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The Neuroscience Research Methods in Management

  • Jyrki Suomala
Chapter

Abstract

Enormous progress in understanding fundamental brain processes by using neuroscientific methods underlying management, marketing and consumers’ choice has been appeared. All thoughts and ideas of people are constituted by neural circuits. However, people have only limited conscious access to these neural circuits. As a result, an estimated 2% of thoughts are conscious, and the weakness of traditional research methods, such as surveys and focus group interviews, is that they concentrate mainly for people’s conscious part of mind. On the contrary, the main benefit by using neuroscientific methods is that there are many possibilities to uncover the unconscious brain processes, which are critical for human choice in management contexts.

The chapter divides neuroscientific methods for biometrics and neuroimaging. The main biometrics methods include eye tracking, face reading, skin conductance, and heart rate measurements. The main neuroimaging methods include electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The description of each method will be presented with examples in management and marketing contexts. The analysis of each method includes benefits and drawbacks in these contexts. And finally, the description how much each method can predict the human behaviour in the real context will be presented. This chapter introduces neuroscientific methods at an elementary level to the management science community. It gives basic concepts and ideas on how to apply neuroscience in order to solve scientific and practical management problems by using neuroscientific methods.

Keywords

Neuroscience Biometrics Neuroimaging Management Marketing 

References

  1. Ackerman, J. M., Nocera, C. C., & Bargh, J. A. (2010). Incidental Haptic Sensations Influence Social Judgments and Decisions. Science, 328(5986), 1712–1715. https://doi.org/10.1126/science.1189993.CrossRefGoogle Scholar
  2. Ariely, D., & Berns, G. S. (2010). Neuromarketing: The Hope and Hype of Neuroimaging in Business. Nature Reviews. Neuroscience, 11(4), 284–292. https://doi.org/10.1038/nrn2795.CrossRefGoogle Scholar
  3. Ashby, F. G. (2011). Statistical Analysis of fMRI Data. Cambridge, MA: MIT Press.Google Scholar
  4. Barden, P. (2013). Decoded: The Science Behind Why We Buy (1st ed.). Chichester: Wiley.Google Scholar
  5. Bargh, J. A. (2013). Our Unconscious Mind. Scientific American, 310(1), 30–37. https://doi.org/10.1038/scientificamerican0114-30.CrossRefGoogle Scholar
  6. Bargh, J. A., Schwader, K. L., Hailey, S. E., Dyer, R. L., & Boothby, E. J. (2012). Automaticity in Social-Cognitive Processes. Trends in Cognitive Sciences, 16(12), 593–605. https://doi.org/10.1016/j.tics.2012.10.002.CrossRefGoogle Scholar
  7. Berkman, E. T., & Falk, E. B. (2013). Beyond Brain Mapping Using Neural Measures to Predict Real-World Outcomes. Current Directions in Psychological Science, 22(1), 45–50. https://doi.org/10.1177/0963721412469394.CrossRefGoogle Scholar
  8. Berns, G., & Moore, S. E. (2010). A Neural Predictor of Cultural Popularity (SSRN Scholarly Paper ID 1742971). Rochester: Social Science Research Network. http://papers.ssrn.com/abstract=1742971
  9. Boksem, M. A. S., & Smidts, A. (2015). Brain Responses to Movie Trailers Predict Individual Preferences for Movies and Their Population-Wide Commercial Success. Journal of Marketing Research, 52(4), 482–492. https://doi.org/10.1509/jmr.13.0572.CrossRefGoogle Scholar
  10. Bridger, D. (2015). Decoding the Irrational Consumer: How to Commission, Run and Generate Insights from Neuromarketing Research (Marketing Science Series). London/Philadelphia: Kogan Page.Google Scholar
  11. Critchley, H. D., Elliott, R., Mathias, C. J., & Dolan, R. J. (2000). Neural Activity Relating to Generation and Representation of Galvanic Skin Conductance Responses: A Functional Magnetic Resonance Imaging Study. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 20(8), 3033–3040.Google Scholar
  12. Dijksterhuis, A., Bos, M. W., Nordgren, L. F., & van Baaren, R. B. (2006). On Making the Right Choice: The Deliberation-Without-Attention Effect. Science, 311(5763), 1005–1007. https://doi.org/10.1126/science.1121629.CrossRefGoogle Scholar
  13. Ekman, P., & Friesen, W. V. (1971). Constants Across Cultures in the Face and Emotion. Journal of Personality and Social Psychology, 17(2), 124–129. https://doi.org/10.1037/h0030377.CrossRefGoogle Scholar
  14. Evans, W. (2012). Eye Tracking Online Metacognition: Cognitive Complexity and Recruiter Decision Making. The Ladders, 1(1), 5.Google Scholar
  15. Falk, E. B., Berkman, E. T., Mann, T., Harrison, B., & Lieberman, M. D. (2010). Predicting Persuasion-Induced Behavior Change from the Brain. The Journal of Neuroscience, 30(25), 8421–8424. https://doi.org/10.1523/JNEUROSCI.0063-10.2010.CrossRefGoogle Scholar
  16. Falk, E. B., Berkman, E. T., & Lieberman, M. D. (2012). From Neural Responses to Population Behavior Neural Focus Group Predicts Population-Level Media Effects. Psychological Science, 23(5), 439–445. https://doi.org/10.1177/0956797611434964.CrossRefGoogle Scholar
  17. Falk, E. B., Morelli, S. A., Locke Welborn, B., Dambacher, K., & Lieberman, M. D. (2013). Creating Buzz the Neural Correlates of Effective Message Propagation. Psychological Science, 24(7), 1234–1242. https://doi.org/10.1177/0956797612474670.CrossRefGoogle Scholar
  18. Falk, E. B., O’Donnell, M. B., Tompson, S., Gonzalez, R., Cin, S. D., Strecher, V., Cummings, K. M., & An, L. (2015, September). Functional Brain Imaging Predicts Public Health Campaign Success. Social Cognitive and Affective Neuroscience, nsv108. doi:https://doi.org/10.1093/scan/nsv108.
  19. Genco, S. J., Pohlmann, A. P., & Steidl, P. (2013). Neuromarketing for Dummies. Mississauga: John Wiley & Sons.Google Scholar
  20. Glimcher, P. W. (2014). Introduction to Neuroscience. Neuroeconomics, 63–75. Elsevier. http://linkinghub.elsevier.com/retrieve/pii/B978012416008800005X.
  21. Heinonen, J., Numminen, J., Hlushchuk, Y., Antell, H., Taatila, V., & Suomala, J. (2016). Default Mode and Executive Networks Areas: Association with the Serial Order in Divergent Thinking (E. A. Stamatakis, Ed.). PLOS ONE, 11(9), e0162234. doi:https://doi.org/10.1371/journal.pone.0162234.
  22. Iyengar, S. S., & Lepper, M. R. (2000). When Choice Is Demotivating: Can One Desire Too Much of a Good Thing? Journal of Personality and Social Psychology, 79(6), 995–1006. https://doi.org/10.1037//0022-3514.79.6.995.CrossRefGoogle Scholar
  23. Levy, D. J., & Glimcher, P. W. (2012). The Root of All Value: A Neural Common Currency for Choice. Current Opinion in Neurobiology, 22(6), 1027–1038. https://doi.org/10.1016/j.conb.2012.06.001.CrossRefGoogle Scholar
  24. Mattson, J., & Simon, M. (1996). The Pioneers of NMR and Magnetic Resonance in Medicine: The Story of MRI. Ramat Gan/Jericho: Bar-Ilan University Press; published in the U.S.A. by Dean Books Co.Google Scholar
  25. Naseer, N., & Hong, K.-S. (2013). Classification of Functional Near-Infrared Spectroscopy Signals Corresponding to the Right- and Left-Wrist Motor Imagery for Development of a Brain–Computer Interface. Neuroscience Letters, 553(October), 84–89. https://doi.org/10.1016/j.neulet.2013.08.021.CrossRefGoogle Scholar
  26. Naseer, N., Hong, M. J., & Hong, K.-S. (2014). Online Binary Decision Decoding Using Functional Near-Infrared Spectroscopy for the Development of Brain–Computer Interface. Experimental Brain Research, 232(2), 555–564. https://doi.org/10.1007/s00221-013-3764-1.CrossRefGoogle Scholar
  27. Plassmann, H., O’Doherty, J., Shiv, B., & Rangel, A. (2008). Marketing Actions Can Modulate Neural Representations of Experienced Pleasantness. Proceedings of the National Academy of Sciences, 105(3), 1050–1054. https://doi.org/10.1073/pnas.0706929105.CrossRefGoogle Scholar
  28. Rayner, K. (1998). Eye Movements in Reading and Information Processing: 20 Years of Research. Psychological Bulletin, 124(3), 372–422. https://doi.org/10.1037/0033-2909.124.3.372.CrossRefGoogle Scholar
  29. Ruff, C. C., & Huettel, S. A. (2014). Experimental Methods in Cognitive Neuroscience. Neuroeconomics, 77–108. Elsevier. http://linkinghub.elsevier.com/retrieve/pii/B9780124160088000061
  30. Shapiro, S., MacInnis, D. J., & Heckler, S. E. (1997). The Effects of Incidental Ad Exposure on the Formation of Consideration Sets. Journal of Consumer Research, 24(1), 94–104. https://doi.org/10.1086/209496.CrossRefGoogle Scholar
  31. Suomala, J., Palokangas, L., Leminen, S., Westerlund, M., Heinonen, J., & Numminen, J. (2012, December). Neuromarketing: Understanding Customers' Subconscious Responses to Marketing. Technology Innovation Management Review, 2, Recent Research, 12–21.Google Scholar
  32. Venkatraman, V., Clithero, J. A., Fitzsimons, G. J., & Huettel, S. A. (2012). New Scanner Data for Brand Marketers: How Neuroscience Can Help Better Understand Differences in Brand Preferences. Journal of Consumer Psychology, 22(1), 143–153. https://doi.org/10.1016/j.jcps.2011.11.008.CrossRefGoogle Scholar
  33. Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., Hershfield, H. E., Ishihara, M., & Winer, R. S. (2015). Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling. Journal of Marketing Research, 52(4), 436–452. https://doi.org/10.1509/jmr.13.0593.CrossRefGoogle Scholar
  34. Walla, P., Brenner, G., & Koller, M. (2011). Objective Measures of Emotion Related to Brand Attitude: A New Way to Quantify Emotion-Related Aspects Relevant to Marketing (W. El-Deredy, Ed.). PLoS ONE, 6(11), e26782. doi:https://doi.org/10.1371/journal.pone.0026782.
  35. Zurawicki, Leon. 2010. Neuromarketing. Berlin/Heidelberg: Springer Berlin Heidelberg. http://link.springer.com/10.1007/978-3-540-77829-5

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Jyrki Suomala
    • 1
  1. 1.Laurea University of Applied Sciences, NeuroLabVantaaFinland

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