deal of economic and environmental benefits. In this vein, the purpose of this study is to contribute to the revitalization of shared mobility services in Korea and to provide service providers with implications for developing consumer-oriented marketing strategies. Research design, data and methodology: Based on the reasons that the users do not use shared mobility service, the factors influencing the behaviors of shared mobility users are structured and analyzed in a reliable, technical and procedural manner. To this end, the theory of reasoned action (TRA) of Ajzen and Fisbbein, the initial trust model (ITM), task technology fit (TTF) and switching cost (SC) are adopted. A total of 202 questionnaires were collected from the respondents who were aware of shared mobility. Then statistical processing of the collected data used SmartPLS(v.3.2.8), a PLS-SEM (Partial Least Squares Structural Equation Modeling) analysis program. The steps of the analysis are as follows. First, a PLS-Algorithm analysis was performed to evaluate the measurement model, and a Bootstraping and Blindfolding analysis was performed to evaluate the structural model and verify the hypotheses. Second, a multi-group analysis (PLS-MGA) was conducted to further analyze the differences depending on whether or not users experienced shared mobility service. Results: The results showed that initial trusts model (ITM) and task technology fit (TTF) have positive effects on users' behaviors through the mediation of the intention to use. As opposed to the assumption, switching costs did not have negative moderating effects in relation to the intention to use and users' behaviors. The influence of IT self-efficacy was significant, depending on the prior experience to use shared mobility services. Conclusions: This study will contribute to the revitalization of domestic shared mobility services and the formulation of service providers' marketing strategies. In future studies, there is a need to explore, reconstruct, and validate factors other than the impact factors of the shared mobility services used in this research model.
- keywords
- Shared Economy, Mobility Service, Initial Trust, Task-Technology Fit, Switching Costs
Reference
Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun : Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.
Ajzen, I., & Fisbbein, M. (1974). Factors influencing intentions and the intention-behavior relation. Human Relations, 27(1), 1-15.
Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs - A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126.
Burton, J. J. F. (1986). The share economy - Conquering stagflation. Industrial and Labor Relations Review, 39(2), 285-291.
Chebat, J. C., Davidow, M., & Borges, A. (2011). More on the role of switching costs in service markets: A research note. Journal of Business Research, 64(8), 823-829.
Colgate, M., & Lang, B. (2001). Switching barriers in consumer makets - An investigation of the financial services industry. Journal of Consumer Marketing, 18(4), 332-347.
Compeau, D. R., & Higgins, C. A. (1995). Computer selfefficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Dishaw, M. T., & Strong, D. M. (1998). Assessing software maintenance tool utilization using task-technology fit and fitness-for-use models. Software Maintenance:Research and Practice, 10(3), 151-179.
Divis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology : A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Farrell, D., Greig, F., & Hamoudi, A. (2018). The Online Platform Economy in 2018: Drivers, Workers, Sellers, and Lessors. New York, NJ: JP Morgan Chase & Co.
Foye, L. (2017). Sharing Economy: 3 Industries Ripe For Disruption. Hampshire, UK: Juniper Research.
Furneaux, B. (2012). Task-technology fit theory: A survey and synopsis of the literature. In: Y. Dwivedi, M. Wade & S. Schneberger (Eds.), Information Systems Theory Integrated Series in Information Systems (pp. 87–106), New York, NJ: Springer.
Fuentes-Blasco, M., Saura, I. G., Berenguer-Contrí, G., & Moliner-Velázquez, B. (2010). Measuring the antecedents of e-loyalty and the effect of switching costs on website. Service Industries Journal, 30(11),1837-1852.
Goodhue, D. L. (1995). Understanding user evaluation of information systems. Management Science, 41(12),1827-1844.
Goodhue, D. L., & Thompson, R. L. (1995). Tasktechnology fit and individual performance. MIS Quarterly, 19(2), 213-236.
Han, H. S., & Joung, S. K. (2011). Exploring the technology fit of digital media on product shopping task. Jounal of Society for e-Business Studies, 16(4), 283-299.
Hsieh, J. K., Hsieh, Y. C., Chiu, H. C., & Feng, Y. C. (2012). Post-adoption switching behavior for online service substitutes: A perspective of the push–pull–mooring framework. Computers in Human Behavior, 28(5),1912-1920.
Jang, S. H. (2016). The Influence of task-technology fit on usage intention of SNS: Focused on social enterprise. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 11(6), 61-69.
Jarupathirun, S., & Zahedi, F. M. (2007). Exploring the influence of perceptual factors in the success of webbased spatial DSS. Decision Support Systems, 43(3), 933-951.
Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2002). Why customers stay - measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 55(6), 441-450.
Ju, N. Y., Kim, J. W., & Kim, E. J. (2017). An empirical study on the factors influencing on the customer satisfaction in case of switching from mobile banking to fintech service. Journal of Information Systems, 26(4), 203-225.
Junglas, I., Abraham, C., & Ives, B. (2009). Mobile technology at the frontlines of patient care:Understanding fit and human drives in utilization decisions and performance. Decision Support Systems, 46(3), 634-647.
Kang, M., Gao, Y., Wang, T., & Wang, M. (2015). The role of switching costs in O2O platforms: Antecedents and consequences. International Journal of Smart Home, 9(3), 135-150.
Kim, G., Kim, W. W., & Lee, H. G. (2005). Investigation of factors influencing consumer initial trust and intention to use mobile banking services. Korean Management Science Review, 22(2), 13-34.
Kim, G., Shin, B., & Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283-311.
Kim, H., & Cho, Y. (2018). Analysis of the bicycle-sharing economy: Strategic issues for sustainable development of society. Journal of Distribution Science, 16(7), 5-16.
Kim, K., & Prabhakar, B. (2004). Initial trust and the adoption of B2C e-commerce - The case of Internet banking. Data Base for Advances in Information Systems, 35(2), 50-64.
Kim, M., Kang, S., & Yang, H. (2008). The effect of tasktechnology fit on groupware usage and performance. Korean Management Review, 37(1), 67-96.
Kim, S., Lim, J., & Yang, S. (2016). An empirical study on influencing factors of untention to use third-party mobile payment services: Applying the task-technology fit model. Journal of Information Technology Services, 15(2), 185-201.
Kim, S. H. (2013). Moderating effects of switching cost on the IT service switching intention. Journal of the Korea Contents Association, 13(10), 452-460.
Kim, Y. (2015). The impact of brand awareness, perceived switching cost, user loyalty on purchase intention:Moderator as a purchase experience. Journal of Internet Electronic Commerce Resarch, 15(1), 75-94.
Klaus, T., Gyires, T., & Wen, H. J. (2003). The use of Webbased information systems for non-work activities - An empirical study. Human Systems Management, 22(3), 105-114.
Laudon, K. C., & Laudon, J. P. (2004). Management Information Systems: Managing the Digital Firm (8th ed.). Upper Saddle River, New Jersey: Prentice-Hall.
Lee, M. K. O., & Turnan, E. (2001). A trust model for consumer Internet shopping. International Journal of Electronic Commerce, 6(1), 75-91.
Lessig, L. (2008). REMIX: Making Art and Commerce Thrive in the Hybrid Economy. London, UK: The Penguin Press.
Li, Q. Z., & Lee, J. H. (2017). The influential relations on sharing economy and consumer traits. International Journal of Industrial Distribution & Business, 8(6), 75-86.
Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410-417.
Lippert, S. K., & Forman, H. (2006). A supply chain study of technology trust and antecedents to technology internalization consequences. International Journal of Physical Distribution & Logistics Management, 36(4), 271-288.
Mathieson, K., & Keil, M. (1998). Beyond the interface -Ease of use and task/technology fit. Information &Management, 34(4), 221-230.
McKnight, D. H., & Chervany, N. L. (2001). What trust means in e-commerce customer relationships - An interdisciplinary conceptual typology. International Journal of Electronic Commerce, 6(2), 35-59.
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: A trust building model. Journal of Strategic Information Systems, 11(3), 297-323.
McKnight, D. H., Cummings, L. L., & Chervany, N. L.(1998). Initial trust formation in new organizational relationships. Academy of Management Review, 23(3), 473-490.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Park, H., & Noh, M. (2011). The influence of product attribute of smart clothing on initial trust and purchase intention: Focused on sensor - based smart clothing. Journal of the Korean Home Economics Association, 49(6), 13-22.
Park, K., Park, S., & Jang, H. (2014). Study on the sse of SNS(social network service) for tasks: Focus on the task-media fit. Journal of Digital Convergence, 12(2), 577-586.
Park, S. J., & Hwang, K. T. (2016). A study on the repurchase intention of customers in the foreign direct sales Internet shopping mall - Focused on the Japanese customers. Journal of Digital Convergence, 14(6), 199-218
Pendharkar, P. C., Khosrowpour, M., & Rodger, J. A.(2001). Development and testing of an instrument for measuring the user evaluations of information technology in health care. Journal of Computer Information Systems, 41(4), 84-89.
Stephany, A. (2015). The Business of Sharing. Seoul, Korea:Hansmedia.
Sundararajan, A. (2016). The Sharing Economy. Seoul, Korea: Kyobobook.
Tian, X. F., Lee, J., & Wu, R. (2017). Use intention of chauffeured car services by O2O and sharing economy. Journal of Distribution Science, 15(12), 73-84.
Venkatesh, V., Morris, M. G., Divis, G. B., & Divis, F. D.(2003). User acceptance of information technology:Toward a unified view. MIS Quarterly, 27(3), 425-478.
Wang, L., & Kim, M. J. (2017). A Study on the customer continuance intention of O2O e-commerce mobile platform. e-Business Studies, 18(3), 187-199.
Wang, S., Beatty, S. E., & Foxx, W. (2004). Signaling the trustworthiness of small online retailers. Journal of Interactive Marketing, 18(1), 53-69.
Wu, J. H., Chen, Y. C., & Lin, L. M. (2007). Empirical evaluation of the revised end user computing acceptance model. Computers in Human Behavior, 23(1), 162-174.
Wu, R., & Lee, J. H. (2017a). The use intention of mobile travel apps by Korea-visiting Chinese tourists. Journal of Distribution Science, 12(5), 53-64.
Wu, R., & Lee, J. H. (2017b). The comparative study on third party mobile payment between UTAUT2 and TTF. Journal of Distribution Science, 15(11), 5-19.
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767.
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