1. Introduction

What drives consumer preferences and purchase behavior - rational or emotional factors - and how can brands cultivate affinity? Traditional economic theory considers consumers as purely rational actors, yet behavioral economics and neuroscience have demonstrated that emotions significantly influence decision-making processes[1]. Emotional states alter value perceptions, modulate risk tolerance, and determine whether consumers prioritize hedonic (pleasure-oriented) or utilitarian (practical) benefits [2].

The growing integration of neuromarketing and psychological research has yielded critical insights into emotional influences on consumer choice. Positive affective states (e.g., happiness, excitement) enhance product appeal, while negative emotions (e.g., fear, sadness) may trigger compensatory purchasing behaviors[1]. By comprehending these psychological mechanisms, businesses can develop more effective marketing strategies that align with consumers' underlying needs [3].

This research examines three key dimensions of emotional influence on consumer behavior. Firstly, the interplay between affective and cognitive processing in decision-making. Secondly, how emotional appeals shape preferences for hedonic versus utilitarian products [2]. Thirdly, the role of emotional engagement in fostering brand loyalty and customer satisfaction. The findings will provide actionable insights for developing sophisticated marketing approaches that simultaneously address consumers' functional requirements and emotional desires [3].

2. Literature review

The dual-process theory shows that consumer decision-making fundamentally involves the interaction between emotional and rational processes. The rapid emotional pathway, mediated by the limbic system and amygdala, drives instinctive motivation and reward-seeking behaviors, while the slower rational pathway engages the prefrontal cortex (PFC) to systematically assess product utility and functionality [4]. This neurobiological distinction explains why consumers often make emotional choices despite having access to rational product information.

Empirical research consistently demonstrates how emotional states modulate purchasing behavior. Positive affective experiences (e.g., joy, excitement) correlate with enhanced brand equity and greater willingness to pay premium prices, whereas negative emotions typically induce either avoidance behaviors or compensatory consumption [1]. Marketing strategies that successfully evoke emotional responses through storytelling or sensory branding elements achieve significantly higher engagement and recall rates [3]. Neuromarketing studies corroborate these findings by demonstrating how emotionally charged stimuli activate the brain's reward centers, thereby enhancing the persuasive power of emotional appeals.

The effectiveness of marketing appeals varies substantially between emotional and functional approaches. Emotional appeals generate stronger, immediate affective responses, particularly for hedonic goods, thereby enhancing brand memorability. Conversely, functional appeals better serve utilitarian products by facilitating consumers' need for practical evaluation and risk reduction [2]. This dichotomy reflects fundamental differences in processing speed and cognitive engagement - emotional responses occur automatically, while functional evaluations require deliberate consideration. Understanding these dynamics enables marketers to tailor strategies according to product type and consumer objectives, whether aiming for immediate emotional connection or building long-term value-based confidence.

Insights from behavioral economics and neuroscience collectively demonstrate that consumer choices rarely result from purely rational deliberation but rather emerge from complex interactions between affective impulses and cognitive assessments. The relative influence of these factors varies according to both consumption context and individual differences. It suggests that optimal marketing approaches should address both emotional and functional dimensions while recognizing their distinct psychological mechanisms and behavioral outcomes.

3. Methodology

3.1. Survey design

We designed three online questionnaires, each with an ad of a fictitious mobile phone and asking participants to read the ad and answer the same set of questions. As shown in Appendix A, the questions ask participants’ age, education level, gender, annual spending power, purchase intention, and decision-making time. There are three groups, including a control group, an emotional experimental group, and a functional experimental group. Each of the groups were shown a unique ad. The ad of the control group only shows basic information of the product without emotional or functional emphasis. On the other hand, the ad of the emotional treatment group is designed to convey emotional value, highlighting emotional connections experienced by people using the product. Also, the functional treatment group is shown an ad focusing on practical features and functional benefits. These distinct advertising strategies allowed us to examine their effects on participants’ decision-making time and purchase intentions.

3.2. Sample

The three questionnaires were distributed on a survey platform, Wenjuanxing, and we received a total of 475 valid responses in a week, including 216 in the control group, 147 in the emotional group, and 112 in the functional group.

3.3. Variables

Dependent Variable. The dependent variables in this research are purchasing intention and decision-making time. Purchasing intention is measured on a scale of 0 to 10, which shows how likely participants are to buy the product. Decision-making time is measured by the real time participants took to determine their purchasing intention.

Independent Variable. The independent variables are the emotional treatment and functional treatment, which represent whether participants were exposed to emotional or functional advertising content. Therefore, they are acting as dummy variables.

Control Variable. The control variables include age, gender, education level, and annual spending, which were included to account for individual differences that might influence the behavior.

3.4. Model

After the data is collected, we first conduct a bivariate regression model that includes only the two independent variables.

Then, we conducted a multiple regression model, adding the control variables to account for demographic differences. The multiple regression equation is shown below.

4. Empirical findings

4.1. Descriptive statistics

According to the summary statistics table, we can notice some key differences in sample characteristics across the three groups. The overall average age of all participants was about 32.5, with roughly 45% being male. However, average ages differed significantly among groups: the control group had the largest average age (mean = 39.7) compared to the emotional group (mean = 28.0) and the functional group (mean = 24.3). Education and spending levels also varied slightly.

For purchasing intention, the emotional group had the highest average (mean = 7.05). In contrast, the functional group had the lowest purchasing intention (mean = 4.98), and the control group had a moderate level of purchasing intention (mean = 5.29). For decision-making time, the functional group had the lowest average, which means participants in this group made decisions more quickly (mean = 1.80) than both the emotional and control groups.

4.2. Purchase intention analysis

***p<0.01, **p<0.05, *p<0.1

First, by examining the regression analysis without control variables (Table 2), the results show that emotional ads significantly increased purchasing intention (β = 1.871, p < 0.01). This finding aligns with previous studies indicating that emotional states could influence product choices [1]. Emotional appeals can effectively activate consumers’ automatic responses by engaging emotional processing areas like the amygdala and limbic system, which drive motivation and reward perception [4]. Additionally, emotional marketing strategies have been consistently associated with stronger consumer responses [3]. In contrast, functional ads had a negative or insignificant effect (β = -0.305, not significant), which does not match our expectation due to several possible reasons, and these limitations will be discussed later.

4.3. Decision time analysis

Second, Table 3 indicates that functional ads significantly reduced participants’ decision-making time (β = -0.210, p < 0.05), while emotional ads did not show a significant effect on decision time. A possible explanation for why the participants in the functional group made quicker decisions is that functional ads provide straightforward information. Therefore, such clear, concise messages reduce cognitive load, which enables faster decisions [5]. Furthermore, younger consumers tend to use heuristic-based, intuitive decision strategies rather than deep analytical thinking [6,7]. Also, according to the summary statistics, the functional group had the youngest average age (24.25) and the lowest average education level, which supports the idea that these participants tend to rely on quicker decision-making strategies. In addition, younger consumers are typically more familiar with smartphone features, allowing them to make rapid evaluations without much consideration [8].

4.4. Regression with control variables

***p<0.01, **p<0.05, *p<0.1

Table 4 shows the regression results while including demographic control variables (age, gender, education, spending). From the table, we can see that even after controlling for these variables, emotional appeals still significantly increased purchase intention (β = 3.025, p < 0.01). In comparison, functional appeals remained insignificant. Thus, Table 3 data further reinforce that emotional ads could effectively influence consumers’ intention to purchase, independent of demographic differences.

4.5. Summary

In summary, emotional appeals consistently and strongly enhanced purchase intention across all models. On the other hand, functional appeals had little to no positive effect on purchasing intention but did significantly reduce decision-making time.

5. Limitations

5.1. Smaller sample size in functional group

The functional treatment group (n = 112) had fewer participants compared to the emotional (n = 147) and control (n = 216) groups. This smaller sample size may limit statistical power and make it harder to detect meaningful effects.

5.2. Homogeneous functional ad content

The functional ads used in the study presented product features and performance characteristics similar to many other smartphone ads. Previous studies have found that repetitive or overly familiar ad content would reduce attention and persuasive effectiveness, known as advertising wear-out [9]. Creative ads are typically more effective because they better capture audience attention and interest [10]. Thus, the similarity between our functional ads and standard market ads might have reduced their persuasive power.

5.3. Age difference across groups

Moreover, there is a significant age difference between the control and treatment groups. Specifically, the participants in the control group had a much higher average age (mean = 39.7 years) compared to the participants in the emotional (mean = 28.0) and functional (mean = 24.3) groups. As age might influence consumers’ response to advertising, this large age gap could bias the control group as a baseline for comparison. Although age was included as a control variable in the regression analysis, the initial differences in samples might still have affected the results.

5.4. Limit of generalizability

The study was conducted in China as the surveys were posted on a Chinese platform, and specifically focused on smartphones. Therefore, our findings might not generalize well across different cultures or product types. Research shows that advertising effectiveness can vary substantially depending on cultural orientation. Ads emphasizing individualistic values (common in Western cultures) perform differently than ads highlighting collectivist values (typical in East Asia) [11]. Moreover, functional versus emotional appeal effectiveness can vary by product type. Emotional appeals tend to be more effective for hedonic or experiential products, while functional appeals may be more suitable for utilitarian products [12]. Thus, results from this smartphone study in China may not directly transfer to other cultural or product contexts.

6. Conclusion

This study investigates how emotional and functional advertising appeals influence consumer decision-making in terms of purchasing intention and decision-making time. By posting three different surveys online, we derived three groups of data: emotional, functional, and control group. According to the result of regression analysis, we found that emotional appeals consistently and significantly increased purchase intention. In contrast, functional appeals did not increase purchasing intention, but they were effective in decreasing decision-making time.

However, there are also several limitations of the study. First, the smaller sample size in the functional group might have weakened the reliability of the comparison. Second, the homogeneous content of the functional ad could have reduced its persuasive impact. In addition, the significant age difference between the control and treatment groups might have biased baseline comparisons. Lastly, this study was based on surveys conducted in China and focused only on smartphones, which limits the generalizability of the findings to other cultures or product categories.

Despite these limitations, this study provides useful insights into advertising. Emotional ads are a useful way to increase consumer interest, which advertisers should pay attention to.