IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v118y2014i3p1205-1228.html
   My bibliography  Save this article

Mobile Phone Usage Preferences: The Contributing Factors of Personality, Social Anxiety and Loneliness

Author

Listed:
  • Suyinn Lee
  • Cai Tam
  • Qiu Chie

Abstract

Psychological factors and social relationships are important components that influence an individual’s communication style. This paper aims to examine the association of personality factors, social anxiety (SA) and loneliness with mobile phone (MP) usage preferences on the basis of voice calling and text messaging. Malaysian MP users (N = 187) completed four questionnaires (Mobile Phone Usage Questionnaire, Big Five Inventory, Interaction Anxiousness Scale and UCLA Loneliness Scale) on paper or online via a web-link. Multiple regression analyses revealed that personality, SA and loneliness broadly predicted preferences for voice calling or text messaging. Further analyses examining the predictability of time spent on voice calls/text messaging and number of people called/exchanged text messages also revealed some significant findings in regards to the openness-to-experience personality dimension, loneliness and SA. The findings of this research have important implications to tailoring the delivery of psychological services to individuals diagnosed with chronic loneliness and SA. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Suyinn Lee & Cai Tam & Qiu Chie, 2014. "Mobile Phone Usage Preferences: The Contributing Factors of Personality, Social Anxiety and Loneliness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(3), pages 1205-1228, September.
  • Handle: RePEc:spr:soinre:v:118:y:2014:i:3:p:1205-1228
    DOI: 10.1007/s11205-013-0460-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11205-013-0460-2
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11205-013-0460-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jana Prodanova & Sonia San-Martín & Nadia Jiménez, 2017. "Enfoque teórico multidisciplinar para la provisión electrónica de servicios," DOCFRADIS Working Papers 1705, Catedra Fundación Ramón Areces de Distribución Comercial, revised Oct 2017.
    2. Sara Thomée, 2018. "Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure," IJERPH, MDPI, vol. 15(12), pages 1-25, November.
    3. Mikaela Irene D Fudolig & Kunal Bhattacharya & Daniel Monsivais & Hang-Hyun Jo & Kimmo Kaski, 2020. "Link-centric analysis of variation by demographics in mobile phone communication patterns," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.
    4. Britta Wetzel & Rüdiger Pryss & Harald Baumeister & Johanna-Sophie Edler & Ana Sofia Oliveira Gonçalves & Caroline Cohrdes, 2021. "“How Come You Don’t Call Me?” Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(12), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    3. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    4. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    5. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    6. Coleman, Stephen, 2005. "Testing Theories with Qualitative and Quantitative Predictions," MPRA Paper 105171, University Library of Munich, Germany.
    7. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    8. Brooks, Jeremy S., 2010. "The Buddha mushroom: Conservation behavior and the development of institutions in Bhutan," Ecological Economics, Elsevier, vol. 69(4), pages 779-795, February.
    9. Brian Knaeble & Seth Dutter, 2017. "Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 97-105, April.
    10. Steven M. Shugan, 2002. "In Search of Data: An Editorial," Marketing Science, INFORMS, vol. 21(4), pages 369-377.
    11. Michael Schomaker & Christian Heumann, 2020. "When and when not to use optimal model averaging," Statistical Papers, Springer, vol. 61(5), pages 2221-2240, October.
    12. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    13. W. Robert Reed, 2009. "The Determinants Of U.S. State Economic Growth: A Less Extreme Bounds Analysis," Economic Inquiry, Western Economic Association International, vol. 47(4), pages 685-700, October.
    14. Nicholas Weller & Jeb Barnes, 2016. "Pathway Analysis and the Search for Causal Mechanisms," Sociological Methods & Research, , vol. 45(3), pages 424-457, August.
    15. S. P. Brooks & E. A. Catchpole & B. J. T. Morgan & M. P. Harris, 2002. "Bayesian methods for analysing ringing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 187-206.
    16. Nick Inglis & Bruce Vanstone & Tobias Hahn, 2019. "Modelling momentum winner/loser asymmetry: the sources of winner and loser returns in the ASX200 and S&P500," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(S1), pages 657-684, April.
    17. Ormerod, Paul, 2015. "The economics of radical uncertainty," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-20.
    18. Sai Ding & John Knight, 2011. "Why has China Grown So Fast? The Role of Physical and Human Capital Formation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 141-174, April.
    19. M. Pir Bavaghar, 2015. "Deforestation modelling using logistic regression and GIS," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 61(5), pages 193-199.
    20. Ke Yang & Langnan Chen & Fengping Tian, 2015. "Realized Volatility Forecast of Stock Index Under Structural Breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(1), pages 57-82, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:118:y:2014:i:3:p:1205-1228. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.