The general adoption of ride-hailing services is contingent on removing the negative media's influence and improving consumers' willingness to use them in the future. It is very important for ride-hailing platforms and governments to promote users' willingness to continue using and retain consumers. On the basis of efficacy theory and adversarial attribution theory, combined with perceived risk and trust, The goal of this study is to figure out what factors influence women's propensity to continue utilizing ride-hailing services. We conducted data analysis on 400 respondents by means of questionnaire survey and structural equation model. Our empirical results suggest that efficacy theory and adversarial attribution theory can provide a strong basis for investigating the continued willingness of female consumers to adopt ride-hailing services. Trust is positively correlated with continuous usage intentions. Self-efficacy and proxy efficacy are positively correlated with trust, while hostile attributional style is negatively correlated with trust. Proxy efficacy is negatively correlated with hostile attributional style. In addition, perceived risk positively affected self-efficacy, and proxy efficacy positively moderated the relationship between self-efficacy and trust.

Yezi Yang, Wei Wang. Understanding consumer continuous usage intention of ride-hailing services: a model based on efficacy and hostility attribution theory. The Frontiers of Society, Science and Technology (2022) Vol. 4, Issue 5: 13-21. https://doi.org/10.25236/FSST.2022.040504.

[1] Contreras, Seth D., & Alexander Paz, 2018, The effects of ride-hailing companies on the taxicab industry in Las Vegas. Nevada. Transportation Research Part A: Policy and Practice 115:63-70

[2] Groß, Michael, 2016, Impediments to mobile shopping continued usage intention: A trust-risk-relationship. Journal of Retailing and Consumer Services 33:109-119.

[3] Ma, L., Xin, Z.; Xiao, Y. D.& Gao, S. W., 2019, Risk perception and intention to discontinue use of ride-hailing services in China: Taking the example of DiDi Chuxing. Transportation Research Part F: Traffic Psychology and Behaviour 66:459-470.

[4] Weng, G.S., Zailani, S., Iranmanesh, M., Hyun, S.S., 2017, Mobile taxi booking application service’s continuance usage intention by users. Transportation Res. Part D:Transport Environ. 57, 207–216.

[5] Martinko, Scott C.D. & Mark J, 2001, Exploring the Role of Individual Differences in the Prediction of Workplace Aggression. Journal of Applied Psychology 86:547-559.

[6] Witte,Kim, 1992, Putting the fear back into fear appeals: The extended parallel process model. Communication Monograph 59(4):329-349.

[7] Witte.K., Cameron, K.A.; McKeon, J.K.& Berkowitz. J.M., 1996, Predicting risk behaviors: development and validation of a diagnostic scale. Journal of Health Communication: International Perspective 1(4): 317-41.

[8] Bauer, R. A.,1960, Consumer behavior as risk taking. In Proceedings of the 43rd National Conference of the American Marketing Assocation, June 15, 16, 17, Chicago, Illinois, 1960. American Marketing Association.

[9] Cox, D. F., & Rich, S. U., 1964, Perceived risk and consumer decision-making—the case of telephone shopping. Journal of marketing research, 1(4), 32-39. 

[10] Witte, K., 1994, Fear control and danger control: A test of the extended parallel process model (EPPM). Communications Monographs, 61(2), 113-134.

[11] Rimal, R. N., & Real, K., 2003, Perceived risk and efficacy beliefs as motivators of change: Use of the risk perception attitude (RPA) framework to understand health behaviors. Human communication research, 29(3), 370-399.

[12] Pappas, N. 2016, Marketing strategies, perceived risks, and consumers trust in online buying behavior. Journal of Retailing and Consumer Services 29:92-103.

[13] Slovic, P., 2010, The feeling of risk. New perspectives on risk perception.

[14] Chang, S. E., Liu, A. Y., & Shen, W. C., 2017, User trust in social networking services: A comparison of Facebook and LinkedIn. Computers in Human Behavior, 69, 207-217.

[15] Chin, A. G., Harris, M. A., & Brookshire, R., 2018, A bidirectional perspective of trust and risk in determining factors that influence mobile app installation. International Journal of Information Management, 39, 49-59.

[16] Kim, G., & Koo, H., 2016, The causal relationship between risk and trust in the online marketplace: A bidirectional perspective. Computers in Human Behavior, 55, 1020-1029.

[17] Heider, F., 2013, The psychology of interpersonal relations. Psychology Press.   

[18] Dobbins, G. H., & Russell, J. M., 1986, Self-serving biases in leadership: A laboratory experiment. Journal of Management, 12(4), 475-483.

[19] Jones, E. E., & Nisbett, R. E., 1971, The Actor and the Observer: Divergent Perspectives on the Causes of Behavior. Morristown.

[20] Dzewaltowski, D. A., Karteroliotis, K., Welk, G., Johnston, J. A., Nyaronga, D., & Estabrooks, P. A. , 2007, Measurement of self-efficacy and proxy efficacy for middle school youth physical activity. Journal of Sport and Exercise Psychology, 29(3), 310-332. 

[21] Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall

[22] Li, X. (2018). Media exposure, perceived efficacy, and protective behaviors in a public health emergency. International Journal of Communication, 12, 20.

[23] Shields, C. A., & Brawley, L. R., 2007, Limiting exercise options: Depending on a proxy may inhibit exercise self-management. Journal of Health Psychology, 12(4), 663-671.

[24] Lent, R. W., & Lopez, F. G., 2002, Cognitive ties that bind: A tripartite view of efficacy beliefs in growth-promoting relationships. Journal of social and Clinical Psychology, 21(3), 256-286.

[25] Witte, K. (1996). Fear as motivator, fear as inhibitor: Using the extended parallel process model to explain fear appeal successes and failures. In Handbook of communication and emotion (pp. 423-450). Academic Press.

[26] Witte, K., Meyer, G., & Martell, D. (2001). Effective health risk messages: A step-by-step guide. Sage.

[27] Hoobler, J. M., & Brass, D. J., 2006, Abusive supervision and family undermining as displaced aggression. Journal of Applied psychology, 91(5), 1125.

[28] Milich, R., & Dodge, K. A., 1984, Social information processing in child psychiatric populations. Journal of abnormal child psychology, 12(3), 471-489. 

[29] Neuwirth, K., Dunwoody, S., & Griffin, R. J., 2000, Protection motivation and risk communication. Risk Analysis, 20(5), 721-734.

[30] Chen, Z. F., & Cheng, Y., 2019, Consumer response to fake news about brands on social media: the effects of self-efficacy, media trust, and persuasion knowledge on brand trust. Journal of Product & Brand Management.

[31] Marriott, H. R., & Williams, M. D., 2018, Exploring consumers perceived risk and trust for mobile shopping: A theoretical framework and empirical study. Journal of retailing and consumer services, 42, 133-146.

[32] Kim, D. J., Ferrin, D. L., & Rao, H. R. ,2009, Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information systems research, 20(2), 237-257.

[33] Yuen, K. F., Wang, X., Wong, Y. D., & Zhou, Q., 2018, The effect of sustainable shipping practices on shippers’ loyalty: The mediating role of perceived value, trust and transaction cost. Transportation Research Part E: Logistics and Transportation Review, 116, 123-135.

[34] Adams, S. H., & John, O. P., 1997, A hostility scale for the California psychological inventory: MMPI, observer Q-sort, and big-five correlates. Journal of Personality Assessment, 69(2), 408-424.

[35] Douglas, S. C., & Martinko, M. J., 2001, Exploring the role of individual differences in the prediction of workplace aggression. Journal of applied psychology, 86(4), 547.

[36] Harvey, P., Harris, K. J., & Martinko, M. J., 2008, The mediated influence of hostile attributional style on turnover intentions. Journal of Business and Psychology, 22(4), 333-343. 

[37] Brislin, R. W., 1980, Cross-cultural research methods. In Environment and culture (pp. 47-82). Springer, Boston.