An integrated-mental brainwave system for analyses and judgments of consumer preference

https://doi.org/10.1016/j.tele.2016.11.002Get rights and content

Highlights

  • Appearance of products is an important topic when they have similar quality.

  • Consumers’ thoughts obtained from their brains can grasp their actual preference.

  • Consumers’ brainwaves are used for the analyses and judgments of their preference.

  • Partial least squares are used to ensure tests accurately meet consumers’ thoughts.

Abstract

With the competition expansion of consumer markets, product appearance becomes important topics when the products, which consumers make decisions to select, have similar quality and content. Hence, enterprises and companies spend a lot of time and money, and pay more attention to enhance product appearances to further attract other consumers. In comparison with using only questionnaires, obtaining consumers’ thoughts directly from their brains can accurately grasp their actual preference. Undoubtedly, it can provide effective and precious decisions for enterprises and companies. In this study, consumers’ brainwaves integrated with mental tasks are extracted through a wearable, portable, wireless electroencephalography (EEG) device. The extracted EEG data are then trained using perceptron learning to make the judgments of integrated mental works well for each subject. They are then applied to the analyses and judgments of consumer preference. Finally, questionnaires are also used as the references on the training process. They are combined with brainwaves data to create a prediction model that can significantly improve accuracy. The partial least squares are used to compare the correlation between different factors in the model and ensure the test can accurately meet consumers’ thoughts.

Introduction

It is a key factor to make products stand out in the market and catch the attention of consumers for increasing profits and creating a storm that sweeps the consumer market. There is a recognition that product design is emerging as a key marketing element (Dawar and Chattopadhyay, 2002, Luo and Tung, 2007, Noble and Kumar, 2010). The visual appearance of a product plays a significant role in determining responses of consumers (Crilly et al., 2004) and is recognized as an opportunity to create a differential advantage in the marketplace (Creusen and Schoormans, 2005).

Many factors affect consumer selections before purchasing. The various aspects of product design play a vital role in the interaction between consumer and product, thereby potentially affecting consumer preference (Hoyer et al., 2010, Kumar et al., 2010). Indeed, consumer preference can be affected during the buying process by the appearance of packaging. Therefore, packaging is crucial because it can create an emotional attachment in the minds of consumers from the very first glance (Harith et al., 2014). Generally, enterprises will use questionnaires or telephone surveys to investigate consumer preference, and then combine this with sales data to help managers make strategies. There is a difference, however, between interview responses and actual buying actions because the emotion during an interview may be different from looking at the physical product. Invalid or bad results from a questionnaire can lead decision makers to make the wrong decision (Ergu and Kou, 2012, Hsu, 2015c).

The goal of the study was to accurately grasp the consumer preference for product appearance. For example, when faced with different products with the same price and similar features, consumers may choose based on the appearance of the products. Sometimes consumers have difficulty making decisions by themselves. It is generally accepted that the look of a product or its package has an important effect on consumer choice at the point of purchase (Garber, 1995). In the situation where consumers are not considering price or function, product appearance plays the most important role. Garber’s (1995) model emphasized the effects of product appearance on consumer attention (Creusen and Schoormans, 2005, Talke et al., 2009). This also makes it more difficult to establish a reference, because there is no way of understanding consumer preferences. In the case of unpredictable preference, it is important to know how to help companies obtain more powerful evaluation criteria for them to design and build the direction of product appearance. To achieve this goal, a system to detect brainwaves has been built to help provide a deeper analysis of consumer preference (Chen and He, 2013, Hsu, 2016).

To efficiently and accurately understand the emotional reactions and degrees of concentration of consumers when they are making response, this system uses a brain-computer interface device to detect consumer brainwaves. It generates electroencephalogram (EEG) data to help analyze and discuss the corresponding relationships between mood swings and the replied answers (Tsai and Shih, 2013, Hsu, 2015a, Hsu, 2015b). Depending on the relationships, companies can craft different marketing strategies to make product sales achieve maximum benefits. Compared to those complex and inconvenient brainwave devices, which are time-consuming and uncomfortable to wear, the device adopted for this study can more quickly and efficiently test every subject. In addition, the system is executed on a personal computer, so it can be performed anytime, anywhere. The computer system quickly generates analysis results and provides reference data of the testing.

We focused on the use of an EEG to detect and integrate subjects’ the changes of degree of mental tasks in this study. With a series of training processes, the study used incremental steps to assess consumer reactions while answering different questions and quickly analyzed the results (Hsu, 2011, Hsu, 2017a). In the future, this system is expected to be used to precisely detect the preference of products from consumers and contribute the data to help companies create more appealing packaging for their products. It not only reduces the risk of releasing a new product design, but also catches consumers’ eyes more precisely.

Section snippets

Neurons and brainwaves

Numerous neurons communicate with each other in a human brain. The action potential generated after activation is transmitted to the synapse by means of axons (Lodish et al., 2000, Hsu, 2017b). The release of neurotransmitters can cause the next neuron to generate postsynaptic potentials, which further stimulate the neuron to generate action potentials. Brainwave intensity under normal circumstances is less than 100 μV, usually just dozens of μV, and the frequency ranges between 0.1 Hz and 40 Hz.

Impacts of emotion on decision-making

The emotional impacts of brainwaves are determined by a lot of factors, within them the most important ones include alpha (α), theta (θ), and gamma (γ) waves. The studies shown that in the case of the good mood, alpha (α) waves will show a positive correlation, while theta (θ) waves are used as the basis for determining shapes and colors (Bastiaansen et al., 2008). Marketing literatures note that observing pleasant or unpleasant advertising leads to an increase in theta (θ) and alpha (α) wave

Materials and methodologies

In this study, EMOTIV EPOC & EEG are applied to acquiring brainwave data of the subjects. The acquired brainwave signals are then stored in the databases for further uses to regress the degree of preference for each subject.

Experimental results

The results shown in Table 1 consist of two parts: The first part is the reports of processed brainwave data and external options of the subjects, which represent the choice of subjects made by them. The second and final one is the correlation between different factors in the model. Repeated testing was executed to identify the subjects’ preference intervals from their brainwave data.

Discussion

The study shows that the concentration situation of a subject can affect his/her degree of preference. When someone is fond of a thing, it is necessary to concentrate on that. It is clear that emotion is influenced by concentration and can aid to determine consumer preference in product design. Accordingly, this study used a model and PLS to prove the accuracy of this assertion. It is important for companies to understand consumers’ emotions regarding their product designs, as this will help

Conclusion

This study provided a way to detect the thoughts of consumers by combining the integrated mental-task brainwave data to increase the accuracy of product preference. This study used computer learning and training skills to automatically improve the quality of data, ensuring the actual performance of product surveys can lead to more accurate and precise decisions. Additionally, the PLS analysis method was used to identify the relationship of integrated mental tasks, including concentration and

Acknowledgements

The author would like to express his sincere appreciation for grants partially from MOST103-2410-H-194-070-MY2 and MOST105-2410-H-194-059-MY3, Ministry of Science and Technology, Taiwan. In addition, he also thanks the students, Jun-Yi Lu, Chih-Chia Chien, Meng-Chiu Hsieh, and Yu-Hsiang Wang, that they help to handle parts of the materials.

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