Influence of Goal-Framing Type and Product Type on Consumer Decision-Making: Dual Evidence from Behavior and Eye Movement

Abstract

In today’s fierce market competition, enterprises must quickly attract consumers’ attention to products and prompt them to make purchases. Based on regulatory focus theory, this study examines the impact of the congruence between different types of goal framing in advertising (promotion vs. prevention) and product types (hedonic vs. utilitarian) on individual consumer decision-making, as well as the underlying psychological mechanisms. The findings are as follows: (1) A goal-framing effect was observed, such that individuals allocated more attention and exhibited higher purchase intentions toward products presented with promotion-framed advertising. (2) A matching effect between goal-framing type and product type was identified: promotion framing increased purchase intentions for hedonic products, whereas prevention framing increased purchase intentions for utilitarian products. (3) Processing fluency mediated the effect of goal–product matching on consumer decision-making. (4) The presence of time pressure amplified the goal-framing effect, leading to stronger preferences under promotion-framed advertisements, as reflected in both longer fixation durations and higher purchase intentions. By integrating regulatory focus theory with product type matching, this study leverages eye-tracking data to reveal the cognitive processes underlying consumer decision-making and the moderating role of time pressure on goal-framing effects. The findings enrich the motivational perspective in consumer behavior research and provide empirical guidance for designing differentiated advertising strategies and optimizing advertising copy.

1. Introduction

In the era of rapidly developing interactive marketing and e-commerce, the strategic use of advertising messages has become an effective means for firms to capture consumer attention [1,2,3]. It is evident that marketers often use behavioral outcomes as the focal point of their messaging, guiding consumer behavior through positive and negative framing [4]. For example, in the context of credit card applications and mutual fund purchases, an advertisement framed positively might state, “Applying for a credit card and purchasing a mutual fund can provide greater security in your life,” whereas a negatively framed version would read, “Failing to apply for a credit card or purchase a mutual fund may leave your life inadequately secured.” According to Levin et al., the goal-framing effect refers to the influence of emphasizing the positive consequences of taking a certain action (positive goal framing) or the negative consequences of not taking a certain action (negative goal framing) on individual decision-making [5]. Existing research has thoroughly explored the application of goal-framing type in the fields of healthcare and environmental protection, and a consensus has been reached regarding its role [6,7,8,9]. However, in the domain of consumer decision-making, there is a lack of consensus on which goal-framing type is more persuasive in promoting individual purchases.

A review of the literature reveals that the inconsistent conclusions regarding goal-framing type in the consumer domain are primarily due to a previously overlooked factor—product type. It is widely acknowledged that an individual’s choice of goods reflects their motivations and needs; the purchase of hedonic products indicates a focus on pleasure, enjoyment, and the pursuit of happiness, while the purchase of utilitarian products suggests an effort to satisfy basic living needs. Consequently, different types of goal framing may be more aligned with consumers’ motivations to purchase hedonic versus utilitarian products. However, existing research has focused only on different attributes of individual products and has not systematically examined the effect of the match between product type and goal framing on consumer decision-making. To further clarify the role of goal-framing type in the consumer domain and to examine its matching effect with product type, this paper focuses on the impact of both on individual consumer decision-making, as well as their psychological mechanisms and boundary conditions. This study not only enriches the research on goal-framing types in the consumer domain but also provides practical guidance for firms to optimize advertising strategies, accurately convey product value and purchase motives, facilitate consumers’ comprehension of information, and support more informed decision-making, thereby enhancing the overall shopping experience and satisfaction.

2. Literature Review and Hypothesis Development

2.1. Goal-Framing Type and Consumer Decision-Making

The Information Framing Theory posits that different descriptions of an objectively identical issue can lead to variations in decision-making judgments, reflecting a cognitive bias in individuals. At the present stage, researchers have begun to focus on the impact of information framing on decision-making, and related content has been sequentially applied to various areas such as fostering healthy behaviors, advertising and marketing, and environmental protection [10]. As the study of information framing has deepened, scholars have also categorized it into more nuanced types, typically including risky framing, attribute framing, and goal framing [11]. Among these, goal framing starts from the potential outcomes of behavior and emphasizes the positive consequences of action and the negative consequences of inaction. Research in related fields indicates that positive goal framing enhances individuals’ perceived self-efficacy, whereas negative goal framing enables them to better monitor the progress of goal pursuit [12]. Accordingly, positive goal framing exerts a stronger motivational effect when individuals are in the novice stage, whereas negative goal framing becomes more beneficial once they reach the expert stage [13,14].

With the rise of advertising and marketing, the field has also begun to gradually utilize different information frames to enhance consumer purchase intention and prompt decisions that benefit corporate development [15]. Among these, goal-framing type is widely used in research on consumer decision-making due to its characteristics of being easily comprehensible and highly applicable. However, existing studies have not reached a consensus on the question of “which goal-framing type is more persuasive in individual consumer decision-making.” Some research suggests that a positive frame effectively facilitates consumer purchases. For instance, Putrevu found that descriptions under a positive frame were more persuasive to consumers in the context of airline service products [16]. Additionally, some researchers have studied the influence of consumer individual characteristics and found that for individuals with high power and high self-esteem, a positive goal frame is more persuasive than a negative goal frame [17,18]. On the other hand, another body of research suggests that a negative goal frame is more persuasive when individuals make consumption decisions. For example, in the domain of green consumption, a negative goal frame has shown a consistent persuasive effect and plays a greater role in self-benefiting consumption scenarios [19]. Research findings on credit card and fund subscription issues have both revealed that consumers exhibit a stronger purchase intention under a negative frame (“Not obtaining a credit card or purchasing funds will leave your financial life without protection”) compared to a positive information frame that highlights how “credit card and fund purchases can bring more security to your financial life” [4]. Other scholars have also found that, in charitable crowdfunding, individuals show a higher level of support for child safety seats under a negative goal framing [14]. Furthermore, Eberhardt et al. also conducted research using health-related elderly care products as an example, and their conclusions were consistent [20]. Contrary to previous views, recent research by Niu et al. in digital advertising shows that positive goal framing has an inverted-U effect on consumer responses: moderate increases enhance responses, but excessive framing reduces them [21].

It is evident from reviewing the above studies that the influence of goal-framing type on consumer decision-making is greatly affected by the product type. However, existing research has seldom focused on this aspect and has several limitations: Firstly, much of the goal-framing type research in the consumer domain is related to health issues, which can lead to confusion in research outcomes. This is because people’s subjective cognition is drawn to negative content when dealing with adverse information, and the adaptive warning system in negative situations results in negative information having a greater impact on judgment [22]. Secondly, most current studies have only examined the applicability of goal-framing type in highlighting different attributes of a product, without fully exploring the impact of the congruence between product type and goal framing on individual consumer decision-making. Lastly, no research has delved into the matching patterns of goal-framing type and product type that influence consumer decision-making: What is the psychological mechanism behind the congruence between the two affecting individual consumer decisions? Could this influence change under certain circumstances? To address the shortcomings of existing research and answer these questions, this paper will primarily clarify the significant role of product type in the influence of goal-framing type on individual consumer decision-making and further investigate its psychological mechanisms and boundary conditions.

2.2. Regulatory Focus Based on Product Type and Its Congruence with Goal-Framing Type

Starting from the perspective of product attributes, the most common categorization in marketing is dividing products into hedonic and utilitarian categories [23]. Specifically, hedonic goods refer to products that “provide aesthetic or sensory pleasure, fantasy, and enjoyment through emotional and sensory experiences” [24]. They offer individuals more consumption related to pleasure, and excitement associated with sensory experiences, such as snacks like potato chips and chocolate. On the other hand, utilitarian goods are defined as “more rationally cognitive, instrumental, and goal-oriented products that serve a function or are used for utilitarian tasks” [25], such as USB flash drives and hand sanitizers, which can meet certain consumer needs. The difference lies in the fact that consumers are regulated by two motivational systems—promotion and prevention—when purchasing hedonic and utilitarian products, respectively [26].

Given that the choice of using positive/negative goal framing in different domains or for different functions of the same product may lead to variations in individual behavioral decisions, many scholars have begun to focus on the role of regulatory focus and regulatory fit in this context. For instance, Kiene et al. found in the context of condom purchase that consumers showed a greater preference when the advertising copy emphasized health protection [27]; whereas, when the copy highlighted the function of condoms in maintaining relationships, individuals had a higher consumption intention under a negative goal framing. This result was also validated in a study on sunscreen. Raymond et al. discovered that consumers were attracted to a positive goal framing when the product’s whitening attribute was emphasized; conversely, when it was categorized as a necessity for preventing sunburn, consumers reported higher purchase intention under a negative goal framing [28]. This further confirmed the importance of regulatory fit in the use of goal-framing type. That is, when emphasizing the product’s attribute of sunburn prevention, which aligns with consumers’ basic functional cognition of sunscreen, the presentation of negative information creates a sense of loss, making the negative goal framing more attention-grabbing. On the other hand, when focusing on whitening effects, which exceeds consumers’ expectations of the product, it provides a more pleasurable and hedonic psychological experience on top of meeting basic functional needs, leading to greater product preference under a positive goal framing. Additionally, scholars have also conducted preliminary explorations into the differential effects of goal-framing type on various product types. However, research directly examining the matching effects of hedonic and utilitarian products with different types of goal framing is still lacking. This study argues that the information conveyed by a product activates different motivational systems, which in turn lead consumers to attend to and prefer different framing types that best align with the product. The resulting increase in purchase intention is thus an outcome of such congruence. Specifically, hedonic products are more compatible with positive goal framing, whereas utilitarian products are more compatible with negative goal framing. Based on this, the following research hypothesis is proposed:

H1. 

In consumer scenarios, individuals will exhibit higher purchase intentions for hedonic products advertised with positive goal framing and for utilitarian products advertised with negative goal framing.

2.3. The Mediating Role of Processing Fluency

Processing fluency, objectively, refers to the dynamic characteristics of the internal information processing in the human brain, primarily concerning processing speed and accuracy [29]. Subjectively, it is an individual’s subjective experience of the ease or difficulty of processing information [30]. In consumer scenarios, individuals’ attitudes and behaviors become more positive with an increase in perceived processing fluency, which ultimately leads to an increase in purchase intention [31]. As research progresses, scholars have begun to further explore the conditions under which processing fluency arises. Shen et al. studied paired products from the perspective of attribute congruence and found that consumers perceive contrast and assimilation effects in response to advertising stimuli under different goal-framing types [32]. That is, when two information stimuli are considered non-integral, the fluency of processing the first stimulus negatively affects the perceived evaluation of the subsequent stimulus. The higher the perceived difficulty in processing the information of the product presented first, the lower the processing fluency, and consequently, the higher the perceived processing fluency when processing the subsequent product, creating a contrast effect. Subsequent scholars have pointed out that the congruence of the match is a crucial factor influencing processing fluency. When product attributes show high congruence with the surrounding environment (such as background music), consumers experience cognitive pleasure and are more inclined to evaluate the product positively, thereby facilitating consumer decision-making [33]. Since information perception primarily involves the processing of content, conceptual alignment is more important than physical attribute congruence when studying products. Prior studies suggest that matching AI customer service and professional influencers with search products, and human customer service and entertainment-oriented influencers with experience products, enhances processing fluency and purchase intention [34,35]. Yet, the effect of matching hedonic versus utilitarian products with different goal-framing types on processing fluency remains unexplored.

As previously mentioned, utilitarian and hedonic products are common product categories in the marketing field. From the perspective of product attributes, utilitarian products primarily serve to meet instrumental and functional needs; the inability to acquire them promptly results in a sense of need deficiency, leading consumers to hold prevention-focused motivations towards them. For hedonic products, which offer more positive sensory experiences, individuals exhibit promotion-focused motivations in consumption activities due to the human pursuit of pleasure [26]. According to regulatory focus theory, the match between promotion focus under a positive goal framing (purchasing hedonic products) and prevention focus under a negative goal framing (purchasing utilitarian products) achieves consistency, which is reflected in the differences in consumer decision-making. When the decision object and goal information framing are matched, the strength of engagement with the information increases, leading to processing fluency effects and enhancing persuasive outcomes [36,37]. Therefore, when the goal-framing type aligns with different types of product regulatory matches, the conceptual relevance is high, enhancing the fluency of information processing. This positive subjective experience increases consumers’ preference for matched products, thereby raising their purchase intention. That is, the fluency of information processing may be a primary mediating mechanism between regulatory fit and consumer decision-making. Based on this, the following research hypothesis is proposed:

H2. 

Processing fluency mediates the influence of goal-framing type and product type on consumer decision-making, such that the congruence between “positive framing-hedonic products” and “negative framing-utilitarian products” initially elicits a fluent information processing experience, which in turn enhances individuals’ purchase intention.

2.4. The Impact of Time Pressure on Goal-Framing Type

In contemporary marketing, especially in contexts of live streaming-based commerce and time-limited flash sales, time pressure has consistently been regarded as an important situational variable affecting decision-making. Time pressure in the realm of consumption refers to the state of anxiety that consumers experience when they feel an increasing urgency to complete a task [38], typically considered to be induced by time constraints. Sanfey and Chang pointed out that when individuals are under high time pressure, due to a lack of sufficient cognitive resources for rational logical processing, they tend to rely more on their intuition and prior experience to handle related tasks [39].

For consumers, the presence of time pressure may also impact their decision-making under different message framings. For example, Martin et al. found that when participants were in situations involving a mix of losses and gains, high time pressure led to an increased aversion to losses and more irrational decisions under loss goal framing, indicating an amplified framing effect [40]. Hao and Liang’s research on live-streaming e-commerce indicates that under conditions of high time scarcity, scarcity messages framed in terms of supply are more effective in eliciting consumers’ impulsive buying and emotional arousal [41]. In recent years, the enhancing effect of time pressure on information framing has been confirmed by numerous studies. Several scholars have extended this conclusion by examining risk framing and promotional contexts, clarifying that time pressure acts as a moderator in consumer decision-making under these two scenarios [42,43,44]. Worth mentioning is that Roberts et al. approached this from a biological perspective, providing further explanations for the enhancing effect of time pressure on information framing by emphasizing the shift in early visual attention and strategic adaptation during the decision-making process [45]. They argue that time constraints lead to a strong shift in visual attention towards reward-predictive cues, and when combined with truncated information search, this magnifies the effect of the framing. The strategic information sampling process guided by attention is sufficient to drive individuals to select more convenient methods of information acquisition to meet their needs, thereby maximizing short-term gains.

However, empirical studies investigating how time pressure influences the effectiveness of different types of goal framing are still limited. Regarding information framing, especially goal-framing type, the dual-process theory attributes its effects to intuitive heuristics that automatically respond to stimuli, meaning that individuals follow an experiential processing strategy when dealing with different types of goal-framing information [46]. When high time pressure is present, the cognitive load increases, making it more difficult for individuals to fully mobilize resources for information analysis. As a result, certain elements under the information framing become more persuasive than usual, which is particularly true in the pursuit of efficient and rapid consumer decision-making [47]. Therefore, both the processing of goal-framing type and consumers’ processing of time information adhere to simple heuristic strategies. The increase in time pressure inevitably leads to a lack of resources for processing goal-framing type, prompting non-rational decision-making, i.e., an enhanced goal-framing effect [39,46]. Existing research has shown that under high cognitive load, individuals rely more on information that matches their regulatory focus [48]. Furthermore, as a presence that occupies cognitive resources, it will inevitably affect individuals’ attention and even memory during the consumption process, which will subsequently be reflected in consumers’ attention to advertising content [49]. To summarize, this paper suggests that eye-tracking technology can be used to clarify the role of time pressure on goal-framing type from the perspective of attention, and the following hypothesis is proposed:

H3. 

The presence of high time pressure will amplify the goal-framing effect. This is manifest as individuals spending longer periods fixating on content under positive goal framing and demonstrating a higher purchase intention.

3. Study 1: The Influence of Goal-Framing Type and Product Type on Consumer Decision-Making and the Mediating Role of Processing Fluency

3.1. Research Objective

Through a review of consumer decision-making research, it has been found that individuals exhibit differences in purchase intention based on different combinations of goal-framing type and product type. Furthermore, the congruence between these two factors may affect purchase intention by eliciting varying levels of processing fluency experiences among individuals, suggesting that processing fluency may mediate the influence of goal-framing type and product type on consumer decision-making. Therefore, the objective of Experiment 1 is to examine the impact of the congruence between different goal-framing types and product types on consumer decision-making and the mediating role of processing fluency.

3.2. Research Method

3.2.1. Experimental Design and Participants

Experiment 1 employed a within-subjects design with a 2 (Goal-Framing Type: Positive vs. Negative) × 2 (Product Type: Hedonic vs. Utilitarian) factorial structure. Using G-power 3.1 software, the effect size f was set to 0.25, the power to 0.95, the number of groups to 1, and the number of measurements to 4 (the experiment primarily examines individual differences in consumer decision-making for hedonic and utilitarian products under two framings). The α value was set at 0.05, resulting in a required sample size of 36. In the formal experiment, a total of 100 students with extensive online shopping experience were recruited to ensure that they could understand the tasks and make realistic purchase decisions. After removing responses that followed a pattern and data with missing content, the final analysis included data from 96 participants (M_age = 22.83), which was considered a sufficient sample size. Of these, 49 were female (51%) and 47 were male (49%). They all had at least a bachelor’s degree, and the average monthly online shopping expenditure was RMB 1630.

3.2.2. Experimental Procedure

The present study referred to the purchase task stimulation paradigm in the formal experiment [50]. Due to its high ecological validity and effective separation of the product, content, and decision-making stages, this paradigm is frequently used in research on consumer purchase decision-making (where purchase intention is used to indicate consumer decisions) [51,52]. The specific experimental flowchart is shown in Figure 1:

In the formal experiment, there were a total of 64 trials, with hedonic and utilitarian products presented completely at random. At the beginning of the experiment, participants were first presented with instructions to ensure they fully understood the content of the experiment. Before each trial started, a fixation point was presented for 500 ms, followed by the product phase, where participants were shown the name of a hedonic or utilitarian product (for 2 s), such as potato chips or umbrellas. Next, the content phase began, where participants were primarily presented with advertising copy for the product from the previous phase under either a positive or negative goal framing (for 4 s). Finally, the decision phase was reached, where participants were asked to evaluate their purchase intention for the product (with no time limit for the keypress response). After the formal experiment, participants were randomly presented with the advertising copy for each product under both positive and negative goal framing, and were asked to assess the processing fluency they experienced while reading each product advertisement. To avoid potential order effects, the presentation order of product type and goal-framing type was randomized and counterbalanced across participants, ensuring that sequence did not introduce systematic bias into the results.

3.2.3. Experimental Materials

To select the experimental materials and ensure their appropriateness, this study first chose 30 hedonic products and 30 utilitarian products, respectively. Subsequently, undergraduate and graduate students from a certain university were recruited as participants to complete a product attribute assessment (1 = utilitarian; 7 = hedonic) and an attractiveness index measurement (1 = very unattractive to me; 7 = very attractive to me) [53,54]. The basic procedure was to first present them with definitions related to hedonic and utilitarian products. After reading the materials, they were asked to self-report whether they understood the related definitions. Then, they were required to assess the product attributes, categorizing products scoring 3 or below as utilitarian products and those scoring 5 or above as hedonic products, while excluding neutral products with scores between 3 and 5. Additionally, to eliminate the influence of product attractiveness on consumer decision-making, products with high attractiveness indices (greater than 5) were removed. A total of 250 questionnaires were distributed, and 248 were returned, resulting in a response rate of 99.2%. Data from participants who self-reported not understanding the concepts of hedonic and utilitarian products after reading the questions were excluded, leaving a final count of 245 questionnaires. Through descriptive statistics, products with an average score of ≤3 in the product attribute assessment were selected as utilitarian products (a total of 16), including tissues, trash bins, shower gel, etc.; products with an average score of ≥5 were selected as hedonic products (a total of 16), including toys, game consoles, potato chips, etc. Moreover, the attractiveness indices for all the above products were below 5 (see Appendix A).

Next, advertising copy was crafted for each selected product under both positive and negative goal framing. Since the focus of goal framing is to emphasize the benefits of doing something or the losses that may occur from not doing it, appropriate advertising copy for each product was developed based on previous scholarly research [55]. After revisions were made based on the evaluations of eight advertising students and faculty, 96 participants were recruited to test the manipulation’s effectiveness. A 7-point Likert scale was used, with the item “This advertisement emphasizes: 1 = the positive outcomes of choosing the product, 7 = the negative outcomes of not choosing the product” [56]. Subsequently, a preference measurement was conducted for the content (1 = strongly dislike; 7 = strongly like).

After excluding participants who failed to understand the concepts of positive and negative goal framing, as well as those with incomplete responses, a total of 91 valid questionnaires were obtained. The material data were then processed using SPSS 24.0, and the effectiveness of the manipulation was assessed through paired-samples t-tests. Specifically, for different products, participants were more likely to perceive the advertisement as emphasizing the positive outcomes of purchasing the product after reading the positive goal framing, and as emphasizing the negative consequences of not purchasing the product after reading the negative goal framing, indicating that the manipulation of goal framing was effective. Moreover, for hedonic products, participants’ liking of the advertising copy did not differ significantly between the two types of goal framing t (n = 90) = 1.75, p = 0.08; similarly, for utilitarian products, participants also showed no significant difference in their liking of the advertising copy across different goal framings t (n = 90) = 1.45, p = 0.15.

Finally, based on the 32 products and advertising copy evaluated in the preliminary phase, Photoshop 2020 software was used to composite the materials, resulting in two versions of advertising copy for each product—one with a positive goal framing and the other with a negative goal framing. An example of the experimental materials is shown in Figure 2:

3.2.4. Measurement

Processing Fluency. In Experiment 1, the measure of participants’ processing fluency was a 5-item scale developed by Laura et al. [57]. This scale used a 5-point Likert scale, where participants were asked to rate their experience on five dimensions after reading the advertising copy. These dimensions included “degree of difficulty”, “fluency”, “ease of comprehension”, “clarity”, and “degree of effort”, with no reverse-scored items. The scale had a reliability coefficient (Cronbach’s alpha) of 0.87 in this experiment, indicating good reliability.

3.3. Results

3.3.1. The Influence of Goal Framing and Product Type on Consumer Decision-Making

To examine the impact of goal framing and product type on participants’ consumer decision-making, Experiment 1 conducted a 2 × 2 repeated measures analysis of variance with goal-framing type (1 = positive framing; 2 = negative framing) and product type (1 = hedonic product; 2 = utilitarian product) as independent variables, and purchase intention as the dependent variable. The consumers’ purchase intentions under different conditions are presented in

Table 1. Consumers’ purchase intentions for hedonic and utilitarian products under different type of goal framings (n = 96).

Goal-Framing Type	Product Type	Purchase Willing (M ± SD)
Positive type	hedonic product	6.06 ± 0.56
	utilitarian product	4.92 ± 0.64
Negative type	hedonic product	2.56 ± 0.78
	utilitarian product	3.61 ± 0.72

:

The results of the ANOVA revealed a significant main effect of goal-framing type on purchase intention, F (1, 95) = 1137.87, p < 0.001, η_²p = 0.92, indicating that individuals had a stronger purchase intention for products presented with a positive goal framing. The main effect of product type on purchase intention was not significant, F (1, 95) = 0.43, p = 0.51. The interaction between goal-framing type and product type was significant, F (1, 95) = 379.86, p < 0.001, η_²p = 0.80. Due to the significant interaction between goal-framing type and product type, a simple effects analysis was conducted. The results showed that under the positive goal framing, there was a significant difference in purchase intention between hedonic and utilitarian products, F (1, 95) = 193.49, p < 0.001, η_²p = 0.67, with a higher purchase intention for hedonic products (M = 6.06) than for utilitarian products (M = 4.92). Under the negative goal framing, there was also a significant difference in purchase intention between hedonic and utilitarian products, F (1, 95) = 123.61, p < 0.001, η_²p = 0.57, but the purchase intention for utilitarian products (M = 3.61) was higher than for hedonic products (M = 2.56). The interaction is illustrated in Figure 3. This finding suggests that consumers are more willing to purchase hedonic products presented with a positive goal framing and utilitarian products presented with a negative goal framing.

3.3.2. Mediating Role of Processing Fluency

Firstly, a 2 × 2 repeated measures ANOVA was conducted with goal-framing type (1 = positive framing; 2 = negative framing) and product type (1 = hedonic product; 2 = utilitarian product) as independent variables, and processing fluency as the dependent variable. The results revealed a significant main effect of goal-framing type, F (1, 95) = 552.92, p < 0.001, η_²p = 0.85, with participants experiencing higher processing fluency under positive framing (M = 14.12) compared to negative framing (M = 10.38). The main effect of product type was not significant, F (1, 95) = 3.90, p = 0.051, η_²p = 0.04. The interaction between goal-framing type and product type was significant, F (1, 95) = 124.94, p < 0.001, η_²p = 0.59. Simple effects analysis indicated that under positive framing, there was a significant difference in processing fluency between hedonic and utilitarian products, F (1, 95) = 172.97, p < 0.001, η_²p = 0.65, with hedonic product slogans (M = 14.74) exhibiting higher processing fluency than utilitarian product slogans (M = 13.51). Similarly, under negative framing, there was a significant difference in processing fluency between hedonic and utilitarian products, F (1, 95) = 48.82, p < 0.001, η_²p = 0.34, but in this case, utilitarian product slogans (M = 12.27) showed higher processing fluency than hedonic product slogans (M = 9.5).

Furthermore, to investigate the mediating role of processing fluency in the influence of goal-framing type and product type on consumer decision-making, the Bootstrap method proposed by Preacher and Hayes was primarily employed [58]. This method, which addresses the issues associated with the classic mediation testing approach, allows for a comparison of the significance of different mediation paths. Specifically, following the coding method of Liu et al. and considering the types of research variables, “positive framing” was coded as “1” and “negative framing” as “2” within the goal-framing type variable; similarly, “hedonic product” was coded as “1” and “utilitarian product” as “2” within the product type variable [56]. The PROCESS macro (Model 8, with 5000 bootstrap samples) was used to conduct a mediated moderation analysis [59]. The results revealed that processing fluency mediated the effect of the interaction between goal-framing type and product type on purchase intention (β = −0.46, SE = 0.01, Bootstrap 95% CI: [−0.55, −0.40]).

3.4. Discussion

Study 1 thoroughly investigated the impact of the congruence between goal-framing type and product type on consumer decision-making, as well as the mediating role of processing fluency. However, in real-world consumer situations, particularly with the acceleration of the pace of life and the rise of online shopping, marketers often use various strategies to create a sense of time urgency among consumers, prompting them to make quick decisions and purchases. The questions that arise are whether the influence of goal-framing type and product type on individual consumer decision-making exists under high time pressure, whether there are any changes in this influence, and whether this impact is also manifested at a more fundamental level of attention. These are the issues that require further investigation in this paper.

4. Study 2: The Impact of Time Pressure on Goal-Framing Type: Dual Evidence from Behavior and Eye Movement

4.1. Research Objective

In real-world consumer situations, besides the content of the product and advertisement itself, situational factors such as time can also significantly influence consumer purchasing behavior. Decision-making often requires a considerable amount of time, making time a crucial variable in facilitating consumer purchases. For consumers, since decisions are made within a continuous time spectrum, the finiteness of time is one of the objective conditions individuals must face. Changes in contemporary marketing practices have further highlighted the importance of time in live-streaming sales and limited-time promotions. Current research on the impact of goal-framing type and product type on consumer decision-making under different time pressures remains controversial. However, the prerequisite for consumer decision-making is attending to relevant information, which often indicates interest. Eye-tracking technology can be effectively used to explore the relationship between the emergence of purchase decisions and the content of interest, starting from the areas of interest attended to by participants. Therefore, Study 2 aims to adopt the same research paradigm as Study 1 and introduce eye-tracking metrics to provide physiological evidence for clarifying the impact of goal-framing type and product type on advertisement fixation and consumer decision-making under different time pressures.

4.2. Research Method

4.2.1. Experimental Design and Participants

Experiment 2 employed a within-subjects design with a 2 (Goal-Framing Type: Positive vs. Negative) × 2 (Product Type: Hedonic vs. Utilitarian) × 2 (Time Pressure: High vs. Low) factorial structure, using the same experimental paradigm as Study 1. The independent variables were goal-framing type, product type, and time pressure, while the dependent variables were purchase intention and fixation duration. Using G-power 3.1 software, the effect size f was set to 0.25, the power to 0.95, the number of groups to 1, and the number of measurements to 6 (the experiment primarily examined the differences in consumer decision-making for hedonic and utilitarian products under high and low time pressure with two advertising frames). The α value was set to 0.05, which calculated the required sample size for the experiment to be 28 participants. In the formal experiment, a total of 100 students with extensive online shopping experience were recruited to ensure that they could understand the tasks and make realistic purchase decisions. After excluding data with patterned responses and missing eye-tracking data, the final analysis included data from 90 participants (M_age = 21.67), which was considered a sufficient sample size. Of these, 48 were female (53%) and 42 were male (47%). They all had at least a bachelor’s degree, and the average monthly online shopping expenditure was RMB 1525.

4.2.2. Experimental Procedure

(1)

Experimental Preparation Phase

Since Experiment 2 controls time pressure as an independent variable, it was necessary to determine the response times for the high and low time pressure groups before the formal experiment began. Descriptive statistical analysis was conducted on the data obtained from Experiment 1, which yielded a mean response time of M ± SD = 1502.09 ± 462.46 milliseconds. Following the method established by previous researchers for determining high and low time pressure groups, the average response time was reduced and increased by 50%, respectively, to define the high time pressure group (751.05 ms) and the low time pressure group (2253.14 ms) [60].

(2)

Formal Experimental Phase

The research paradigm used in the formal experiment of Experiment 2 was the same as that of Experiment 1, and each participant was required to complete the experimental tasks for both the high and low time pressure groups. The experimental equipment used during the formal experiment was the Eyelink 1000 Plus eye-tracking system(manufactured by SR Research Ltd., Oakville, ON, Canada), set with a sampling rate of 500 Hz. The display resolution of the monitor presenting the experimental stimuli was 1024 × 768, with a refresh rate of 60 Hz. Before the experiment began, the participants’ eyes were calibrated, with the requirement that they sit 60 cm away from the computer screen and rest their chins on a U-shaped chin rest. The Hv9 calibration mode was used, which involved first calibrating the participants’ eye movements, followed by a validation step. The specific procedure is described as follows:

The formal experiment consisted of 64 trials, using the same experimental materials as Study 1. The high and low time pressure groups were divided into two sessions, each containing 32 trials, ensuring that the different time pressure conditions were balanced for each participant. Specifically, both hedonic and utilitarian products were divided into two groups: each of the high and low time pressure groups included 8 hedonic and 8 utilitarian products, along with their corresponding advertising slogans under positive and negative goal framing conditions. The entire experimental procedure was the same as Experiment 1, with the only difference being the variation in response time during the decision-making phase between the high and low time pressure groups. After completing the tasks in each time pressure group, participants were asked to fill out a time pressure scale to assess the perceived level of time pressure during the task.

In Study 1, to avoid potential order effects, the presentation order of products and goal-framing type was randomized and counterbalanced across participants, ensuring that sequence did not introduce systematic bias into the results.

4.2.3. Measurement

Time Pressure. In Experiment 2, the measure of participants’ perceived time pressure was a 5-item scale developed by Putrevu and Ratchford [61]. This scale used a 7-point Likert scale and required participants to rate the level of time tension they experienced while performing the task. The items included statements such as “I felt rushed to make my purchase decision” and “I had to make my purchase decision quickly to ensure the smooth progress of the next task”, with one reverse-scored item. This scale has been widely used in Chinese scholarly research and has demonstrated good reliability and validity [45]. In this experiment, the alpha coefficient was 0.79, indicating good reliability.

4.3. Results

4.3.1. Manipulation Check of Time Pressure

Data were analyzed using SPSS 24.0, and the results of the paired-samples t-test indicated t (89) = 10.88, p < 0.001, Cohen’s d = 1.15. This suggests that participants could distinctly perceive the difference in time pressure when completing the experimental tasks in the high and low time pressure groups. Specifically, individuals reported significantly lower levels of perceived time scarcity and urgency in the low time pressure condition (M = 2.76, SD = 0.88) compared to the high time pressure condition (M = 4.56, SD = 1.19). These results indicate that the manipulation was effective not only in terms of response times but also demonstrated consistency at the level of participants’ subjective perceptions.

4.3.2. The Impact of Goal-Framing Type and Product Type on Consumer Decision-Making and Advertisement Fixation Under Different Time Pressures

To investigate the influence of goal-framing type and product type on consumer purchase intention and advertisement dwell time under different time pressures, repeated measures ANOVAs were conducted separately with purchase intention and dwell duration as dependent variables, and time pressure, goal-framing type, and product type as independent variables. The data on consumer purchase intention and dwell times under different conditions are presented in

Table 2. Consumers’ purchase intentions and fixation times for positive and negative goal framing of different products under various time pressures (n = 90).

Time Pressure	Framing Type	Product Type	Purchase Willing (M ± SD)	Dwell Time (M ± SD)
High	Positive	Hedonic	6.70 ± 0.16	944.57 ± 208.50
		Utilitarian	5.66 ± 0.28	1144.70 ± 55.38
	Negative	Hedonic	2.37 ± 0.66	663.35 ± 69.69
		Utilitarian	3.46 ± 0.89	573.50 ± 87.82
Low	Positive	Hedonic	6.10 ± 0.19	803.24 ± 65.06
		Utilitarian	5.10 ± 0.25	926.04 ± 46.02
	Negative	Hedonic	2.36 ± 0.62	655.85 ± 84.11
		Utilitarian	3.33 ± 0.79	526.37 ± 57.10

.

• The Impact of Goal-Framing Type and Product Type on Consumer Decision-Making under Different Time Pressures

The analysis results indicate that the main effect of time pressure on purchase intention was significant, F (1, 89) = 71.56, p < 0.001, η_²p = 0.45, suggesting that participants reported higher purchase intentions under high time pressure. The main effect of goal-framing type on purchase intention was also significant, F (1, 89) = 2991.50, p < 0.001, η_²p = 0.97, indicating that participants were more inclined to purchase products presented in a positive goal framing. Moreover, the interaction between time pressure and goal-framing type was significant, F (1, 89) = 36.10, p = 0.001, η_²p = 0.29. Further simple effects analysis revealed that under high time pressure, there was a significant difference in purchase intention after viewing advertisements with positive and negative goal framing, F (1, 89) = 2252.43, p < 0.001, η_²p = 0.96. Specifically, participants showed a higher purchase intention for products advertised with positive goal framing (M = 6.18) compared to negative goal framing (M = 2.92). Similarly, under low time pressure, there was a significant difference in purchase intention after viewing advertisements with different goal-framing type, F (1, 89) = 1532.96, p < 0.001, η_²p = 0.95. Again, participants reported higher purchase intentions for products presented with positive goal framing (M = 5.60) than those with negative goal framing (M = 2.85). Moreover, participants under high time pressure reported significantly higher purchase intentions for products with positively framed advertising compared to those under low time pressure, F (1, 89) = 652.55, p < 0.001, η_²p = 0.88 (see Figure 4).

Additionally, the interaction effect between goal-framing type and product type was significant, F (1, 89) = 717.03, p < 0.001, η_²p = 0.89. Further simple effects analysis revealed that under the positive goal framing, there was a significant difference in purchase intention between hedonic and utilitarian products, F (1, 89) = 2180.04, p < 0.001, η_²p = 0.96. Specifically, the purchase intention for hedonic products (M = 6.40) was higher than that for utilitarian products (M = 5.38). Under the negative goal framing, there was also a significant difference in purchase intention between hedonic and utilitarian products, F (1, 89) = 223.54, p < 0.001, η_²p = 0.72, with a higher purchase intention for utilitarian products (M = 3.40) compared to hedonic products (M = 2.38) (see Figure 5).

2.

The Impact of Goal-Framing Type and Product Type on Ad Fixation under Different Time Pressures

Before analyzing the eye-tracking data, the relevant content was preprocessed. Eye-tracking metrics can generally be divided into fixation indices and saccade indices, both of which can reveal issues related to individual attention. In the field of consumer decision-making, dwell time is the most valuable reference, as it refers to the duration of attention an individual focuses on a region of interest, and its length is related to the extraction and processing of information [62,63]. During the product selection process, consumers are strongly influenced by product-related information. The visual cognitive system controls eye fixations to direct attention to the most information-rich and important areas of the viewing scene based on actual needs. It can be said that dwell time directly reflects the processing of individual visual cognition. Therefore, this paper primarily selects dwell time as the main analytical metric.

Definition of Regions of Interest. This study primarily divides the eye-tracking areas to include the positive and negative goal framing information content within the advertisement copy, as shown in Figure 6.

The analysis results indicate that the main effect of time pressure on advertisement fixation time was significant, F (1, 89) = 212.08, p < 0.001, η_²p = 0.70, suggesting that the greater the time pressure, the longer the participants’ fixation on the advertisement. The main effect of goal-framing type on advertisement fixation time was significant, F (1, 89) = 2275.14, p < 0.001, η_²p = 0.96, indicating that participants paid more attention to the information content under positive goal framing. The main effect of product type on advertisement fixation time was significant, F (1, 89) = 11.09, p < 0.005, η_²p = 0.11, showing that participants were more likely to focus on hedonic products. The interaction effect between time pressure and goal-framing type was significant, F (1, 89) = 115.61, p < 0.001, η_²p = 0.64. Further simple effects analysis revealed that under high time pressure, there was a significant difference in fixation time between positive and negative goal-framing messages, F (1, 89) = 1396.93, p < 0.001, η_²p = 0.94. Specifically, participants fixated longer on advertisement messages under positive goal framing (M = 1044.63) compared to negative goal framing (M = 618.43). Similarly, under low time pressure, there was a significant difference in fixation time between positive and negative goal framing advertisement messages, F (1, 89) = 1425.97, p < 0.001, η_²p = 0.94. Again, participants fixated longer on advertisement messages under positive goal framing (M = 864.64) than under negative goal framing (M = 591.11) (see Figure 7).

In addition, the interaction between time pressure and product type was significant, F (1, 89) = 17.29, p < 0.001, η_²p = 0.16. Further simple effects analysis revealed that under high time pressure, there was a significant difference in fixation time between hedonic and utilitarian product advertisements, F (1, 89) = 18.04, p < 0.001, η_²p = 0.17. Specifically, individuals spent more time fixating on utilitarian product advertisements (M = 859.10) compared to hedonic product advertisements (M = 803.96). In contrast, under low time pressure, there was no significant difference in fixation time between hedonic and utilitarian product advertisements, F (1, 89) = 0.22, p = 0.64. Moreover, participants under high time pressure exhibited significantly longer fixation durations for products with positively framed advertising compared to those under low time pressure, F(1, 89) = 264.32, p < 0.001, η_²p = 0.75 (see Figure 8).

Finally, the interaction between goal-framing type and product type was significant, F (1, 89) = 296.80, p < 0.001, η_²p = 0.77. Further simple effects analysis revealed that for hedonic products, there was a significant difference in fixation time between positive and negative goal-framing information, F (1, 89) = 256.77, p < 0.001, η_²p = 0.74. Specifically, participants fixated longer on positive goal-framing information (M = 873.90) than on negative goal-framing information (M = 659.60). For utilitarian products, there was also a significant difference in fixation time between positive and negative goal-framing information, F (1, 89) = 4480.40, p < 0.001, η_²p = 0.98. Similarly, participants fixated longer on positive goal-framing information (M = 1035.37) than on negative goal-framing information (M = 549.90) (see Figure 9).

4.4. Discussion

The eye-tracking results of Experiment 2 not only reveal the influence of time pressure on consumers’ attentional allocation and behavioral tendencies during information processing and decision-making but also provide new empirical evidence for understanding attentional mechanisms in consumer decision-making. Specifically, regardless of product type, consumers tended to focus more on positive framing, and this attentional preference was particularly pronounced under high time pressure, further influencing their purchase intentions. In addition, the findings indicate that time pressure strengthens consumers’ attentional engagement with utilitarian products, suggesting that under conditions of urgency, individuals are more likely to rely on functional and diagnostic cues. It is noteworthy that although positive framing universally enhances consumers’ attention, its effect appears to lie primarily in attracting attention rather than directly driving purchase behavior. Only when advertising messages are aligned with product attributes can the attentional advantage induced by positive framing be more effectively transformed into purchase intention, thereby facilitating actual consumption behavior.

5. General Discussion

Based on the Regulatory Focus Theory, this paper examines the influence of different goal-framing type (positive vs. negative) and product types (hedonic vs. utilitarian products) on consumer decision-making. Through two experiments, this paper finds that product advertisements with positive framing always elicit higher purchase intentions from individuals, an effect that is supported by both behavioral and eye-tracking experiments. Additionally, this paper verifies that hedonic products with positive framing and utilitarian products with negative framing enhance individual consumption intentions. Furthermore, it clarifies the internal mechanism by which the congruence between goal-framing type and product type affects individual consumer decisions (Study 1). Specifically, purchasing different types of products initially activates an individual’s motivation system for promotion (hedonic products) or prevention (utilitarian products). Therefore, when the information presented in the goal-framing type is congruent with this motivation, the processing difficulty of the relevant information is reduced, bringing about a psychological experience of processing fluency. Subsequently, this positive emotional experience further promotes the individual’s purchase intentions for the related products. Building on this, Study 2 employed eye-tracking technology to clarify the amplifying effect of time pressure on the goal-framing effect. Specifically, when consumers perceived a high level of time pressure during the decision-making process, they allocated more visual attention to positive framing and exhibited higher purchase intentions.

In addition, the eye-tracking experiment revealed several previously overlooked findings. Experiment 2 showed that positive framing significantly increased consumers’ purchase intentions under both high and low time pressure conditions, exhibiting a consistent positive effect. In contrast, when fixation duration was used as the dependent variable, the interaction between time pressure and goal-framing type showed a similar pattern; however, an additional interaction between time pressure and product type emerged, such that under high time pressure, consumers exhibited significantly longer fixations on utilitarian products than on hedonic products. This finding is consistent with the conclusion drawn by Wei et al. that in the early stage of cognitive processing, the match between positive framing and utilitarian products can significantly enhance consumers’ cognitive attention [64]. Moreover, the interaction between goal-framing type and product type was also significant, yet unlike the matching effect observed for purchase intention, consumers displayed longer fixation durations for positively framed information regardless of whether the product was hedonic or utilitarian.

This aforementioned pattern can be explained by differences between attention and decision-making at the cognitive processing level. First, fixation duration reflects not only interest or preference but also cognitive effort and information evaluation, so “looking longer” does not necessarily equate to “wanting to buy more” [65,66]. Positive framing appears to confer a general attentional advantage, likely driven by affective and motivational factors [67]: positive framing messages elicit approach tendencies, positive affect, or hope, and their semantic familiarity and processing fluency generate a dual effect of ease and engagement, leading consumers to allocate more attention to positive information during the attentional stage. In contrast, purchase intention represents an integrative decision stage, in which individuals weigh product attributes against message framing: when purchasing hedonic products, positive framing aligns with goals, facilitating self-justification, and social acceptability; when purchasing utilitarian products, negative framing may be more persuasive due to risk-avoidance logic. Thus, matching effects are primarily observed at the decision stage, whereas the general attentional attraction of positive framing dominates at the attentional stage.

Furthermore, time pressure moderates the aforementioned process. Under high time pressure, consumers tend to rely on heuristic processing and prioritize cues that quickly reduce uncertainty or enhance controllability [68], thereby increasing attention to utilitarian products. At the same time, high pressure amplifies the influence of fluent cues, making positive information more likely to capture attention [69]. This dual mechanism explains why, under high-pressure conditions, attention allocation is biased toward utilitarian cues while positive framing still elicits longer fixations. Finally, fixation duration primarily reflects immersion and engagement rather than processing difficulty alone, which accounts for why positive information is associated with longer fixation times in eye-tracking data. Overall, these findings suggest that the general positive attentional bias at the attentional level and the framing–product matching effect at the decision level operate independently across different cognitive processing stages, jointly shaping consumers’ information processing and purchase behavior, and providing new theoretical and empirical insights into attention mechanisms in consumer decision-making.

6. Implications

6.1. Theoretical Contributions

Firstly, this study enriches the application of goal-framing type in the field of consumer research. Previous studies have not reached a consensus on whether positive or negative goal framing is more effective in enhancing consumer purchase intentions. Some research suggests that positive goal framing facilitates purchases [16,18,70], while other studies find that negative goal framing has a better persuasive effect [27]. This study argues that these contradictory conclusions are due to the use of different types of products as experimental materials across studies. By deeply examining the variable of product type, this study offers new insights into the field: the effectiveness of goal-framing type should consider not only the attributes of the framing content but also its alignment with specific product types. While highlighting positive information content is crucial, the congruence of hedonic product advertisements with positive goal framing and utilitarian product advertisements with negative goal framing is an important factor in driving consumer purchases.

Secondly, this study is the first to explore the psychological mechanism by which the congruence between goal-framing type and product type influences individual consumer decision-making. Previous research has suggested that negative goal framing is more persuasive based on the perspective that individuals are more inclined to attend to negative content, as subjective cognition is drawn to it and the adaptive warning system in negative contexts amplifies its impact on judgment [22]. However, this mechanism fails to explain findings in some studies where positive goal framing in advertising copy is more effective in enhancing purchase intentions. Drawing on Regulatory Focus Theory and Match Theory, this study provides an explanation for the persuasive effects of positive and negative goal framing in consumer decision-making from a motivational perspective, clarifying the mediating role of processing fluency, and offers a new perspective for future research.

Finally, this study also enriches the research on the effects of time pressure on goal-framing effects based on real-life live marketing situations. Previous studies have demonstrated that the time pressure promotes non-rational decision-making under risk framing and promotional scenarios from both behavioral and attentional perspectives [43,44,45], but its impact on goal-framing type remains unclear. In Experiment 2, by systematically manipulating time pressure and the scientific evaluation of goal-framing type experimental materials, this study collectively demonstrated the amplifying effect of time pressure on goal-framing type from both behavioral and eye-tracking perspectives.

6.2. Practical Implications

In the era of rapid consumption, effectively attracting consumer attention and persuading them to make purchases holds significant practical importance [70]. Meanwhile, new media technologies are playing a crucial role in transforming interactive marketing [71,72]. This study offers several actionable insights for marketing practice.

First, leveraging positive goal framing. Since positively framed messages significantly increase attention and purchase intentions, marketers should prioritize gain-focused wording (e.g., “enhance your lifestyle”) rather than loss-focused expressions. Advertising copy should emphasize benefits such as enjoyment, health, or convenience to maximize persuasive impact.

Second, matching frame type with product category. Findings highlight a matching effect: hedonic products perform better under positive framing, whereas utilitarian products benefit from negative framing. Managers should design differentiated advertising templates based on product type. For example, food, cosmetics, and leisure products should emphasize enjoyment and gains, while insurance or cleaning products can highlight risk avoidance. A/B testing can be employed to evaluate frame–product congruence before large-scale campaigns.

Third, optimizing attention capture. Although behavioral responses depend on frame–product matching, attention was consistently higher for positively framed ads regardless of product type. This suggests that positive framing should be applied in high-traffic channels (e.g., e-commerce homepages, social media feeds) to attract initial attention, followed by frame–product matching strategies to convert attention into purchases.

Finally, incorporating time pressure. Time pressure amplifies the persuasive power of positive framing. Managers can leverage this by integrating countdowns, flash sales, and limited-time promotions with positively framed copy (e.g., “Limited time—enjoy a better life today”). This dual strategy enhances both attention and purchase intentions under urgency. Overall, firms should: (a) adopt positive framing as the default approach; (b) align framing with product attributes for effective persuasion; (c) use positive framing in attention-intensive contexts; and (d) combine positive framing with time-limited promotions to maximize conversion. These strategies allow firms to allocate marketing resources more effectively, strengthen consumer engagement, and ultimately improve profitability.

7. Limitation and Future Research

Although this study has established that the congruence between goal-framing type and product type can influence consumer decision-making, providing new insights for the existing literature and managerial implications, several limitations remain. First, in terms of research methodology, this study relied on experimental methods, and the research design was constrained by the information presented in the experimental materials. Future research could consider employing VR or field experiments to further investigate the dynamic effects of goal-framing type on individual consumer decision processes [73].

Second, with respect to the relevant variables, this study focused solely on the moderating role of time pressure in the effect of goal-framing type and product type on consumer decision-making, as well as its amplifying effect on goal framing. Future research could further explore how individual characteristics and cultural or value-related factors [74,75] influence the effects of goal framing, providing richer and more compelling narratives [76].

Third, regarding the selection of participants and experimental materials, this study primarily examined the behavior of young consumers in the context of material consumption and accordingly designed purchase tasks. Since consumption can be broadly categorized into material and experiential types [65]. Therefore, future research could extend this framework to experiential consumption. Additionally, it could examine consumer decision-making across different cultural backgrounds and age groups under conditions of alignment between goal framing and product type, thereby further enhancing the external validity of the findings. Finally, although eye-tracking technology was employed to examine the impact of time pressure on attentional allocation, the study did not investigate the underlying neural mechanisms in depth. Future research could incorporate cognitive neuroscience methods, such as ERP and functional near-infrared spectroscopy (fNIRS), to examine how external environmental factors affect brain activity during consumer decision-making.