Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.
Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.
Purpose The purpose of this paper is to explore the domain relevance of a comprehensive yet almost overlooked theoretical framework for studying organic food purchase behavior in a global context. This conceptual paper argues that there exists an apparently powerful model in health behavior domain that may readily be brought into organic food purchase behavior research. The paper argues for domain relevance and proposes that Montano and Kasprzyk’s integrated behavior model may readily be used in organic food behavior studies with some relevant modification. Design/methodology/approach The paper follows an exploratory approach and shows how variables used in the past may be aggregated to the model in question. The challenge is addressed by following both the inductive and the deductive reasoning. Deductive reasoning calls for investigating whether such behavior may be classified as health behavior. Inductive reasoning calls for proving relevance of all the variables in the aforesaid model to the organic food research context. Findings The paper concludes that the Montano and Kasprzyk’s model is theoretically relevant to the organic food behavior domain. However, it is observed that the domain-specific operationalization is necessary for further empirical studies. Research limitations/implications Since the model was rarely tested empirically in predicting organic food purchase intention, the variable-specific relevance may not warrant the relevance of the whole model with intertwined relationships at the same time. Practical implications The paper may pave a way toward further empirical research and may also explain the apparent intention-behavior gap as often reported in literature. Originality/value The paper may provide a useful direction in future organic food purchase behavior studies by showing the domain relevance of an apparently powerful model, along with addition of some newer variables that may enrich the existing model.
The global demand for organic foods has inspired the academicians and practicing professionals to explore consumer purchase behavior in this sector. The multiple promises that organic foods hold for the future -like sustainable food production, food safety, food security, nutrition and reduction of green-house gases -all might have influenced the recent rise of behavioral research in the organic food sector. Interestingly, Bangladesh has been a producer of organic foods since the early '80s; however, only a handful of studies could be traced that actually studied consumer behavior in this sector. The current paper explored the important roles that organic foods might play in Bangladesh, synthesized findings of past studies under Bangladesh context, and justified probable areas that might be investigated in future. Therefore, plausible gaps were explored in the existing literature pertaining to Bangladesh context and a tentative research agenda for future researchers was proposed. JEL classification: M30, M31, M39Keywords: organic food purchase behavior; organic foods in Bangladesh; roles of organic foods in a developing country; behavior of organic foods consumer; green marketing and organic foods.
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