Why he buys it and she doesn't – Exploring self-reported and neural gender differences in the perception of eCommerce websites

Gender effect in human-machine communication: a neurophysiological study

Purpose

This study aimed to investigate the neural mechanism by which virtual chatbots' gender might influence users' usage intention and gender differences in human-machine communication.

Approach

Event-related potentials (ERPs) and subjective questionnaire methods were used to explore the usage intention of virtual chatbots, and statistical analysis was conducted through repeated measures ANOVA.

Results/findings

The findings of ERPs revealed that female virtual chatbots, compared to male virtual chatbots, evoked a larger amplitude of P100 and P200, implying a greater allocation of attentional resources toward female virtual chatbots. Considering participants' gender, the gender factors of virtual chatbots continued to influence N100, P100, and P200. Specifically, among female participants, female virtual chatbots induced a larger P100 and P200 amplitude than male virtual chatbots, indicating that female participants exhibited more attentional resources and positive emotions toward same-gender chatbots. Conversely, among male participants, male virtual chatbots induced a larger N100 amplitude than female virtual chatbots, indicating that male participants allocated more attentional resources toward male virtual chatbots. The results of the subjective questionnaire showed that regardless of participants' gender, users have a larger usage intention toward female virtual chatbots than male virtual chatbots.

Value

Our findings could provide designers with neurophysiological insights into designing better virtual chatbots that cater to users' psychological needs.

Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study

Introduction

This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers.

Methods

The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns.

Results

Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences.

Discussion

This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.

Changes in consumer spending behavior during the COVID-19 pandemic across product categories

We examine changes in online consumer spending during the COVID-19 pandemic. Specifically, we compare consumer spending on various product categories before the pandemic started to after. Unlike previous work, we not only look at the different consumer demographic profiles but also different product categories, providing a better understanding of spending behavior. E-commerce has been a favorite way of shopping for consumers before the pandemic, while some demographic groups were reluctant to use e-commerce. However, as pandemic-related restrictions on physical shopping were put in place, consumers found e-commerce the only way to shop for many essential products. As such, consumer online spending changed. We employ freshly released Canadian Perspectives Survey Series (CPSS) data on consumer spending by Statistics Canada. We examine the association between various demographic factors and the change in online spending empirically. Our findings indicate that, compared to their counterparts, younger consumers are more likely to have increased online spending on product categories related to internet connectivity and streaming services. Female consumers exhibit increased online spending on similar products, in addition to online learning services and home exercise equipment. Consumers living in urban areas exhibit a higher probability of increased online spending on computers, smartphone devices, internet connectivity, and food delivery services. Consumers with at least one child exhibit a similar trend. Larger households exhibit a considerable increase in the probability of increased online spending on food delivery services. Lastly, consumers with higher education exhibit a higher probability of increased online spending on products related to online learning and streaming services.

Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy

It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.

Brain-computer interface to predict impulse buying behavior using functional near-infrared spectroscopy

As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people's preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain-Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain's prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.

Metaverse-based virtual reality experience and endurance performance in sports economy: Mediating role of mental health and performance anxiety

Metaverse sports arena is gaining popularity globally that empowers virtual reality sporting experience through digital avatars. The main objective of the current study is to explore the impact of the Metaverse-based virtual reality sporting experience on the endurance performance of young Chinese athletes, with the mediating role of their mental health condition and performance anxiety. The study's participants mainly included Chinese athletes, especially the sample group is an accurate depiction of young athletes using a convenience sampling approach. SEM-AMOS statistical software was used for the analysis and validation of the proposed relationships. The study findings statistically validate that mental health and performance anxiety fully mediate the direct associations between virtual reality sporting experiences and the endurance performance of young Chinese athletes. Interestingly, the mental health condition of the young Chinese athletes imposes a greater impact on their endurance performance, in contrast to the adverse effects of their performance anxiety. The outcomes of the present research guide young athletes on the opportunities to enhance their virtual reality sporting abilities and boost their endurance performance. Policymakers can also build systems to dissolve physical and geographical barriers, reduce performance anxiety, and sustain mental health in virtual reality sporting events through the metaverse.