The Application of Mobile fNIRS in Marketing Research—Detecting the “First-Choice-Brand” Effect

Krampe, Caspar; Gier, Nadine R.; Kenning, Peter

doi:10.3389/fnhum.2018.00433

Cited by 47 publications

(52 citation statements)

References 65 publications

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“…For example, in food-related value-based decisionmaking increased neural activity in brain areas of the dlPFC have been identified for participants that execute a greater selfcontrol on their food choice (Hare et al, 2009). Simultaneously, a reduced neural activity of the dlPFC has been associated for brand-related decisions that require less strategy-based reasoning (Deppe et al, 2005;Schaefer and Rotte, 2007;Koenigs and Tranel, 2008;Krampe et al, 2018a). First shown in the study by Deppe et al (2005), decision sets that include the participants favoured brand, emotionalise the choice, which allows a quicker, straightforward and less complex decision-making process in favour of the preferred product, a replicated and robust effect called cortical relief effect.…”

Section: Introductionmentioning

confidence: 99%

“…In conclusion, preferred choice options seem to be easier to process, which makes it easier to choose for the favoured product during a decision-making process that seem to be less cognitively controlled and assumed to elicit a reduced activity in brain regions of the dlPFC (Deppe et al, 2005;Schaefer and Rotte, 2007;Koenigs and Tranel, 2008;Krampe et al, 2018a). Less self-controlled decisions might, therefore, result in more impulsive decision-making, choosing the option that is preferentially presented in a choice situation (Boettiger et al, 2007;Kable and Glimcher, 2007;Hare et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale – A fNIRS Approach

2020

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The (re-)launch of products is frequently accompanied by point-of-sale (PoS) marketing campaigns in order to foster sales. Predicting the success of these merchandising elements at the PoS on sales is of interest to research and practice, as the misinvestments that are based on the fragmented PoS literature are tremendous. Likewise, the predictive power of neuropsychological methods has been demonstrated in various research work. Nevertheless, the practical application of these neuropsychological methods is still limited. In order to foster the application of neuropsychological methods in research and practice, the current research work aims to explore, whether mobile functional near-infrared spectroscopy (fNIRS) – as a portable neuroimaging method – has the potential to predict the success of PoS merchandising elements by rendering significant neural signatures of brain regions of the dorsolateral prefrontal cortex (dlPFC), highlighting its potential to forecast shoppers’ behaviour aka sales at the PoS. Building on previous research findings, the results of the given research work indicate that the neural signal of brain regions of the dlPFC, measured with mobile fNIRS, is able to predict actual sales associated with PoS merchandising elements, relying on the cortical relief effect. More precisely, the research findings support the hypothesis that the reduced neural activity of brain regions associated with the dlPFC can predict sales at the PoS, emphasising another crucial neural signature to predict shoppers’ purchase behaviour, next to the frequently cited reward association system. The research findings offer an innovative perspective on how to design and evaluate PoS merchandising elements, indicating fruitful theoretical and practical implications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale – A fNIRS Approach

2020

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“…In addition to these methods, one new approach for measuring brain functioning is functional near-infrared spectroscopy (fNIRS), which measures changes in oxygenated and deoxygenated hemoglobin (León-Carrión & León-Domínguez, 2012). Within decision neuroscience, fNIRS has been used to study branding (Krampe et al, 2018), risk-taking behavior in the BART task (Cazzell et al, 2012), and purchasing behavior (Cakir et al, 2018).…”

Section: Methodological Challengesmentioning

confidence: 99%

Decision Neuroscience and Neuroeconomics: Recent Progress and Ongoing Challenges

Dennison¹,

Sazhin

Smith

2020

Preprint

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In the past decade, decision neuroscience and its subfield of neuroeconomics have developed many new insights in the study of decision making. This review provides a comprehensive update on how the field has advanced in this time. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across psychology, neuroscience, and economics. We will first review progress in basic value processes involved in reward learning, explore-exploit decisions, risk and uncertainty, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choice, and exciting developments in prediction and neuroforecasting of future decisions. Finally, we will consider overall progress in the field of decision neuroscience in reconciling past challenges, identifying new challenges, and recent exciting applications of this research.

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“…EEG has better temporal resolution than fNIRS, which is a relatively new neuroimaging technique in neuromarketing research. However, recent fNIRS research is still in the substantial validation phase [13,14]. EEG is most commonly used in neuromarketing research due to its advantages that are detailed in the next subsection.…”

Section: Bcismentioning

confidence: 99%

“…We used the PSD technique in this study to divide each EEG signal into four different frequency bands: theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). The Python signal processing toolbox (MNE) was used for PSD calculation, and the average power over the frequency bands was computed to build a feature using the avgpower function in the MNE toolbox.…”

Section: Psdmentioning

confidence: 99%

Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020

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The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

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The Application of Mobile fNIRS in Marketing Research—Detecting the “First-Choice-Brand” Effect

Cited by 47 publications

References 65 publications

Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale – A fNIRS Approach

Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale – A fNIRS Approach

Decision Neuroscience and Neuroeconomics: Recent Progress and Ongoing Challenges

Deep Learning for EEG-Based Preference Classification in Neuromarketing

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