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Users Intention for Continuous Usage of Mobile News Apps: the Roles of Quality, Switching Costs, and Personalization

  • Qiongwei Ye
  • Yumei LuoEmail author
  • Guoqing Chen
  • Xunhua Guo
  • Qiang Wei
  • Shuyan Tan
Article
  • 2 Downloads

Abstract

Mobile news apps have emerged as a significant means for learning about latest news and trends. However, in light of numerous news apps and information overload, motivating users to adopt one app is a major concern for both the industry and academia. Therefore, considering the attributes of mobile news and the debate on switching costs in the Internet context, based on the expectation-confirmation model (ECM), this study suggests that switching costs still exist and have a significant moderating effect on user satisfaction and continuous usage of mobile news apps. Furthermore, the different influences of information quality, system quality and service quality on continuance intention, user satisfaction and switching costs are discussed, showing that quality of information has a significant impact on users’ continuous usage of mobile news apps through increasing perceived usefulness, whereas personalized service quality have stronger effects through increasing user satisfaction and switching costs.

Keywords

Personalized recommendation switching costs mobile news apps expectation-confirmation model 

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Notes

Acknowledgements

We would like to thank the anonymous reviewers for their immensely helpful suggestions suggestions and critique. This work was partly supported by the National Natural Science Foundation of China (grant numbers 71402159, 71362016, 71490721/4, and 71572092), the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities (17JJD630006), Yunnan Province Young Academic and Technical Leader candidate Program (2018HB), Yunnan Science and Technology Funds (2017FA034, 2014FB116), Yunnan Provincial E-Business Entrepreneur Innovation Interactive Space (2017DS012), Kunming Key Laboratory of E-Business and Internet Finance (2017-1A-14684, KGF[2018]18), Educational and Teaching Reform Funds of Yunnan University (2015), and Yunnan Provincial E-Business Innovation and Entrepreneurship Key Laboratory of colleges and universities (YES 2014 [16]).

References

  1. Ajzen I, Fishbein M (1980). Understanding Attitudes and Predicting Social Behaviour.Google Scholar
  2. Anderson EW, Sullivan MW (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science 12(2): 125–143.Google Scholar
  3. Anderson JC, Gerbing DW (1988). Structural equation modeling in practice: Areviewand recommended twostep approach. Psychological Bulletin 103(3): 411.Google Scholar
  4. Augusto de Matos C, Luiz Henrique J, de Rosa F (2013). Customer reactions to service failure and recovery in the banking industry: The influence of switching costs. Journal of Services Marketing 27(7): 526–538.Google Scholar
  5. Aydin S, Özer G, Arasil Ö (2005). Customer loyalty and the effect of switching costs as a moderator variable: A case in the turkish mobile phone market. Marketing Intelligence & Planning 23(1): 89–103.Google Scholar
  6. Bagozzi RP, Yi Y, Phillips LW (1991). Assessing construct validity in organizational research. Administrative Science Quarterly 421–458.Google Scholar
  7. Balabanis G, Reynolds N, Simintiras A (2006). Bases of e–store loyalty: Perceived switching barriers and satisfaction. Journal of Business Research 59(2): 214–224.Google Scholar
  8. Bell SJ,Auh S, Smalley K (2005). Customer relationship dynamics: service quality and customer loyalty in the context of varying levels of customer expertise and switching costs. Journal of the Academy of Marketing Science 33(2): 169–183.Google Scholar
  9. Bhattacherjee A (2001). Understanding information systems continuance: an expectation–confirmation model. MIS Quarterly 351–370.Google Scholar
  10. Bhattacherjee A, Premkumar G (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly 229–254.Google Scholar
  11. Brislin RW (1976). Comparative research methodology: Cross–cultural studies. International Journal of Psychology 11(3): 215–229.Google Scholar
  12. Burnham TA, Frels JK, Mahajan V (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science 31(2): 109–126.Google Scholar
  13. Cardozo RN (1965). An experimental study of customer effort, expectation, and satisfaction. Journal of Marketing Research 244–249.Google Scholar
  14. Caruana A (2003). The impact of switching costs on customer loyalty: A study among corporate customers of mobile telephony. Journal of Targeting, Measurement and Analysis for marketing 12(3): 256–268.Google Scholar
  15. Chang IC, Liu CC, Chen K (2014). The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal 24(4): 323–346.Google Scholar
  16. Chen IY (2007). The factors influencing members’ continuance intentions in professional virtual communities— a longitudinal study. Journal of Information Science 33(4): 451–467.Google Scholar
  17. Chen PY, Hitt LM (2002). Measuring switching costs and the determinants of customer retention in internetenabled businesses: A study of the online brokerage industry. Information Systems Research 13(3): 255–274.Google Scholar
  18. Chen SC, Yen DC, Hwang MI (2012). Factors influencing the continuance intention to the usage of web 2.0: An empirical study. Computers in Human Behavior 28(3): 933–941.Google Scholar
  19. Chin WW, Marcolin BL, Newsted PR (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a monte carlo simulation study and an electronic–mail emotion/adoption study. Information Systems Research 14(2): 189–217.Google Scholar
  20. Churchill Jr GA (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 64–73.Google Scholar
  21. Cohen P, West SG, Aiken LS (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Psychology Press.Google Scholar
  22. Cohen P, West SG, Aiken LS (2014). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Psychology Press.Google Scholar
  23. Colgate M, Lang B (2001). Switching barriers in consumer markets: an investigation of the financial services industry. Journal of Consumer Marketing 18(4): 332–347.Google Scholar
  24. Davis FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 319–340.Google Scholar
  25. Davis FD, Bagozzi RP, Warshaw PR (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science 35(8): 982–1003.Google Scholar
  26. DeLone WH, McLean ER (1992). Information systems success: The quest for the dependent variable. Information Systems Research 3(1): 60–95.Google Scholar
  27. Delone WH, McLean ER (2003). The delone and mclean model of information systems success: a ten–year update. Journal of Management Information Systems 19(4): 9–30.Google Scholar
  28. Dick AS, Basu K (1994). Customer loyalty: toward an integrated conceptual framework. Journal of the Academy of Marketing Science 22(2): 99–113.Google Scholar
  29. Doong HS, Lai H (2008). Exploring usage continuance of enegotiation systems: expectation and disconfirmation approach. Group Decision and Negotiation 17(2): 111–126.Google Scholar
  30. Fornell C, Larcker DF (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 39–50.Google Scholar
  31. Fu HP, Su HY, Wang FH, Chang CY, Lee HH(2011). Factors of 3gwc systems for drawing up customized strategies of promotion. Journal of Systems Science and Systems Engineering 20(3): 365.Google Scholar
  32. GAN C, XIAO D (2015). An empirical study on continuance intention of mobile reading. Journal of Data and Information Science 7(2): 69–82.MathSciNetGoogle Scholar
  33. Ganesh J, Arnold MJ, Reynolds KE (2000). Understanding the customer base of service providers: an examination of the differences between switchers and stayers. Journal of Marketing 64(3): 65–87.Google Scholar
  34. Gefen D, Straub D (2005). A practical guide to factorial validity using pls–graph: Tutorial and annotated example. Communications of the Association for Information Systems 16(1): 5.Google Scholar
  35. Hadji B, Degoulet P (2016). Information system end–user satisfaction and continuance intention: A unified modeling approach. Journal of Biomedical Informatics 61: 185–193.Google Scholar
  36. Hauser JR, Simester DI, Wernerfelt B (1994). Customer satisfaction incentives. Marketing Science 13(4): 327–350.Google Scholar
  37. Hong H, Xu D (2017). An empirical study of mobile social app continuance intention: integrating flow experience and switching costs. International Journal of Networking and Virtual Organisations 17(4): 410–424.Google Scholar
  38. Hong S, Kim J, Lee H (2008). Antecedents of usecontinuance in information systems: Toward an inegrative view. Journal of Computer Information Systems 48(3): 61–73.Google Scholar
  39. Hsieh CC, Kuo PL, Yang SC, Lin SH (2010). Assessing blog–user satisfaction using the expectation and disconfirmation approach. Computers in Human Behavior 26(6): 1434–1444.Google Scholar
  40. Hsu CL, Lu HP (2004). Why do people play on–line games? an extended tam with social influences and flow experience. Information & Management 41(7): 853–868.Google Scholar
  41. iResearch (2017). Research report on media value of mobile information app in media channels. iResearch Report.Google Scholar
  42. Jennex ME, Olfman L (2006). A model of knowledge management success. International Journal of Knowledge Management (IJKM) 2(3): 51–68.Google Scholar
  43. Jennex ME, Olfman L (2009). A model of knowledge management success. Selected Readings on Information Technology Management: Contemporary Issues, 76–93 (IGI Global).Google Scholar
  44. Jin XL, Lee MK, Cheung CM (2010). Predicting continuance in online communities: model development and empirical test. Behaviour & Information Technology 29(4): 383–394.Google Scholar
  45. Jones MA, Mothersbaugh DL, Beatty SE (2000). Switching barriers and repurchase intentions in services. Journal of Retailing 76(2): 259–274.Google Scholar
  46. Jones MA, Mothersbaugh DL, Beatty SE (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.Google Scholar
  47. Jones MA, Reynolds KE, Mothersbaugh DL, Beatty SE (2007). The positive and negative effects of switching costs on relational outcomes. Journal of Service Research 9(4): 335–355.Google Scholar
  48. Kouser R, Abbas SS, Azeem M, et al. (2014). Consumer attitudes and intentions to adopt smartphone apps: Case of business students. Pakistan Journal of Commerce and Social Sciences (PJCSS) 8(3): 763–779.Google Scholar
  49. Lee J, Lee J, Feick L (2001). The impact of switching costs on the customer satisfaction–loyalty link: mobile phone service in france. Journal of Services Marketing 15(1): 35–48.Google Scholar
  50. Lee MC (2010). Explaining and predicting users’ continuance intention toward e–learning: An extension of the expectation–confirmation model. Computers & Education 54(2): 506–516.Google Scholar
  51. Lee Y, Kwon O (2011). Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web–based services. Electronic Commerce Research and Applications 10(3): 342–357.MathSciNetGoogle Scholar
  52. Liang TP, Lai HJ, Ku YC (2006). Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems 23(3): 45–70.Google Scholar
  53. Lin CS, Wu S, Tsai RJ (2005). Integrating perceived playfulness into expectation–confirmation model for web portal context. Information & Management 42(5): 683–693.Google Scholar
  54. Lin HF (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research 17(2): 119–138.Google Scholar
  55. Lin HH, Wang YS (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management 43(3): 271–282.Google Scholar
  56. Lin WS, Wang CH (2012). Antecedences to continued intentions of adopting e–learning system in blended learning instruction: A contingency framework based on models of information system success and tasktechnology fit. Computers & Education 58(1): 88–99.Google Scholar
  57. Liu L, Sun K (2011). The post–adoption of mobile digital reading service user continuance usage: A theoretical model and empirical test. Library and Information Service 55(10): 78–82.Google Scholar
  58. McKinney V, Yoon K, Zahedi FM (2002). The measurement of web–customer satisfaction: An expectation and disconfirmation approach. Information Systems Research 13(3): 296–315.Google Scholar
  59. Murray KB (1991). A test of services marketing theory: consumer information acquisition activities. The Journal of Marketing 10–25.Google Scholar
  60. Muter P, Maurutto P (1991). Reading and skimming from computer screens and books: the paperless office revisited? Behaviour & Information Technology 10(4): 257–266.Google Scholar
  61. Oghuma AP, Libaque–Saenz CF, Wong SF, Chang Y (2016). An expectation–confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics 33(1): 34–47.Google Scholar
  62. Oliver RL (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research 460–469.Google Scholar
  63. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003). Common method biases in behavioral research: A critical reviewof the literature and recommended remedies. Journal of Applied Psychology 88(5): 879.Google Scholar
  64. Podsakoff PM, Organ DW (1986). Self–reports in organizational research: Problems and prospects. Journal of Management 12(4): 531–544.Google Scholar
  65. Porter ME (1980). Competitive Strategy: Techniques for Analyzing Industries and Competitors, Simon and Schuster.Google Scholar
  66. Ranaweera C, Prabhu J (2003). The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. International Journal of Service Industry Management 14(4): 374–395.Google Scholar
  67. Reibstein DJ (2002). What attracts customers to online stores, and what keeps them coming back? Journal of the Academy of Marketing Science 30(4): 465.Google Scholar
  68. Reichheld FF, Schefter P (2000). E–loyalty: your secret weapon on the web. Harvard Business Review 78(4): 105–113.Google Scholar
  69. Seddon PB (1997). A respecification and extension of the delone and mclean model of is success. Information Systems Research 8(3): 240–253.Google Scholar
  70. Shi N, Lee MK, Cheung CM, Chen H (2010). The continuance of online social networks: how to keep people using facebook? System Sciences (HICSS), 2010 43rd Hawaii International Conference on, 1–10 (IEEE).Google Scholar
  71. Srinivasan SS, Anderson R, Ponnavolu K (2002). Customer loyalty in e–commerce: an exploration of its antecedents and consequences. Journal of Retailing 78(1): 41–50.Google Scholar
  72. Sun Y, Liu D, Chen S, Wu X, Shen XL, Zhang X (2017). Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push–pull–mooring framework. Computers in Human Behavior 75: 727–738.Google Scholar
  73. Thong JY, Hong SJ, Tam KY (2006). The effects of postadoption beliefs on the expectation–confirmation model for information technology continuance. International Journal of Human–Computer Studies 64(9): 799–810.Google Scholar
  74. Venkatesh V, Morris MG, Davis GB, Davis FD (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly 425–478.Google Scholar
  75. Wangenheim F (2003). Situational characteristics as moderators of the satisfaction–loyalty link: an investigation in a business–to–business context. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior 16: 145.Google Scholar
  76. Wu J, Sun H, Tan Y (2013). Social media research: A review. Journal of Systems Science and Systems Engineering 22(3): 257–282.Google Scholar
  77. Yang G (2015). An empirical study on continuance intention of mobile reading service. Journal of Modern Information 35(3): 57–63.Google Scholar
  78. Yung YF, Bentler PM (1996). Bootstrapping techniques in analysis of mean and covariance structures. Advanced Structural Equation Modeling: Issues and Techniques 195–226.zbMATHGoogle Scholar
  79. Zhao Yang, Gao Ting (2015). An empirical research on influence factors of users’ continuance usage of mobile library app. Infomation Science 33(6): 95–125.Google Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Qiongwei Ye
    • 1
    • 2
  • Yumei Luo
    • 3
    Email author
  • Guoqing Chen
    • 1
  • Xunhua Guo
    • 1
  • Qiang Wei
    • 1
  • Shuyan Tan
    • 2
  1. 1.China Retail Research Center, School of Economics and ManagementTsinghua UniversityBeijingChina
  2. 2.Business SchoolYunnan University of Finance and EconomicsKunmingChina
  3. 3.College of Business and Tourism ManagementYunnan UniversityKunmingChina

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