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Exploring continued use of mobile shopping channel in China: the effects of active coping and its antecedents

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Abstract

Online B2C retailers in China rely heavily on exclusive promotions for mobile shopping to attract new consumers and to promote their mobile shopping channels. However, it is unknown what factors may drive new consumers to continuously do mobile shopping without further promotional incentives. Motivated by the theoretical gap and practical salience of this question, in this study we propose a research model based on coping theory that highlights the critical role of active coping in handling mobile shopping procedures, and further identifies important dispositional traits of consumers and situation-specific factors that are either beneficial or harmful to their coping efforts. We recruited participants for a real mobile shopping task and collected pre-task and post-task survey data to test the research hypotheses. The results reveal important factors that are influential to consumers’ continued use of mobile shopping through a coping mechanism, which contributes to deepening the understanding of user behavior in mobile shopping both from an academic perspective and in practice.

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Notes

  1. http://www.statista.com/statistics/278204/china-mobile-users-by-month/, Accessed on Dec 12, 2015.

  2. http://www.statista.com/statistics/364715/alibaba-singles-day-1111-mobile-share/, Accessed on Dec 12, 2015.

References

  1. Wang, R. J. H., Malthouse, E. C., & Krishnamurthi, L. (2015). On the go: How mobile shopping affects customer purchase behavior. Journal of Retailing, 91(2), 217–234.

    Article  Google Scholar 

  2. Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study–A case of China. Computers in Human Behavior, 53, 249–262.

    Article  Google Scholar 

  3. Lazarus, R. S., & Folkman, S. (1984). Stress, Appraisal, and Coping. New York: Springer Publishing Company.

    Google Scholar 

  4. Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 134–159.

    Article  Google Scholar 

  5. Animesh, A., Pinsonneault, A., Yang, S., & Oh, W. (2011). An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments on intention to purchase virtual products. MIS Quarterly, 35(3), 789–810.

    Google Scholar 

  6. Goel, L., Johnson, N. A., Junglas, I., & Ives, B. (2011). From space to place: Predicting users’ intentions to return to virtual worlds. MIS Quarterly, 35(3), 749–771.

    Google Scholar 

  7. Wells, J. D., Valacich, J. S., & Hess, T. J. (2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly, 35(2), 373–396.

    Google Scholar 

  8. Carroll, J. (2008). Theorizing the IT artifact for mobility: A portfolio, not a singularity. In Proceeding of twenty ninth international conference on information systems, Paris, France.

  9. Mano, H., & Oliver, R. L. (1993). Assessing the dimensionality and structure of consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20, 451–466.

    Article  Google Scholar 

  10. Westbrook, R. A., & Oliver, R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction. Journal of Consumer Research, 18, 84–91.

    Article  Google Scholar 

  11. Yi, S., & Baumgartner, H. (2004). Coping with negative emotions in purchase-related situations. Journal of Consumer Psychology, 14(3), 303–317.

    Article  Google Scholar 

  12. Pinsonneault, A., & Rivard, S. (1998). The impact of information technologies on managerial work: From the productivity paradox to the Icarus paradox? MIS Quarterly, 22(3), 287–312.

    Article  Google Scholar 

  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–342.

    Article  Google Scholar 

  14. Davis, 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.

    Article  Google Scholar 

  15. Kao, D. T. (2009). The impact of transaction trust on consumers’ intentions to adopt M-commerce: A cross-cultural investigation. Cyber Psychology & Behavior, 12(2), 225–229.

    Article  Google Scholar 

  16. Ko, E., Kim, E. Y., & Lee, E. K. (2009). Modeling consumer adoption of mobile shopping for fashion products in Korea. Psychology & Marketing, 26(7), 669–687.

    Article  Google Scholar 

  17. Magura, B. (2003). What hooks M-commerce customers? MIT Sloan Management Review, 44(3), 9.

    Google Scholar 

  18. Yang, B., Kim, Y., & Yoo, C. (2013). The integrated mobile advertising model: The effects of technology- and emotion-based evaluations. Journal of Business Research, 66(9), 1345–1352.

    Article  Google Scholar 

  19. Khalifa, K., & Shen, N. (2008). Explaining the adoption of transactional B2C mobile commerce. Journal of Enterprise Information Management, 21(2), 110–124.

    Article  Google Scholar 

  20. Fang, X. W., Chan, S., Brzezinski, J., & Shuang, X. U. (2005). Moderating effects of task type on wireless technology acceptance. Journal of Management Information Systems, 22(3), 123–157.

    Article  Google Scholar 

  21. Min, Q. F., Ji, S. B., & Qu, G. (2008). Mobile commerce user acceptance study in China: A revised UTAUT model. Tsinghua Science and Technology, 13, 257–264.

    Article  Google Scholar 

  22. Choi, Y. K., & Totten, J. W. (2012). Self-construal’s role in mobile TV acceptance: Extension of TAM across cultures. Journal of Business Research, 65, 1525–1533.

    Article  Google Scholar 

  23. Li, Y. M., & Yeh, Y. S. (2010). Increasing trust in mobile commerce through design aesthetics. Computers in Human Behavior, 26(4), 673–684.

    Article  Google Scholar 

  24. Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management, 43(3), 271–282.

    Article  Google Scholar 

  25. Zhou, T., & Lu, Y. B. (2011). The effects of personality traits on user acceptance of mobile commerce. International Journal of Human-Computer Interaction, 27(6), 545–561.

    Article  Google Scholar 

  26. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior. Reading, MA: Addison-Wesley.

    Google Scholar 

  27. Ajzen, I. (1985). From intentions to actions: a theory of planned behavior. In J. Kuhl & J. Bechmann (Eds.), Action-control: from cognition to behavior (pp. 11–39). Heidelberg: Springer.

    Chapter  Google Scholar 

  28. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Google Scholar 

  29. Gebauer, J., & Shaw, M. (2004). Success factors and benefits of mobile business applications: Results from a mobile e-procurement study. International Journal of Electronic Commerce, 8(3), 19–41.

    Google Scholar 

  30. Dai, H., & Palvia, P. C. (2009). Mobile commerce adoption in china and the United States: A cross-cultural study. Data Base for Advances in Information Systems, 40(4), 43–61.

    Article  Google Scholar 

  31. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

    Article  Google Scholar 

  32. Lee, Y., Park, J., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65, 1590–1599.

    Article  Google Scholar 

  33. Gebauer, J., & Ginsburg, M. (2009). Exploring the black box of task-technology fit. Communications of the ACM, 52(1), 130–135.

    Article  Google Scholar 

  34. Junglas, I., Abraham, C., & Watson, R. T. (2008). Task-technology fit for mobile locatable information systems. Decision Support Systems, 45(4), 1046–1057.

    Article  Google Scholar 

  35. Ozok, A. A., & Wei, J. N. (2010). An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: Results from a college student population. Electronic Commerce Research, 10(2), 111–137.

    Article  Google Scholar 

  36. Hung, M., Yang, S., & Hsieh, T. (2012). An examination of the determinants of mobile shopping continuance. International Journal of Electronic Business Management, 10(1), 29–37.

    Google Scholar 

  37. Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091.

    Article  Google Scholar 

  38. Yang, S., Lu, Y., Chen, Y., & Gupta, S. (2015). Understanding consumers’ mobile channel continuance: An empirical investigation of two fitness mechanisms. Behaviour & Information Technology, 34(12), 1135–1146.

    Article  Google Scholar 

  39. Khedhaouria, A., & Beldi, A. (2014). Perceived enjoyment and the effect of gender on continuance intention for mobile Internet services. International Journal of Technology and Human Interaction (IJTHI), 10(2), 1–20.

    Article  Google Scholar 

  40. Hsiao, W., & Chang, T. (2014). Understanding consumers’ continuance intention towards mobile advertising: A theoretical framework and empirical study. Behaviour & Information Technology, 33(7), 730–742.

    Article  Google Scholar 

  41. Lee, H., & Chen, T. (2014). Perceived quality as a key antecedent in continuance intention in mobile commerce. International Journal of Electronic Commerce Studies, 5(2), 123–142.

    Article  Google Scholar 

  42. Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. K. (2012). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision Support Systems, 52(3), 645–656.

    Article  Google Scholar 

  43. Duhachek, A., & Iacobucci, D. (2005). Consumer personality and coping: Testing rival theories of process. Journal of Consumer Psychology, 15(1), 52–63.

    Article  Google Scholar 

  44. McKinney, V., Yoon, K., & Zahedi, F. M. (2002). The measurement of web-customer satisfaction: An expectation and discontinuation approach. Information Systems Research, 13(3), 296–315.

    Article  Google Scholar 

  45. Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.

    Google Scholar 

  46. Folkman, S., & Moskowitz, J. T. (2004). Coping: Pitfalls and promise. Annual Review of Psychology, 55, 745–774.

    Article  Google Scholar 

  47. Beaudry, A., & Pinsonneault, A. (2005). Understanding user responses to information technology: A coping model of user adaptation. MIS Quarterly, 39, 493–524.

    Google Scholar 

  48. Lazarus, R. S. (1991). Emotion and adaptation. New York: Oxford University Press.

    Google Scholar 

  49. Luce, M. F. (1998). Choosing to avoid: coping with negatively emotion-laden consumer decisions. Journal of Consumer Research, 24(4), 409–433.

    Article  Google Scholar 

  50. Luce, M. F., Bettman, J. R., & Payne, J. W. (2001). Emotional decisions: Tradeoff difficulty and coping in consumer choice. Chicago: University of Chicago Press.

    Google Scholar 

  51. Mick, D. G., & Fournier, S. (1998). Paradoxes of technology: Consumer cognizance, emotions, and coping strategies. Journal of Consumer Research, 25(2), 123–143.

    Article  Google Scholar 

  52. Sujan, M., Sujan, H., Bettman, J., & Verhallen, T. (1999). Sources of consumers’ stress and their coping strategies. European Advances in Consumer Research, 4, 182–187.

    Google Scholar 

  53. Duhachek, A. (2005). Coping: a multidimensional, hierarchical framework of responses to stressful consumption episodes. Journal of Consumer Research, 32(1), 41–53.

    Article  Google Scholar 

  54. Morrison, P. D., Roberts, J. H., & von Hippel, E. (2000). Determinants of user innovation and innovation sharing in a local market. Management Science, 46(12), 1513–1527.

    Article  Google Scholar 

  55. Hoffman, D. L., Kopalle, P. K., & Novak, T. P. (2009). The “right” consumers for better concepts: Identifying consumers high in emergent nature to develop new product concepts. Journal of Marketing Research, 47(5), 854–865.

    Article  Google Scholar 

  56. Folkman, S. (1992). Making the case for coping. In B. N. Carpenter (Ed.), Personal coping: Theory, research, and application. Westport, CT: Praeger/Greenwood.

    Google Scholar 

  57. Simon, H. A. (1957). Models of man, social and rational: Mathematical essays on rational human behavior. New York: Wiley.

    Google Scholar 

  58. McCrae, R. R. (1987). Creativity, divergent thinking, and openness to experience. Journal of Personality and Social Psychology, 52, 1258–1265.

    Article  Google Scholar 

  59. Trapnell, P. D., & Campbell, J. D. (1999). Private self-consciousness and the five-factor model of personality: Distinguishing rumination from reflection. Journal of Personality and Social Psychology, 76(2), 284–304.

    Article  Google Scholar 

  60. Novak, T. P., & Hoffman, D. L. (2009). The fit of thinking style and situation: New measures of situation-specific experiential and rational cognition. Journal of Consumer Research, 36, 56–72.

    Article  Google Scholar 

  61. Kopalle, P., & Lehmann, D. R. (2001). Strategic management of expectations: Role of disconfirmation sensitivity and perfectionism. Journal of Marketing Research, 38, 386–394.

    Article  Google Scholar 

  62. Dennis, A. R., Wixom, B. H., & Vandenberg, R. J. (2001). Understanding fit and appropriation effects in group support systems via meta-analysis. MIS Quarterly, 25(2), 167–193.

    Article  Google Scholar 

  63. Fuller, R. M., & Dennis, A. R. (2009). Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks. Information Systems Research, 20(1), 2–17.

    Article  Google Scholar 

  64. Teo, T. S. H., & Men, B. B. M. (2008). Knowledge portals in Chinese consulting firms: A task–technology fit perspective. European Journal of Information Systems, 17(6), 557–574.

    Article  Google Scholar 

  65. Zigurs, I., & Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313–334.

    Article  Google Scholar 

  66. 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.

    Article  Google Scholar 

  67. Lee, C. C., Cheng, H. K., & Cheng, H. H. (2007). An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences. Decision Support Systems, 43(1), 95–110.

    Article  Google Scholar 

  68. Zeidner, M., & Saklofske, D. (1996). Adaptive maladaptive coping. In M. Zeidner & N. S. Endler (Eds.), Handbook of coping (pp. 505–531). New York: Wiley.

    Google Scholar 

  69. Carroll, J. (2008). Theorizing the IT artifact for mobility: A portfolio, not a singularity. In: Proceeding of twenty ninth international conference on information systems, Paris, France.

  70. Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: investigating the moderating effect of customer characteristics. Journal of Marketing Research, 38(1), 131–142.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the valuable comments and suggestions provided by the Guest Editors and three anonymous reviewers, which have greatly helped improve the quality of the earlier drafts. This research was partially supported by the research grant provided by National Science Foundation China (NSFC, 71102039, 71572079) and ESSEC Research Center (Budget Code: 043-220-1-2-05-P-1).

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Authors and Affiliations

  1. Department of Marketing and Electronic Business, School of Business (Management), Nanjing University, Hankou Lu 22, Nanjing, 210093, Jiangsu, China

    Xue Yang

  2. Department of Information Systems, Decision Sciences & Statistics, ESSEC Business School, Avenue Bernard Hirsch, B.P. 50105, 95021, Cergy-Pontoise Cedex, France

    Yan Li

  3. College of Business and Entrepreneurship, The University of Texas Rio Grande Valley , One West University Blvd., Brownsville, TX, 78520, USA

    Qinyu Liao

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  1. Xue Yang
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Yang, X., Li, Y. & Liao, Q. Exploring continued use of mobile shopping channel in China: the effects of active coping and its antecedents. Electron Commer Res 16, 245–267 (2016). https://doi.org/10.1007/s10660-016-9224-9

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