Consumer preferences for novel products: eye-tracking and EEG insights into gift-giving vs. self consumption

Introduction

Consumer purchase decisions are shaped by product-related factors (price, brand, product type) as well as contextual variables, such as location, purchase channel (online versus in-store) and purchase occasion (Boncinelli et al., 2019; Wei et al., 2024). Past research shows that gift-giving is a highly involved process (Belk, 1982; Sherry, 1983), resulting in increased evaluation and engagement as it often involves impression and risk management compared to self-consumption (Carsana and Jolibert, 2017; Scammon et al., 1982). Research has also highlighted gift-giving to be an emotional process as it can often induce perceived risks (Moreau et al., 2011) due to unfamiliarity with the recipient's taste and pressure to make a good impression (Wooten, 2000).

Wine is often purchased for both personal consumption and as a gift (Collins, 2023; Hatak and Stöckl, 2008). It therefore provides an appropriate context to study the differences in the decision-making process between self-consumption and gift-giving (hereafter the “purchase occasion”) as the same consumer may exhibit different preferences depending on the occasion for which they are choosing the wine (Boncinelli et al., 2019; Lockshin et al., 2006; Lockshin and Corsi, 2012). Purchasing wine in general can be considered risky given the number of options and factors to consider. The risk perceptions can be even higher when buying for others depending on the givers’ level of expertise, givers’ knowledge of the recipients’ preferences and recipients’ wine knowledge (Olsen et al., 2003). Further, consumers often lack the opportunity to evaluate the quality of the wine prior to purchase and therefore rely on extrinsic cues, such as price, labels and reviews. This reliance may be amplified in the case of unfamiliar wines such as wine made from disease-resistant varieties of grapes, which are environmentally beneficial and naturally resistant to common fungal diseases (Borrello et al., 2024). Cross breeding elite commercial grape varieties with disease resistance species to produce ‘disease resistant varieties’ (DRVs) of grapes can help significantly reduce chemical dependency, improve soil health and even decrease the grape producer's exposure to harmful chemicals. However, these wines are largely unfamiliar to consumers and may potentially raise concerns regarding taste, quality and confusion with genetically modified organisms (GMOs) (Borrello et al., 2024). This newness and uncertainty may increase consumers’ dependence on visual attributes like the label, price and even customer reviews to reduce this uncertainty (Lockshin and Corsi, 2012; Oczkowski, 2001). This setting provides a theoretically meaningful and policy-relevant context to examine how consumers process product attributes both visually and cognitively under uncertainty and risk, and how these processes differ across purchase occasions.

Most existing research comparing consumer behavior regarding wine in the contexts of self-consumption and gift-giving (Boncinelli et al., 2019; Carsana and Jolibert, 2017) have relied on surveys and choice experiments. While these techniques provide critical insights into people's choice and behavior, they provide limited insights into the underlying cognitive processes (Telpaz et al., 2015). Recent advances in consumer neuroscience uses tools like electroencephalogram (EEG) and eye tracking to study the underlying attentional and neural responses during the decision making process (Alsharif et al., 2021; Glimcher, 2010; Oliveira et al., 2022). Eye-tracking has become an important tool in consumer research to explore visual attention, as it can capture eye movements like gaze co-ordinates, fixation times, fixations durations, gaze paths, pupil size, saccades and many other metrics (Bednarik et al., 2005; Kim and Lee, 2021). In consumer research, eye tracking has been used to study how consumers assess various marketing stimuli like products, advertisements, product attributes and ultimately how these affect their purchase decisions (e.g., Pšurný et al., 2024). EEG is becoming more popular as it offers many benefits, it is non-intrusive allowing participants to operate normally in the lab or on site, is more cost effective compared to other brain imaging techniques and has a high temporal resolution which can record brain activity in milliseconds (Ozkara and Bagozzi, 2021; Zhu and Lv, 2023). Thus, EEG complements eye tracking by measuring the temporal dynamics of cognitive and affective processing that cannot be observed through eye movements alone. Whereas eye tracking reveals where consumers look, EEG provides insights into how information is processed following visual attention, allowing researchers to distinguish early attentional responses from later evaluative and emotional processing (Ozkara and Bagozzi, 2021; Telpaz et al., 2015). For example, Horská et al. (2016) analyzed data from EEG and facial expressions (using a face reader) to study consumer's emotions while tasting eight blank samples of wine. They were able to identify the wine samples that resulted in the highest values for happiness and the EEG data revealed that women reacted more emotionally than men and that their emotional engagement was also higher immediately after wine tasting. Similarly, Alvino et al. (2019) investigated consumer preferences for wines using EEG during a wine tasting task and found significant changes in the frontal beta oscillations and stated preferences upon tasting the wines. While prior research has employed EEG to understand how consumers evaluate and respond to food labels and packaging (Byrne et al., 2022; Jin et al., 2017; Pšurný et al., 2024), to our knowledge, no prior research has compared gift-giving and self-consumption contexts using EEG.

We employed a multimodal design that combines conjoint analysis, eye tracking, and electroencephalography (EEG) to examine how consumers evaluate DRV wines when purchasing for themselves versus as gifts. Specifically, this study investigates whether purchase occasion (self-consumption versus gift-giving) influences (1) consumers’ purchase intentions and willingness to pay for sustainability-related wine labels, (2) visual attention toward different wine label attributes as measured by eye-tracking, and (3) the temporal dynamics of neural processing elicited by sustainability badges, customer reviews, and price information as measured through event-related potentials (ERPs). By doing so, this research makes important contributions to existing literature. Theoretically, this is the first study, to our knowledge, to have simultaneously combined behavioral, attentional, and neurophysiological measures to explore the underlying mechanisms of decision-making under self-consumption and gift-giving. Methodologically, previous studies using EEG or eye-tracking have mainly focused on understanding product-level differences or studying the impact of an attribute using different product images (Barbierato et al., 2023; Wang et al., 2020). Different from previous studies, we examine the simultaneous impact of multiple attributes on the front label of the wine, without altering the overall design of the label using a conjoint analysis (García-Madariaga et al., 2019). In this study, participants repeatedly rated alternative wine labels that differ in sustainability badges, customer ratings, price, and promotional messages. This helps isolate the attribute-specific effects without confounding the variations in neural and visual processing with factors that were not the focus of a study.

Empirically, to our knowledge, this study is the first to combine eye-tracking and EEG to study consumer preferences for novel wines, specifically, disease-resistant wines. Therefore, this study not only contributes to consumer neuroscience literature but provides directional strategic insights to growers, wineries and marketers seeking to expand consumer acceptance of the next generation sustainable wines.

Methodology

Sample

Participants (N = 47) were recruited for the study through an online screening survey via Qualtrics^TM and participated in lab sessions in Minneapolis (December 2024) and Portland (May 2025). A total of 19 participants were recruited in Minneapolis and 28 participants in Portland. In Minneapolis, 10 participants were assigned to the gift-giving condition and 9 to the self-consumption condition, whereas in Portland, 13 participants were assigned to the gift-giving condition and 15 to the self-consumption condition. Thus, both purchase conditions were represented at each data collection site, reducing the potential for confounding between experimental condition and collection location.

All participants were 21 years or older, had consumed wine at least once in the past year, had normal or corrected-to-normal vision, and no reported history of neurological disorders. Each session lasted approximately 80 min and participants received $60 for their time. All participants provided written informed consent prior to participation. The study protocol was reviewed and approved by the Institutional Review Board (IRB) at the University of Minnesota and the study number is STUDY00016999.

Prior to the analysis a total of 10 participants were excluded, 6 due to messy EEG data even after pre-processing and 4 participants had incomplete decision data from the eye-tracking software. The final sample consisted of 37 participants, with n = 18 assigned to the self-consumption condition and n = 19 assigned to the gift-giving condition.

Experiment design/stimulus

The wine front-labels varied across four attributes: badge type (4 levels), creative phrase (3 levels), price (3 levels) and customer reviews (2 levels), see Table 1 and Figure 1 for the list of the attributes and visual depiction of the labels, respectively. Figure 2 shows the eco-badges used in the experiment. The price levels were chosen based on the average price of white wine in the US. The attributes were selected based on a literature review, the Alcohol and Tobacco Tax and Trade Bureau regulations, consultation with industry experts. The badges served as the primary attribute of interest as they communicate the disease resistance property of the wine and previously environmental attributes have shown to positively influence consumers’ perceptions of product quality and purchase intentions, even more so in the context of gift-giving (Boncinelli et al., 2019). Research by Lockshin and Corsi (2012) and Oczkowski (2001) has shown that wine purchasers rely on various quality factors which can be both extrinsic (prices, reviews, etc.) and intrinsic (wine attributes–region, vintage, brand, etc.). In this experiment we chose to manipulate price and customer reviews while keeping the intrinsic factors constant. Further, results from a focus group study (Steede et al., 2026) showed consumers often misinterpreted DRVs as genetically modified. Therefore, a non-GMO logo was also included on the label. A balanced fractional factorial design was used resulting in 24 unique label combinations. A control label without sustainability badges was included.

Table 1. Product attributes and levels used in the experiment.

The overall design including a fictitious brand logo, grape blend, alcohol by volume, volume, a non-GMO logo, color scheme, and typography were kept constant across the stimuli. ‘White Blend’ was chosen as most disease resistant grape varieties currently being developed are white grapes and are mostly used as a blend. While the position of the various label attributes was beyond the scope of this research and were kept constant, the positioning of the various label attributes was based on previous eye-tracking research. Research shows that consumers often process stimuli in a Z-pattern, starting from the top left corner moving across the top right and then diagonally to the bottom left (Buscher, Cutrell, and Morris, 2009; Nielsen, 2006). To ensure a balanced design, we placed the badges on the top left corner and the non-GMO label at the bottom right corner.

A between-subjects design was used where participants in either gift-giving or self-consumption condition viewed all 24 stimuli. A between-subjects design was selected to prevent carryover effects that could arise if participants evaluated the same wine labels under both purchase contexts. Presenting only one purchase scenario to each participant also better approximated real-world purchasing decisions. Participants were randomly assigned to one of two purchase-occasion conditions, self-consumption or gift-giving, and were instructed to evaluate each wine label as they were purchasing for themselves or as a gift. The instructions provided to the participants are shown in Appendix A.

In total, each participant viewed 408 trials (24 stimuli × 17 repetitions each) plus two practice trials at the start of the experiment. Each trial (see Figure 3) began with a 500 ms fixation cross, stimulus presentation (4000 ms), fixation cross (500 ms) and then participants were asked to rate their likelihood of purchase on a 5-point Likert scale using the mouse (no time limit). Repeated presentations were necessary because ERP components are extracted by averaging across multiple trials to improve the signal-to-noise ratio and obtain stable estimates of stimulus-locked neural activity (Luck, 2014). To decrease fatigue and ensure participants were actively making decisions, they were encouraged to take a break halfway through the experiment. The two halves of the experiment were identical, and trials were randomized for each participant. Participants were seated approximately 68.5 cm from a 27-inch monitor (3840 × 2160 pixels at 60 Hz). The visual stimulus subtended approximately 20° horizontally and 27° vertically. The experiment room was sound-attenuated and free from visual distraction to improve the quality of data collected from the eye-tracker and EEG systems.

Participants first completed the eye-tracking and EEG experiment, during which they evaluated the conjoint choice stimuli. After the experiment, participants filled out an online questionnaire about their preferences for wine, knowledge about disease-resistant grape cultivars and demographics variables.

Eye data was recorded simultaneously with EEG using a Tobii Pro Fusion screen-based eye-tracker with a sampling rate of 60 Hz. A standard 9-point calibration and validation procedure was performed, and recalibration was conducted as needed. EEG data was recorded using the Ant Neuro eego sport 64 channel gel-based cap with Ag/AgCl electrodes positioned according to the international 10–20 system and sampled at 500 Hz. Stimulus presentation was controlled using Tobii Pro lab and TTL markers were sent from this software to the EEG acquisition system for time synchronization. To get the behavioral responses, five AOIs were created on the decision screen that followed the stimulus presentation, one for each of the five-point Likert scale ratings. Mouse click data were extracted from the eye-tracking software to determine each participant's trial level purchase intention.

Data analysis

Eye-tracking data was analyzed in Tobii Pro lab. Eye-tracking metrics used included fixation duration (ms) (time spent on an AOI), fixation count (indicates repeated visual sampling of an attribute), time to first fixation (ms) (reflects the speed with which an attribute initially attracts attention) and duration of first fixation (ms) (captures early processing immediately following attention captures) during the 4 s stimulus presentation time window for 9 areas of interest (AOIs, see Figure 4). These metrics were selected because they capture complementary aspects of visual attention and have been widely used in consumer research to examine how individuals process product labels and packaging (García-Madariaga et al., 2019; Krucien et al., 2017; Motoki et al., 2021; Van Loo et al., 2021; Zhu and Lv, 2023). EEG data was preprocessed and analyzed in MATLAB (2025) using EEGLAB. Continuous EEG signals were first re-referenced to the average of the left and right mastoids. A 0.1–45 Hz bandpass filter was applied to remove low-frequency drift and high-frequency noise, and 60 Hz notch filter was applied to remove electrical line noise. Continuous EEG data was then segmented into 2.5 s epochs, which included 500 ms pre-stimulus baseline and 2 s post-stimulus onset. Baseline correction was done using the 500 ms fixation cross period before stimulus onset. Channels with a lot of noise or flat channels were identified manually and then interpolated using spherical spline interpolation. Messy trials with excessive channel or psychological noise were manually selected and removed from the data. Once epoched, we performed independent component analysis (ICA) on the data to remove eye-blinks, muscle artifacts and other horizontal eye movements. Event related potential (ERP) components were used as metrics to analyze the EEG data. ERPs are “a set of voltage changes that are consistent with a single generator site and that systematically vary in amplitude across conditions, time, individuals, and so forth and a source of systematic and reliable variability in an ERP data set” (Luck, 2014). Thus, EEG based ERPs can be used to study variations in cognitive and affective processing (Jartarkar et al., 2022) of stimuli and the time windows and electrode sites were selected based on the typical windows from previous literature. The ERP peak amplitudes used included P100, N200, P300, N400, and LPP.

P100 is a positive peak at around 100 ms after the stimulus onset and is a very early signal that is associated with visual attention and unconscious processing and emotions (Herzberg et al., 2025). In this study, P100 was between 0 and 200 ms measured at Oz, PO7, PO8, O1, O2. N200 and N400 components are often related to conflict, unfamiliarity or response inhibition. N200 wave is a negative ERP and represents unconscious conflict processing. N200 was between 150 and 300 ms measured at Fz, FCz, C3, C4, FC1, FC2, F3, F4. The N400 ERP is also a negative wave that peaks around 400 ms and is modulated by violations in sematic processing and unexpected stimuli (Byrne et al., 2022). In our study, N400 was between 350 and 500 ms measured at Fz, C3, C4, Cz, P3, Pz, P4, PO7, Oz, PO8 (Kutas and Federmeier, 2011; White et al., 2009). The P300 ERP component is a positive going wave that typically peaks between 250 and 500 ms after the stimulus onset and is typically located in the posterior region of the brain (Polich, 2007). In this experiment, P300 was measured between 250 and 450 ms at Fz, C3, C4, Cz, P3, Pz, P4, PO7, Oz, PO8. P300 amplitude reflects cognitive processing like working memory updates and allocation of attentional resources (Byrne et al., 2022). Research has shown that stimuli that are salient, motivationally relevant or require increased evaluative processing generally result in higher P300 amplitudes (Nieuwenhuis et al., 2005). Lastly, late positive potential (LPP) are late stage ERP components that generally reflect conscious emotional processing (Byrne et al., 2022) to visual stimuli that convey emotion related information (Goto et al., 2019). We measured LPP between 600–2000ms at C1, C2, CP1, CP2, P1, Pz, P2 (Hajcak et al., 2009; Hajcak and Foti, 2020).

Further data cleaning and analysis was performed in Stata 17. Differences in participant characteristics between purchase occasions were assessed using chi-square tests and independent-samples t-tests. Purchase intentions were analyzed using mixed-effects Tobit regression models. Differences in price sensitivity between purchase occasions were evaluated using a Wald test of the interaction between purchase occasion and price. Eye-tracking metrics were compared using Wilcoxon rank-sum tests following assessment of normality using the Shapiro–Wilk test. ERP amplitudes were analyzed using mixed-effects linear regression models. Mixed-effects linear regression models were estimated using Stata's mixed command, while mixed-effects Tobit models were estimated using the metobit command. Robust standard errors were used. The willingness to pay estimates were calculated as the negative ratio of the attribute coefficient to the price coefficient from the mixed-effects Tobit model.

Mixed-effects regression models were used to account for the repeated structure of the data, where each participant (i=1,…,N) rated multiple labels j=1,…,J. There were repeated observations nested within individuals. As a result, the data have a two-level hierarchical structure, with individual ratings (Level 1) nested within participants (Level 2). To account for within subject correlation and unobserved individual heterogeneity, parameters were estimated using a mixed effects specification with random intercepts at the participant level. The general model in Equation 1 for observation j and individual i is:where Xij is a vector of label attributes, ui∼N(0,σu2) captures individual specific effects and εij∼N(0,σε2) is the idiosyncratic error term. All models were estimated by maximum likelihood estimation.

yij=Xijβ+ui+εij(1)

The regressions with purchase intention (rating) as the dependent variable were estimated using mixed-effects Tobit regressions since the variable is continuous between 1 and 5 after averaging across the 17 trials. Equation 2 is the estimating equation with purchase intentions as the dependent variable for either purchase occasion. The EEG data was modeled using mixed-effects linear regressions with interactions on purchase occasion, as in Equation 3.where DR denotes disease resistant, CR denotes climate resilient, RA denotes reduced agrichemicals, FOE denotes “Friend of the Environment” and FOW denotes “Future of Wine”.

Ratingij=β0+β1DRij+β2CRij+β3RAij+β4FOEij+β5FOWij+β6Priceij+β7CustomerReviewsij+ui+εij(2)

EEGij=β0+β1(GiftiDRij)+β2(GiftiCRij)+β3(GiftiRAij)+β4(GiftiFOEij)+β5(GiftiFOWij)+β6(GiftiPriceij)+β7(Gifti*CustomerReviewsij)+ui+εij(3)

Results

Summary statistics of the sample

The final sample consisted of 37 participants, aged 21-70, mean age was between 40 and 49 years. The sample was 73% female, 24% male and 3% non-binary. Chi-square tests confirmed that there were no statistically significant differences between the self-consumption and gift-giving groups with respect to familiarity with disease-resistant crops and demographics (refer to Table 2 for a summary of the variables). These results indicated that random assignment of participants yielded balanced and comparable groups regarding these characteristics.

Table 2. Socio-demographic summary statistics for the pooled sample and self-consumption and gift-giving condition samples separately.

Conjoint analysis results

No significant differences in reaction times were observed between conditions. Table 3 presents the results of the mixed-effects Tobit regressions. For the self-consumption group, customer reviews (5-star vs 3-star) had the largest positive and significant impact on purchase intent (β = 1.44, p < 0.01). Relative to the no-badge condition, all three badge types significantly increased purchase intention (p < 0.01), with the largest coefficient observed for the reduced agrichemicals badge (β = 0.44). Neither creative phrases (“Friend of the Environment,” and “Future of Wine”) were statistically significant. Price had a significant negative effect (β = -0.120, p < 0.01).

Table 3. Mixed effects tobit regression on purchase intent for self-consumption and gift-giving conditions.

The reference category for badge is ‘no-badge’. Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

For the gift-giving condition, customer reviews had the largest positive effect (β = 1.04, p < 0.01) and creative phrases were not statistically significant. The disease-resistant badge was not significant, climate-resilient badge had the largest positive effect (β = 0.383, p < 0.01), followed by reduced-agrichemicals badge (β = 0.270, p < 0.05). Price remained negative and significant. A Wald test on the interaction between price and purchase occasion confirmed that price sensitivity was lower in the gift-giving condition (β = 0.046, p < 0.001).

Across most attributes, WTP was higher in the gift-giving condition than in the self-consumption condition (see Table 4). The largest increase was observed for the climate-resilient badge (+48.2%), followed by customer reviews (+20.0%), whereas the disease-resistant badge was not significant in the gift-giving condition.

Table 4. Willingness to pay for each badge type by purchase occasion.

Eye-tracking results

Eye-tracking metrics¹ were first tested for normality using the Shapiro–Wilk test, which indicated violations of normality for both purchase occasions. Consequently, non-parametric Wilcoxon tests were used to compare visual attention metrics across conditions. Analyses focused on four predefined areas of interest (AOIs): badge, customer reviews, non-GMO logo, and price. Table 6 ²reports the corresponding p-values.

Table 5. Summary of eye-tracking metrics by purchase occasion and AOIs.

Table 6. P-values of the Wilcoxon test to compare the metrics across each AOI between the purchase occasions.

For the badge AOI, participants in the self-consumption condition exhibited longer total fixation duration (500.19 ms vs. 453.63 ms, p = 0.016), whereas differences in the number of fixations, time to first fixation, and duration of first fixation were not statistically significant. For the customer review AOI, self-consumers exhibited significantly longer duration of first fixation (207.99 ms vs. 188.89 ms, p = 0.012). For the non-GMO AOI, significant differences were observed for total fixation duration (61.09 ms vs. 74.66 ms, p < 0.001), number of fixations (0.28 vs. 0.35, p < 0.001), and time to first fixation (2376.59 ms vs. 2197.13 ms, p < 0.001). Duration of first fixation did not differ significantly (p = 0.132). No statistically significant differences in the metrics were found between the two conditions for the price AOI.

Heatmaps (Figure 5) provide a descriptive visualization of fixation distributions across the wine labels. Across both purchase occasions, fixations were concentrated primarily on the brand and badge regions, confirming that participants attended to the intended areas of interest during the experiment. Visual inspection also suggests similar fixation patterns between the self-consumption and gift-giving conditions (left and right panels, respectively).

EEG results

Table 7 presents the regression results on the EEG data with interactions between self-consumption and gift-giving with the attributes. In the self-consumption condition, all badge types significantly increased P100, P300 and LPP peak amplitudes (p < 0.01) and reduced N200 and N400 (p < 0.01) amplitudes relative to the no-badge condition. The “Friend of the Environment” creative phrase significantly decreased P100, P300 and LPP amplitudes and resulted in significantly more negative N400 amplitudes. Customer reviews significantly affected only N400 (β = 0.517, p < 0.05), whereas price did not significantly affect any ERP component.

Table 7. EEG mixed effects regression results comparing results between self-consumption and gift-giving.

Confidence intervals correspond to the regression coefficients reported in Table 7. The reference category for badge is No badge in the self-consumption condition. Participant-level random intercepts were included in all mixed-effects regression models.

The reference category for badge is ‘no-badge’. Standard errors in parentheses.

p < 0.10.

p < 0.05.

p < 0.01. 95% confidence intervals can be found in Appendix B.

In the gift-giving condition, the three badges significantly increased P100 (p < 0.05), P300 (p < 0.01) and LPP (p < 0.05) amplitudes compared to the baseline no-badge condition under self-consumption occasion. Disease-resistant and climate-resilient badges showed marginal reductions in N400 (p < 0.10), whereas no significant effects were observed for N200. Interestingly, the magnitudes across P100, P300, N400 and LPPs were higher overall compared to the self-consumption condition, including in the no-badge condition. Neither creative phrases nor customer reviews significantly affected ERP amplitudes. Price significantly increased N200 (β = −0.047, p < 0.05) and N400 (β = −0.060, p < 0.01) amplitudes while decreasing P300 (β = −0.056, p < 0.05).

Figures 6–17 present the grand average ERP waveforms, illustrating the differences in P100, N200, P300, N400 and LPP amplitudes across the label attributes and purchase occasions.

Figures 18, 19 present the scalp activity distribution differences between the two purchase occasions for two-time windows 0-600 ms and 600–2,000 ms across badge types and price levels. We find higher activity across most attribute levels in the gift-giving condition compared to the self-consumption condition, with increased activity in the 600–2,000 m time window. Across components, higher neural activity was observed in the gift-giving condition relative to self-consumption, particularly in later processing stages (600–2,000 ms).

Discussion and conclusions

This study examined how purchase occasion (self-consumption vs. gift-giving) influences consumer responses to novel, disease-resistant wines by integrating conjoint analysis, eye-tracking, and EEG measures. While previous studies have shown that purchase occasion affects wine preferences (Boncinelli et al., 2019; Carsana and Jolibert, 2017), little is known about the underlying attentional and neural processes associated with these decisions. Our findings provide preliminary evidence that purchase occasion influences not only stated preferences but also the processing of label attributes throughout the decision-making process. Because this is one of the first studies to combine behavioral, eye-tracking, and EEG measures in this context, the interpretations presented below should be regarded as preliminary and hypothesis-generating rather than definitive. To facilitate comparison across the three analytical approaches, a summary of the principal findings from the conjoint analysis, eye-tracking, and EEG analyses is provided in Appendix C.

The behavioral results on purchase intention revealed distinct preferences for messaging strategies related to disease-resistant wines between the two purchase occasions. For self-consumption, consumers valued disease resistant, reduced agrichemicals, and climate resilient more compared to no badge. In contrast, gift-givers exhibited a more selective decision-making process, consistent with previous literature that describes gift-giving as a highly involved process (Scammon et al., 1982). They exhibited a strong preference for the climate-resilient badge, followed by reduced agrichemicals, and showed no significant preference for the disease-resistant badge compared to no badge. These findings suggest general acceptance of novel wines across purchase occasions, with gift-givers demonstrating greater scrutiny of messaging and a preference for sustainability-related badges. Previous research found that organic certification served as a quality signal in gift-giving contexts (Boncinelli et al., 2019). It may be that the climate-resilient badge was perceived as a clearer indicator of sustainability, leading to its higher preference. Additionally, the function of a gift is to communicate value to the recipient. Given the unfamiliarity of disease-resistant wines, consumers may reduce the risk of an inappropriate gift by selecting badges that are widely recognized or easily interpreted.

The neurophysiological measures were generally consistent with these behavioral patterns. Across both purchase occasions, the three badges elicited higher P100, P300 and LPP amplitudes compared to no badge with higher amplitudes observed in the gift-giving condition. Previous literature has related P100 to rapid, automatic attention captured by visually salient stimuli (Di Russo and Spinelli, 1999), larger P300 amplitudes with increased attentional allocation and evaluative processing (Nieuwenhuis et al., 2005; Polich, 2007), and larger LPP amplitudes with sustained evaluative and affective processing of motivationally relevant stimuli (Byrne et al., 2022; Goto et al., 2019; Hajcak et al., 2009). The increased amplitudes in the gift-giving condition could suggest heightened automatic attention and engagement to visually salient badges, supporting the theory that gift giving involves increased attention and effort to manage the impressions of the gift-giver (Moreau et al., 2011).

Among the three badge types, the climate resilient message produced one of the strongest and most consistent neural response patterns. In the self-consumption condition, elicited relatively large P100, P300, and LPP responses while also showing the largest reductions in both N200 and N400 amplitudes. Lower N200 and N400 amplitudes have been previously associated reduced conflict monitoring (Folstein and Van Petten, 2008) and easier semantic integration of information (Kutas and Federmeier, 2011). Thus, the results could suggest that the climate resilient message was processed relatively fluently compared with the other badge types. Similarly, in the gift-giving condition, the climate-resilient badge had the highest effect on P100 and P300 and less negative N400 amplitudes. These findings are broadly consistent with Jin et al. (2018), who reported reduced N200 amplitudes and greater purchase intention for eco-labeled food products. One possible explanation is that climate-related sustainability messaging may represent a broader and more familiar environmental concept that consumers can readily interpret, although familiarity was not directly measured in this study. Interestingly, this neural pattern parallels the behavioral findings, where the climate resilient badge generated the highest willingness-to-pay premium during gift-giving condition (+48%). While we did not directly test whether ERP responses mediated purchase intention, the convergence of the behavioral and neurophysiological findings suggests that this message was processed more favorably than the other sustainability messages, particularly when participants selected wine as a gift.

From the behavioral results, we found consumers were willing to spend more on a bottle of wine used as a gift compared to self-consumption differences in the magnitude of the price coefficients between the two conditions, which aligns with the results from Boncinelli et al. (2019). This behavior can be tied to the phenomenon of social desirability bias (Boncinelli et al., 2019), where the giver tends to present oneself in the best possible light to leave a positive influence on the recipient.

While consumers indicated behaviorally that they were less price sensitive in gift giving, the ERP data revealed decreased P300 and more negative N200 and N400 amplitudes due to higher prices among gift givers. These findings are in line with research by Wang et al. (2018) who studied decision making on e-commerce and found that negative reviews or incongruent pricing increase N200 amplitude. For gift givers, price may thus represent a source of dissonance, balancing generosity norms against cost, leading to greater conflict monitoring. Similarly, Fu et al. (2019) found that incongruent or unexpected price-value pairings resulted in higher N400 amplitudes. These results further support previous literature of the role of a gift as a social and economic exchange and anxiety or stress on the gift giver to reduce the risk of making the ‘wrong’ choice. Higher prices also reduced P300 in the gift condition, suggesting reduced evaluative positivity or motivational engagement when costly products conflicted with gifting goals.

Customer reviews exerted the largest behavioral influence on purchase intention across both purchase occasions, with willingness-to-pay estimates indicating an even larger premium among gift givers. In the absence of the opportunity to taste the wine, consumers often rely on various product cues to ensure quality (Lockshin et al., 2006). This is especially important in the case of new wines. In this study, we did not test the effect of brand; therefore, it makes sense that consumers increasingly rely on customer reviews for information on the wine's quality.

Customer reviews did not strongly modulate P100, N200 or P300 suggesting that reviews were not automatic visual attention cues but required more cognitive elaboration. We found that reviews reduced N400 for self–consumption participants, consistent with their role in easing semantic integration (providing quality assurance). However, unlike Sun et al. (2020) who found that product ratings modulated emotional arousal under different prices, we did not find any significant association with LPP, but the behavioral data showed the importance of customer reviews in decision making across both conditions. This could suggest that customer reviews function as a cognitive heuristic rather than an emotional driver. They reduced uncertainty and cognitive conflict but did not necessarily evoke strong evaluative or affective engagement.

The eye-tracking data served two purposes; first, it provided evidence that participant's truly looked at the relevant AOIs and that their decisions were based on them attending to the attributes. Second, it investigated any differences in the visual attention patterns between the two conditions. The heat maps revealed that while the brand received the most fixations overall (especially in labels without a badge), the presence of a badge shifted more attention towards it. This indicates that the presence of a badge on the label helps focus participants’ visual attention. Overall, while some differences in visual attention were observed between self-consumption and gift-giving conditions, attention was primarily influenced by visual and product attributes rather than by purchase occasion.

The study has several limitations that should be taken into consideration before interpreting the findings. The sample size was modest, with 37 participants, limiting statistical power for detecting small effects and restricting sub-group analyses. Although this aligns with typical EEG study sizes (Alvino et al., 2021; Jin et al., 2017; Khushaba et al., 2013), replication with larger samples is needed. Multiple ERP components, eye-tracking metrics, and label attributes were analyzed, the possibility of inflated Type I error cannot be completely excluded. Although the analyses were hypothesis-driven, future studies should consider formal adjustments for multiple comparisons. Each participant viewed the same label configurations repeatedly to obtain reliable ERP estimates. Although stimulus presentation was randomized and brief rest periods were provided, repeated exposure may have introduced habituation, learning, or fatigue effects that could influence behavioral and neurophysiological responses. Additionally, although a non-GMO logo was included on all labels to reduce potential confusion and participant reported a low familiarity with disease-resistant grape varieties, the study did not include a direct manipulation check to verify how participants interpreted the “Disease Resistant” badge. Therefore, we may not be able to rule out the possibility that some responses were driven by the semantic salience of the word “disease” rather than by the intended concept of disease-resistant grape varieties. Finally, participants made hypothetical purchase decisions in a controlled laboratory environment using a single wine label design. Consequently, the results may not fully generalize to real purchasing environments, other product categories, or broader consumer populations. Future research should replicate these findings using incentive-compatible purchase tasks, more diverse samples, and commercially available wine products.

Theoretically, this research provides early and preliminary neurophysiological evidence on the differences in the decision-making process between self-consumption and gift-giving. Practically, we find that sustainability-related messages, particularly the climate-resilient badge, were consistently associated with stronger behavioral responses across purchase occasions and may therefore represent an effective communication strategy for introducing these unfamiliar products to consumers. Customer reviews was a strong driver of purchase intention, suggesting that providing credible quality information may help reduce uncertainty surrounding novel wine varieties. Furthermore, the role of purchase occasion may be important when designing marketing communication. For example, positioning DRV wines as a gift may be an advantageous launch strategy in the market to increase consumer confidence and popularity. Together, these findings provide preliminary evidence that purchase context influences not only stated preferences but also the way consumers attend to and evaluate information presented on novel wine labels.

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Footnotes

Edited by: Shinsuke Suzuki, Hitotsubashi University, Japan

Reviewed by: Toshiki Saito, Toyo University, Japan

Airton Rodrigues, São Paulo State Technological College, Brazil

Footnotes

¹Table 5 presents the summary of the selected eye-tracking metrics across AOIs by purchase occasions.

²Trial level data was aggregated at the participant level.