From E-Commerce to the Metaverse: A Neuroscientific Analysis of Digital Consumer Behavior.

The growing interest in consumer behavior in the digital environment is leading scholars and companies to focus on consumer behavior and choices on digital platforms, such as the metaverse. On this immersive digital shopping platform, consumer neuroscience provides an optimal opportunity to explore consumers' emotions and cognitions. In this study, neuroscience techniques (EEG, SC, BVP) were used to compare emotional and cognitive aspects of shopping between metaverse and traditional e-commerce platforms. Participants were asked to purchase the same product once on a metaverse platform (Second Life, SL) and once via an e-commerce website (EC). After each task, questionnaires were administered to measure perceived enjoyment, informativeness, ease of use, cognitive effort, and flow. Statistical analyses were conducted to examine differences between SL and EC at the neurophysiological and self-report levels, as well as between different stages of the purchase process. The results show that SL elicits greater cognitive engagement than EC, but it is also more mentally demanding, with a higher workload and more memorization, and fails to elicit a strong positive emotional response, leading to a poorer shopping experience. These findings provide insights not only for digital-related consumer research but also for companies to improve their metaverse shopping experience. Before investing in the platform or creating a digital retail space, companies should thoroughly analyze it, focusing on how to enhance users' cognition and emotions, ultimately promoting a better consumer experience. Despite its limitations, this pilot study sheds light on the emotional and cognitive aspects of metaverse shopping and suggests potential for further research with a consumer neuroscience approach in the metaverse field.

Neuroselling: applying neuroscience to selling for a new business perspective. An analysis on teleshopping advertising.

This paper presents an innovative research project that aims to study the emotional factors influencing decision-making elicited by infomercials, a powerful sales technique that uses emotional communication to engage viewers, capture attention, and build trust. Using cutting-edge consumer neuroscience techniques, this study focuses on the identification of the variables that most impact the Call-to-Action and Purchase Intention. Forty participants were selected and divided into two groups, with each group exposed to one of two infomercials (condition A = male seller; condition B = female seller). EEG signals were recorded as well as Eye-tracking data. After the viewing, participants completed a self-report questionnaire. Results show that seller characteristics such as Performance and Trustworthiness, as well as Neurophysiological variables such as Approach-Withdrawal Index, Willingness to Pay, Attention and Engagement, significantly impact the final Call-to-Action, Purchase Intention, and infomercial Likeability responses. Moreover, eye-tracking data revealed that the more time is spent observing crucial areas of the infomercial, the more it will increase our Willingness to Pay and our interest and willingness to approach the infomercial and product. These findings highlight the importance of considering both the Seller attributes and the consumers' Neurophysiological responses to understand and predict their behaviors in response to marketing stimuli since they all seem to play a crucial role in shaping consumers' attitudes and purchase intentions. Overall, the study is a significant pilot in the new field of neuroselling, shedding light on crucial emotional aspects of the seller/buyer relationship and providing valuable insights for researchers and marketers.

From Coaching to Neurocoaching: A Neuroscientific Approach during a Coaching Session to Assess the Relational Dynamics between Coach and Coachee-A Pilot Study.

Life transitions represent moments characterized by changes that can profoundly influence individual life trajectories and subjective well-being. Recently, career coaching has become an important method of helping people expand their self-awareness, facilitate personal development, and increase their performance in the school-to-work transition. Although previous studies have confirmed that one of the most important keys to the success of a coaching program is the quality of the relationship between coach and coachee, there is a lack of knowledge regarding how to objectively measure it. In this pilot study, we adopted a neuroscientific approach to introduce objective measures of the relationship between coach and coachee through the phases of a coaching session. A sample of 14 university students and a professional coach participated in career-coaching sessions while their affective states were measured by recording brain (EEG) and physiological (Skin conductance) activity. Electroencephalographic indicators of valence, arousal, and engagement showed differences between session phases, highlighting the possibility of a neurophysiological measurement of relational dynamics. Our results provide initial evidence that neurophysiological activity can be considered a way to understand differences in the coach-coachee relationship, thereby providing information on the effectiveness of coaching interventions and facilitating a better life transition from school to work.

Can Traditional Food Product Communication Convey Safety to the Younger Generations? The Role of Sustainable Packaging.

Traditional food products (TFPs) represent a defining part of one's culture, identity, and heritage with crucial economic, cultural, and environmental benefits in society. Younger generations have a positive idea of TFPs, even if this does not lead to actual purchase, possibly due to the fact that they are often perceived to not meet safety criteria. This study focuses on product communication (CP) and packaging referring to the territory (PT) and to sustainability (SP) in order to verify if these have a direct or mediated impact on perceived product safety (PPS). A structural equation model was conducted on a sample of 1079 young Italian cheese consumers. The results allowed us to confirm the hypothesized impact of CP on PPS through the mediation of PT and, particularly, SP. SP has a crucial communicative role in the model, demonstrating the ability to enhance the perception of the safety of TFPs. This research adds to the knowledge in the field of TFPs, focusing on communication and sustainable packaging as crucial factors conveying healthiness, nutritiousness, and perceived safety, consequently leading to a greater diffusion of the products themselves in the market.

The Role of the Emotional Sequence in the Communication of the Territorial Cheeses: A Neuromarketing Approach.

Over the past few years, many studies have shown how territoriality can be considered a driver for purchasing agri-food products. Products with certification of origin are perceived as more sustainable, safer and of better quality. At the same time, producers of traditional products often belong to small entities that struggle to compete with large multinational food corporations, having less budget to allocate to product promotion. In this study, we propose a neuromarketing approach, showing how the use of these techniques can help in choosing the most effective commercial in terms of likeability and ability to activate mnemonic processes. Two commercials were filmed for the purpose of this study. They differed from each other in terms of emotional sequence. The first aimed primarily at eliciting positive emotions derived from the product description. The second aimed to generate negative emotions during the early stages, highlighting the negative consequences of humans' loss of contact with nature and tradition and then eliciting positive emotions by presenting cheese production using traditional techniques as a solution to the problem. Based on the literature on the emotional sequences in social advertising, we hypothesised that the second commercial would generate an overall better emotional reaction and activate mnemonic processes to a greater extent. Our results partially support the research hypotheses, providing useful insights both to marketers and for future research on the topic.

Dairy Products with Certification Marks: The Role of Territoriality and Safety Perception on Intention to Buy.

Over the years, the territorial origins of agri-food products have become a consolidated marketing model which stand as an alternative to mass production. References to territory, whether on packaging or in advertising, have become an increasingly popular way for marketers to differentiate products, by attributing specific characteristics to them, derived from specific cultural identities and traditions. The aim of this study is to capture the possible differences between two groups, Italian and French, in the perception and intention to buy products with certification marks. We tested a multi-group structural equations model, assessing the mediation of the Perceived Product Safety (PPS) between Packaging with reference to Territoriality (PT) and Intention to Buy (IB). Our findings show that in both groups PT has a positive association with IB and PPS and that PPS has a positive association with IB. The difference is the mediation of PPS, present only in the Italian group. This opens important considerations on the role of the perception of safety, particularly in the pandemic period, in the presentation of products, particularly in products with certification marks linked to sustainability and territoriality.

Job Assessment Through Bioelectrical Measures: A Neuromanagement Perspective.

During recruitment, human resource departments face two challenges: finding the right people for the job and attracting talent. Therefore, the hiring process requires both the ability to communicate a good company brand image and to understand the characteristics and potential of candidates. In this study, we used a neuroscientific approach to measure the experience of candidates during a job interview. The experiment involved 30 participants that individually took part in a job interview lasting 40 min. During the experiment, their engagement and stress levels were measured in real-time with skin conductance and electroencephalographic (EEG) data. From the results, we identified both the most stressful phases (the second and the fourth parts, relating to the explanation of the job and remuneration) and the most engaging phases (the first and the third phases, relating to the presentation of the company and the explanation of the career process) of the interview, suggesting implications for the assessment process. This study is a contribution to the field of neuromanagement, as a neuroscientific approach was applied to management issues in light of work and organizational psychology.

Assessing the Emotional Response in Social Communication: The Role of Neuromarketing.

Social advertising is designed to have an impact on the behavior of the target audience to improve the welfare of both the individuals and the society. The challenge for social marketing is to respond to the exchange process in a social perspective, considering that non-profit actions are perceived as intangible since they deal with services. As donations, the neuroscience applied to consumer behavior is an added value since it offers elements explaining the reactions of the individuals to emotional contents. Understanding the emotions in the moment in which they are felt allows to understand the experimentation of a message by individuals and to understand the possibility that the message can change the behavior of the target audience. The aim of the study is to assess the effectiveness of the Unicef bequest campaign in terms of emotional response, comparing different creative proposals to optimize communication, applying neuromarketing tools to the social area. The experiment involved 70 participants (35 males; 35 females; mean age 68.94 years) and compared two different spots and flyers. The progeny factor was introduced to assess the different impacts of bequests depending on the presence or absence of potential heirs. The neuromarketing tools such as electroencephalography (EEG), skin conductance (SC), and eye-tracker were used for instrumentation purposes. Analysis of the two spots showed statistically significant differences in both the Approach-Withdrawal Index (AWI), for the cognitive involvement, and the SC, the emotional activation indicator, particularly for those not having children (target audience) and in a specific spot that linked the possibility to live after death. The detection of the emotional responses through neuromarketing tools, associated with the non-profit communication, resulted particularly effective and verified an increment of 35% of the donations. Analyses performed with neuromarketing techniques allowed to understand both emotional intensity and cognitive involvement and to understand the best solution, according to the target audience and the aim of Unicef.

ESB: A low-cost EEG Synchronization Box.

Electroencephalography (EEG) is a neuroimaging technique with a temporal resolution in the millisecond scale. Popular ERPs and ERD/ERS functions, as well as EEG-fRMI data and hyperscanning methods requires a proper temporal alignment (namely, synchronization) with stimulus onsets and other devices. Hardware-based synchronization, based on a SYNC signal injected into the device, ensures a reliable timing. In this paper we describe the design, test and validation of an EEG Synchronization Box (ESB), able to condition and distribute a SYNC signal (analog and digital) to different devices simultaneously. ESB can be easily built by individuals with basic soldering skills and represents a cost-effective solution to the available commercial synchronization boxes, while preserving similar electrical and functional features.

Applying machine learning EEG signal classification to emotion­related brain anticipatory activity.

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach.

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a 'linear discriminant classifier' to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional "mental telecommunications" between physically and sensorially separated participants.

A new method to detect event-related potentials based on Pearson's correlation.

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.  Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.  The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP's waveform, these waveforms being time- and phase-locked.  In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson's correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase-in consonance with the stimuli-in EEG signal correlation over all channels.  This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.  These hidden components seem to be caused by variations (between each successive stimulus) of the ERP's inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology.  The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language.

EEG correlates of social interaction at distance.

This study investigated EEG correlates of social interaction at distance between twenty-five pairs of participants who were not connected by any traditional channels of communication. Each session involved the application of 128 stimulations separated by intervals of random duration ranging from 4 to 6 seconds. One of the pair received a one-second stimulation from a light signal produced by an arrangement of red LEDs, and a simultaneous 500 Hz sinusoidal audio signal of the same length. The other member of the pair sat in an isolated sound-proof room, such that any sensory interaction between the pair was impossible. An analysis of the Event-Related Potentials associated with sensory stimulation using traditional averaging methods showed a distinct peak at approximately 300 ms, but only in the EEG activity of subjects who were directly stimulated. However, when a new algorithm was applied to the EEG activity based on the correlation between signals from all active electrodes, a weak but robust response was also detected in the EEG activity of the passive member of the pair, particularly within 9 - 10 Hz in the Alpha range. Using the Bootstrap method and the Monte Carlo emulation, this signal was found to be statistically significant.