Use of ‘Habit’ Is not a Habit in Understanding Individual Technology Adoption: A Review of UTAUT2 Based Empirical Studies

  • Kuttimani TamilmaniEmail author
  • Nripendra P. Rana
  • Yogesh K. Dwivedi
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 533)


‘Habit’ was the most important theoretical addition into UTAUT2 to challenge the role of behavioural intention as a lone predictor of technology use. However, systematic review and meta-analysis of Price value the other UTAUT2 additional construct revealed major inconsistency of the model with just 41% UTAUT2 based studies including the construct in their research. Thus, the aim of this research is to understand the appropriateness of ‘habit’ construct usage among UTAUT2 based empirical studies and their reason for omission or inclusion. The findings from 66 empirical studies revealed only 23 studies a meagre (35%) utilised ‘habit’ construct and the remaining massive 43 studies (65%) excluded the construct from their research model. The major reason for studies not including “habit” construct was they were examining users of new technology at early stage of adoption where sufficient time hasn’t elapsed for users to form habit. Moreover this study caution the use of experience as an alternative for habit. Since experience can be gained under mandatory settings which is not sufficient enough to form habit that occurs more naturally under voluntary settings. This study also provided number of recommendations for theory and practice based on the findings.


Meta-analysis Habit UTAUT2 


  1. 1.
    Dwivedi, Y.K., Wastell, D., Laumer, S., Henriksen, H.Z., Myers, M.D., Bunker, D., Elbanna, A., Ravishankar, M.N., Srivastava, S.C.: Research on information systems failures and successes: status update and future directions. Inf. Syst. Front. 17(1), 143–157 (2015)CrossRefGoogle Scholar
  2. 2.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)CrossRefGoogle Scholar
  3. 3.
    Venkatesh, V., Thong, J.Y., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)CrossRefGoogle Scholar
  4. 4.
    Venkatesh, V., Thong, J.Y., Xu, X.: Unified theory of acceptance and use of technology: a synthesis and the road ahead. J. Assoc. Inf. Syst. 17(5), 328–376 (2016)Google Scholar
  5. 5.
    Tamilmani, K., Rana, N.P., Dwivedi, Y.K.: A systematic review of citations of UTAUT2 article and its usage trends. In: Kar, A.K., et al. (eds.) I3E 2017. LNCS, vol. 10595, pp. 38–49. Springer, Cham (2017). Scholar
  6. 6.
    Tamilmani, K., Rana, N.P., Dwivedi, Y.K., Sahu, G.P., Roderick, S.: Exploring the role of ‘Price Value’ for understanding consumer adoption of technology: a review and meta-analysis of UTAUT2 based empirical studies. In: Twenty-Second Pacific Asia Conference on Information Systems, Japan (2018)Google Scholar
  7. 7.
    Kim, S.S., Malhotra, N.K., Narasimhan, S.: Research note—two competing perspectives on automatic use: a theoretical and empirical comparison. Inf. Syst. Res. 16(4), 418–432 (2005)CrossRefGoogle Scholar
  8. 8.
    Ajzen, I., Fishbein, M.: Attitudes and the attitude-behavior relation: reasoned and automatic processes. Eur. Rev. Soc. Psychol. 11(1), 1–33 (2000)CrossRefGoogle Scholar
  9. 9.
    King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inf. Manag. 43(6), 740–755 (2006)CrossRefGoogle Scholar
  10. 10.
    Dwivedi, Y.K., Rana, N.P., Jeyaraj, A., Clement, M., Williams, M.D.: Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): towards a revised theoretical model. Inf. Syst. Front. 1–16 (2017).
  11. 11.
    Field, A.P.: Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed-and random-effects methods. Psychol. Methods 6(2), 161–180 (2001)CrossRefGoogle Scholar
  12. 12.
    Wu, J., Du, H.: Toward a better understanding of behavioral intention and system usage constructs. Eur. J. Inf. Syst. 21(6), 680–698 (2012)CrossRefGoogle Scholar
  13. 13.
    Grinstein, A.: The relationships between market orientation and alternative strategic orientations: a meta-analysis. Eur. J. Mark. 42(1/2), 115–134 (2008)CrossRefGoogle Scholar
  14. 14.
    Schmidt, F.L.: What do data really mean? Research findings, meta-analysis, and cumulative knowledge in psychology. Am. Psychol. 47(10), 1173 (1992)CrossRefGoogle Scholar
  15. 15.
    Alalwan, A.A., Dwivedi, Y.K., Rana, N.P.: Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. Int. J. Inf. Manage. 37(3), 99–110 (2017)CrossRefGoogle Scholar
  16. 16.
    Alalwan, A.A., Dwivedi, Y.K., Williams, M.D.: Customers’ intention and adoption of telebanking in Jordan. Inf. Syst. Manage. 33(2), 154–178 (2016)CrossRefGoogle Scholar
  17. 17.
    Slade, E.L., Dwivedi, Y.K., Piercy, N.C., Williams, M.D.: Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust. Psychol. Mark. 32(8), 860–873 (2015)CrossRefGoogle Scholar
  18. 18.
    Wong, C.-H., Tan, G.W.-H., Tan, B.-I., Ooi, K.-B.: Mobile advertising: the changing landscape of the advertising industry. Telematics Inform. 32(4), 720–734 (2015)CrossRefGoogle Scholar
  19. 19.
    Koohikamali, M., Gerhart, N., Mousavizadeh, M.: Location disclosure on LB-SNAs: the role of incentives on sharing behavior. Decis. Support Syst. 71, 78–87 (2015)CrossRefGoogle Scholar
  20. 20.
    Koohikamali, M., Peak, D.A., Prybutok, V.R.: Beyond self-disclosure: disclosure of information about others in social network sites. Comput. Hum. Behav. 69, 29–42 (2017)CrossRefGoogle Scholar
  21. 21.
    Wagner, T.M., Hess, T.: What drives users to pay for freemium services? Examining people’s willingness to pay for music services. In: Proceedings of the Nineteenth American Conference on Information Systems, Chicago, Illinois (2013)Google Scholar
  22. 22.
    Kourouthanassis, P., Boletsis, C., Bardaki, C., Chasanidou, D.: Tourists responses to mobile augmented reality travel guides: the role of emotions on adoption behavior. Pervasive Mobile Comput. 18, 71–87 (2015)CrossRefGoogle Scholar
  23. 23.
    Lallmahomed, M.Z., Lallmahomed, N., Lallmahomed, G.M.: Factors influencing the adoption of e-Government Services in Mauritius. Telematics Inform. 34(4), 57–72 (2017)CrossRefGoogle Scholar
  24. 24.
    Lin, S., Zimmer, J.C., Lee, V.: Podcasting acceptance on campus: the differing perspectives of teachers and students. Comput. Educ. 68, 416–428 (2013)CrossRefGoogle Scholar
  25. 25.
    Stefi, A.: Do Developers make unbiased decisions?-The effect of mindfulness and not-invented-here bias on the adoption of software components. In: Paper presented at the ECIS (2015)Google Scholar
  26. 26.
    Jia, L., Hall, D., Sun, S.: Trust building in consumer learning process and its effect on consumers’ behavioral intention toward mobile payments. In: Proceedings of Twenty-first Americas Conference on Information Systems, Puerto Rico (2015)Google Scholar
  27. 27.
    Koenig-Lewis, N., Marquet, M., Palmer, A., Zhao, A.L.: Enjoyment and social influence: predicting mobile payment adoption. Serv. Ind. J. 35(10), 537–554 (2015)CrossRefGoogle Scholar
  28. 28.
    Oliveira, T., Thomas, M., Baptista, G., Campos, F.: Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Comput. Hum. Behav. 61, 404–414 (2016)CrossRefGoogle Scholar
  29. 29.
    Qasim, H., Abu-Shanab, E.: Drivers of mobile payment acceptance: the impact of network externalities. Inf. Syst. Front. 18(5), 1021–1034 (2016)CrossRefGoogle Scholar
  30. 30.
    Shaw, N.: The mediating influence of trust in the adoption of the mobile wallet. J. Retail. Consum. Serv. 21(4), 449–459 (2014)CrossRefGoogle Scholar
  31. 31.
    Teo, A.-C., Tan, G.W.-H., Ooi, K.-B., Hew, T.-S., Yew, K.-T.: The effects of convenience and speed in m-payment. Ind. Manage. Data Syst. 115(2), 311–331 (2015)CrossRefGoogle Scholar
  32. 32.
    Choudrie, J., Pheeraphuttharangkoon, S., Zamani, E., Giaglis, G.: Investigating the adoption and use of smartphones in the UK: a silver-surfers perspective. In: Hertfordshire Business School Working Paper (2014)Google Scholar
  33. 33.
    Gao, S., Krogstie, J., Yang, Y.: Differences in the adoption of smartphones between middle aged adults and older adults in China. In: Zhou, J., Salvendy, G. (eds.) ITAP 2015. LNCS, vol. 9193, pp. 451–462. Springer, Cham (2015). Scholar
  34. 34.
    Gao, S., Yang, Y., Krogstie, J.: The adoption of smartphones among older adults in China. In: Liu, K., Nakata, K., Li, W., Galarreta, D. (eds.) ICISO 2015. IAICT, vol. 449, pp. 112–122. Springer, Cham (2015). Scholar
  35. 35.
    Bere, A.: Exploring determinants for mobile learning user acceptance and use: An application of UTAUT. In: 11th International Conference on IEEE Information Technology: New Generations (ITNG) (2014)Google Scholar
  36. 36.
    Wong, C.-H., Tan, G.W.-H., Loke, S.-P., Ooi, K.-B.: Adoption of mobile social networking sites for learning? Online Inf. Rev. 39(6), 762–778 (2015)CrossRefGoogle Scholar
  37. 37.
    Mahfuz, M.A., Hu, W., Khanam, L.: The Influence of Cultural Dimensions and Website Quality on m-banking Services Adoption in Bangladesh: Applying the UTAUT2 Model Using PLS. WHICEB (2016)Google Scholar
  38. 38.
    Wendy Zhu, W., Morosan, C.: An empirical examination of guests’ adoption of interactive mobile technologies in hotels: revisiting cognitive absorption, playfulness, and security. J. Hospitality Tourism Technol. 5(1), 78–94 (2014)CrossRefGoogle Scholar
  39. 39.
    Morosan, C., DeFranco, A.: When tradition meets the new technology: an examination of the antecedents of attitudes and intentions to use mobile devices in private clubs. Int. J. Hospitality Manage. 42, 126–136 (2014)CrossRefGoogle Scholar
  40. 40.
    Hajli, N., Lin, X.: Exploring the security of information sharing on social networking sites: the role of perceived control of information. J. Bus. Ethics 133(1), 111–123 (2016)CrossRefGoogle Scholar
  41. 41.
    Lallmahomed, M.Z., Rahim, N.Z.A., Ibrahim, R., Rahman, A.A.: Predicting different conceptualizations of system use: acceptance in hedonic volitional context (Facebook). Comput. Hum. Behav. 29(6), 2776–2787 (2013)CrossRefGoogle Scholar
  42. 42.
    Sharifi fard, S., Tamam, E., Hj Hassan, M.S., Waheed, M., Zaremohzzabieh, Z.: Factors affecting Malaysian university students’ purchase intention in social networkingsites. Cogent Bus. Manage. 3(1) (2016).
  43. 43.
    Lai, C., Wang, Q., Li, X., Hu, X.: The influence of individual espoused cultural values on self-directed use of technology for language learning beyond the classroom. Comput. Hum. Behav. 62, 676–688 (2016)CrossRefGoogle Scholar
  44. 44.
    Chaouali, W., Yahia, I.B., Souiden, N.: The interplay of counter-conformity motivation, social influence, and trust in customers’ intention to adopt Internet banking services: the case of an emerging country. J. Retail. Consum. Serv. 28, 209–218 (2016)CrossRefGoogle Scholar
  45. 45.
    Salim, B.F., Mahmoud, M.H., Khair, H.M.: Perceived factors affecting the internet banking implementation in sudan: an application of (UTAUT2). Int. J. Appl. Bus. Econ. Res. 14(1), 1–16 (2016)Google Scholar
  46. 46.
    Wagner, T.M., Benlian, A., Hess, T.: Converting freemium customers from free to premium—the role of the perceived premium fit in the case of music as a service. Electron. Markets 24(4), 259–268 (2014)CrossRefGoogle Scholar
  47. 47.
    Ahn, M., Kang, J., Hustvedt, G.: A model of sustainable household technology acceptance. Int. J. Consum. Stud. 40(1), 83–91 (2016)CrossRefGoogle Scholar
  48. 48.
    Ramantoko, G., Putra, G., Ariyanti, M., Sianturi, N.V.: Early adoption characteristic of consumers’: a behavioral intention to use home digital services in Indonesia. In: 3rd International Seminar and Conference on Learning Organization (ISCLO) (2015)Google Scholar
  49. 49.
    Gao, Y., Li, H., Luo, Y.: An empirical study of wearable technology acceptance in healthcare. Ind. Manage. Data Syst. 115(9), 1704–1723 (2015)CrossRefGoogle Scholar
  50. 50.
    Segura, A.S., Thiesse, F.: Extending UTAUT2 to explore pervasive information systems. In: Paper presented at the ECIS (2015)Google Scholar
  51. 51.
    An, L., Han, Y., Tong, L.: Study on the factors of online shopping intention for fresh agricultural products based on UTAUT2. In: 2nd Information Technology and Mechatronics Engineering Conference (2016)Google Scholar
  52. 52.
    Pletikosa Cvijikj, I., Kadar, C., Ivan, B., Te, F.: Prevention or panic: design and evaluation of a crime prevention IS. In: Proceedings of the 2015 International Conference on Information Systems (2015)Google Scholar
  53. 53.
    Degirmenci, K., Breitner, M.H.: Consumer purchase intentions for electric vehicles: is green more important than price and range? Transp. Res. Part D Transp. Environ. 51, 250–260 (2017)CrossRefGoogle Scholar
  54. 54.
    Morosan, C.: An empirical examination of US travelers’ intentions to use biometric e-gates in airports. J. Air Transport Manage. 55, 120–128 (2016)CrossRefGoogle Scholar
  55. 55.
    Muraina, I.D., Osman, W.R.B.S., Ahmad, A.: The roles of some antecedents of broadband user behavioural intention among students in the rural areas through PLS-SEM. Am. J. Appl. Sci. 12(11), 820–829 (2015)CrossRefGoogle Scholar
  56. 56.
    Yoo, D.K., Roh, J.J.: Use and uptake of e-books in the lens of unified theory of acceptance and use of technology. In: Paper presented at the Proceedings of Pacific Asia Conference on Information Systems (PACIS) (2016)Google Scholar
  57. 57.
    Alalwan, A.A., Dwivedi, Y.K., Rana, N.P., Lal, B., Williams, M.D.: Consumer adoption of Internet banking in Jordan: Examining the role of hedonic motivation, habit, self-efficacy and trust. J. Financ. Serv. Mark. 20(2), 145–157 (2015)CrossRefGoogle Scholar
  58. 58.
    Ain, N., Kaur, K., Waheed, M.: The influence of learning value on learning management system use: an extension of UTAUT2. Inf. Dev. 32(5), 1306–1321 (2016)CrossRefGoogle Scholar
  59. 59.
    Juaneda-Ayensa, E., Mosquera, A., Murillo, Y.S.: Omnichannel customer behavior: key drivers of technology acceptance and use and their effects on purchase intention. Front. Psychol. 7(1117) (2016).
  60. 60.
    Escobar-Rodrguez, T., Carvajal-Trujillo, E., Monge-Lozano, P.: Factors that influence the perceived advantages and relevance of Facebook as a learning tool: An extension of the UTAUT. Australas. J. Educ. Technol. 30(2), 136–151 (2014)Google Scholar
  61. 61.
    Buettner, R.: Getting a job via career-oriented social networking sites: the weakness of ties. In: 49th Hawaii International Conference on System Sciences (2016)Google Scholar
  62. 62.
    Dernbecher, S., Beck, R., Weber, S.: Switch to your own to work with the known: an empirical study on consumerization of IT. In: Proceedings of the Nineteenth American Conference on Information Systems, Chicago (2013)Google Scholar
  63. 63.
    Huang, K.-Y., Chuang, Y.-R.: A task–technology fit view of job search website impact on performance effects: An empirical analysis from Taiwan. Cogent Bus. Manage. 3(1), 1–18 (2016)CrossRefGoogle Scholar
  64. 64.
    Ali, F., Nair, P.K., Hussain, K.: An assessment of students’ acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. J. Hospitality Leisure Sport Tourism Educ. 18, 51–60 (2016)CrossRefGoogle Scholar
  65. 65.
    Baptista, G., Oliveira, T.: Understanding mobile banking: the unified theory of acceptance and use of technology combined with cultural moderators. Comput. Hum. Behav. 50, 418–430 (2015)CrossRefGoogle Scholar
  66. 66.
    Raman, A., Don, Y.: Preservice teachers’ acceptance of learning management software: an application of the UTAUT2 model. Int. Educ. Stud. 6(7), 157–168 (2013)CrossRefGoogle Scholar
  67. 67.
    Chong, A.Y.-L., Ngai, E.W.: What influences travellers’ adoption of a location-based social media service for their travel planning? In: PACIS (2013)Google Scholar
  68. 68.
    Järvinen, J., Ohtonen, R., Karjaluoto, H.: Consumer acceptance and use of Instagram. In: 49th Hawaii International Conference on System Sciences (2016)Google Scholar
  69. 69.
    Nair, P.K., Ali, F., Leong, L.C.: Factors affecting acceptance & use of ReWIND: validating the extended unified theory of acceptance and use of technology. Interact. Technol. Smart Educ. 12(3), 183–201 (2015)CrossRefGoogle Scholar
  70. 70.
    Herrero, Á., San Martín, H.: Explaining the adoption of social networks sites for sharing user-generated content: a revision of the UTAUT2. Comput. Hum. Behav. 71, 209–217 (2017)CrossRefGoogle Scholar
  71. 71.
    Morosan, C., DeFranco, A.: Co-creating value in hotels using mobile devices: a conceptual model with empirical validation. Int. J. Hospitality Manage. 52, 131–142 (2016)CrossRefGoogle Scholar
  72. 72.
    Jia, L., Hall, D., Sun, S.: The effect of technology usage habits on consumers’ intention to continue use mobile payments. In: Proceedings of the 20th Americas Conference on Information Systems. AIS. Savannah (2014)Google Scholar
  73. 73.
    Chong, A.Y.-L.: A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Syst. Appl. 40(4), 1240–1247 (2013)CrossRefGoogle Scholar
  74. 74.
    Baptista, G., Baptista, G., Oliveira, T., Oliveira, T.: Why so serious? Gamification impact in the acceptance of mobile banking services. Internet Res. 27(1), 118–139 (2017)CrossRefGoogle Scholar
  75. 75.
    Wong, C.-H., Wei-Han Tan, G., Loke, S.-P., Ooi, K.-B.: Mobile TV: a new form of entertainment? Ind. Manage. Data Syst. 114(7), 1050–1067 (2014)CrossRefGoogle Scholar
  76. 76.
    Ramírez-Correa, P.E., Rondán-Cataluña, F.J., Arenas-Gaitán, J.: An empirical analysis of mobile Internet acceptance in Chile. Inf. Res. 19(3), 19-3 (2014)Google Scholar
  77. 77.
    Escobar-Rodríguez, T., Carvajal-Trujillo, E.: Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tour. Manage. 43, 70–88 (2014)CrossRefGoogle Scholar
  78. 78.
    Rana, N.P., Dwivedi, Y.K., Williams, M.D.: A meta-analysis of existing research on citizen adoption of e-government. Inf. Syst. Front. 17(3), 547–563 (2015)CrossRefGoogle Scholar
  79. 79.
    Limayem, M., Hirt, S.G., Cheung, C.M.: How habit limits the predictive power of intention: the case of information systems continuance. MIS Q. 31(4), 705–737 (2007)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Kuttimani Tamilmani
    • 1
    Email author
  • Nripendra P. Rana
    • 1
  • Yogesh K. Dwivedi
    • 1
  1. 1.Emerging Markets Research Centre (EMaRC), School of ManagementSwansea University Bay CampusSwanseaUK

Personalised recommendations