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

Qiongwei Ye , Yumei Luo , Guoqing Chen , Xunhua Guo , Qiang Wei , Shuyan Tan

Journal of Systems Science and Systems Engineering ›› 2019, Vol. 28 ›› Issue (1) : 91 -109.

PDF
Journal of Systems Science and Systems Engineering ›› 2019, Vol. 28 ›› Issue (1) : 91 -109. DOI: 10.1007/s11518-019-5405-0
Article

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

Author information +
History +
PDF

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

Cite this article

Download citation ▾
Qiongwei Ye, Yumei Luo, Guoqing Chen, Xunhua Guo, Qiang Wei, Shuyan Tan. Users Intention for Continuous Usage of Mobile News Apps: the Roles of Quality, Switching Costs, and Personalization. Journal of Systems Science and Systems Engineering, 2019, 28(1): 91-109 DOI:10.1007/s11518-019-5405-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Ajzen I, Fishbein M. Understanding Attitudes and Predicting Social Behaviour, 1980

[2]

Anderson EW, Sullivan MW. The antecedents and consequences of customer satisfaction for firms. Marketing Science, 1993, 12(2): 125-143.

[3]

Anderson JC, Gerbing DW. Structural equation modeling in practice: Areviewand recommended twostep approach. Psychological Bulletin, 1988, 103(3): 411.

[4]

Augusto de Matos C, Luiz Henrique J, de Rosa F. Customer reactions to service failure and recovery in the banking industry: The influence of switching costs. Journal of Services Marketing, 2013, 27(7): 526-538.

[5]

Aydin S, Özer G, Arasil. Customer loyalty and the effect of switching costs as a moderator variable:^A case in the turkish mobile phone market. Marketing Intelligence & Planning, 2005, 23(1): 89-103.

[6]

Bagozzi RP, Yi Y, Phillips LW. Assessing construct validity in organizational research, 1991.

[7]

Balabanis G, Reynolds N, Simintiras A. Bases of e–store loyalty: Perceived switching barriers and satisfaction. Journal of Business Research, 2006, 59(2): 214-224.

[8]

Bell SJ, Auh S, Smalley K. 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, 2005, 33(2): 169-183.

[9]

Bhattacherjee A. Understanding information systems continuance: an expectation–confirmation model, 2001

[10]

Bhattacherjee A, Premkumar G. Understanding changes in belief and attitude toward information technology usage:^A theoretical model and longitudinal test, 2004

[11]

Brislin RW. Comparative research methodology: Cross–cultural studies. International Journal of Psychology, 1976, 11(3): 215-229.

[12]

Burnham TA, Frels JK, Mahajan V. Consumer switching costs:^A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 2003, 31(2): 109-126.

[13]

Cardozo RN. An experimental study of customer effort, expectation, and satisfaction, 1965

[14]

Caruana A. The impact of switching costs on customer loyalty:^A study among corporate customers of mobile telephony. Journal of Targeting, Measurement and Analysis for marketing, 2003, 12(3): 256-268.

[15]

Chang IC, Liu CC, Chen K. The push, pull and mooring effects in virtual migration for social networking sites. Information Systems Journal, 2014, 24(4): 323-346.

[16]

Chen IY. The factors influencing members’ continuance intentions in professional virtual communities— a longitudinal study. Journal of Information Science, 2007, 33(4): 451-467.

[17]

Chen PY, Hitt LM. Measuring switching costs and the determinants of customer retention in internetenabled businesses:^A study of the online brokerage industry. Information Systems Research, 2002, 13(3): 255-274.

[18]

Chen SC, Yen DC, Hwang MI. Factors influencing the continuance intention to the usage of web 2.0: An empirical study. Computers in Human Behavior, 2012, 28(3): 933-941.

[19]

Chin WW, Marcolin BL, Newsted PR. 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, 2003, 14(2): 189-217.

[20]

Churchill GA Jr. A paradigm for developing better measures of marketing constructs, 1979

[21]

Cohen P, West SG, Aiken LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2003

[22]

Cohen P, West SG, Aiken LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2014.

[23]

Colgate M, Lang B. Switching barriers in consumer markets: an investigation of the financial services industry. Journal of Consumer Marketing, 2001, 18(4): 332-347.

[24]

Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology, 1989.

[25]

Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Management Science, 1989, 35(8): 982-1003.

[26]

DeLone WH, McLean ER. Information systems success: The quest for the dependent variable. Information Systems Research, 1992, 3(1): 60-95.

[27]

Delone WH, McLean ER. The delone and mclean model of information systems success: a ten–year update. Journal of Management Information Systems, 2003, 19(4): 9-30.

[28]

Dick AS, Basu K. Customer loyalty: toward an integrated conceptual framework. Journal of the Academy of Marketing Science, 1994, 22(2): 99-113.

[29]

Doong HS, Lai H. Exploring usage continuance of enegotiation systems: expectation and disconfirmation approach. Group Decision and Negotiation, 2008, 17(2): 111-126.

[30]

Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error, 1981

[31]

Fu HP, Su HY, Wang FH, Chang CY, Lee HH. Factors of 3gwc systems for drawing up customized strategies of promotion. Journal of Systems Science and Systems Engineering, 2011, 20(3): 365.

[32]

GAN C, XIAO D. An empirical study on continuance intention of mobile reading. Journal of Data and Information Science, 2015, 7(2): 69-82.

[33]

Ganesh J, Arnold MJ, Reynolds KE. Understanding the customer base of service providers: an examination of the differences between switchers and stayers. Journal of Marketing, 2000, 64(3): 65-87.

[34]

Gefen D, Straub D. A practical guide to factorial validity using pls–graph: Tutorial and annotated example. Communications of the Association for Information Systems, 2005, 16(1): 5

[35]

Hadji B, Degoulet P. Information system end–user satisfaction and continuance intention:^A unified modeling approach. Journal of Biomedical Informatics, 2016, 61: 185-193.

[36]

Hauser JR, Simester DI, Wernerfelt B. Customer satisfaction incentives. Marketing Science, 1994, 13(4): 327-350.

[37]

Hong H, Xu D. An empirical study of mobile social app continuance intention: integrating flow experience and switching costs. International Journal of Networking and Virtual Organisations, 2017, 17(4): 410-424.

[38]

Hong S, Kim J, Lee H. Antecedents of usecontinuance in information systems: Toward an inegrative view. Journal of Computer Information Systems, 2008, 48(3): 61-73.

[39]

Hsieh CC, Kuo PL, Yang SC, Lin SH. Assessing blog–user satisfaction using the expectation and disconfirmation approach. Computers in Human Behavior, 2010, 26(6): 1434-1444.

[40]

Hsu CL, Lu HP. Why do people play on–line games? an extended tam with social influences and flow experience. Information & Management, 2004, 41(7): 853-868.

[41]

iResearch Research report on media value of mobile information app in media channels. iResearch Report, 2017

[42]

Jennex ME, Olfman L. A model of knowledge management success. International Journal of Knowledge Management (IJKM), 2006, 2(3): 51-68.

[43]

Jennex ME, Olfman L. A model of knowledge management success. Selected Readings on Information Technology Management: Contemporary Issues, 76–93 (IGI Global), 2009

[44]

Jin XL, Lee MK, Cheung CM. Predicting continuance in online communities: model development and empirical test. Behaviour & Information Technology, 2010, 29(4): 383-394.

[45]

Jones MA, Mothersbaugh DL, Beatty SE. Switching barriers and repurchase intentions in services. Journal of Retailing, 2000, 76(2): 259-274.

[46]

Jones MA, Mothersbaugh DL, Beatty SE. Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 2002, 55(6): 441-450.

[47]

Jones MA, Reynolds KE, Mothersbaugh DL, Beatty SE. The positive and negative effects of switching costs on relational outcomes. Journal of Service Research, 2007, 9(4): 335-355.

[48]

Kouser R, Abbas SS, Azeem M, et al. Consumer attitudes and intentions to adopt smartphone apps: Case of business students. Pakistan Journal of Commerce and Social Sciences (PJCSS), 2014, 8(3): 763-779.

[49]

Lee J, Lee J, Feick L. The impact of switching costs on the customer satisfaction–loyalty link: mobile phone service in france. Journal of Services Marketing, 2001, 15(1): 35-48.

[50]

Lee MC. Explaining and predicting users’ continuance intention toward e–learning: An extension of the expectation–confirmation model. Computers & Education, 2010, 54(2): 506-516.

[51]

Lee Y, Kwon O. Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web–based services. Electronic Commerce Research and Applications, 2011, 10(3): 342-357.

[52]

Liang TP, Lai HJ, Ku YC. Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems, 2006, 23(3): 45-70.

[53]

Lin CS, Wu CS, Tsai RJ. Integrating perceived playfulness into expectation–confirmation model for web portal context. Information & Management, 2005, 42(5): 683-693.

[54]

Lin HF. The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 2007, 17(2): 119-138.

[55]

Lin HH, Wang YS. An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management, 2006, 43(3): 271-282.

[56]

Lin WS, Wang CH. 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, 2012, 58(1): 88-99.

[57]

Liu L, Sun K. The post–adoption of mobile digital reading service user continuance usage:^A theoretical model and empirical test. Library and Information Service, 2011, 55(10): 78-82.

[58]

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

[59]

Murray KB. A test of services marketing theory: consumer information acquisition activities, 1991

[60]

Muter P, Maurutto P. Reading and skimming from computer screens and books: the paperless office revisited. Behaviour & Information Technology, 1991, 10(4): 257-266.

[61]

Oghuma AP, Libaque–Saenz CF, Wong SF, Chang Y. An expectation–confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 2016, 33(1): 34-47.

[62]

Oliver RL. A cognitive model of the antecedents and consequences of satisfaction decisions, 1980

[63]

Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research:^A critical reviewof the literature and recommended remedies. Journal of Applied Psychology, 2003, 88(5): 879.

[64]

Podsakoff PM, Organ DW. Self–reports in organizational research: Problems and prospects. Journal of Management, 1986, 12(4): 531-544.

[65]

Porter ME. Competitive Strategy: Techniques for Analyzing Industries and Competitors, Simon and Schuster, 1980

[66]

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.

[67]

Reibstein DJ. What attracts customers to online stores, and what keeps them coming back. Journal of the Academy of Marketing Science, 2002, 30(4): 465.

[68]

Reichheld FF, Schefter P. E–loyalty: your secret weapon on the web. Harvard Business Review, 2000, 78(4): 105-113.

[69]

Seddon PB. A respecification and extension of the delone and mclean model of is success. Information Systems Research, 1997, 8(3): 240-253.

[70]

Shi N, Lee MK, Cheung CM, Chen H. The continuance of online social networks: how to keep people using facebook?, 2010

[71]

Srinivasan SS, Anderson R, Ponnavolu K. Customer loyalty in e–commerce: an exploration of its antecedents and consequences. Journal of Retailing, 2002, 78(1): 41-50.

[72]

Sun Y, Liu D, Chen S, Wu X, Shen XL, Zhang X. Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push–pull–mooring framework. Computers in Human Behavior, 2017, 75: 727-738.

[73]

Thong JY, Hong SJ, Tam KY. The effects of postadoption beliefs on the expectation–confirmation model for information technology continuance. International Journal of Human–Computer Studies, 2006, 64(9): 799-810.

[74]

Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view, 2003

[75]

Wangenheim F. 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, 2003, 16: 145

[76]

Wu J, Sun H, Tan Y. Social media research:^A review. Journal of Systems Science and Systems Engineering, 2013, 22(3): 257-282.

[77]

Yang G. An empirical study on continuance intention of mobile reading service. Journal of Modern Information, 2015, 35(3): 57-63.

[78]

Yung YF, Bentler PM. Bootstrapping techniques in analysis of mean and covariance structures, 1996, Advanced Structural Equation Modeling: Issues and Techniques 195–226.

[79]

Zhao Y, Gao T. An empirical research on influence factors of users’ continuance usage of mobile library app. Infomation Science, 2015, 33(6): 95-125.

AI Summary AI Mindmap
PDF

251

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/