Price played an important role in most purchases. Buying behavior was strongly determined by consumers' price expectations. Emotion as a research hotspot was demonstrated to be ubiquitous in marketing and influenced purchase processing as well. This study addressed interests upon whether emotion arousal would influence consumers' price perceptions and their willingness to purchase. Compared to such emotion researches which normally adopted emotional pictures as priming stimuli, we creatively employed a two-player “Finger Play” (FP) game without monetary gains or losses to arouse subjects' emotion in the experiment. A 2 (FP Game Results: Continuous Win vs. Continuous Lose) by 2 (Price Conditions: High Price vs. Low Price) Event-Related Potentials (ERPs) experiment was designed to investigate whether game results would arouse different emotions and influence subjects' perception of product price. Both behavioral and ERP results indicated that subjects' price perception was deeply impacted by emotions induced from continuous win/lose experiences.

It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram (EEG) signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding (t-SNE) neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions (whether to accept the brand extension or not). The recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory (LSTM) layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis (PCA) network, a recurrent independent component correlation algorithm [independent component analysis (ICA)] network, a t-SNE support vector machine (SVM) network, a t-SNE back propagation (BP) neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency.

In business practice, companies prefer to find highly attractive commercial spokesmen to represent and promote their products and brands. This study mainly focused on the investigation of whether female facial attractiveness influenced the preference attitudes of male subjects toward a no-named and unfamiliar logo and determined the underlying reasons via neuroscientific methods. We designed two ERP (event-related potential) experiments. Experiment 1 comprised a formal experiment with facial stimuli. The purpose of experiment 2 was to confirm whether the logos that were used did not present a significant difference for the subjects. According to the behavioural results of experiment 1, when other conditions were not significantly different, the preference degree of the logos correlated with attractive female faces was increased compared with the logos correlated with unattractive faces. Reasons to explain these behavioural phenomena were identified via ERP measures, and preference cross-category transfer mainly caused the results. Additionally, the preference developed associated with emotion. This study is the first to report a novel concept referred to as the “Preference Cross-Category Transfer Effect”. Moreover, a three-phase neural process of the face evaluation subsequently explained how the cross-category transfer of preference occurred and influenced subject preference attitude toward brand logos.