Advertisement

A Pilot Study on the Effects of Personality Traits on the Usage of Mobile Applications: A Case Study on Office Workers and Tertiary Students in the Bangkok Area

  • Charnsak Srisawatsakul
  • Gerald Quirchmayr
  • Borworn Papasratorn
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)

Abstract

Recent research suggests that the “big five personality traits” influence the purchasing and usage preferences of mobile application. However, the impact of monetizing of applications and personality traits has so far been largely unattended. We have therefore extended our research to cover monetizing models of mobile applications. In this paper, we aim to enhance the understanding of the relationship between the “big five personality traits” and the usages and purchase intention of mobile applications in difference categories. Our initial data for the pilot study consists of 173 individuals, collected from smart device consumers who live in Bangkok, Thailand. Pearson’s correlation and multiple linear regressions were used to analyze the data. The initial results indicate that some personality traits are associated with the usages and intention to purchase mobile applications. It is highly possible to conclude from the data that conscientious persons placed more intention to use productive applications. Specifically, this personality trait has a positive relationship with utilities, education, business and maps and navigation. Neuroticism reported only significant relation with in-app purchase in utilities applications. Agreeableness showed no significance during our regressions analysis. The most widely used paid application among all traits is entertainment. The findings of this pilot study will serve as indicators for the direction of our planned future research in this field.

Keywords

Mobile Applications Personality Traits Monetizing Purchase Intention In-App Purchase Application Usages 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    ITU: ITU World Telecommunication/ICT Indicators database, http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2013.pdf
  2. 2.
    Research, A.B.I.: The Mobile App Market will be Worth $27 Billion in, as Tablet Revenue Grows (2013), https://www.abiresearch.com/press/the-mobile-app-market-will-be-worth-27-billion-in-
  3. 3.
    Gu, X., Xu, Z., Wang, T., Fang, Y.: Trusted Service Application Framework on Mobile Network. In: Gu, X., Xu, Z., Wang, T., Fang, Y. (eds.) 2012 9th Int. Conf. Ubiquitous Intell. Comput. 9th Int. Conf. Auton. Trust. Comput., pp. 979–984 (2012)Google Scholar
  4. 4.
    Charland, A., LeRoux, B.: Mobile Application Development: Web vs. Native. Queue 9, 20 (2011)CrossRefGoogle Scholar
  5. 5.
    Holzer, A., Ondrus, J.: Mobile application market: A developer’s perspective. Telemat. Informatics 28, 22–31 (2011)CrossRefGoogle Scholar
  6. 6.
    Liu, C., Zhu, Q., Holroyd, K.A., Seng, E.K.: Status and trends of mobile-health applications for iOS devices: A developer’s perspective. J. Syst. Softw. 84, 2022–2033 (2011)CrossRefGoogle Scholar
  7. 7.
    Lane, W., Manner, C.: The Influence of Personality Traits on Mobile Phone Application Preferences. J. Econ. Behav. Stud. 4, 252–260 (2012)Google Scholar
  8. 8.
    Chittaranjan, G., Blom, J., Gatica-Perez, D.: Who’s Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones. In: 2011 15th Annu. Int. Symp. Wearable Comput., pp. 29–36 (2011)Google Scholar
  9. 9.
    Valentine, C.W.: Personality—A Psychological Interpretation by Gordon W. Allport, pp. xiv + 588. Constable, London (1943); Br. J. Educ. Psychol. 13, 48–50 (1943)Google Scholar
  10. 10.
    McCrae, R.R., John, O.P.: An introduction to the five-factor model and its applications. J. Pers. 60, 175–215 (1992)CrossRefGoogle Scholar
  11. 11.
    Costa, P.T., MacCrae, R.R.: Psychological Assessment Resources, I.: Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO FFI): Professional Manual. Psychological Assessment Resources (1992)Google Scholar
  12. 12.
    Donnellan, M.B., Oswald, F.L., Baird, B.M., Lucas, R.E.: The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. Psychol. Assess. 18, 192–203 (2006)CrossRefGoogle Scholar
  13. 13.
    Goldberg, L.R., Johnson, J.A., Eber, H.W., Hogan, R., Ashton, M.C., Cloninger, C.R., Gough, H.G.: The international personality item pool and the future of public-domain personality measures. J. Res. Pers. 40, 84–96 (2006)CrossRefGoogle Scholar
  14. 14.
    John, O.P., Srivastava, S.: The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In: Pervin, L.A., John, O.P. (eds.) Handbook of Personality: Theory and Research, pp. 102–138. Guilford Press, New York (1999)Google Scholar
  15. 15.
    Saucier, G.: Mini-Markers: A Brief Version of Goldberg’s Unipolar Big-Five Markers. J. Pers. Assess. 63, 506–516 (1994)CrossRefGoogle Scholar
  16. 16.
    Tsaousis, I., Kerpelis, P.: The Traits Personality Questionnaire 5 (TPQue5). Eur. J. Psychol. Assess. 20, 180–191 (2004)CrossRefGoogle Scholar
  17. 17.
    Cooper, A.J., Smillie, L.D., Corr, P.J.: A confirmatory factor analysis of the Mini-IPIP five-factor model personality scale. Pers. Individ. Dif. 48, 688–691 (2010)CrossRefGoogle Scholar
  18. 18.
    Gosling, S.D., Rentfrow, P.J., Swann, W.B.: A very brief measure of the Big-Five personality domains. J. Res. Pers. 37, 504–528 (2003)CrossRefGoogle Scholar
  19. 19.
    Bernard, L., Walsh, R., Mills, M.: Ask once, may tell: Comparative validity of single and multiple item measurement of the Big-Five personality factors. Couns. Clin. Psychol. J. 2, 40–57 (2005)Google Scholar
  20. 20.
    Aronson, Z.H., Reilly, R.R., Lynn, G.S.: The impact of leader personality on new product development teamwork and performance: The moderating role of uncertainty. J. Eng. Technol. Manag. 23, 221–247 (2006)CrossRefGoogle Scholar
  21. 21.
    Woods, S., Hampson, S.: Measuring the Big Five with single items using a bipolar response scale. Eur. J. Pers. 390, 373–390 (2005)CrossRefGoogle Scholar
  22. 22.
    Baldasaro, R.E., Shanahan, M.J., Bauer, D.J.: Psychometric properties of the mini-IPIP in a large, nationally representative sample of young adults. J. Pers. Assess. 95, 74–84 (2013)CrossRefGoogle Scholar
  23. 23.
    Credé, M., Harms, P., Niehorster, S., Gaye-Valentine, A.: An evaluation of the consequences of using short measures of the Big Five personality traits. J. Pers. Soc. Psychol. 102, 874–888 (2012)CrossRefGoogle Scholar
  24. 24.
    Shafer, A.B.: Brief Bipolar Markers for The Five Factor Model of Personality. Psychol. Rep. 84, 1173–1179 (1999)CrossRefGoogle Scholar
  25. 25.
    Apple Press: Apple - Press Info - Apple’s App Store Marks Historic 50 Billionth Download, http://www.apple.com/pr/library/2013/05/16Apples-App-Store-Marks-Historic-50-Billionth-Download.html
  26. 26.
  27. 27.
    Srisawatsakul, C., Papasratorn, B.: Factors Affecting Consumer Acceptance Mobile Broadband Services with Add-on Advertising: Thailand Case Study. Wirel. Pers. Commun. 69, 1055–1065 (2013)CrossRefGoogle Scholar
  28. 28.
    Svendsen, G.B., Johnsen, J.-A.K., Almås-Sørensen, L., Vittersø, J.: Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behav. Inf. Technol. 32, 323–334 (2013)CrossRefGoogle Scholar
  29. 29.
    Cortimiglia, M., Ghezzi, A., Renga, F.: Mobile Applications and Their Delivery Platforms. IT Prof. 13, 51–56 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Charnsak Srisawatsakul
    • 1
  • Gerald Quirchmayr
    • 2
  • Borworn Papasratorn
    • 1
  1. 1.Requirement Engineering Laboratory, School of Information TechnologyKing Mongkut’s University of Technology ThonburiBangkokThailand
  2. 2.Faculty of Computer ScienceUniversity of ViennaViennaAustria

Personalised recommendations