Multimedia Tools and Applications

, Volume 74, Issue 14, pp 5041–5054 | Cite as

Relative weight evaluation of the factors inducing social media service use

  • Cheol-Rim Choi
  • Hwa-Young Jeong
  • Jong Hyuk Park
  • Young-Sik Jeong
Article

Abstract

As an influence of social media service (SMS) is extending enormously and SMS becomes an important marketing way and a field of modern industry beyond a method to transfer informations. Under these circumstances that the influence of SMS is getting bigger bigger, which factors make users participate and work in the social media service and how relatively important these factors are between them need to be examined. But SMS are different from the existing information systems. Most information systems are task-oriented, that is, the systems aim to provide users with useful information for better decision making. Therefore, a different approach is required. In this research, we examine and select the main factors which affect users’ social media service use, and evaluate relative importance degrees of each factor on the basis of relationships between the factors with Analytic Network Process (ANP).

Keywords

Social media service Relative importance degree Analytic network process 

References

  1. 1.
    Agarwal R, Karahanna E (2000) Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Q 24:665–694CrossRefGoogle Scholar
  2. 2.
    Agarwal R, Prasad J (1999) Are individual differences germane to the acceptance of new information technologies? Decis Sci 30:361–391CrossRefGoogle Scholar
  3. 3.
    Ashtona MC, Paunonena SV, Helmesa E, Jacksona DN (1988) Kin altruism, reciprocal altruism, and the big five personality factors. Evol Hum Behav 19(4):243–255CrossRefGoogle Scholar
  4. 4.
    Bard JF, Sousk SF (1990) A tradeoff analysis for rough terrain cargo handlers using the AHP: an example of group decision-making. Eng Manag IEEE Trans 37(3):222–227CrossRefGoogle Scholar
  5. 5.
    Cle’ment R, Noels K, Doeneault B (2001) Interethnic contact, identity, and psychological adjustments in the mediating and moderating roles of communication. J Soc Issues 57:559–578CrossRefGoogle Scholar
  6. 6.
    Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13:319–340CrossRefGoogle Scholar
  7. 7.
    Erickson T (2002) Some problems with the notion of context-aware computing. Commun ACM 45:102–104CrossRefGoogle Scholar
  8. 8.
    Evans D (2008) Social media marketing: An hour a day. Wiley Publisher, IndianapolisGoogle Scholar
  9. 9.
    Gallego D, Huecas G (2012) An empirical case of a context-aware mobile recommender system in a banking environment. J Converg 3(4):41–48Google Scholar
  10. 10.
    Heitzmann CA, Kaplan RM (1988) Assessment of methods for measuring social support. Health Psychol 7(1):75–109CrossRefGoogle Scholar
  11. 11.
    Hyde KM, Maier HR, Colby CB (2003) Incorporating Uncertainty in the PROMETHEE MCDA Method. J Multi-Criteria Decis Anal 12:245–259CrossRefGoogle Scholar
  12. 12.
    Kailiponi P (2010) Analyzing evacuation decisions using multi-attribute utility theory (MAUT). Procedia Eng 3:163–174CrossRefGoogle Scholar
  13. 13.
    Kim T, Biocca F (2004) Telepresence via television: Two dimensions of telepresence may have different connections to memory and persuasion J Computer-Mediated Commun 3(2)Google Scholar
  14. 14.
    Kwon O (2004) Modeling and generating context-aware agent-based applications with amended colored petri-nets. Expert Syst Appl 27:609–621CrossRefGoogle Scholar
  15. 15.
    Kwon O, Wen Y (2010) An empirical study of the factors affecting social network service use. Comput Hum Behav 26:254–263CrossRefGoogle Scholar
  16. 16.
    McDowell M, Morda D (2011) Socializing Securely: Using Social Networking Services, Carnegie Mellon University (Produced for US-CERT)Google Scholar
  17. 17.
    Oommen BJ, Yazidi A, Granmo O-C (2012) An adaptive approach to learning the preferences of users in a social network using weak estimators. J Inf Process Syst 8(No. 2):191–212CrossRefGoogle Scholar
  18. 18.
    Rachung Y, Tzeng G-H (2006) A soft computing method for multi-criteria decision making with dependence and feedback. Appl Math Comput 180(6):63–75MATHMathSciNetGoogle Scholar
  19. 19.
    Rau PP, Gao Q, Ding Y (2008) Relationship between the level of intimacy and lurking in online social network services. Comput Hum Behav 24(6):2757–2770CrossRefGoogle Scholar
  20. 20.
    Riedlinger ME, Gallois C, Mckay S, Pittam J (2004) Impact of social group processes and functional diversity on communication in networked organizations. J Appl Commun Res 32(1):55–79CrossRefGoogle Scholar
  21. 21.
    Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkMATHGoogle Scholar
  22. 22.
    Saaty TL (1996) Decision making with dependence and feedback: The analytic network process. RWS Publications, PittsburghGoogle Scholar
  23. 23.
    Saaty TL (2001) Decision making with dependence feedback: The analytic network process. RWS Publications, PittsburghGoogle Scholar
  24. 24.
    See Siew S, Khalil Md N, Al-Agaga AM (2012) Factors Affecting Malaysian young consumers’ online purchase intention in social media websites. Procedia - Soc Behav Sci 40:326–333CrossRefGoogle Scholar
  25. 25.
    Tang X, Feng J (2006) ANP theory and application expectation. Stat Decis Mak 12(3):138–140Google Scholar
  26. 26.
    Turban E, King D, Lang J (2009) Introduction to electronic commerce. Pearson Education, Inc., Upper Saddle RiverGoogle Scholar
  27. 27.
    Venkatesh V (1999) Creation of favorable uses perceptions: exploring the role of intrinsic motivation. MIS Q 23:239–260CrossRefGoogle Scholar
  28. 28.
    Venkatesh V, Morris M, Davis G, Davis F (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478Google Scholar
  29. 29.
    Yanghoon K, Chang H (2012) IT convergence index and measurement design in the manufacturing industry. J Converg 3(3):47–50Google Scholar
  30. 30.
    Zhu Y, Jin Q (2012) An adaptively emerging mechanism for context-aware service selections regulated by feedback distributions. Human-centric Comput Inf Sci 2:1–15CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Cheol-Rim Choi
    • 1
  • Hwa-Young Jeong
    • 1
  • Jong Hyuk Park
    • 2
  • Young-Sik Jeong
    • 3
  1. 1.Humanitas CollegeKyung Hee UniversitySeoulSouth Korea
  2. 2.Department of Computer Science and EngineeringSeoul National University of Science and TechnologySeoulSouth Korea
  3. 3.Department of Multimedia EngineeringDongguk UniversitySeoulSouth Korea

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