Brand Unhappiness on Social Media

  • Kumru Uyar
  • Esra Kahya Ozyirmidokuz
  • Eduard Alexandru Stoica
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

Social media is one of the places where brands can communicate with their customers and potential customers easily and fast. Although there are lots of advantages to using social media including low cost, being able to measure customers’ feelings, having fast and easy access to consumers, etc., it is very important for a firm not to make any mistakes on social media, because the online brand is lonely against to the crowd. Customers can easily give feedback about brands. Unhappy customers can share their opinion with the masses through social media and negatively affect the firm. It is important for firms to control and manage brand unhappiness. Firms should understand brand unhappiness factors in order to increase the happiness of their customers. This research aims to discover a better understanding of the brand unhappiness concept by examining the online social media feedback of customers. For this purpose, 17 different firms’ Facebook customer comments are analyzed. Therefore, the brand unhappiness factors on social media are extracted.

Keywords

Brand unhappiness Customer feedback analysis Social media analysis Customer well-being 

Notes

Acknowledgment

This research was supported by Erciyes University Research Fund under the title “Formalizing and Analyzing Users’ Feedback to Evolve of STSs” project (Project id: FBA-2014-4850).

References

  1. Abdel-Khalek, A.M.: Happiness among Kuwaiti college students. J. Happiness Stud. 5(1), 93–97 (2004)CrossRefGoogle Scholar
  2. Almaliki, M. et al.: The design of adaptive acquisition of users feedback: an empirical study. In: The IEEE Eighth International Conference on Research Challenges in Information Science (RCIS 2014), Marrakesh, Morocco, 28–30 May 2014Google Scholar
  3. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)CrossRefGoogle Scholar
  4. Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31, 102–107 (2016)CrossRefGoogle Scholar
  5. Creswell, J.W.: Qualitative Inquiry and Research Design: Choosing among five Approaches, 2nd edn. Sage Publications, Thousand Oaks (2007)Google Scholar
  6. Csikszentmihalyi, M.: Akış: Mutluluk bilimi. HYB Yayınları, Ankara (2005)Google Scholar
  7. Desmeules, R.: The impact of variety on consumer happiness: marketing and the tyranny of freedom. Acad. Mark. Sci. Rev. 1, 1–18 (2002)Google Scholar
  8. Diener, E., et al.: The satisfaction with life scale. J. Pers. Assess. 49(1), 71–75 (1985)CrossRefGoogle Scholar
  9. Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: songs, blogs and presidents. J. Happiness Stud. 11(4), 441–456 (2010)CrossRefGoogle Scholar
  10. Fang, R.-Y. et al.: Emotion detection based on concept inference and spoken sentence analysis for customer service. In: Twelfth Annual Conference of the International Speech Communication Association (INTERSPEECH 2011), pp. 1409–1412, Florence, Italy, August 27–31 2011Google Scholar
  11. Gibbs, G.R.: Thematic Coding and Categorizing. Analyzing Qualitative Data. Sage Publications, London (2007)Google Scholar
  12. Guest, G., et al.: Applied Thematic Analysis. SAGE Publications, Los Angeles (2012)CrossRefGoogle Scholar
  13. Gupta, N., et al.: Emotion detection in email customer care. Comput. Intell. 29(3), 10–16 (2013)CrossRefGoogle Scholar
  14. Han, J.K., et al.: Market orientation and organizational performance: is innovation a missing link? J. Mark. 62, 30–45 (1998)CrossRefGoogle Scholar
  15. Hills, P., Argyle, M.: The Oxford happiness questionnaire: a compact scale for the measurement of psychological well-being. Personal. Individ. Differ. 33, 1073–1082 (2002)CrossRefGoogle Scholar
  16. Hsieh, Y.-C., et al.: Does raising value co-creation increase all customers’ happiness? J. Bus. Ethics., Springer. 1, 1–15 (2016). https://doi.org/10.1007/s10551-016-3293-5 Google Scholar
  17. Kahneman, D., et al.: A survey method for characterizing daily life experience: the day reconstruction method. Science. 306(5702), 1776–1780 (2004)CrossRefGoogle Scholar
  18. Kim, G. W. et al.: Understanding characteristics of user-generated content as a source of extracting user value. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 4–7 Dec. 2016 pp. 1751–1755, Bali, Indonesia (2016) doi: https://doi.org/10.1109/IEEM.2016.7798178
  19. Lichtman, M.V.: Qualitative Research in Education: a user's Guide, 3rd edn. Sage Publications, Los Angeles (2013)Google Scholar
  20. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRefGoogle Scholar
  21. Lyubomirsky, S., Lepper, H.S.: A measure of subjective happiness: preliminary reliability and construct validation. Soc. Indic. Res. 46(2), 137–155 (1999)CrossRefGoogle Scholar
  22. Mogaji, E., et al.: Factors shaping attitudes towards UK bank brands: an exploratory analysis of social media data. Cogent Bus. Manag. 3(1.):1223389), 1–15 (2016)Google Scholar
  23. Namey, E., et al.: Data reduction techniques for large qualitative data sets. In: Guest, G., MacQueen, K.M. (eds.) Handbook for Team-Based Qualitative Research, pp. 137–163. Altamira Press, Lanham (2008)Google Scholar
  24. Norton, D.W., et al.: Producing customer happiness: the job to do for brand innovation. Des. Manag. Rev. 21(3), 6–15 (2010)CrossRefGoogle Scholar
  25. Ren, F., Quan, C.: Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing. Inf. Technol. Manag. 13(4), 321–332 (2012)CrossRefGoogle Scholar
  26. Ryan, G.W., Bernard, H.R.: Techniques to identify themes. Field Methods. 15(1), 85–109 (2003). https://doi.org/10.1177/1525822X02239569 CrossRefGoogle Scholar
  27. Stoica, E.A., Kahya Ozyirmidokuz, E.: Mining customer feedback data. Int. J. Knowl. Eng. 1(1), 68–71 (2015)CrossRefGoogle Scholar
  28. Stoica, E.A., et al.: A novel model for e-business and e-government processes on social media, proceedings of the international economic conference of Sibiu 2013 post crisis economy: challenges and opportunities, IECS 2013. Proc. Econ. Finan. 6, 760–769 (2013)CrossRefGoogle Scholar
  29. Stoica, E.A., et al.: New media e-marketing campaign. Case study for a Romanian press trust. Proc. Econ. Finan. 16, 635–640 (2014)CrossRefGoogle Scholar
  30. Ura, K. et al.: A Short Guide to Gross National Happiness Index. The Centre for Bhutan Studies, Thimphu, http://www.ophi.org.uk/wp-content/uploads/Ura-et-al-BhutanHappiness-Chapter.pdf?cda6c1 (2012.) Accessed 14 Feb 2016
  31. Weiner, B.: Attributional thoughts about consumer behavior. J. Consum. Res. 27(3), 382–387 (2000)CrossRefGoogle Scholar
  32. Yen, H., et al.: Emotional product design and perceived brand emotion. Int. J. Adv. Psychol. 3(2), 59–66 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Kumru Uyar
    • 1
  • Esra Kahya Ozyirmidokuz
    • 2
  • Eduard Alexandru Stoica
    • 3
  1. 1.Nuh Naci Yazgan UniversityKayseriTurkey
  2. 2.Erciyes UniversityKayseriTurkey
  3. 3.Lucian Blaga University of SibiuSibiuRomania

Personalised recommendations