Skip to main content

Advertisement

Springer Nature Link
Log in

Understanding the effect of flow on user adoption of mobile games

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Mobile games as an emerging service have not received wide adoption among users; especially, presenting a compelling experience to users may be crucial to their usage. Drawing on the flow theory, this research identified the factors affecting user adoption of mobile games. The results indicated that perceived ease of use, connection quality and content quality affect flow. Among them, content quality has the largest effect. Flow, social influence and usage cost determine usage intention. The results imply that service providers need to improve users’ experience in order to facilitate their adoption and usage of mobile games.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103(3):411–423

    Article  Google Scholar 

  2. Animesh A, Pinsonneault A, Yang SB, Oh W (2011) An odyssey into virtual worlds: exploring the impacts of technological and spatial environments on intention to purchase virtual products. MIS Q 35(3):789–810

    Google Scholar 

  3. Carlson J, O’Cass A (2011) Creating commercially compelling website-service encounters: an examination of the effect of website-service interface performance components on flow experiences. Elect Mark 21(4):237–253

    Article  Google Scholar 

  4. Chang HH, Wang IC (2008) An investigation of user communication behavior in computer mediated environments. Comput Hum Behav 24(5):2336–2356

    Article  Google Scholar 

  5. CNNIC (2012) 30th statistical survey report on the internet development in China, China Internet Network Information Center

  6. Csikszentmihalyi M, Csikszentmihalyi IS (1988) Optimal experience: psychological studies of flow in consciousness. Cambridge University Press, Cambridge

    Book  Google Scholar 

  7. Davis FD, Bagozzi RP, Warshaw PR (1992) Extrinsic and intrinsic motivation to use computers in the workplace. J Appl Soc Psychol 22(14):1111–1132

    Article  Google Scholar 

  8. Gefen D, Straub DW, Boudreau MC (2000) Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inform Syst 4(7):1–70

    Google Scholar 

  9. Guo YM, Poole MS (2009) Antecedents of flow in online shopping: a test of alternative models. Inform Syst J 19(4):369–390

    Article  Google Scholar 

  10. Hausman AV, Siekpe JS (2009) The effect of web interface features on consumer online purchase intentions. J Bus Res 62(1):5–13

    Article  Google Scholar 

  11. Ho L-A, Kuo T-H (2010) How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Comput Hum Behav 26(1):23–31

    Article  Google Scholar 

  12. Hoffman DL, Novak TP (1996) Marketing in hypermedia computer-mediated environments: conceptual foundations. J Mark 60(3):50–68

    Article  Google Scholar 

  13. Hoffman DL, Novak TP (2009) Flow online: lessons learned and future prospects. J Inter Mark 23(1):23–34

    Article  Google Scholar 

  14. Hsu C-L, Lu H-P (2004) Why do people play on-line games? An extended TAM with social influences and flow experience. Inform Manage 41:853–868

    Article  Google Scholar 

  15. Jung Y, Perez-Mira B, Wiley-Patton S (2009) Consumer adoption of mobile TV: examining psychological flow and media content. Comput Hum Behav 25(1):123–129

    Article  Google Scholar 

  16. Junglas I, Abraham C, Watson RT (2008) Task-technology fit for mobile locatable information systems. Decis Support Syst 45(4):1046–1057

    Article  Google Scholar 

  17. Kamis A, Stern T, Ladik DM (2010) A flow-based model of web site intentions when users customize products in business-to-consumer electronic commerce. Inform Syst Frontier 12(2):157–168

    Article  Google Scholar 

  18. Kim C, Mirusmonov M, Lee I (2010) An empirical examination of factors influencing the intention to use mobile payment. Comput Hum Behav 26(3):310–322

    Article  Google Scholar 

  19. Kim DJ, Hwang Y (2012) A study of mobile internet user’s service quality perceptions from a user’s utilitarian and hedonic value tendency perspectives. Inform Syst Frontier 14(2):409–421

    Article  Google Scholar 

  20. Kim KK, Shin HK, Kim B (2011) The role of psychological traits and social factors in using new mobile communication services. Electron Commer Res Appl 10(4):408–417

    Article  Google Scholar 

  21. Kuo Y-F, Yen S-N (2009) Towards an understanding of the behavioral intention to use 3G mobile value-added services. Comput Hum Behav 25(1):103–110

    Article  Google Scholar 

  22. Lee KC, Kang IW, McKnight DH (2007) Transfer from offline trust to key online perceptions: an empirical study. IEEE Trans Eng Manage 54(4):729–741

    Article  Google Scholar 

  23. Lee T (2005) The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. J Elect Comm Res 6(3):165–180

    Google Scholar 

  24. Lee YE, Benbasat I (2004) A framework for the study of customer interface design for mobile commerce. Int J Elect Comm 8(3):79–102

    Google Scholar 

  25. Lin H-F (2011) An empirical investigation of mobile banking adoption: the effect of innovation attributes and knowledge-based trust. Int J Inf Manage 31(3):252–260

    Article  Google Scholar 

  26. Liu Z, Min Q, Ji S (2010) An empirical study of mobile securities management systems adoption: a task-technology fit perspective. Int J Mobile Commun 8(2):230–243

    Article  Google Scholar 

  27. Lu Y, Deng Z, Wang B (2010) Exploring factors affecting Chinese consumers’ usage of short message service for personal communication. Inform Syst J 20(2):183–208

    Article  Google Scholar 

  28. Malhotra NK, Kim SS, Patil A (2006) Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research. Manage Sci 52(12):1865–1883

    Article  Google Scholar 

  29. Mallat N (2007) Exploring consumer adoption of mobile payments—a qualitative study. J Strateg Inf Syst 16(4):413–432

    Article  Google Scholar 

  30. Nunnally JC (1978) Psychometric theory. McGraw-Hill, New York

    Google Scholar 

  31. O’Cass A, Carlson J (2010) Examining the effects of website induced flow in professional sporting team websites. Internet Res 20(2):115–134

    Article  Google Scholar 

  32. Park J, Yang S, Lehto X (2007) Adoption of mobile technologies for Chinese consumers. J Elect Comm Res 8(3):196–206

    Google Scholar 

  33. Podsakoff PM, Organ DW (1986) Self-reports in organizational research: problems and prospects. J Manage 12(4):531–544

    Article  Google Scholar 

  34. Shen AXL, Cheung CMK, Lee MKO, Chen H (2011) How social influence affects we-intention to use instant messaging: the moderating effect of usage experience. Inform Sys Frontier 13(2):157–169

    Article  Google Scholar 

  35. Shin YM, Lee SC, Shin B, Lee HG (2010) Examining influencing factors of post-adoption usage of mobile internet: focus on the user perception of supplier-side attributes. Inform Syst Frontier 12(5):595–606

    Article  Google Scholar 

  36. Straub D, Boudreau M-C, Gefen D (2004) Validation guidelines for IS positivist research. Commun Assoc Inform Syst 13:380–427

    Google Scholar 

  37. Thong JYL, Venkatesh V, Xu X, Hong S-J, Tam KY (2011) Consumer acceptance of personal information and communication technology services. IEEE Trans Eng Manage 58(4):613–625

    Article  Google Scholar 

  38. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Quarterly 27(3):425–478

    Google Scholar 

  39. Wang LC, Baker J, Wagner JA, Wakefield K (2007) Can a retail web site be social? J Mark 71:143–157

    Article  Google Scholar 

  40. Wei TT, Marthandan G, Chong AYL, Ooi KB, Arumugam S (2009) What drives Malaysian m-commerce adoption? An empirical analysis. Ind Manage Data Syst 109(3–4):370–388

    Article  Google Scholar 

  41. Wu I-L, Li J-Y, Fu C-Y (2011) The adoption of mobile healthcare by hospital’s professionals: an integrative perspective. Decis Support Syst 51(3):587–596

    Article  Google Scholar 

  42. Wu JH, Wang SC (2005) What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inform Manag 42(5):719–729

    Article  Google Scholar 

  43. Yuan Y, Archer N, Connelly CE, Zheng W (2010) Identifying the ideal fit between mobile work and mobile work support. Inform Manag 47(3):125–137

    Article  Google Scholar 

  44. Yun H, Lee CC, Kim BG, Kettinger WJ (2011) What determines actual use of mobile web browsing services? A contextual study in Korea. Commun Assoc Inform Syst 28(1):313–328

    Google Scholar 

  45. Zaman M, Anandarajan M, Dai Q (2010) Experiencing flow with instant messaging and its facilitating role on creative behaviors. Comput Hum Behav 26(5):1009–1018

    Article  Google Scholar 

  46. Zhou T, Lu Y (2011) Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Comput Hum Behav 27(2):883–889

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by a grant from the National Natural Science Foundation of China (71001030) and a grant from Zhejiang Provincial Zhijiang Social Science Young Scholar Plan (G94).

Author information

Authors and Affiliations

  1. School of Management, Hangzhou Dianzi University, Hangzhou, 310018, People’s Republic of China

    Tao Zhou

Authors
  1. Tao Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Tao Zhou.

Appendix: Measurement scales and items

Appendix: Measurement scales and items

  • Perceived ease of use (PEOU) (adapted from Jung et al. [15])

  • PEOU1: Learning to use this mobile game is easy for me.

  • PEOU2: Skillfully using this mobile game is easy for me.

  • PEOU3: I find this mobile game easy to use.

  • Connection quality (CNQ) (adapted from Kim and Hwang [19])

  • CNQ1: This mobile game has a rapid initial connection speed.

  • CNQ2: This mobile game has a rapid data transferring speed.

  • CNQ3: This mobile game has a stable connection.

  • Content quality (CTQ) (adapted from Jung et al. [15])

  • CTQ1: This mobile game provides up-to-date contents.

  • CTQ2: This mobile game provides attractive contents.

  • CTQ3: This mobile game provides contents pertaining to my needs.

  • Social influence (SOI) (adapted from Venkatesh et al. [38 ])

  • SOI1: People who influence my behavior think that I should use this mobile game.

  • SOI2: People who are important to me think that I should use this mobile game.

  • Flow (FLOW) (adapted from Lee et al. [22 ])

  • FLOW1: When using this mobile game, my attention is focused on the activity.

  • FLOW2: When using this mobile game, I feel in control.

  • FLOW3: When using this mobile game, I find a lot of pleasure.

  • Usage cost (COST) (adapted from Wu and Wang [42 ])

  • COST1: The access cost of using this mobile game is expensive.

  • COST2: The transaction fee of using this mobile game is expensive.

  • COST3: I feel that the usage cost of this mobile game is expensive.

  • Usage intention (USE) (adapted from Lee [23])

  • USE1: Given the chance, I intend to use this mobile game.

  • USE2: I expect my use of this mobile game to continue in the future.

  • USE3: I have intention to use this mobile game.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, T. Understanding the effect of flow on user adoption of mobile games. Pers Ubiquit Comput 17, 741–748 (2013). https://doi.org/10.1007/s00779-012-0613-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-012-0613-3

Keywords