487
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

An Integrated Model of Continued M-Commerce Applications Usage

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon

References

  • Pew Research Center. Mobile fact sheet. 2021. https://www.pewresearch.org/internet/fact-sheet/mobile/
  • Oberlo. Mobile commerce sales in 2021. 2021. https://www.oberlo.com/statistics/mobile-commerce-sales
  • Narang U, Shankar V. Mobile app introduction and online and offline purchases and product returns. Marketing Sci. 2019;38(5):756–72. doi:10.1287/mksc.2019.1169.
  • Taylor DG, Levin M. Predicting mobile app usage for purchasing and information-sharing. Int J Retail Distrib Manage. 2014;42(8):759–74. doi:10.1108/ijrdm-11-2012-0108.
  • Verkijika SF, De Wet L. Understanding word-of-mouth (WOM) intentions of mobile app users: the role of simplicity and emotions during the first interaction. Telematics Inf. 2019;41:218–28. doi:10.1016/j.tele.2019.05.003.
  • Fang Y. Beyond the usefulness of branded applications: insights from consumer–brand engagement and self‐construal perspectives. Psychol Marketing. 2017;34(1):40–58. doi:10.1002/mar.20972.
  • Stocchi L, Michaelidou N, Pourazad N, Micevski M. The rules of engagement: how to motivate individuals to engage with branded mobile apps. J Marketing Manage. 2018;34(13–14):1196–226. doi:10.1080/0267257x.2018.1544167.
  • Tarute A, Nikou S, Gatautis R. Mobile application driven consumer engagement. Telematics Inf. 2017;34(4):145–56. doi:10.1016/j.tele.2017.01.006.
  • Alnawas I, Aburub F. The effect of benefits generated from interacting with branded mobile apps on consumer satisfaction and purchase intentions. J Retailing Consum Serv. 2016;31:313–22. doi:10.1016/j.jretconser.2016.04.004.
  • Bellman S, Potter RF, Treleaven-Hassard S, Robinson JA, Varan D. The effectiveness of branded mobile phone apps. J Interact Marketing. 2011;25(4):191–200. doi:10.1016/j.intmar.2011.06.001.
  • Seitz VA, Aldebasi NM. The effectiveness of branded mobile apps on user’s brand attitudes and purchase intentions. Rev Econ Bus Stud. 2016;9(1):141–54. doi:10.1515/rebs-2016-0029.
  • Statista. Mobile app user retention rate worldwide 2020. 2021. https://www.statista.com/statistics/259329/ios-and-android-app-user-retention-rate/
  • Bhattacherjee A. Understanding information systems continuance: an expectation-confirmation model. MIS Q. 2001;25(3):351–70. doi:10.2307/3250921.
  • Ashraf AR, Razzaque MA, Thongpapanl NT. The role of customer regulatory orientation and fit in online shopping across cultural contexts. J Bus Res. 2016;69(12):6040–47. doi:10.1016/j.jbusres.2016.05.019.
  • Ashraf AR, Thongpapanl NT. Connecting with and converting shoppers into customers: investigating the role of regulatory fit in the online customer’s decision-making process. J Interact Marketing. 2015;32:13–25. doi:10.1016/j.intmar.2015.09.004.
  • Ashraf AR, Thongpapanl NT, Anwar A, Lapa L, Venkatesh V. Perceived values and motivations influencing m-commerce use: a nine-country comparative study. Int J Inf Manage. 2021;59:102318. doi:10.1016/j.ijinfomgt.2021.102318.
  • Ashraf AR, Thongpapanl NT, Spyropoulou S. The connection and disconnection between e-commerce businesses and their customers: exploring the role of engagement, perceived usefulness, and perceived ease-of-use. Electron Commer Res Appl. 2016;20:69–86. doi:10.1016/j.elerap.2016.10.001.
  • Lin CP, Bhattacherjee A. Extending technology usage models to interactive hedonic technologies: a theoretical model and empirical test. Inf Syst J. 2010;20(2):163–81. doi:10.1111/j.1365-2575.2007.00265.x.
  • Thongpapanl N, Ashraf AR, Lapa L, Venkatesh V. Differential effects of customers’ regulatory fit on trust, perceived value, and m-commerce use among developing and developed countries. J Int Marketing. 2018;26(3):22–44. doi:10.1509/jim.17.0129.
  • Pereira R, Tam C. Impact of enjoyment on the usage continuance intention of video-on-demand services. Inf Manage. 2021;58(7):103501. doi:10.1016/j.im.2021.103501.
  • Van der Heijden H. User acceptance of hedonic information systems. MIS Q. 2004;28(4):695–704. doi:10.2307/25148660.
  • Wu J, Du H. Toward a better understanding of behavioral intention and system usage constructs. Eur J Inf Syst. 2012;21(6):680–98. doi:10.1057/ejis.2012.15.
  • Wu J, Lu X. Effects of extrinsic and intrinsic motivators on using utilitarian, hedonic, and dual-purposed information systems: a meta-analysis. J Assoc Inf Syst. 2013;14(3):153–91. doi:10.17705/1jais.00325.
  • Oliver RL. A cognitive model for the antecedents and consequences of satisfaction. J Marketing Res. 1980;17:460–69. doi:10.1177/002224378001700405.
  • Hossain MA, Quaddus M. Expectation–confirmation theory in information system research: a review and analysis. In: Dwivedi YK, et al. editors. Information systems theory: theory: explaining and predicting, our digital society, Vol. 1, integrated series in information systems 28. Springer Science+Business Media; 2012. p. 441–69. doi:10.1007/978-1-4419-6108-2_21.
  • Hung MC, Hwang HG, Hsieh TC. An exploratory study on the continuance of mobile commerce: an extended expectation-confirmation model of information system use. Int J Mobile Commun. 2007;5(4):409–22. doi:10.1504/ijmc.2007.012788.
  • Nguyen GD, Ha MT. The role of user adaptation and trust in understanding continuance intention towards mobile shopping: an extended expectation-confirmation model. Cogent Bus Manage. 2021;8(1):1980248. doi:10.1080/23311975.2021.1980248.
  • Tam C, Santos D, Oliveira T. Exploring the influential factors of continuance intention to use mobile Apps: extending the expectation confirmation model. Inf Syst Front. 2020;22(1):243–57. doi:10.1007/s10796-018-9864-5.
  • Zhou T. An empirical examination of users’ post-adoption behaviour of mobile services. Behav Inf Technol. 2011;30(2):241–50. doi:10.1080/0144929X.2010.543702.
  • Lin HH, Wang YS. An examination of the determinants of customer loyalty in mobile commerce contexts. Inf Manage. 2006;43(3):271–82. doi:10.1016/j.im.2005.08.001.
  • Turel O, Serenko A. Satisfaction with mobile services in Canada: an empirical investigation. Telecomm Policy. 2006;30(5/6):314–31. doi:10.1016/j.telpol.2005.10.003.
  • Lin YM, Shih DH. Deconstructing mobile commerce service with continuance intention. Int J Mobile Commun. 2008;6(1):67–87. doi:10.1504/ijmc.2008.016000.
  • Brown SA, Venkatesh V, Goyal S. Expectation confirmation in technology use. Inf Syst Res. 2012;23(2):474–87. doi:10.1287/isre.1110.0357.
  • Ho YC, Wu J, Tan Y. Disconfirmation effect on online rating behavior: a structural model. Inf Syst Res. 2017;28(3):626–42. doi:10.1287/isre.2017.0694.
  • Venkatesh V, Goyal S. Expectation disconfirmation and technology adoption: polynomial modeling and response surface analysis. MIS Q. 2010;34:281–303. doi:10.2307/20721428.
  • Nam K, Baker J, Ahmad N, Goo J. Determinants of writing positive and negative electronic word-of-mouth: empirical evidence for two types of expectation confirmation. Decis Support Syst. 2020;129:113168. doi:10.1016/j.dss.2019.113168.
  • Chong AY-L. Understanding mobile commerce continuance intentions: an empirical analysis of Chinese consumers. J Comput Inf Syst. 2013;53(4):22–30. doi:10.1080/08874417.2013.11645647.
  • Lee MC. Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation-confirmation model. Comput Educ. 2010;54(2):506–16. doi:10.1016/j.compedu.2009.09.002.
  • Deci EL. Intrinsic motivation. New York (NY): Plenum Press; 1975.
  • Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and intrinsic motivation to use computers in the workplace. J Appl Soc Psychol. 1992;22(14):1111–32. doi:10.1111/j.1559-1816.1992.tb00945.x.
  • Shang R, Chen Y-C, Shen L. Extrinsic versus intrinsic motivations for individuals to shop online. Inf Manage. 2005;42(3):401–13. doi:10.1016/j.im.2004.01.009.
  • Fagan MH, Neill S, Wooldridge BR. Exploring the intention to use computers: an empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. J Comput Inf Syst. 2008;48(3):31–37. doi:10.1080/08874417.2008.11646019.
  • Agrifoglio R, Black S, Metallo C, Ferrara M. Extrinsic versus intrinsic motivation in continued twitter usage. J Comput Inf Syst. 2012;53(1):33–41. doi:10.1080/08874417.2012.11645594.
  • Fang J, Wen C, George B, Prybutok VR. Consumer heterogeneity, perceived value, and repurchase decision-making in online shopping: the role of gender, age, and shopping motives. J Electron Commerce Res. 2016;17:116.
  • Childers TL, Carr CL, Peck J, Carson S. Hedonic and utilitarian motivations for online retail shopping behavior. J Retailing. 2001;77(4):511–35. doi:10.1016/S0022-4359(01)00056-2.
  • Cheng X, Fu S, Yin G. Does subsidy work? An investigation of post-adoption switching on car-hailing apps. J Electron Commerce Res. 2017;18(4): 317–40. https://ssrn.com/abstract=2953448
  • Han JH, Kim H-M. The role of information technology use for increasing consumer informedness in cross-border electronic commerce: an empirical study. Electron Commer Res Appl. 2019;34:100826. doi:10.1016/j.elerap.2019.100826.
  • Kakar AK. How does the value provided by a software product and users’ psychological needs interact to impact user loyalty. Inf Software Technol. 2018;97:135–45. doi:10.1016/j.infsof.2018.01.007.
  • Chong AY-L. A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Syst Appl. 2013;40(4):1240–47. doi:10.1016/j.eswa.2012.08.067.
  • Chong AY-L. Predicting m-commerce adoption determinants: a neural network approach. Expert Syst Appl. 2013;40(2):523–30. doi:10.1016/j.eswa.2012.07.068.
  • Ashraf RU, Hou F, Ahmad W. Understanding continuance intention to use social media in China: the roles of personality drivers, hedonic value, and utilitarian value. Int J Human Comput Interact. 2019;35(13):1216–28. doi:10.1080/10447318.2018.1519145.
  • Mittal A, Aggarwal A, Mittal R. Predicting university students’ adoption of mobile news applications: the role of perceived hedonic value and news motivation. Int J E-Serv Mobile Appl. 2020;12(4):42–59. doi:10.4018/IJESMA.2020100103.
  • Choi H, Kim Y, Kim J. Driving factors of post adoption behavior in mobile data services. J Bus Res. 2011;64(11):1212–17. doi:10.1016/j.jbusres.2011.06.025.
  • Choi K, Wang Y, Sparks B. Travel app users’ continued use intentions: it’s a matter of value and trust. J Travel Tourism Marketing. 2019;36(1):131–43. doi:10.1080/10548408.2018.1505580.
  • Ozturk A, Nusair K, Okumus F, Hua N. The role of utilitarian and hedonic values on users’ continued usage intention in a mobile hotel booking environment. Int J Hospitality Manage. 2016;57:106–15. doi:10.1016/j.ijhm.2016.06.007.
  • Akdim K, Casaló LV, Flavián C. The role of utilitarian and hedonic aspects in the continuance intention to use social mobile apps. J Retailing Consum Serv. 2022;66:102888. doi:10.1016/j.jretconser.2021.102888.
  • Chen JH, Fu JR. On the effects of perceived value in the mobile moment. Electron Commer Res Appl. 2018;27:118–28. doi:10.1016/j.elerap.2017.12.009.
  • Lyer P, Davari A, Mukherjee A. Investigating the effectiveness of retailers’ mobile applications in determining customer satisfaction and repatronage intentions? A congruency perspective. J Retailing Consum Serv. 2018;44:235–43. doi:10.1016/j.jretconser.2018.07.017.
  • Li M, Dong ZY, Chen X. Factors influencing consumption experience of mobile commerce: a study from experiential view. Internet Res. 2012;22(2):120–41. doi:10.1108/10662241211214539.
  • Parasuraman A, Zeithaml VA, Malhotra A. E-S-QUAL: a multiple-item scale for assessing electronic service quality. J Serv Res. 2005;7(3):213–33. doi:10.1177/1094670504271156.
  • Wolfinbarger M, Gilly MC. eTailQ: dimensionalizing, measuring and predicting etail quality. J Retailing. 2003;79(3):183–98. doi:10.1016/S0022-4359(03)00034-4.
  • Wakefield RL, Whitten D. Mobile computing: a user study on hedonic/utilitarian mobile device usage. Eur J Inf Syst. 2006;15(3):292–300. doi:10.1057/palgrave.ejis.3000619.
  • Molinillo S, Aguilar-Illescas S, Anaya-Sánchez R, Carvajal-Trujillo E. The customer retail app experience: implications for customer loyalty. J Retailing Consum Serv. 2022;65:102842. doi:10.1016/j.jretconser.2021.102842.
  • Al-Natour S, Benbasat I. The adoption and use of IT artifacts: a new interaction-centric model for the study of user-artifact relationships. J Assoc Inf Syst. 2009;10(9):661–85. doi:10.17705/1jais.00208.
  • Köse DB, Morschheuser B, Hamari J. Is it a tool or a toy? How user’s conception of a system’s purpose affects their experience and use. Int J Inf Manage. 2019;49:461–74. doi:10.1016/j.ijinfomgt.2019.07.016.
  • Ono A, Nakamura A, Okuno A, Sumikawa M. Individual motivations in browsing online stores with mobile devices. Int J Electron Commerce. 2012;16(4):153–78. doi:10.2753/jec1086-4415160406.
  • Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. doi:10.1016/0749-5978(91)90020-T.
  • Chang I-C, Liu CC, Chen K. The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games. Internet Res. 2014;24(1):21–45. doi:10.1108/intr-02-2012-0025.
  • Xu C, Ryan S, Prybutok V, Wen C. It is not for fun: an examination of social network site usage. Inf Manage. 2012;49(5):210–17. doi:10.1016/j.im.2012.05.001.
  • Zhang M, Guo L, Hu M, Liu W. Influence of customer engagement with company social networks on stickiness: mediating effect of customer value creation. Int J Inf Manage. 2017;37(3):229–40. doi:10.1016/j.ijinfomgt.2016.04.010.
  • Sun H. A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Q. 2013;37(4):1013–41. https://www.jstor.org/stable/43825780
  • Terlaak A, Gong Y. Vicarious learning and inferential accuracy in adoption processes. Acad Manag Rev. 2008;33(4):846–68. doi:10.5465/amr.2008.34421979.
  • Rao H, Greve HR, Davis GF. Fool’s gold: social proof in the initiation and abandonment of coverage by Wall Street analysts. Adm Sci Q. 2001;46(3):502–26. doi:10.2307/3094873.
  • Thompson RL, Higgins CA, Howell JM. Personal computing: toward a conceptual model of utilization. MIS Q. 1991;15(1):125–43. doi:10.2307/249443.
  • Wang P. Chasing the hottest IT: effects of information technology fashion on organizations. MIS Q. 2010;34(1):63–85. doi:10.2307/20721415.
  • Wang N. Managers’ noticing of new organizational IT and influences of IT attributes. Inf Manage. 2020;57(5):103232. doi:10.1016/j.im.2019.103232.
  • Li M, Dong ZY, Chen XI. Factors influencing consumption experience of mobile commerce: a study from experiential view. Internet Res. 2012;22(2):120–41. doi:10.1108/10662241211214539.
  • Hair JF, Sarstedt M, Ringle CM, Gudergan SP. Advanced issues in partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: SAGE; 2018.
  • Ringle CM, Sarstedt M, Straub DW. Editor’s comments: a critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Q. 2012;36(1):iii–xiv. doi:10.2307/41410402.
  • Hair JF, Ringle CM, Sarstedt M. PLS-SEM: indeed a silver bullet. J Marketing Theory Pract. 2011;19(2):139–51. doi:10.2753/MTP1069-6679190202.
  • Fornell C, Larcker DF. Evaluating structural equations with unobservable variables and measurement error. J Marketing Res. 1981;18(1):39–50. doi:10.1177/002224378101800104.
  • Hair JF, Hult GTM, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed. SAGE Publications; 2016. doi:10.1509/jim.17.0151.
  • Anwar A, Thongpapanl N, Ashraf AR. Strategic imperatives of mobile commerce in developing countries: the influence of consumer innovativeness, ubiquity, perceived value, risk, and cost on usage. J Strategic Marketing. 2021;29(8):722–42. doi:10.1080/0965254X.2020.1786847.
  • Ashraf AR, Thongpapanl N, Menguc B, Northey G. The role of m-commerce readiness in emerging and developed markets. J Int Marketing. 2017;25(2):25–51. doi:10.1509/jim.16.0033.
  • Hong SJ, Thong YL, Tam KY. Understanding continued information technology usage behavior: a comparison of three models in the context of mobile internet. Decis Support Syst. 2006;42(3):1819–34. doi:10.1016/j.dss.2006.03.009.
  • Ko E, Kim EY, Lee EK. Modeling individual adoption of mobile shopping for fashion products in Korea. Psychol Marketing. 2009;26(7):669–87. doi:10.1002/mar.20294.
  • Chan FT, Chong AY-L. Analysis of the determinants of individuals’ m-commerce usage activities. Online Inf Rev. 2013;37(3):443–61. doi:10.1108/OIR-01-2012-0012.
  • Lee T, Jun J. Contextual expectation confirmation?: investigating the role of contextual marketing for customer relationship management in a mobile commerce context. Bus Process Manage J. 2007;13(6):798–814. doi:10.1108/14637150710834569.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.