Skip to main content
SpringerLink
Log in

Exploring Critical Success Factors of Mobile Recommendation Systems: The End User Perspective

  • Conference paper
  • First Online:
Transactions on Engineering Technologies

Abstract

This study is intended to critically explore key factors for the user experience of mobile recommendations, evaluate the findings, and use the generated critical success factors (CSFs) to propose a framework to assist in the Chinese mobile marketplace. The proposed framework provides a guideline for academics and practitioners and highlights the significant role of each factor in developing and sustaining effective mobile recommendation systems practice. The findings can help managers to derive a better understanding and measurement of mobile marketing activities that appropriately balance between traditional and mobile marketing practices. At the same time, the CSFs can be integrated into the companies to determine the level of marketing performance in mobile marketplace.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pathak B, Garfinkel R, Gopal R, Venkatesan R, Yin F (2010) Empirical analysis of the impact of recommender systems on sales. J Manage Inf Syst 27(2):159–188

    Article  Google Scholar 

  2. Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 40:10059–10072

    Article  Google Scholar 

  3. Bobadilla J, Ortega F, Hernando A, Gutierrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109

    Google Scholar 

  4. Konstan J, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User-Adap Inter 22(1/2):101–123

    Article  Google Scholar 

  5. Gallego D, Woerndl W, Huecas G (2013) Evaluating the impact of proactivity in the user experience of a context-aware restaurant recommender for Android smartphones. J Syst Archit 59(9):748

    Google Scholar 

  6. Shani G, Gunawardana A (2013) Tutorial on application-oriented evaluation of recommendation system. AI Commun 26(2):225–236

    MathSciNet  Google Scholar 

  7. Liu J, Zhou T, Zhang Z, Yang Z, Liu C, Li W (2014) Promoting cold-start items in recommender systems; http://arxiv.org/abs/1404.4936

    Google Scholar 

  8. Aldrich SE (2014) Recommender systems in commercial use. AI Mag 32(3):28–34

    Google Scholar 

  9. Hostler RE, Yoon VY, Guimaraes T (2012) Recommendation agent impact on consumer online shopping: the movie magic case study. Expert Syst Appl 39(3):2989 (2012)

    Google Scholar 

  10. Pu P, Li C, Kumar P (2008) Evaluating product search and recommender systems for E-commerce environments. Electron Commer Res 8(1/2):1–27

    Article  MATH  Google Scholar 

  11. Dabrowski M, Acton T (2013) The performance of recommender systems in online shopping: a user-centric study. Expert Syst Appl 40(14):5551

    Google Scholar 

  12. Polatidis N, Georgiadis C (2013) Mobile recommender systems: an overview of technologies and challenges. In: Informatics and applications (ICIA), second international conference on IEEE, pp 282–287

    Google Scholar 

  13. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333

    Article  Google Scholar 

  14. Joon YC, Hee SS, Soung HK (2007) MCORE: a context-sensitive recommendation system for the mobile Web. Expert Syst 24(1):32–46

    Google Scholar 

  15. Xia X, Wang X, Li J, Zhou X (2014) Multi-objective mobile app recommendation: a system-level collaboration approach. Comput Electr Eng 40(1):203

    Google Scholar 

  16. Jannach D (2011) Recommender systems: an introduction. Cambridge University Press, New York

    Google Scholar 

  17. Owusu TD, Hoffman C (2014) The personalization and prediction innovation of mobile recommender systems. Issues Inf Syst 15(2):168–174

    Google Scholar 

  18. Gao H, Wang S, Yang B, Yang H (2014) User preference-oriented collaborative recommendation algorithm in E-commerce. J Softw 9(7):1886

    Google Scholar 

  19. Shen X, Sun Y, Wang N (2013) Recommendations from friends anytime and anywhere: toward a model of contextual offer and consumption values. CyberPsychol Behav Soc Network 16(5):349–356

    Article  Google Scholar 

  20. Tintarev N, Masthoff J (2012) Evaluating the effectiveness of explanations for recommender systems. User Model User-Adap Inter 22(4/5):399–439

    Article  Google Scholar 

  21. Pu P, Li C, Hu R (2012) Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model User-Adap Inter 22(4/5):317–355

    Article  Google Scholar 

  22. Liu Q, Ma H, Chen E, Xiong H (2013) A survey of context-aware mobile recommendations. Int J Inf Technol Decis Making 12(1):139–172

    Article  Google Scholar 

  23. Knijnenburg B, Willemsen M, Gantner Z, Soncu H, Newell C (2012) Explaining the user experience of recommender systems. User Model User-Adap Inter 22(4/5):441–504

    Article  Google Scholar 

  24. Polatidis N, Georgiadis C (2014) Factors influencing the quality of the user experience in ubiquitous recommender systems. In: Distributed, ambient, and pervasive interactions, pp 369–379

    Google Scholar 

  25. Hor-Meyll LF, Correia LM, Ferreira JB (2014) Why should I accept ads on my mobile phone? Factors affecting the acceptance by Brazilian teenagers. Braz Bus Rev (English Edition) 11(4):130–150

    Google Scholar 

  26. Oulasvirta A, Ratternbury T, Raita E (2012) Habits make smartphone use more pervasive. Pers Ubiquit Comput 16(1):105–114

    Article  Google Scholar 

  27. Bryman A (2012) Social research methods. Oxford University Press, New York

    Google Scholar 

  28. MarketLine Industry Profile (2014) Profile Global Mobile Phones April 2014. Mobile Phones Industry Profile: Global, pp 1–34

    Google Scholar 

  29. MarketLine Industry Profile (2012) Mobile Phones in China September 2012, Mobile Phones Industry Profile: China, pp 1–35

    Google Scholar 

  30. Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2(11):559–572

    Article  MATH  Google Scholar 

  31. Russell DW (2002) In search of underlying dimensions: the use (and abuse) of factor analysis in personality and social psychology bulletin. Pers Soc Psychol Bull 28(12):1629–1646

    Article  Google Scholar 

  32. Wienclaw AR (2009) Correlation, research starters sociology. Res Starters, EBSCOhost, viewed 30 April 2015

    Google Scholar 

  33. Hair JF, Black C, Babin J, Anderson E (1998) Multivariate data analysis. Prentice Hall, Upper Saddle River, N.J

    Google Scholar 

  34. Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometricka 13:93–96

    Google Scholar 

  35. Gliem J, Gliem R (2003) Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. Midwest research-to-practice conference in adult, continuing, and community education

    Google Scholar 

  36. Kline P (2000) The handbook of psychological testing. Routledge, London

    Google Scholar 

  37. George D, Mallery P (2003) SPSS for windows step by step: a simple guide and reference. Allyn & Bacon, Boston

    Google Scholar 

  38. Bartlett MS (1950) Tests of significance in factor analysis. Br J Psychol 3:77–85

    Google Scholar 

  39. Kaiser HF, Rice J (1974) Little jiffy, mark iv. Educ Psychol Measur 34(1):111–117

    Article  Google Scholar 

  40. Brace N, Snelgar R, Kemp R (2012) SPSS for psychologists. Psychology Press, New York

    Google Scholar 

  41. Kutner MH, Nachtsheim CJ, Neter J (2004) Applied linear regression models. McGraw-Hill Irwin, New York

    Google Scholar 

  42. Bauer HH et al (2015) Driving consumer acceptance of mobile marketing: a theoretical framework and empirical study. J Electron Commer Res 6:181–192

    Google Scholar 

  43. Sultan F, Rohm A, Gao T (2009) Factors affecting consumer acceptance of mobile marketing: a two-country study of youth markets. J Interact Mark 23:308–320

    Article  Google Scholar 

  44. Tsang MM, Ho SC, Liang TP (2004) Consumer attitudes toward mobile advertising: an empirical study. Int J Electron Commer 8:65–78

    Google Scholar 

  45. Zhang J, Mao E (2008) Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychol Mark 25(8):787–805

    Article  Google Scholar 

  46. Sun Y, Chong K, Han YS, Rho S, Man KL (2015) Key factors affecting user experience of mobile recommendation systems. In: Lecture notes in engineering and computer science: proceedings of the international multiconference of engineers and computer scientists, 18–20 March, 2015, Hong Kong, pp 724–728

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, No. 111 Ren Ai Road, Suzhou Dushu Lake Higher Education Town, Suzhou Industrial Park, 215123, Jiangsu Province, People’s Republic of China

    Yan Sun & Woon Kian Chong

  2. Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, No. 111 Ren Ai Road, Suzhou Dushu Lake Higher Education Town, Suzhou Industrial Park, 215123, Jiangsu Province, People’s Republic of China

    Ka Lok Man

  3. Department of Multimedia, Sungkyul University, Anyang, Republic of Korea

    Seungmin Rho

  4. Department of Financial Mathematics and Financial Engineering, South University of Science and Technology of China, 1088 Xueyuan Avenue, Nanshan District, 518055, Shenzhen, China

    Dejun Xie

Authors
  1. Yan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Woon Kian Chong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ka Lok Man
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seungmin Rho
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dejun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Woon Kian Chong .

Editor information

Editors and Affiliations

  1. Multimedia Engineering, Mokpo National University, Mokpo, Cholla-namdo, Korea (Republic of)

    Gi-Chul Yang

  2. Unit 1, 1F, International Association of Engineers, Hong Kong, Hong Kong

    Sio-Iong Ao

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Xu Huang

  4. Calzada Tecnologico s/n, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Sun, Y., Chong, W.K., Man, K.L., Rho, S., Xie, D. (2016). Exploring Critical Success Factors of Mobile Recommendation Systems: The End User Perspective. In: Yang, GC., Ao, SI., Huang, X., Castillo, O. (eds) Transactions on Engineering Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-10-0551-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0551-0_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0550-3

  • Online ISBN: 978-981-10-0551-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation