Measuring Neurophysiological Signals, Fixations and Self-report Data for Product Placement Effectiveness Assessment in Music Videos

  • Ana C. Martinez-LevyEmail author
  • Giulia Cartocci
  • Enrica Modica
  • Dario Rossi
  • Marco Mancini
  • Arianna Trettel
  • Fabio Babiloni
  • Patrizia Cherubino
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


Product placement is a marketing technique that, by inserting products into a narrative structure, constitutes a likely effective tool to increase the visibility and notoriety of a brand. For years, the opportunities for product placement in music videos were limited. Recently, there has been a growth of interest for this tool/advertising modality since the digital community allowed the possibility to move videos from television to the Internet. The scope of the present study is to investigate the effectiveness of the product placement in music videos. An electroencephalographic (EEG) index called mental effort (ME) has been analyzed, in addition to the emotional index (EI), calculated by the combination of galvanic skin response (GSR) and heart rate (HR) signals. Self-report responses have also been collected through an online questionnaire and interviews, since one experimental question was to investigate whether viewing a video containing a commercial product could influence the declared recall of the product inserted in it and the spontaneous recall of the video itself. Furthermore, fixations related to the product inserted in videos have been obtained by the eye-tracking technique (ET). Higher values of the ME (p = 0.016) and EI (p = 0.033) have been found for videos with product placement in comparison to videos without it. In addition, results show that the number of fixations affects the recall of the showed products (p < 0.001). These findings highlight that using product placement in famous singers’ music videos is an effective technique for prompting product recall and how it helps to focus the visual attention on them.


Product placement EEG Fixations Emotion Mental effort Recall 


  1. 1.
    Aricò, P., Borghini, G., Graziani, I., et al.: Towards a multimodal bioelectrical framework for the online mental workload evaluation. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3001–3004 (2014)Google Scholar
  2. 2.
    Auty, S., Lewis, C.: The ‘delicious paradox’: preconscious processing of product placements by children. In: The Psychology of Entertainment Media: Blurring the Lines Between Entertainment and Persuasion, pp. 117–133 (2004)Google Scholar
  3. 3.
    Baddeley, A.: Working Memory, Thought, and Action, vol. 45. OUP, Oxford (2007)Google Scholar
  4. 4.
    Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., et al.: A blind source separation technique using second-order statistics. IEEE Trans. Sig. Process. 45(2), 434–444 (1997)CrossRefGoogle Scholar
  5. 5.
    Benedek, M., Kaernbach, C.: A continuous measure of phasic electrodermal activity. J. Neurosci. Methods 190, 80–91 (2010)CrossRefGoogle Scholar
  6. 6.
    Berka, C., Levendowski, D.J., Lumicao, M.N., et al.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5), B231–B244 (2007)Google Scholar
  7. 7.
    Borghini, G., Astolfi, L., Vecchiato, G., et al.: Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci. Biobehav. Rev. 44, 58–75 (2014)CrossRefGoogle Scholar
  8. 8.
    Boucsein, W.: Electrodermal Activity. Springer Science & Business Media (2012)Google Scholar
  9. 9.
    Cartocci, G., Caratù, M., Modica, E., et al.: Electroencephalographic, heart rate, and galvanic skin response assessment for an advertising perception study: application to antismoking public service announcements. J. Vis. Exp. JoVE (126) (2017)Google Scholar
  10. 10.
    Cartocci, G., Maglione, A.G., Vecchiato, G., et al.: Mental workload estimations in unilateral deafened children. In: Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE, pp. 1654–1657, Aug 2015Google Scholar
  11. 11.
    Cartocci, G., Maglione, A.G., Rossi, D., et al.: The influence of different cochlear implant features use on the mental workload index during a word in noise recognition task. Int. J. Bioelectromagn. 18, 60–66 (2016)Google Scholar
  12. 12.
    Cartocci, G., Maglione, A.G., Modica, E., et al.: Is the younger the less effortful? An electroencephalographic comparison among consecutive generations of cochlear implant sound processors. Int. J. Bioelectromagn. 19(1), 11–17 (2017)Google Scholar
  13. 13.
    Cherubino, P., Cartocci, G., Modica, E., et al.: Wine tasting: how much is the contribution of the olfaction? In: International Conference on Computational Methods in Experimental Economics, pp. 199–209. Springer, Cham (2017)CrossRefGoogle Scholar
  14. 14.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)CrossRefGoogle Scholar
  15. 15.
    Di Flumeri, G., Aricó, P., Borghini, G., et al.: A new regression-based method for the eye blinks artifacts correction in the EEG signal, without using any EOG channel. In: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference, Aug 2016Google Scholar
  16. 16.
    González, M.B., García, T.R.: Imagen de marca y product placement, pp. 120–121. ESIC Editorial (2012)Google Scholar
  17. 17.
    Law, S., Braun, K.A.: I’ll have what she’s having: gauging the impact of product placements on viewers. Psychol. Mark. 17(12), 1059–1075 (2000)CrossRefGoogle Scholar
  18. 18.
    Lehmann, D., Michel, C.M.: Intracerebral dipole source localization for FFT power maps. Clin. Neurophysiol. 76(3), 271–276 (1990)CrossRefGoogle Scholar
  19. 19.
    Martinez-Levy, A., Cherubino, P., Cartocci, G.: Gender differences evaluation in charity campaigns perception by measuring neurophysiological signals and behavioural data. Int. J. Bioelectromagn. 19(1), 25–36 (2017)Google Scholar
  20. 20.
    Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23, 209–237 (2009)CrossRefGoogle Scholar
  21. 21.
    Modica, E., Cartocci, G., Rossi, D., et al.: Neurophysiological responses to different product experiences. Comput. Intell. Neurosci. 2018 (2018)Google Scholar
  22. 22.
    Nebenzahl, I.D., Secunda, E.: Consumers’ attitudes toward product placement in movies. Int. J. Advert. 12(1), 1–11 (2015)CrossRefGoogle Scholar
  23. 23.
    Nelli, R.P., Bensi, P.: Il product placement nelle strategie di convergenza della marca nel settore dell’intrattenimento, vol. 23. Vita e Pensiero (2007)Google Scholar
  24. 24.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)CrossRefGoogle Scholar
  25. 25.
    Plambeck, J.: Product placement grows in music videos. N. Y. Times 5, B8 (2010)Google Scholar
  26. 26.
    Posner, J., Russell, J.A., Peterson, B.S.: The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715–734 (2005)CrossRefGoogle Scholar
  27. 27.
    PQ Media.: Global Branded Entertainment Marketing Forecast 2018. (2018)Google Scholar
  28. 28.
    Russell, J.A., Barrett, L.F.: Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. J. Pers. Soc. Psychol. 76, 805–819 (1999)CrossRefGoogle Scholar
  29. 29.
    Russell, C.A., Norman, A.T., Heckler, S.E.: People and “their” television shows: an overview of television connectedness. In: The Psychology of Entertainment Media: Blurring the Lines Between Entertainment and Persuasion, pp. 275–290 (2004)Google Scholar
  30. 30.
    Scott, J., Craig-Lees, M.: Audience engagement and its effects on product placement recognition. J. Promot. Manag. 16(1–2), 39–58 (2010)CrossRefGoogle Scholar
  31. 31.
    Unsworth, N., Spillers, G.: Working memory capacity: attention control, secondary memory, or both? A direct test of the dual-component model. J. Mem. Lang. 62(4), 392–406 (2010)CrossRefGoogle Scholar
  32. 32.
    Van Reijmersdal, E.A., Neijens, P.C., Smit, E.G.: Effects of television brand placement on brand image. Psychol. Mark. 24(5), 403–420 (2007)CrossRefGoogle Scholar
  33. 33.
    Vecchiato, G., Maglione, A.G., Cherubino, P., et al.: Neurophysiological tools to investigate consumer’s gender differences during the observation of TV commercials. Comput. Math. Methods Med. 2014 (2014)Google Scholar
  34. 34.
    Wisniewski, M.G., Thompson, E.R., Iyer, N., et al.: Frontal midline θ power as an index of listening effort. NeuroReport 26(2), 94–99 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ana C. Martinez-Levy
    • 1
    • 3
    Email author
  • Giulia Cartocci
    • 2
    • 3
  • Enrica Modica
    • 2
  • Dario Rossi
    • 2
  • Marco Mancini
    • 3
  • Arianna Trettel
    • 3
  • Fabio Babiloni
    • 2
    • 3
    • 4
  • Patrizia Cherubino
    • 2
    • 3
  1. 1.Department of Communication and Social ScienceSapienza University of RomeRomeItaly
  2. 2.Department of Molecular MedicineSapienza University of RomeRomeItaly
  3. 3.BrainSigns SrlRomeItaly
  4. 4.College of Computer Science and TechnologyUniversity Hangzhou DianziHangzhouChina

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