Skip to main content

Advertisement

SpringerLink
Log in

Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

The use of mobile applications (apps) has been growing in the world of technology, a phenomenon related to the increasing number of smartphone users. Even though the mobile apps market is huge, few studies have been made on what makes individuals continue to use a mobile app or stop using it. This study aims to uncover the factors that underlie the continuance intention to use mobile apps, addressing two theoretical models: Expectation confirmation model (ECM) and the extended unified theory of acceptance and use of technology (UTAUT2). A total of 304 questionnaires were collected by survey to test the theoretical framework proposal, using structural equation modelling (SEM). Our findings indicate that the most important drivers of continuance intention of mobile apps are satisfaction, habit, performance expectancy, and effort expectancy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Albashrawi, M., & Motiwalla, L. (2017). Privacy and personalization in continued usage intention of mobile banking: an integrative perspective. Information Systems Frontiers, 1–13. https://doi.org/10.1007/s10796-017-9814-7.

    Article  Google Scholar 

  • Alwahaishi, S., & Snásel, V. (2013). Acceptance and Use of Information and Communications Technology: A UTAUT and Flow Based Theoretical Model. Journal of Technology Management & Innovation, 8(2), 61–73.

    Article  Google Scholar 

  • Amoroso, D., & Lim, R. (2017). The mediating effects of habit on continuance intention. International Journal of Information Management, 37(6), 693–702.

    Article  Google Scholar 

  • Baharuddin, R., Singh, D., & Razali, R. (2013). Usability dimensions for mobile applications—a review. Research Journal of Applied Sciences, Engineering and Technology, 5(6), 2225–2231.

    Article  Google Scholar 

  • Barnes, S. J. (2011). Understanding use continuance in virtual worlds: empirical test of a research model. Information Management, 48(8), 313–319.

    Article  Google Scholar 

  • Bellman, S., Potter, R. F., Treleaven-Hassard, S., Robinson, J. A., & Varan, D. (2011). The effectiveness of branded mobile phone apps. Journal of Interactive Marketing, 25(4), 191–200.

    Article  Google Scholar 

  • Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214.

    Article  Google Scholar 

  • Bhattacherjee, A. (2001b). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 351–370.

    Article  Google Scholar 

  • Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 399–426.

  • Chang, I.-C., Liu, C.-C., & Chen, K. (2014). The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games. Internet Research, 24(1), 21–45.

    Article  Google Scholar 

  • Chin, W. (1998). Issues and opinion on structural equation modelling. Management Information Systems Quarterly, 22(1), 1–8.

    Article  Google Scholar 

  • Chin, W. W., Marcolin, B. L., & Newsted, P. R. (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.

    Article  Google Scholar 

  • Chiu, C.-M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information Management, 45(3), 194–201.

    Article  Google Scholar 

  • Chiu, C.-M., Hsu, M.-H., Sun, S.-Y., Lin, T.-C., & Sun, P.-C. (2005). Usability, quality, value and e-learning continuance decisions. Computers & Education, 45(4), 399–416.

    Article  Google Scholar 

  • Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics, 87, 75–83.

    Article  Google Scholar 

  • Chopdar, P. K., Korfiatis, N., Sivakumar, V. J., & Lytras, M. D. (2018). Mobile shopping apps adoption and perceived risks: a cross-country perspective utilizing the Unified Theory of Acceptance and use of Technology. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2018.04.017.

    Article  Google Scholar 

  • Corbeil, J. R., & Valdes-Corbeil, M. E. (2007). Are you ready for mobile learning? Educause Quarterly, 30(2), 51–58.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology, 22(14), 1111–1132.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39–50.

  • Forrester (2016). Integrating mobile in your strategy as a key brand differentiator. In: T. Husson (Ed.) 2016 Mobile And App Marketing Trends.

  • Gartner, (2015). Gartner says demand for enterprise mobile apps will outstrip available development capacity five to one. available at: http://www.gartner.com/newsroom/id/3076817.

  • Gartner, (2016). Gartner survey reveals enterprise spending on mobile app development remains low. available at: http://www.gartner.com/newsroom/id/3353317.

  • Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing (JOEUC), 15(3), 1–13.

    Article  Google Scholar 

  • 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 

  • Hair Jr., J., Sarstedt, M., Hopkins, L., & Kuppelwieser, G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121.

    Article  Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

    Article  Google Scholar 

  • Harris, M. A., Brookshire, R., & Chin, A. G. (2016). Identifying factors influencing consumers’ intent to install mobile applications. International Journal of Information Management, 36(3), 441–450.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20(1), 277–319.

    Article  Google Scholar 

  • Hoehle, H., & Venkatesh, V. (2015). Mobile application usability: conceptualization and instrument development. MIS Quarterly, 39(2), 435–472.

    Article  Google Scholar 

  • Hoehle, H., Zhang, X., & Venkatesh, V. (2015). An espoused cultural perspective to understand continued intention to use mobile applications: a four-country study of mobile social media application usability. European Journal of Information Systems, 24(3), 337–359.

    Article  Google Scholar 

  • Hong, S.-J., Thong, J. Y. L., Moon, J.-Y., & Tam, K.-Y. (2008). Understanding the behavior of mobile data services consumers. Information Systems Frontiers, 10(4), 431–445.

    Article  Google Scholar 

  • Hong, J.-C., Lin, P.-H., & Hsieh, P.-C. (2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior, 67, 264–272.

    Article  Google Scholar 

  • Hsiao, C.-H., Chang, J.-J., & Tang, K.-Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342–355.

    Article  Google Scholar 

  • Hsu, C.-L., & Lin, J. C.-C. (2015). What drives purchase intention for paid mobile apps?–An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1), 46–57.

    Article  Google Scholar 

  • Idemudia, E. C., Raisinghani, M. S., and Samuel-Ojo, O. (2016). The contributing factors of continuance usage of social media: An empirical analysis. Information Systems Frontiers. https://doi.org/10.1007/s10796-016-9721-3.

    Article  Google Scholar 

  • Johnson, R. E., Rosen, C. C., & Djurdjevic, E. (2011). Assessing the impact of common method variance on higher order multidimensional constructs. Journal of Applied Psychology, 96(4), 744.

    Article  Google Scholar 

  • Kang, S. (2014). Factors influencing intention of mobile application use. International Journal of Mobile Communications, 12(4), 360–379.

    Article  Google Scholar 

  • Kim, S. S., & Malhotra, N. K. (2005). A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena. Management Science, 51(5), 741–755.

    Article  Google Scholar 

  • Kim, D., Chun, H., & Lee, H. (2014). Determining the factors that influence college students’ adoption of smartphones. Journal of the Association for Information Science and Technology, 65(3), 578–588.

    Article  Google Scholar 

  • Kim, S. J., Wang, R. J.-H., & Malthouse, E. C. (2015). The Effects of Adopting and Using a Brand's Mobile Application on Customers' Subsequent Purchase Behavior. Journal of Interactive Marketing, 31, 28–41.

    Article  Google Scholar 

  • Krishnaraju, V., Mathew, S. K., & Sugumaran, V. (2016). Web personalization for user acceptance of technology: An empirical investigation of E-government services. Information Systems Frontiers, 18(3), 579–595.

    Article  Google Scholar 

  • Kulviwat, S., Bruner, G. C., & Al-Shuridah, O. (2009). The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption. Journal of Business Research, 62(7), 706–712.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Li, H., Liu, Y., Xu, X., Heikkilä, J., & Van Der Heijden, H. (2015). Modeling hedonic is continuance through the uses and gratifications theory: An empirical study in online games. Computers in Human Behavior, 48, 261–272.

    Article  Google Scholar 

  • Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 31(4), 705–737.

    Article  Google Scholar 

  • Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information Management, 42(5), 683–693.

    Article  Google Scholar 

  • Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114.

    Google Scholar 

  • Nysveen, H., & Pedersen, P. E. (2016). Consumer adoption of RFID-enabled services. Applying an extended UTAUT model. Information Systems Frontiers, 18(2), 293–314.

    Article  Google Scholar 

  • Oliver, R. L. (1980). "A cognitive model of the antecedents and consequences of satisfaction decisions", Journal of Marketing Research, 17(4), 460–469.

    Article  Google Scholar 

  • Perez, S. (2014). "Mobile App Usage Increases In 2014, As Mobile Web Surfing Declines", Flurry Analytics.

    Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.

    Article  Google Scholar 

  • Porter, M. E. (1980). Competitive strategy: techniques for analyzing industries and competitors. New York: Free Press.

    Google Scholar 

  • Prensky, M. (2001). Digital natives, digital immigrants part 1. On the horizon, 9(5), 1–6.

    Article  Google Scholar 

  • Qasim, H., & Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers, 18(5), 1021–1034.

    Article  Google Scholar 

  • Remus, W. (1986). Graduate students as surrogates for managers in experiments on business decision making. Journal of Business Research, 14(1), 19–25.

    Article  Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2014). Smartpls 3. Hamburg: SmartPLS.

    Google Scholar 

  • Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683–696.

    Article  Google Scholar 

  • Ryans, A. B. (1974). Estimating consumer preferences for a new durable brand in an established product class. Journal of Marketing Research, 11(4), 434–443.

    Article  Google Scholar 

  • Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information Management, 42(2), 317–327.

    Article  Google Scholar 

  • Seethamraju, R., Diatha, K. S., & Garg, S. (2018). Intention to Use a Mobile-Based Information Technology Solution for Tuberculosis Treatment Monitoring–Applying a UTAUT Model. Information Systems Frontiers, 20(1), 163–181.

    Article  Google Scholar 

  • Shen, A. X., Cheung, C. M., Lee, M. K., & Chen, H. (2011). How social influence affects we-intention to use instant messaging: The moderating effect of usage experience. Information Systems Frontiers, 13(2), 157–169.

    Article  Google Scholar 

  • Song, J., Kim, J., Jones, D. R., Baker, J., & Chin, W. W. (2014). Application discoverability and user satisfaction in mobile application stores: An environmental psychology perspective. Decision Support Systems, 59, 37–51.

    Article  Google Scholar 

  • Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984–990.

    Article  Google Scholar 

  • Thong, J. Y., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810.

    Article  Google Scholar 

  • Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71–102.

  • Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.

  • Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60.

    Article  Google Scholar 

  • 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.

  • Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502.

  • Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555.

    Article  Google Scholar 

  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

    Article  Google Scholar 

  • Vila, N., & Kuster, I. (2011). Consumer feelings and behaviours towards well designed websites. Information Management, 48(4), 166–177.

    Article  Google Scholar 

  • Wang, H.-Y., Liao, C., & Yang, L.-H. (2013a). What Affects Mobile Application Use? The Roles of Consumption Values. International Journal of Marketing Studies, 5(2), 11–22.

    Article  Google Scholar 

  • Wang, Y., Meister, D. B., & Gray, P. H. (2013b). Social Influence and Knowledge Management Systems Use: Evidence from Panel Data. MIS Quarterly, 37(1), 299–313.

    Article  Google Scholar 

  • Wani, M., Raghavan, V., Abraham, D., & Kleist, V. (2017). Beyond utilitarian factors: User experience and travel company website successes. Information Systems Frontiers, 19(4), 769–785.

    Article  Google Scholar 

  • Xu, C., Peak, D., & Prybutok, V. (2015). A customer value, satisfaction, and loyalty perspective of mobile application recommendations. Decision Support Systems, 79, 171–183.

    Article  Google Scholar 

  • Yang, H.-L., & Lin, S.-L. (2015). User continuance intention to use cloud storage service. Computers in Human Behavior, 52, 219–232.

    Article  Google Scholar 

  • Zhang, N., Guo, X., & Chen, G. (2011). Why adoption and use behavior of IT/IS cannot last?—two studies in China. Information Systems Frontiers, 13(3), 381–395.

    Article  Google Scholar 

  • Zhang, X., Yan, X., Cao, X., Sun, Y., Chen, H., & She, J. (2017). The role of perceived e-health literacy in users’ continuance intention to use mobile healthcare applications: an exploratory empirical study in China. Information Technology for Development, 24(2), 198–223.

  • Zhou, T., & Li, H. (2014). Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern. Computers in Human Behavior, 37, 283–289.

    Article  Google Scholar 

  • Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NOVA Information Management School (NOVA IMS), Campus de Campolide, 1070-312, Lisbon, Portugal

    Carlos Tam, Diogo Santos & Tiago Oliveira

Authors
  1. Carlos Tam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diogo Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tiago Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Tam.

Appendix A

Appendix A

Constructs

Items

Adapted from

Performance Expectancy

PE1. I find mobile apps useful in my daily life.

PE2. Using mobile apps increases my chances of achieving things that are important to me.

PE3. Using mobile apps helps me accomplish things more quickly.

PE4. Using mobile apps increases my productivity.

(Venkatesh et al. 2011)

Effort Expectancy

EE1. Learning how to use mobile apps is easy for me.

EE2. My interaction with mobile apps is clear and understandable.

EE3. I find mobile apps easy to use.

EE4. It is easy for me to become skilful at using mobile apps.

(Venkatesh et al. 2011)

Social Influence

SI1. People who are important to me think that I should use mobile apps.

SI2. People who influence my behaviour think that I should use mobile apps.

SI3. People whose opinions that I value prefer that I use mobile apps.

(Venkatesh et al. 2011)

Facilitating Conditions

FC1. I have the resources necessary to use mobile apps.

FC2. I have the knowledge necessary to use mobile apps.

FC3. Mobile apps are compatible with other technologies I use.

FC4. I can get help from others when I have difficulties using mobile apps.

(Venkatesh et al. 2011)

Hedonic Motivation

HM1. Using mobile apps is fun.

HM2. Using mobile apps is enjoyable.

HM3. Using mobile apps is very entertaining.

(Venkatesh et al. 2011)

Price Value

PV1. Mobile apps are reasonably priced.

PV2. Mobile apps are a good value for the money.

PV3. At the current price, mobile apps provide a good value.

(Venkatesh et al. 2011)

Habit

HAB1. The use of mobile apps has become a habit for me.

HAB2. I am addicted to using mobile apps.

HAB3. I must use mobile apps.

HAB4. Using mobile apps has become natural to me.

(Venkatesh et al. 2011)

Confirmation

CONF1. Using mobile apps was better than I expected.

CONF2. The service level or function provided for mobile apps in general was better than I predicted.

CONF3. Overall, most of my expectations from using mobile apps were confirmed.

(Bhattacherjee 2001b)

Satisfaction

SAT1. I believe I made the correct decision in using a certain app.

SAT2. Using mobile apps makes me feel very satisfied.

SAT3. I am pleased with the mobile apps I have downloaded.

(Vila and Kuster 2011)

Continuance Intention

CI1. I intend to continue using mobile apps in the future.

CI2. I will always try to use mobile apps in my daily life

CI3. I will keep using mobile apps as regularly as I do now.

(Bhattacherjee 2001b, Venkatesh et al. 2011)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 22, 243–257 (2020). https://doi.org/10.1007/s10796-018-9864-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-018-9864-5

Keywords

Search

Navigation