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Design and optimization of mobile learning applications based on Hierarchical Bayes conjoint models of user preferences

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Abstract

When developing new mobile applications, it is critical to accurately understand the needs of the target users at the early stage, so that the designed products can better match user preferences and improve user experience. Using the powerful method of conjoint analysis and Hierarchical Bayes (HB) statistical estimation, this exploratory study sought to better model individual user needs and desired functions for a mobile English learning application, in comparison with traditional user research approaches. Participants in a Web-based conjoint experiment evaluated a series of hypothetical mobile applications for English learning, in which learning contents, duration of learning time, and frequency of study per week were found to have the biggest impacts on their choices. Based on HB models that better captured individual differences among users and therefore quantified the importance of each design attribute more accurately, an optimized mobile application was designed, which was predicted to maximize the share of preference among target consumers.

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The experimental data used to support the findings of this study are available as supplement material on the publisher’s website.

References

  1. Annis DH (2006) Bayesian Statistics and Marketing. Peter Rossi, Greg Allenby, and Rob McCulloch. J Am Stat Assoc 101(476):1732–1733. https://doi.org/10.1198/jasa.2006.s158

    Article  Google Scholar 

  2. Bauer HH, Reichardt T, Exler S, Tranka E (2007) Utility-based design of mobile ticketing applications–a conjoint-analytical approach. Int J Mob Commun 5(4):457–473. https://doi.org/10.1504/IJMC.2007.012791

    Article  Google Scholar 

  3. Butcher M (2020) Language platform Busuu acquires video tutor startup, now plans IPO. https://techcrunch.com/2020/01/22/language-platform-busuu-acquires-video-tutor-startup-now-plans-ipo/. Accessed 14 Apr 2020

  4. Chen CM, Chen LC, Yang SM (2019) An English vocabulary learning app with self-regulated learning mechanism to improve learning performance and motivation. Comput Assist Lang Learn 32(3):237–260. https://doi.org/10.1080/09588221.2018.1485708

    Article  Google Scholar 

  5. Chen YH, Hsu IC, Lin CC (2010) Website attributes that increase consumer purchase intention: A conjoint analysis. J Bus Res 63(9–10):1007–1014. https://doi.org/10.1016/j.jbusres.2009.01.023

    Article  Google Scholar 

  6. Cobb GW, Moore DS (1997) Mathematics, Statistics, and Teaching. Am Math Mon 104(9):801–823. https://doi.org/10.2307/2975286

    Article  MathSciNet  Google Scholar 

  7. Davis FD (1985) A technology acceptance model for empirically testing new end-user information systems: Theory and results. Dissertation, Massachusetts Institute of Technology

  8. Do Bagus MR, Murata T (2016) Conjoint Analysis of Costumers' Preferences with Kansei Engineering System for Product Exterior Design. 2016 5th IIAI Int Congr Adv Appl Inform 1026–1031. https://doi.org/10.1109/IIAI-AAI.2016.86.

  9. Goldberg SM, Green PE, Wind Y (1984) Conjoint analysis of price premiums for hotel amenities. J Bus 57(1):S111–S132

    Article  Google Scholar 

  10. Hao Y, Lee KS, Chen ST, Sim SC (2019) An evaluative study of a mobile application for middle school students struggling with English vocabulary learning. Comput Hum Behav 95:208–216. https://doi.org/10.1016/j.chb.2018.10.013

    Article  Google Scholar 

  11. Head M, Ziolkowski N (2012) Understanding student attitudes of mobile phone features: Rethinking adoption through conjoint, cluster and SEM analyses. Comput Hum Behav 28(6):2331–2339. https://doi.org/10.1016/j.chb.2012.07.003

    Article  Google Scholar 

  12. Hing N, Vitartas P, Lamont M (2017) Understanding persuasive attributes of sports betting advertisements: A conjoint analysis of selected elements. J Behav Addict 6(4):658–668. https://doi.org/10.1556/2006.6.2017.062

    Article  Google Scholar 

  13. Huertas-Garcia R, Laguna GM, Consolación C (2014) Conjoint analysis of tourist choice of hotel attributes presented in travel agent brochures. Int J Tour Res 16(1):65–75. https://doi.org/10.1002/jtr.1899

    Article  Google Scholar 

  14. Ibrahim NK, Hammed H, Zaidan AA et al (2019) Multi-criteria evaluation and benchmarking for young learners’ English language mobile applications in terms of LSRW skills. IEEE Access 7:146620–146651. https://doi.org/10.1109/ACCESS.2019.2941640

    Article  Google Scholar 

  15. Kétyi A (2015) Practical evaluation of a mobile language learning tool in higher education. In: Helm F, Bradley L, Guarda M, Thouësny S (eds) Critical CALL – Proceedings of the 2015 EUROCALL Conference, Research-publishing.net, Dublin, pp 306–311

  16. Kétyi A (2013) Using smart phones in language learning–A pilot study to turn CALL into MALL. In: Bradley L, Thouësny S (eds) 20 Years of eurocall: learning from the past, looking to the future. Research-publishing.net, Dublin, pp 129–134

    Chapter  Google Scholar 

  17. Kim NY, Cha Y, Kim HS (2019) Future English learning: Chatbots and artificial intelligence. Multimedia Assist Lang Learn 22(3):32–53

    Google Scholar 

  18. Kohli R, Mahajan V (1991) A reservation-price model for optimal pricing of multiattribute products in conjoint analysis. J Mark Res 28(3):347–354. https://doi.org/10.1177/002224379102800309

    Article  Google Scholar 

  19. Lee SM, Park M (2020) Reconceptualization of the context in language learning with a location-based AR app. Comput Assist Lang Learn 33(8):936–959. https://doi.org/10.1080/09588221.2019.1602545

    Article  Google Scholar 

  20. Leib T, Reynolds D, Taylor J, Baker W (2018) Web-based menu design: a conjoint value analysis. Int J Hosp Tour Adm 19(4):361–373. https://doi.org/10.1080/15256480.2017.1348923

    Article  Google Scholar 

  21. Liu H (2016) Research on Elements of Independent English Study of College Students in Network Era. Int Conf Educ Sports Arts Manag Eng 2016:208–212. https://doi.org/10.2991/icesame-16.2016.44

    Article  Google Scholar 

  22. Liu TP, Wu XY, Sun P, Wang HW (2016) Frameworks for Exploring the User Experience of Mobile Apps. Int Conf Sustain Energ Environ Inf Eng 2016:307–312

    Google Scholar 

  23. Luce RD, Tukey JW (1964) Simultaneous conjoint measurement: A new type of fundamental measurement. J Math Psychol 1(1):1–27. https://doi.org/10.1016/0022-2496(64)90015-X

    Article  Google Scholar 

  24. Makoe M, Shandu T (2018) Developing a mobile app for learning English vocabulary in an open distance learning context. Int Rev Res Open Distrib Learn 19(4). https://doi.org/10.19173/irrodl.v19i4.3746

  25. Malerba ML (2015) Learners behaviours and autonomy in Livemocha and Busuu online communities. In: EDEN 2015 Annual Conference. Expanding Learning Scenarios, pp 1–6

  26. McFadden D (1973) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in Econometrics New York. Academic Press, Cambridge, pp 104–142

    Google Scholar 

  27. Nguyen-Thi H, Hwang WY, Pham XL, Chen GD (2016) Understanding the participant behaviors in the online English learning mobile app: A case study among 106,912 learners via Google Play App store. 2016 IEEE 16th Int Conf Adv Learn Technol 104–108. https://doi.org/10.1109/ICALT.2016.137

  28. Nikou S, Bouwman H, De Reuver M (2012) The potential of converged mobile telecommunication services: a conjoint analysis. Info 14(5):21–35. https://doi.org/10.1108/14636691211256287

    Article  Google Scholar 

  29. Ohkawa Y, Kodama M, Konno Y, Zhao X, Mitsuishi T (2018) A study on UI design of smartphone app for continuous blended language learning. 2018 5th Int Conf Bus Ind Res 584–589. https://doi.org/10.1109/ICBIR.2018.8391265

  30. Orme B (2013) Which conjoint method should I use. Sawtooth Software Research Paper Serie. https://sawtoothsoftware.com/resources/technical-papers/which-conjoint-method-should-i-use. Accessed 15 Apr 2020

  31. Orme B (2010) Getting started with conjoint analysis: strategies for product design and pricing research. Research Publishers LLC, Madison

    Google Scholar 

  32. Orme BK, Chrzan K (2017) Becoming an expert in conjoint analysis: choice modelling for pros. Publisher: Sawtooth Software, Inc., Provo

  33. Pemba D, Mann V, Sarkar T, Azartash K (2016) Learning English in China: A tablet-based app using the voices of native speakers. Open J Soc Sci 4(7):85–91. https://doi.org/10.4236/jss.2016.47014

    Article  Google Scholar 

  34. Quan W (2017) Research on Development and Application of User Experience. Int Conf Mechatron Inf Technol 2017:329–332

    Google Scholar 

  35. Rivera M, Croes R, Zhong Y (2016) Developing mobile services: A look at first-time and repeat visitors in a small island destination. Int J Contemp Hosp Manag 28(12):2721–2747. https://doi.org/10.1108/IJCHM-02-2015-0052

    Article  Google Scholar 

  36. Rosell-Aguilar F (2018) Autonomous language learning through a mobile application: a user evaluation of the busuu app. Comput Assist Lang Learn 31(8):854–881. https://doi.org/10.1080/09588221.2018.1456465

    Article  Google Scholar 

  37. Rossi PE, Allenby GM (2003) Bayesian statistics and marketing. Mark Sci 22(3):304–328. https://doi.org/10.1287/mksc.22.3.304.17739

    Article  Google Scholar 

  38. Shih RC, Lee C, Cheng TF (2015) Effects of English spelling learning experience through a mobile LINE APP for college students. Procedia-Soc Behav Sci 174:2634–2638. https://doi.org/10.1016/j.sbspro.2015.01.945

    Article  Google Scholar 

  39. Shon M, Shin J, Hwang J, Lee D (2021) Free contents vs. inconvenience costs: Two faces of online video advertising. Telemat Inform 56:101476. https://doi.org/10.1016/j.tele.2020.101476

    Article  Google Scholar 

  40. Silverman S, Calderon A, Kaw K, Childers TB, Stafford BA, Brynildsen W, Focil A, Koenig M, Gold DT (2013) Patient weighting of osteoporosis medication attributes across racial and ethnic groups: a study of osteoporosis medication preferences using conjoint analysis. Osteoporos Int 24(7):2067–2077. https://doi.org/10.1007/s00198-012-2241-1

    Article  Google Scholar 

  41. Srinivasan V, Flachsbart PG, Dajani JS, Hartley RG (1981) Forecasting the effectiveness of work-trip gasoline conservation policies through conjoint analysis. J Mark 45(3):157–172. https://doi.org/10.1177/002224298104500313

    Article  Google Scholar 

  42. Tseng CC, Lin JY, Chen MS, Lin MC (2011) The use of conjoint analysis in the design of bicycle-related clothing. J Chin Inst Eng 28(4):298–315. https://doi.org/10.1080/10170669.2011.579477

    Article  Google Scholar 

  43. Wang BT (2017) Designing mobile apps for English vocabulary learning. Int J Inf Educ Technol 7(4):279. https://doi.org/10.18178/ijiet.2017.7.4.881

    Article  Google Scholar 

  44. Wang YC, Hsu L (2020) Shall we go to the MALL?—Students’ perceptions of a business English learning app. Int J Inf Educ Technol 10(2):110–116. https://doi.org/10.18178/ijiet.2020.10.2.1348

    Article  Google Scholar 

  45. Wooliscroft B, Ganglmair-Wooliscroft A (2014) Improving conditions for potential New Zealand cyclists: An application of conjoint analysis. Transp Res Pt A-Policy Pract 69:11–19. https://doi.org/10.1016/j.tra.2014.08.005

    Article  Google Scholar 

  46. Yao J, Johnson MB, Beck KH (2014) Predicting DUI decisions in different legal environments: investigating deterrence with a conjoint experiment. Traffic Inj Prev 15(3):213–221. https://doi.org/10.1080/15389588.2013.808338

    Article  Google Scholar 

  47. Yao J, Tao X, Hou S (2021) Risk perceptions and DUI decisions of drivers in different legal environments: New evidence on differential deterrence from a Chinese sample. Accid Anal Prev 157:106188. https://doi.org/10.1016/j.aap.2021.106188

    Article  Google Scholar 

  48. Zhang S (2016) Mobile English learning: An empirical study on an APP, English fun dubbing. Int J Emerg Technol 11(12):4–8. https://doi.org/10.3991/ijet.v11i12.6314

    Article  Google Scholar 

  49. Zinkhan GM, Christian ZF (1994) Capital budgeting: Emerging issues and trends. Manag Financ 20(7):2–9. https://doi.org/10.1108/eb018478

    Article  Google Scholar 

  50. Zinkhan FC, Zinkhan GM (1994) An application of conjoint analysis to capital budgeting: The case of innovative land management systems. Manag Financ 20(7):37–50. https://doi.org/10.1108/eb018480

    Article  Google Scholar 

  51. Zou B, Li H, Li J (2018) Exploring a curriculum app and a social communication app for EFL learning. Comput Assist Lang Learn 31(7):694–713. https://doi.org/10.1080/09588221.2018.1438474

    Article  Google Scholar 

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Acknowledgements

This study was funded by the National Social Science Fund of China (General Grant 20BTY120). The authors would also like to thank Huanshu Jiang for her help in manuscript revision.

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Authors and Affiliations

  1. College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, China

    Tao Xiao & Huiyi Zheng

  2. Department of Design, School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China

    Jing Wang & Jie Yao

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  1. Tao Xiao
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Correspondence to Jie Yao.

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Tao Xiao and Jing Wang are Co-first-authors

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Appendix 1

Appendix 1

Tables 7 and 8

Table 7 Changes in preference shares resulting from changing the level of each attribute of the base case English learning application product
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Table 8 Histogram of each utility parameter across respondents
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Xiao, T., Wang, J., Zheng, H. et al. Design and optimization of mobile learning applications based on Hierarchical Bayes conjoint models of user preferences. Multimed Tools Appl 83, 17001–17024 (2024). https://doi.org/10.1007/s11042-023-16229-5

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