The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance1
Abstract
Past research in the area of information systems acceptance has primarily focused on initial adoption under the implicit assumption that IS usage is mainly determined by intention. While plausible in the case of initial IS adoption, this assumption may not be as readily applicable to continued IS usage behavior since it ignores that frequently performed behaviors tend to become habitual and thus automatic over time. This paper is a step forward in defining and incorporating the “habit” construct into IS research. Specifically, the purpose of this study is to explore the role of habit and its antecedents in the context of continued IS usage. Building on previous work in other disciplines, we define habit in the context of IS usage as the extent to which people tend to perform behaviors (use IS) automatically because of learning. Using recent work on the continued usage of IS (IS continuance), we have developed a model suggesting that continued IS usage is not only a consequence of intention, but also of habit. In particular, in our research model, we propose IS habit to moderate the influence of intention such that its importance in determining behavior decreases as the behavior in question takes on a more habitual nature. Integrating past research on habit and IS continuance further, we suggest how antecedents of behavior/behavioral intention as identified by IS continuance research relate to drivers of habitualization. We empirically tested the model in the context of voluntary continued WWW usage. Our results support the argument that habit acts as a moderating variable of the relationship between intentions and IS continuance behavior, which may put a boundary condition on the explanatory power of intentions in the context of continued IS usage. The data also support that satisfaction, frequency of past behavior, and comprehensiveness of usage are key to habit formation and thus relevant in the context of IS continuance behavior. Implications of these findings are discussed and managerial guidelines presented.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
A Theory of Cognitive Dissonance
Festinger, L. (1957) · doi:10.1515/9781503620766 · Cited by 24,241
From Intentions to Actions: A Theory of Planned Behavior
Ajzen, I. (1985) · Action Control · doi:10.1007/978-3-642-69746-3_2 · Cited by 19,057
A Paradigm for Developing Better Measures of Marketing Constructs
Churchill, G. A. (1979) · Journal of Marketing Research · doi:10.1177/002224377901600110 · Cited by 16,549
Cited by
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Venkatesh, V., & Morris, M. G. (2000) · MIS Quarterly · doi:10.2307/3250981 · Cited by 2,780
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., et al. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102542 · Cited by 2,753
Self-citationDeveloping and validating a service robot integration willingness scale
Lu, L., Cai, R., & Gursoy, D. (2019) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2019.01.005 · Cited by 1,013
Baptista, G., & Oliveira, T. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.04.024 · Cited by 922
Strzelecki, A. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2209881 · Cited by 807
The benefits and dangers of enjoyment with social networking websites
Turel, O., & Serenko, A. (2012) · European Journal of Information Systems · doi:10.1057/ejis.2012.1 · Cited by 684
Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2011.08.019 · Cited by 679
Polites, G. L., & Karahanna, E. (2012) · MIS Quarterly · doi:10.25300/misq/2012/36.1.02 · Cited by 215
Hsu, C.-L., & Lin, J. C.-C. (2015) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2014.11.003 · Cited by 662
Chen, S.-C., & Lin, C.-P. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.11.025 · Cited by 606
Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014) · Tourism Management · doi:10.1016/j.tourman.2014.01.017 · Cited by 590
e-Government Adoption Model (GAM): Differing service maturity levels
Shareef, M. A., Kumar, V., Kumar, U., & Dwivedi, Y. K. (2011) · Government Information Quarterly · doi:10.1016/j.giq.2010.05.006 · Cited by 581
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2017.08.026 · Cited by 576
Merhi, M., Hone, K., & Tarhini, A. (2019) · Technology in Society · doi:10.1016/j.techsoc.2019.101151 · Cited by 555
Integrating Technology Addiction and Use: an Empirical Investigation of Online Auction Users1
Turel, O., Serenko, A., & Giles, P. (2011) · MIS Quarterly · doi:10.2307/41409972 · Cited by 547
Individual Swift Trust and Knowledge-Based Trust in Face-to-Face and Virtual Team Members
Robert, L. P., Denis, A. R., & Hung, Y.-T. C. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222260210 · Cited by 514
Chopdar, P. K., Korfiatis, N., Sivakumar, V. J., & Lytras, M. D. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.04.017 · Cited by 468
Strzelecki, A. (2024) · Innovative Higher Education · doi:10.1007/s10755-023-09686-1 · Cited by 477
“Venkatesh et al. (2012) and Limayem et al. (2007) have explained HT as a perceptual construct, and it has been found to be a significant predictor of “Behavioral intention” and technology use (Tamilmani et al., 2019).”
Macedo, I. M. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.06.013 · Cited by 402
A Longitudinal Study of Herd Behavior in the Adoption and Continued Use of Technology1
Sun, H. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.4.02 · Cited by 375
Point of adoption and beyond. Initial trust and mobile-payment continuation intention
Talwar, S., Dhir, A., Khalil, A., Mohan, G., & Islam, A. K. M. N. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102086 · Cited by 363
A unified model of IT continuance: Three complementary perspectives and crossover effects
Bhattacherjee, A., & Lin, C.-P. (2015) · European Journal of Information Systems · doi:10.1057/ejis.2013.36 · Cited by 336
Hsieh, J.-K., Hsieh, Y.-C., Chiu, H.-C., & Feng, Y.-C. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2012.05.010 · Cited by 291
Acceptance of mobile banking in Islamic banks: Evidence from modified UTAUT model
Raza, S. A., Shah, N., & Ali, M. (2019) · Journal of Islamic marketing · doi:10.1108/jima-04-2017-0038 · Cited by 257
Understanding Situational Online Information Disclosure as a Privacy Calculus
Li, H., Sarathy, R., & Xu, H. (2010) · Journal of Computer Information Systems · doi:10.1080/08874417.2010.11645450 · Cited by 246
A theory of social media dependence: Evidence from microblog users
Wang, C., Lee, M. K. O., & Hua, Z. (2015) · Decision Support Systems · doi:10.1016/j.dss.2014.11.002 · Cited by 247
Digital Behaviour Change Interventions to Break and Form Habits
Pinder, C., Vermeulen, J., Cowan, B. R., & Beale, R. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3196830 · Cited by 235
Why different trust relationships matter for information systems users
Söllner, M., Hoffmann, A., & Leimeister, J. M. (2016) · European Journal of Information Systems · doi:10.1057/ejis.2015.17 · Cited by 223
User acceptance of agile information systems: a model and empirical test
Hong, W., Thong, J. Y. L., Chasalow, L. C., & Dhillon, G. (2011) · Journal of Management Information Systems · doi:10.2753/mis0742-1222280108 · Cited by 212
Consumers’ intentions to use online food delivery systems in the USA
Gunden, N., Morosan, C., & DeFranco, A. (2020) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-06-2019-0595 · Cited by 207
Friend, mentor, lover: does chatbot engagement lead to psychological dependence?
Xie, T., Pentina, I., & Hancock, T. (2023) · Journal of Service Management · doi:10.1108/josm-02-2022-0072 · Cited by 204
Predicting the continued use of Internet-based learning technologies: the role of habit
Limayem, M., & Cheung, C. M. K. (2011) · Behaviour & Information Technology · doi:10.1080/0144929x.2010.490956 · Cited by 191
Self-citationMobile apps for healthy living: Factors influencing continuance intention for health apps
Yan, M., Filieri, R., Raguseo, E., & Gorton, M. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120644 · Cited by 182
Using TikTok in tourism destination choice: A young Chinese tourists' perspective
Zhou, Q., Sotiriadis, M., & Shen, S. (2023) · Tourism Management Perspectives · doi:10.1016/j.tmp.2023.101101 · Cited by 187
Teenagers in social virtual worlds: Continuous use and purchasing behavior in Habbo Hotel
Mäntymäki, M., & Salo, J. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2011.06.003 · Cited by 178
Polites, G. L., & Karahanna, E. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.1.10 · Cited by 174
Magni, M., Susan Taylor, M., & Venkatesh, V. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2010.03.004 · Cited by 173
A trust-risk perspective on social commerce use: an examination of the biasing role of habit
Farivar, S., Turel, O., & Yuan, Y. (2017) · Internet Research · doi:10.1108/intr-06-2016-0175 · Cited by 169
The corporeality of practice-based learning
Yakhlef, A. (2010) · Organization Studies · doi:10.1177/0170840609357384 · Cited by 166
Jang, M., Aavakare, M., Nikou, S., & Kim, S. (2021) · Telecommunications Policy · doi:10.1016/j.telpol.2021.102154 · Cited by 162
Bae, M. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2017.10.026 · Cited by 157
Tandon, U., Kiran, R., & Sah, A. N. (2018) · Information Systems and e-Business Management · doi:10.1007/s10257-017-0341-3 · Cited by 157
M-payment adoption for bottom of pyramid segment: an empirical investigation
Hussain, M., Mollik, A. T., Johns, R., & Rahman, M. S. (2019) · International Journal of Bank Marketing · doi:10.1108/ijbm-01-2018-0013 · Cited by 147
Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables
Cabrera-Sánchez, J.-P., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., & Shaikh, A. A. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101529 · Cited by 146
Zhu, W., Huang, L., Zhou, X., Li, X., Shi, G., Ying, J., & Wang, C. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2323277 · Cited by 143
Examining consumers’ usage intention of contactless payment systems
Karjaluoto, H., Shaikh, A. A., Leppäniemi, M., & Luomala, R. (2019) · International Journal of Bank Marketing · doi:10.1108/ijbm-04-2019-0155 · Cited by 136
Kim, B. (2012) · Telecommunications Policy · doi:10.1016/j.telpol.2011.11.011 · Cited by 133
Chen, S.-C., Liu, M.-L., & Lin, C.-P. (2013) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2012.0606 · Cited by 127
Motivating Employees to Explore Collaboration Technology in Team Contexts1
Maruping, L. M., & Magni, M. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.1.01 · Cited by 126
The Janus face of Facebook: Positive and negative sides of social networking site use
Mäntymäki, M., & Islam, A. K. M. N. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.02.078 · Cited by 119
Habits and the electronic herd: the psychology behind social media’s successes and failures
Anderson, I. A., & Wood, W. (2021) · Consumer Psychology Review · doi:10.1002/arcp.1063 · Cited by 108
“As explained in the section, “Motivation fades as habits form,” consumers continue to use sites habitually despite motivations to act otherwise (Limayem & Cheung, 2008; Limayem et al., 2007).”
BackgroundNajmul Islam, A. K. M. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2013.09.010 · Cited by 107
Lee, K., Lee, K. Y., & Sheehan, L. (2020) · Information Systems Frontiers · doi:10.1007/s10796-019-09975-1 · Cited by 95
Tang, Z., & Chen, L. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102066 · Cited by 93
Intention to use e-commerce vs physical shopping. Difference between consumers in the post-COVID era
Higueras-Castillo, E., Liébana-Cabanillas, F. J., & Villarejo-Ramos, Á. F. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113622 · Cited by 84
Biloš, A., & Budimir, B. (2024) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer19020045 · Cited by 84
Maxwell, D., Oyarzun, B., Kim, S., & Bong, J. Y. (2025) · TechTrends · doi:10.1007/s11528-025-01109-6 · Cited by 80
Investigating the reasons for storage of WEEE by residents – a potential for removal from households
Nowakowski, P. (2019) · Waste Management · doi:10.1016/j.wasman.2019.02.008 · Cited by 78
Cheng, X., Qiao, L., Yang, B., & Li, Z. (2024) · Internet Research · doi:10.1108/intr-06-2022-0402 · Cited by 76
An empirical examination of the “Vicious Cycle” of Facebook addiction
Turel, O. (2015) · Journal of Computer Information Systems · doi:10.1080/08874417.2015.11645775 · Cited by 68
Compulsive smartphone usage and users' ill-being among young Indians: Does personality matter?
Panda, A., & Jain, N. K. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2018.03.006 · Cited by 67
Do cultural dimensions differentiate ERP acceptance? A study in the context of Saudi Arabia
Alhirz, H., & Sajeev, A. S. M. (2015) · Information Technology & People · doi:10.1108/itp-07-2013-0127 · Cited by 62
Bile Hassan, I., Murad, M. A. A., El-Shekeil, I., & Liu, J. (2022) · Informatics · doi:10.3390/informatics9020031 · Cited by 55
Faraon, M., Rönkkö, K., Milrad, M., & Tsui, E. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13492-x · Cited by 50
Why are experienced users of WeChat likely to continue using the app?
Gong, X., Liu, Z., Zheng, X., & Wu, T. (2018) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-10-2017-0246 · Cited by 47
Anatomy in the metaverse: Exploring student technology acceptance through the UTAUT2 model
Kalınkara, Y., & Özdemir, O. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2353 · Cited by 46
Towards a Decomposed Expectation-Confirmation Model of IT Continuance: The Role of Usability
University of Turku, Islam, A. K. M. N., Mäntymäki, M., University of Turku, Bhattacherjee, A., & University of South Florida (2017) · Communications of the Association for Information Systems · doi:10.17705/1cais.04023 · Cited by 46
Factors Influencing Behavioural Intention to Adopt the QR-Code Payment: Extending UTAUT2 Model
Suo, W.-J., Goi, C.-L., Goi, M.-T., & Sim, A. K. S. (2021) · International Journal of Asian Business and Information Management · doi:10.4018/ijabim.20220701.oa8 · Cited by 41
Soror, A. A., Steelman, Z. R., & Limayem, M. (2012) · 2012 45th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2012.219 · Cited by 34
Self-citationSugumar, M., & Chandra, S. (2021) · Journal of International Technology and Information Management · doi:10.58729/1941-6679.1501 · Cited by 34
Exploring contemporary screen time in Australian adolescents: A qualitative study
Thomas, G., Bennie, J. A., De Cocker, K., & Biddle, S. J. H. (2021) · Health Promotion Journal of Australia · doi:10.1002/hpja.440 · Cited by 29
Addiction to Mobile Phone or Addiction through Mobile Phone?
Davazdahemami, B., Hammer, B., & Soror, A. (2016) · 2016 49th Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2016.186 · Cited by 29
The Fine Line Between Persuasion and Digital Addiction
Cemiloglu, D., Naiseh, M., Catania, M., Oinas-Kukkonen, H., & Ali, R. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-79460-6_23 · Cited by 28
Chen, Y., Li, X., Li, Q., & Li, W. (2022) · Industrial Management & Data Systems · doi:10.1108/imds-04-2022-0234 · Cited by 20
Huang, F.-H. (2022) · Promet - Traffic&Transportation · doi:10.7307/ptt.v34i1.3762 · Cited by 10
Handayanto, E., & Ambarwati, R. (2022) · AMCA Journal of Science and Technology · doi:10.51773/ajst.v2i1.131 · Cited by 8
Liu, Q., & Geertshuis, S. (2016) · ASCILITE Publications · doi:10.14742/apubs.2016.809 · Cited by 7
An Empirical Study of User Acceptance Behaviours of Internet Banking in Tunisia Using UTAUT2 Model
Nasri, W. (2021) · International Journal of Innovation in the Digital Economy · doi:10.4018/ijide.2021100102 · Cited by 4
Xu, W., Zhu, K., Zhou, D., Wang, C., & Wen, C. (2025) · Education Sciences · doi:10.3390/educsci15070828 · Cited by 4
Metadata
- Authors
- Limayem, M., Hirt, S. G., & Cheung, C. M. K.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2007-12-01
- Volume
- 31
- Issue
- 4
- Pages
- 705-737
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W2160123621
- Cited by
- 2,308
- References
- 27
- FWCI
- 82.09
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.