Skip to main content
Log in

Identifying and structuring service functions of mobile applications in Google’s Android Market

  • Original Article
  • Published:
Information Systems and e-Business Management Aims and scope Submit manuscript

Abstract

Recently, mobile applications are considered one of the most opportune issues in mobile industry since the explosive growth of mobile applications has generated the myriad of service functions. In response, it is needed to extract useful information on dominant service functions and structural relationship among the service functions. However, there have been few methods for encapsulating information on major service functions and their relationships. Thus, this study aims at identifying dominant service functions and their structural relationships of mobile applications in Google’s Android Market using frequent pattern (FP)-tree algorithm which retrieves and summarizes frequently used items according to association rules. In this study, the FP-tree algorithm is used for extracting information on service functions in terms of three factors: frequency, association, and hierarchical structure. Using the information of service function tree, the developers refer the systematic and mash-up combinations of service functions to design new mobile applications.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Agrawal R, Srikant R (1994) Fast algorithm for mining association rules in large database. In: Proceeding of the 20th international conference on very large databases, pp 487–499

  • Cai J (2011) Mobile communications in China: levels of technological dynamism. Technol Anal Strateg 23(2):123–143

    Article  Google Scholar 

  • Corley JK, Jourdan Z, Ingram WR (2013) Internet marketing: a content analysis of the research. Electron Mark 23(3):177–204

    Article  Google Scholar 

  • Danado J, Davies M, Ricca P, Fensel A (2010) An authoring tool for user-generated mobile services. Lect Notes Comput Sci 6369:118–127

    Article  Google Scholar 

  • Deng Z, Lu Y, Wang B, Zhang J, Wei K (2010) An empirical analysis of factors influencing users’ adoption and use of mobile services in China. Int J Mob Commun 8(5):561–585

    Article  Google Scholar 

  • Feijoo C, Maghiros L, Abadie F, Gomez-Barroso JL (2008) Exploring a heterogeneous and fragmented digital ecosystem: mobile content. Telemat Inform 26(3):282–292

    Article  Google Scholar 

  • Gartner (2014) Predicts 2014: Apps, personal cloud and data analytics will drive new consumer interactions. http://www.gartnercom/document/2628016. Accessed 4 Feb 2016

  • Ghose A, Han SP (2014) Estimating demand for mobile applications in the new economy. Manag Sci 60(6):1470–1488

    Article  Google Scholar 

  • Giachetti C, Marchi G (2017) Successive changes in leadership in the worldwide mobile phone industry: the role of windows of opportunity and firms’ competitive action. Res Policy 46(2):352–364

    Article  Google Scholar 

  • Gopalan R, Sucahyo Y (2004) High performance frequent patterns extraction using compressed FP-tree. In: Proceedings of the third international conference on machine learning and cybernetics, Shanghai, China, pp 26–29

  • Greengard S (2015) Automotive systems get smarter. Commun ACM 58(10):18–20

    Article  Google Scholar 

  • Gretzel U, Sigala M, Xiang Z, Koo C (2015) Smart tourism: foundations and developments. Electron Marks 25(3):179–188

    Article  Google Scholar 

  • Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov 8(1):53–87

    Article  Google Scholar 

  • Holzer A, Ondrus J (2011) Mobile application market: a developer’s perspective. Telemat Inform 28(1):22–31

    Article  Google Scholar 

  • Hsu C-L, Lin J (2015) What drives purchase intention for paid mobile apps?—an expectation confirmation model with perceived value. Electron Commer Res Appl 14(1):4657

    Article  Google Scholar 

  • Hu Y-H, Yeh T-W (2014) Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowl-Based Syst 61:76–88

    Article  Google Scholar 

  • Kim C, Lee H (2012) A database-centered approach to the development of new mobile service concepts. Int J Mob Commun 10(3):248–264

    Article  Google Scholar 

  • Kim J, Park Y, Kim C, Lee H (2014) Mobile application service networks: Apple’s App Store. Serv Bus 14(1):1–27

    Article  Google Scholar 

  • Kotsiantis S, Kanellopoulos D (2006) Association rules mining: a recent overview. GESTS Int Trans Comput Sci Eng 32(1):71–82

    Google Scholar 

  • Kumar D, Sachan A (2014) Bridging the gap between disabled people and new technology in interactive web application with the help of voice. In: IEEE international conference on advances in engineering and technology research (ICAETR), August 2014, Unnao, India, pp 1–5. https://doi.org/10.1109/ICAETR.2014.7012885

  • Laudon K, Traver CG (2010) E-commerce: business, technology, society, 6th edn. Prentice-Hill, New York

    Google Scholar 

  • Lin R-H (2009) Potential use of FP-growth algorithm for identifying competitive suppliers in SCM. J Oper Res Soc 60(8):1135–1141

    Article  Google Scholar 

  • Nagi EWT, Gunasekaran A (2007) A review for mobile commerce research and applications. Decis Support Syst 43(1):3–15

    Article  Google Scholar 

  • Narvekar M, Syed SF (2015) An optimized algorithm for association rule mining using FP tree. Proc Comput Sci 45:101–110

    Article  Google Scholar 

  • Noh MJ, Lee KT (2016) An analysis of the relationship between quality and user acceptance in smartphone apps. Inf Syst E-Bus Manag 14(2):273–291

    Article  Google Scholar 

  • Portet F, Vacher M, Golanski C, Roux C, Meillon B (2013) Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects. Pers Ubiquit Comput 17(1):127–144

    Article  Google Scholar 

  • Reuver M, Bouwman H, Haaker T (2009) Mobile business models: organizational and financial design issues that matter. Electron Marks 19(1):3–13

    Article  Google Scholar 

  • Schuster M (2010) Speech recognition for mobile devices at Google. Lect Notes Comput Sci 6230:8–10

    Article  Google Scholar 

  • Shen L, Shen H, Cheng L (1999) New algorithms for efficient mining of association rules. Inform Sci 118(1–4):251–268

    Article  Google Scholar 

  • Statista (2017) Worldwide mobile app revenues in 2015, 2016 and 2020. https://www.statista.com/statistics/269025/worldwide-mobile-app-revenue-forecast/

  • Suchacka G, Chodak G (2016) Using association rules to assess purchase probability in online stores. Inf Syst E-Bus Manag. https://doi.org/10.1007/s10257-016-0329-4

    Google Scholar 

  • Suh Y, Lee H (2017) Developing ecological index for identifying roles of ICT industries in mobile ecosystems: the inter industry analysis approach. Telemat Inform 34(1):425–437

    Article  Google Scholar 

  • Suh Y, Lee H, Park Y (2012) Analysis and visualization of structure of smartphone application services using text mining and the set-covering algorithm: a case of App Store. Int J Mob Commun 10(1):1–20

    Article  Google Scholar 

  • Suh Y, Kim G, Seol H (2017) Roadmapping for prioritisation of smartphone feature requirements based on user experiences. Technol Anal Strateg 27(8):886–902

    Article  Google Scholar 

  • Tan P, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson Education, Boston

    Google Scholar 

  • Tsay YJ, Chiang JY (2005) CBAR: an efficient method for mining association rules. Knowl-Based Syst 18(2–3):99–105

    Article  Google Scholar 

  • Turban E, King D, Lee JK, Viehland D (2004) Electronic commerce 2004: a managerial perspective. Pearson Prentice Hall, New Jersey

    Google Scholar 

  • Ulrich KT, Eppinger SD (2003) Product design and development. McGraw-Hill, New York

    Google Scholar 

  • Wang J, Lai J-Y, Chang C-H (2016) Modeling and analysis for mobile application services: the perspective of mobile network operators. Technol Forecast Soc 111:146–163

    Article  Google Scholar 

  • West J, Mace M (2010) Browsing as the killer app: explaining the rapid success of Apple’s iPhone. Telecommun Policy 34(5/6):270–286

    Article  Google Scholar 

  • Wu C-M, Huang Y-F (2011) Generalized association rule mining using an efficient data structure. Expert Syst Appl 38(6):7277–7290

    Article  Google Scholar 

  • Yoon B (2010) Strategic visualisation tools for managing technological information. Technol Anal Strateg 22(3):377–397

    Article  Google Scholar 

  • Yu J (2011) From 3G to 4G: technology evolution and path dynamics in China’s mobile telecommunications sector. Technol Anal Strateg 23(10):1079–1093

    Article  Google Scholar 

  • Yun J, Won D, Jeong E, Par K, Yang J, Park J (2016) The relationship between technology, business model, and market in autonomous car and intelligent robot industries. Technol Forecast Soc 103:142–155

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (2011-0030814).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yongtae Park.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suh, Y., Park, Y. Identifying and structuring service functions of mobile applications in Google’s Android Market. Inf Syst E-Bus Manage 16, 383–406 (2018). https://doi.org/10.1007/s10257-017-0366-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10257-017-0366-7

Keywords

Navigation