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The effect of an individual’s age on the perceived importance and usage intensity of communications services—A Bayesian Network analysis

Author

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  • Pekka Kekolahti

    (Aalto University)

  • Juuso Karikoski

    (Aalto University)

  • Antti Riikonen

    (Aalto University)

Abstract

Multiple novel interpersonal communications services have emerged recently, but how their usage and perceived importance are related to the personal characteristics of the users is still relatively unexplored. Therefore, the aim of this study is to explore the effect of an individual’s age on the perceived importance and usage intensity of communications services based on Bayesian Networks using a survey of 3008 Finns during 2011. In the case of Short Message Service (SMS), Instant Messaging (IM), Internet forums and communities (e.g., Facebook & Twitter), and e-mail the results indicate that the perceived importance of the communications services decreases as the age increases. With phone calls and letters, however, no clear dependencies with age were identified. In the causal analysis the importance of Internet forums and communities was the only variable which can be stated to be directly caused by an individual’s age. This variable also acts as a mediator in the path from age towards perceived importance of other communication services and also towards their usage intensity. These results about the central role of Internet forums and communities can be exploited, for example, by device manufacturers when designing their products, and by service providers when designing their consumer services. The study also provides new information for mobile operators about the dependencies between mobile communications services and a documented example workflow for research community to construct a causal Bayesian Network from a combination of observational data and domain expertise.

Suggested Citation

  • Pekka Kekolahti & Juuso Karikoski & Antti Riikonen, 2015. "The effect of an individual’s age on the perceived importance and usage intensity of communications services—A Bayesian Network analysis," Information Systems Frontiers, Springer, vol. 17(6), pages 1313-1333, December.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:6:d:10.1007_s10796-014-9502-9
    DOI: 10.1007/s10796-014-9502-9
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    References listed on IDEAS

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    1. Zhi Geng & Guangwei Li, 2002. "Conditions for Non‐confounding and Collapsibility without Knowledge of Completely Constructed Causal Diagrams," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 169-181, March.
    2. Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
    3. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
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    Cited by:

    1. Arnold, René & Hildebrandt, Christian & Tas, Serpil & Kroon, Peter, 2017. "More than Words: A global analysis of the socio-economic impact of Rich Interaction Applications (RIAs)," 28th European Regional ITS Conference, Passau 2017 169445, International Telecommunications Society (ITS).
    2. Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.
    3. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.

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