The emergence of inclusive and exclusive virtual communities determined by the preferences of their users

  • Debora Di Caprio
  • Francisco J. Santos-Arteaga
  • Madjid Tavana
Original Research


Consider the decision faced by the user of a social network site (SNS) regarding whether or not to accept a friendship request from another user. The user making such a decision is constrained by the limited amount of information available about the requester. Therefore, the decision must be based on incomplete information about the main characteristics and preferences describing the requester. We formalize this decision problem by defining the expected utility tradeoffs derived from the request and simulate the resulting acceptance and rejection incentives numerically. These incentives provide the basis on which to build inclusive and exclusive social networks determined by the different expectations and preferences of their users. Social networks are generated using a self-organizing map to cluster the decision makers (DMs) by their friendship acceptance behavior. We analyze the effects on the cluster structure of the resulting social network that follow from modifying the distribution of requesters relative to the preferences of the DMs, the disutility derived from accepting the friendship of an unwanted requester, the costs incurred when searching for potential friends to expand the network of connections, and the minimum networking capacities of the friendship requesters demanded by the DMs.


Virtual communities Expected utility Preference similarity Self-organizing map Social networks 


  1. Abell L, Brewer G (2014) Machiavellianism, self-monitoring, self-promotion and relational aggression on Facebook. Comput Hum Behav 36:258–262CrossRefGoogle Scholar
  2. Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25:211–230CrossRefGoogle Scholar
  3. Atif Y, Mathew SS, Lakas A (2015) Building a smart campus to support ubiquitous learning. J Ambient Intell Humaniz Comput 6:223–238CrossRefGoogle Scholar
  4. Błachnio A, Przepiórka A, Rudnicka P (2013) Psychological determinants of using Facebook: a research review. Int J Human Computer Interact 29:775–787CrossRefGoogle Scholar
  5. Błachnio A, Przepiorka A, Boruch W, Bałakier E (2016) Self-presentation styles, privacy, and loneliness as predictors of Facebook use in young people. Personal Individ Differ 94:26–31CrossRefGoogle Scholar
  6. Buzzanca M, Carchiolo V, Longheu A, Malgeri M, Mangioni G (2016) Direct trust assignment using social reputation and aging. J Ambient Intell Humaniz Comput. doi: 10.1007/s12652-016-0413-0 Google Scholar
  7. Choi G, Chung H (2013) Applying the technology acceptance model to social networking sites (SNS): impact of subjective norm and social capital on the acceptance of SNS. Int J Human Computer Interact 29:619–628CrossRefGoogle Scholar
  8. Giannakos MN, Chorianopoulos K, Giotopoulos K, Vlamos P (2013) Using Facebook out of habit. Behav Inf Technol 32:594–602CrossRefGoogle Scholar
  9. Guo H, Pathak P, Cheng HK (2015) Estimating social influences from social networking sites—articulated friendships versus communication interactions. Decis Sci 46:135–163CrossRefGoogle Scholar
  10. Jiang Z, Heng CS, Choi BCF (2013) Privacy concerns and privacy-protective behavior in synchronous online social interactions. Inf Syst Res 24:579–595CrossRefGoogle Scholar
  11. Kahneman D, Tversky A (2000) Choices, values, and frames. Cambridge University Press, CambridgezbMATHGoogle Scholar
  12. Kaptein MC, Markopoulos P, de Ruyter B, Aarts E (2010) Persuasion in ambient intelligence. J Ambient Intell Humaniz 1:43–56CrossRefGoogle Scholar
  13. Klein A, Ahlf H, Sharma V (2015) Social activity and structural centrality in online social networks. Telematics Inform 32:321–332CrossRefGoogle Scholar
  14. Kohonen T (2001) Self-organizing maps. Springer series in information sciences, vol 30, 3rd edn. Springer, BerlinzbMATHGoogle Scholar
  15. Krasnova H, Veltri NF, Günther O (2012) Self-disclosure and privacy calculus on social networking sites: the role of culture. Bus Inf Syst Eng 4:127–135CrossRefGoogle Scholar
  16. Lee H, Park H, Kim J (2013) Why do people share their context information on Social Network Services? A qualitative study and an experimental study on users’ behavior of balancing perceived benefit and risk. Int J Hum Comput Stud 71:862–877CrossRefGoogle Scholar
  17. Marey O, Bentahar J, Khosrowshahi-Asl E, Sultan K, Dssouli R (2015) Decision making under subjective uncertainty in argumentation-based agent negotiation. J Ambient Intell Humaniz Comput 6:307–323CrossRefGoogle Scholar
  18. Nadkarni A, Hofmann SG (2012) Why do people use Facebook? Personal Individ Differ 52:243–249CrossRefGoogle Scholar
  19. Oldmeadow JA, Quinn S, Kowert R (2013) Attachment style, social skills, and Facebook use amongst adults. Comput Hum Behav 29:1142–1149CrossRefGoogle Scholar
  20. Qin L, Kim Y, Hsu J, Tan X (2011) The effects of social influence on user acceptance of online social networks. Int J Hum Comput Intera 27:885–899CrossRefGoogle Scholar
  21. Samiee S, Shimp TA, Sharma S (2005) Brand origin recognition accuracy: its antecedents and consumers’ cognitive limitations. J Int Bus Stud 36:379–397CrossRefGoogle Scholar
  22. Simon HA (1997) Administrative behaviour. Free Press, New YorkGoogle Scholar
  23. Stefanone MA, Hurley CM, Egnoto MJ, Covert JM (2015) Information asymmetry and social exchange: exploring compliance gaining online. Inf Commun Soc 18:376–389CrossRefGoogle Scholar
  24. Sulkava M, Sepponen AM, Yli-Heikkilä M, Latukka A (2015) Clustering of the self-organizing map reveals profiles of farm profitability and upscaling weights. Neurocomputing 147:197–206CrossRefGoogle Scholar
  25. Tavana M, Di Caprio D, Santos-Arteaga FJ (2016a) Modeling sequential information acquisition behavior in rational decision making. Decis Sci 47:720–761Google Scholar
  26. Tavana M, Di Caprio D, Santos-Arteaga FJ, Tierney K (2016b) Modeling signal-based decisions in online search environments: a non-recursive forward-looking approach. Inf Manag 53:207–226Google Scholar
  27. Tavana M, Di Caprio D, Santos-Arteaga FJ (2016c) Loyal customer bases as innovation disincentives for duopolistic firms using strategic signaling and Bayesian analysis. Ann Oper Res 244:647–676Google Scholar
  28. Tosun LP (2012) Motives for Facebook use and expressing “true self” on the Internet. Comput Hum Behav 28:1510–1517CrossRefGoogle Scholar
  29. Trepte S, Dienlin T, Reinecke L (2015) Influence of social support received in online and offline contexts on satisfaction with social support and satisfaction with life: a longitudinal study. Media Psychol 18:74–105CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Debora Di Caprio
    • 1
    • 2
  • Francisco J. Santos-Arteaga
    • 3
    • 4
  • Madjid Tavana
    • 5
    • 6
  1. 1.Department of Mathematics and StatisticsYork UniversityTorontoCanada
  2. 2.Polo Tecnologico IISS G. GalileiBolzanoItaly
  3. 3.School of Economics and ManagementFree University of BolzanoBolzanoItaly
  4. 4.Instituto Complutense de Estudios InternacionalesUniversidad Complutense de MadridMadridSpain
  5. 5.Business Systems and Analytics Department, Distinguished Chair of Business Systems and AnalyticsLa Salle UniversityPhiladelphiaUSA
  6. 6.Business Information Systems Department, Faculty of Business Administration and EconomicsUniversity of PaderbornPaderbornGermany

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