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Brand trust and decision-making

Master’s thesis research by Berkay İlber Tunakan, supervised by Prof. Dr. Aydın Akan.

State of the Art

Trust is not a single feeling, nor the work of a single brain region. Reviews of the biology of trust show that the decision relies on the basal ganglia, linked to reward expectation, limbic structures that process threat and uncertainty, and frontal regions that carry out evaluation, all working together [1]. An EEG study using an investment game likewise found trust-related activity spread across frontal, fronto-central and occipital areas, and concluded that no single brain region responds to trust on its own [2]. Prior reputation is also known to change how much people rely on the feedback they later receive [1].

With brands, the picture becomes richer. Knowing and liking a brand is not the same as trusting it. Consumer research shows that brand familiarity shapes the attitudes that lead to investment decisions [5], that brand trust and familiarity each affect brand attachment [6], and that some brand traits feed trust more while others feed emotional closeness more [7]. Scales built to measure brand trust define it as a construct with several sub-dimensions [8].

The main gap lies in how EEG studies of trust are designed. Most work with labels defined before the experiment, such as “high / low trust” or “trustworthy / untrustworthy”, and use the brain signal to predict them [3, 4]. This squeezes the components inside trust, namely expectation, risk, remembered experience and felt closeness, into a single label, and leaves open whether trust in a familiar brand follows a different neural route than trust in a new company.

Excellence

Objectives

  • O1. Identify which EEG patterns recorded at the moment of a trust decision group together on their own, without labels imposed in advance.
  • O2. Relate these data-driven clusters to brand familiarity, likeability, the trustworthiness a brand has shown, the decision made and its outcome.
  • O3. Observe the building, maintenance and breakdown of trust towards real brands within a single experiment.

Ambition and Progress Beyond the State of the Art

  • Moving from label-driven classification to unsupervised discovery lets the components of trust emerge from the data instead of being defined from the outside.
  • Using well-known real brands, rather than abstract partners, connects the neuroscience of trust with consumer research on familiarity, liking and brand attachment.
  • An in-house experiment interface that aligns EEG with decision moments at millisecond precision provides a reusable platform for decision-making studies in the lab.

Methodology

Participants play an investment game featuring well-known brands from different sectors. On each round they decide whether to invest in a brand and then see the outcome. Some brands behave consistently while others let that trust down over time. EEG is aligned with each decision moment by a web-based interface we developed, and participants also complete a brand trust scale. In the analysis, unsupervised learning methods group EEG patterns by their similarity, and the resulting clusters are compared with behaviour, scale scores and brand characteristics.

References

  1. Riedl, R., Javor, A. (2012). The biology of trust: Integrating evidence from genetics, endocrinology, and functional brain imaging. Journal of Neuroscience, Psychology, and Economics, 5(2), 63–91. doi:10.1037/a0026318
  2. Wang, M., Hussein, A., Rojas, R. F., Shafi, K., Abbass, H. A. (2018). EEG-based neural correlates of trust in human-autonomy interaction. IEEE Symposium Series on Computational Intelligence (SSCI), 350–357. doi:10.1109/SSCI.2018.8628649
  3. Choo, S., Nam, C. S. (2022). Detecting human trust calibration in automation: A convolutional neural network approach. IEEE Transactions on Human-Machine Systems, 52(4), 774–783. doi:10.1109/THMS.2021.3137015
  4. Orozco, J. A., Artemiadis, P. (2024). Extracting human levels of trust in human–swarm interaction using EEG signals. IEEE Transactions on Human-Machine Systems, 54(2), 182–191. doi:10.1109/THMS.2024.3356421
  5. Md Husin, M., Aziz, S., Bhatti, T. (2022). The impact of brand familiarity, perceived trust and attitude on investors’ decision-making in Islamic stock market. Journal of Islamic Marketing. doi:10.1108/JIMA-04-2020-0093
  6. Barijan, D., Ariningsih, E. P., Rahmawati, F. (2021). The influence of brand trust, brand familiarity, and brand experience on brand attachments. Journal of Digital Marketing and Halal Industry, 3(1). doi:10.21580/jdmhi.2021.3.1.7440
  7. Sung, Y., Kim, J. (2010). Effects of brand personality on brand trust and brand affect. Psychology & Marketing, 27(7), 639–661. doi:10.1002/mar.20349
  8. Roest, H., De Graaf, P. (2023). In brands we trust: The development and validation of a contemporary brand trust scale. Proceedings of the European Marketing Academy (EMAC), 52nd.