Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2, 1283-1300 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i2.4750 © 2024 by the author; licensee Learning Gate © 2025 by the author; licensee Learning Gate History: Received: 19 December 2024; Revised: 22 January 2025; Accepted: 27 January 2025; Published: 12 February 2025 * Correspondence: adilfait@yahoo.com The Mental Image of a Product and its Influence on Purchase and Repeat Purchase Decisions by Saudi Consumers Adil Mohamed Ahmed Fait1* 1Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Kingdom of Saudi Arabia; adilfait@yahoo.com (A.M.A.F.). Abstract: This study examines how product aesthetics, self-image congruence, perceived quality, and exposure to advertising collectively contribute to consumer perception and drive purchasing behavior among Saudi consumers. Grounded in Consumer Perception Theory (CPT) and Self-Congruity Theory (SCT), the study employs quantitative research on data collected from 871 respondents. The findings indicate that product aesthetics and perceived quality strongly influence purchasing decisions, while self-image congruence and exposure to advertising play significant but secondary roles. Furthermore, purchasing decisions mediate the relationship between these independent variables and repeat purchase behavior, reinforcing the importance of a product’s mental image in fostering long-term consumer loyalty. The study offers practical insights for marketers, emphasizing the need for brands to align their design, quality, and promotional efforts with consumer expectations and cultural values in the Saudi market. Future research should explore longitudinal impacts and the impact of external factors, such as technological advancements and market competition, on product perception and consumer loyalty. Keywords: Mental image, product aesthetics, self-image congruence, perceived quality, exposure to advertising, purchase decisions, and repeat purchase behavior. 1. Introduction A product’s mental image is critical to consumers’ purchasing decisions in competitive world markets. The mental image is formed from diverse processes, including consumers’ prior experience, advertising, branding, and social influences. It significantly affects how people assess and perceive products [1, 2]. The mental image is a complex construct involving functional attributes (e.g., design and quality) and the emotional and psychological associations people give the product. Saudi Arabia is a fast-changing market with increasing consumer power and digital penetration. It is critical for companies involved in the Saudi market to understand how product image affects consumer behavior [3]. This study seeks to understand how Saudi consumers' image of a product affects their purchasing behavior. Brands strive to distinguish themselves from others in the ever-more competitive and globalized economy and depend on how consumers perceive their products. This mental image is formed from aesthetics, emotional resonance, brand reputation, and perceptions of quality [4]. Saudi consumers experience a marketplace with diverse local and foreign products, so it is essential to understand how their mental images of these products affect the decisions they make regarding initial purchases and repeat purchases [5]. However, marketers need to know precisely which aspects of these product images are most significant in their effect on purchasing decisions, and they need to use that knowledge to develop more effective brand images [6]. A product’s mental image in the Saudi market is not only affected by the quality and a brand’s traditional aspects of the brand but also by how compatible it is with local cultural values and consumer 1284 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate preferences [7]. This study is, therefore, essential for two reasons. First, it adds to growing academic knowledge about how non-Western, particularly Middle-Eastern consumers behave. The Kingdom of Saudi Arabia has experienced massive change in terms of economic diversification and social and cultural reforms promoted by the Saudi Vision 2030 [8]. Second, the study gives critical practical insights for companies involved in the Saudi market so that they can position their products better by understanding how Saudi consumers view and interact with product images [9] thereby enhancing their marketing strategies, resonating with local consumers better and capitalizing on market growth. This study, therefore, aims to explore comprehensively the diverse aspects of people’s mental images of products and their effect on purchasing behaviour. In particular, the study seeks to assess the impact of aesthetics, self-image congruence, perceived quality, and exposure to advertising on the initial purchasing decisions made by Saudi consumers [10] and how the mental image thus formed affects repeat purchases and customer loyalty [11]. 2. Literature Review The mental image of a product represents the collection of perceptions, associations, and beliefs that consumers develop regarding a specific product, encompassing both tangible and intangible attributes [2]. This construct is multidimensional and influenced by several critical factors, such as product aesthetics, perceived quality, functionality, branding strategies, and marketing communications [12]. The mental image significantly impacts consumers' evaluations of products and serves as a key determinant of their purchase intentions and decision-making processes [13]. Product aesthetics, including design, color, and overall appearance, foster a positive mental image by enhancing visual appeal and establishing emotional connections [14]. Likewise, perceived quality, which includes attributes like durability, reliability, and performance, reinforces consumer trust and mitigates perceived risks, further shaping the product’s mental representation [15]. Branding efforts and advertising contribute significantly by associating the product with specific cultural values, narratives, or aspirational lifestyles, enhancing its desirability and relevance to target audiences [4]. The mental image of a product is dynamic, constantly evolving through consumer experiences, exposure to marketing communications, and the influence of word-of-mouth interactions [16]. A robust and positive mental image strengthens brand equity, fosters emotional engagement, and cultivates customer loyalty by ensuring alignment between the product and consumer expectations and preferences [2]. Conversely, inconsistencies in product quality or discrepancies between advertised claims and actual performance can result in negative associations, potentially eroding consumer trust and damaging the brand's reputation [12]. The mental image’s significance lies in its function as a cognitive shortcut, allowing consumers to rapidly evaluate a product’s suitability based on pre-existing perceptions rather than engaging in exhaustive deliberation [13]. Therefore, maintaining a strong and positive mental image through strategic product design, consistent branding, and customer-focused marketing practices is essential for ensuring long-term competitiveness, market sustainability, and lasting consumer relationships [4]. 2.1. Theoretical Basis This study of the influence of product mental image on consumer purchasing decisions is rooted in several significant theories, which seek to explain how consumers form mental pictures of products and how they influence their behavior. Consumer perception theory (CPT), coming from cognitive psychology, proposes that consumers make decisions about purchases based on their perceptions of products, which are formed by such factors as advertising, product appearance, and prior experience [17] along with perceived quality and sensory information, from visual design, texture and packaging, and such abstract qualities as the alignment of products with their identities [18]. Mental images influence consumer behavior by generating expectations about product performance and value, thus influencing purchasing decisions. CPT explains how consumers interpret and respond to market stimuli, with perceived quality, aesthetics, and self-image congruence shaping their decisions [19]. 1285 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate Self-congruity theory (SCT) considers how people’s concepts of self-interact with their product preferences [20] proposing that consumers tend to choose products they perceive as aligning with their self-image. Mental images of products, therefore, have a critical role in determining consumers’ belief in the alignment of products with their identities [21]. When consumers view a product as a reflection of who they are or what they aspire to be, they are more likely to purchase it and do so again in the future [22]. SCT, therefore, suggests that a product’s alignment with someone’s concept of self (its symbolic meaning) is often more important than price or functional attributes like perceived quality. This theory is especially relevant to this study because it links consumers’ ideas of self with external cues such as branding and advertising in forming mental images that promote purchasing behavior [23]. CPT and SCT form the theoretical basis for understanding how mental images influence purchasing decisions. However, they are supported by additional consumer behavior theories, such as attitude theory (AT), which proposes that people’s feelings and beliefs contribute to their attitudes to products, directly affecting purchasing decisions [24]. Positive mental images created by advertising and prior experience can thus make consumer attitudes to products more positive, increasing the likelihood of purchasing them. Brand image theory (BIT) emphasizes the significance of brand image in developing consumer perceptions. A strong brand image contributes to a positive mental image of purchased products, leading to more repeat purchases [25]. These four theories offer a comprehensive framework for exploring how buying decisions are affected by mental images, which are, in turn, formed by factors such as perceived quality, aesthetics, self-image congruence, and exposure to advertising. 2.2. Conceptual Model for the Study The conceptual framework (refer to Figure 1) systematically explores the relationship between the independent variables, the mediating variable of Purchasing Decision (PD), and the dependent variable of Repeat Purchase Behavior (RPB). The independent variables—Product Aesthetics (PA), Self-Image Congruence (SIC), Perceived Quality (PQ), and Exposure to Advertising (ETA)—collectively influence consumer perceptions and shape their purchasing decisions. Product Aesthetics enhance consumer attraction through visual appeal, while Self-Image Congruence fosters emotional resonance by aligning product attributes with the consumer’s identity. Perceived Quality plays a critical role in building trust and mitigating perceived risks, while Exposure to Advertising strengthens the mental image of the product and amplifies its appeal. The mediating variable, Purchasing Decision (PD), is the link between the independent variables and Repeat Purchase Behavior (RPB), reflecting the consumer’s decision to buy a product based on the aggregate influence of the variables. A robust purchasing decision signifies the successful establishment of a compelling mental image of the product. Meanwhile, the dependent variable, Repeat Purchase Behavior (RPB), assesses the probability of consumers re-engaging with the product, highlighting long- term loyalty driven by consistent satisfaction and alignment of the product with consumer expectations. This framework illustrates the intricate interplay between the mental image of a product, consumer decision-making processes, and loyalty behaviors, emphasizing the pivotal role of strategic marketing initiatives and innovative approaches in fostering initial purchases and sustained repurchase behaviors. 1286 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate Figure 1. Conceptual model for the study. 2.3. Product Aesthetics, Purchase Decisions Research has found that aesthetic appeal is significant in consumers' initial product assessments and affects purchasing decisions [26]. Likewise, the aesthetics of shop design and products considerably influence buying decisions and customer retention in the long term [27]. A study on consumer electronics showed that aesthetic appeal affects consumer willingness to purchase again directly [28]. In fast-moving consumer goods, aesthetics enhanced customers' satisfaction, leading to RPB [29]. Mobile phone users value the aesthetics of their devices highly, which influences initial purchase and, thereafter, their brand loyalty [30]. A strong relationship has been shown between the visual appeal of packaging and repeat purchases, especially in highly competitive markets [31]. Regarding luxury products, aesthetics are a key factor in RPB by affluent consumers, who value design uniqueness and product exclusivity [32]. This leads to the first hypothesis for this study. H1: The aesthetics of product design makes purchasing more likely and increases the likelihood of repeat purchases. 2.4. Self-Image Congruence, Purchase Decisions Consumers are more likely to buy products that align with their self-image and buy them frequently. People repeat purchases when products fit their ideal self-image [33]. SIC significantly affects purchase decisions in the beauty and personal care sector [34]. SIC encourages repeat purchases of consumer electronics [35] luxury goods [36] and fashion accessories, particularly status-based items [37]. Similarly, guests who identify with a hotel brand image are more likely to return (Nguyen et al., 2020). Research on environment-friendly products has shown that SIC, concerning sustainability messages and brand, has a strong effect on initial and repeat purchasing [38-40]. Product packaging for organic goods draws on SIC to affect purchase decision-making and RPB [41]. This leads to the second hypothesis for this study. H2: Self-image congruence with the product affects purchase decisions significantly and encourages repeat purchases. 2.5. Perceived Quality, Purchase Decisions Much research has considered PQ, purchasing decisions, and RPB in diverse sectors, with PQ being the significant determinant of RPB [42, 43]. High quality is essential in the car industry for shaping purchasing decisions and RPB [44]. PQ of smartphones enhances customer loyalty and, therefore, RPB [45] and with luxury goods, it is linked to brand image and RPB, especially with high-prestige products [46]. Likewise, PQ is essential in hospitality, leading to repeat patronage and stronger customer relationships [47]. In the service and e-commerce sectors, service quality is all the more 1287 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate critical, as consumers can evaluate the product or service in advance, so high service quality significantly influences RPB [48, 49]. This leads to the third hypothesis of this study. H3: Perceived quality decides to purchase more likely and encourages repeat purchasing behavior. 2.6. Exposure to Advertising, Purchase Decisions Many studies in diverse industries have examined ETA, purchasing decisions, and RPB. In retail, exposure to targeted advertising positively affects initial and subsequent purchases, as more personalized advertising has a more significant impact [50]. Creative advertising increases initial purchasing and stimulates repeat purchases by maintaining awareness of a brand [51]. Exposure to car advertisements directly influences consumers' purchasing decisions and leads to repeat purchases, especially when they emphasize product features with which consumers feel connected [52]. In the fashion industry, visual advertising in magazines and online affects RPB, especially where there is a focus on desirable lifestyles [53]. ETA in the luxury goods sector significantly affects purchases and RPB [54]. Long-term ETA increases positive brand associations and, thus, the likelihood of repeat purchases [55]. Repeated exposure to print advertising has a significant impact on recall, having a positive influence on initial purchasing and RPB [56]. Television advertising affects purchasing decisions deeply, with repeated exposure increasing interest in brands and repeat purchases [57]. In e-commerce, exposure to advertisements that remind past customers of previous purchases significantly affects their repeat purchasing [58]. Online banner advertising helps shape purchasing decisions, as repeated exposure increases the likelihood of RPB [59]. All of this leads to the fourth hypothesis of this study. H4: Exposure to advertising positively impacts purchasing decisions and enhances repeat purchase behavior. 2.7. Purchasing Decisions, Repeat Purchasing Behaviour Those consumers who make initial purchasing decisions according to strong brand associations tend to repurchase the same brands, as has been demonstrated for electronics [60]. Similarly, those who decide to buy based on price and convenience tend to engage in RPB according to habitual purchasing patterns in the fast-moving consumer goods sector [61]. Product features, reliability, and performance in the mobile phone and car sectors affect initial purchases and generate customer loyalty when the product meets their functionality expectations [62, 63]. Personal recommendations and ease of use in e- commerce directly affect RPB as customers return to platforms offering a seamless purchasing experience [64]. Advertising affects these relationships, as those exposed to persuasive advertisements are likelier to exhibit RPB when those advertisements fit their motivations [65]. Similarly, when fashion consumers purchase based on visual appeal and brand identity, they tend to buy again from the same brand as long as the product aligns with their style preferences [66]. All of this leads to the fifth hypothesis of this study. H5: A favorable purchase decision strongly influences repeat purchasing behavior. 3. Study Methodology The study employed a quantitative research methodology to investigate the impact of a product's mental image on purchase and repeat purchase decisions among Saudi consumers. This approach was selected for its effectiveness in analysing relationships among key variables, including product aesthetics, self-image congruence, perceived quality, exposure to advertising, purchasing decisions, and repeat purchase behavior, through rigorous statistical analysis and empirical testing. The research design adopted a descriptive and explanatory framework, emphasizing identifying causal relationships and patterns within the data. The study sample comprised 871 participants, carefully chosen to represent a diverse cross-section of Saudi consumers. A stratified random sampling technique was utilized to ensure balanced representation across demographic categories such as age, gender, income, and educational background. 1288 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate This approach enhances the validity and generalizability of the findings to the broader population. Data collection was facilitated through a structured questionnaire to capture comprehensive insights into the examined variables. The survey instrument was constructed using validated scales derived from existing literature, with appropriate modifications to align with Saudi Arabia's cultural context and the study's specific objectives. It included Likert-scale items to measure the independent variables (product aesthetics, self- image congruence, perceived quality, and exposure to advertising), the mediating variable (purchasing decision), and the dependent variable (repeat purchase behavior). The questionnaire was distributed through both online platforms and physical channels, ensuring broad accessibility and encouraging high levels of participation among respondents. 4. Data Analysis and Results The researcher personally surveyed a sample of consumers in Saudi Arabia, using a random sampling approach and manual and e-mail distribution in October 2024. Of the 1021 questionnaires distributed, 871 were deemed suitable for analysis. The instrument's reliability was assessed using Cronbach’s Alpha, and structured equation modelling (SEM) analysis was conducted using AMOS to derive the results. The first section of the questionnaire was devoted to demographic information, such as age, gender, monthly income, and product categories primarily purchased. Generally, the active population among Saudi consumers is appropriate for this research. Table 1. Frequencies of respondents’ answers to the demographic questions. Demographic Information Options Number Percentage Age Under 18 98 0.11 18-25 188 0.21 26-35 163 0.18 36-45 177 0.20 Over 45 245 0.28 Gender Male 401 0.46 Female 470 0.54 Monthly income Under 5,000 SAR 179 0.21 5,000-10,000 SAR 271 0.31 10,000-15,000 SAR 288 0.33 Over 15,000 SAR 133 0.15 What products do you buy? Electronics 167 0.19 Fashion 202 0.23 Automotive 174 0.20 Household Goods 230 0.26 Other 98 0.11 Total 871 100 Table 1 shows that the largest group of respondents (28%) is over 45. More than half (54%) of the respondents were females. Respondents with a monthly income ranging between 10,000 and 15,000 SAR represented 33% of the sample, followed by those whose income ranged between 5,000 and 10,000 SAR) (31%). When asked which product categories they purchase primarily, their answers were most commonly household goods, at 26%, followed by fashion, at 23% of the total sample. 4.1. Goodness of Fit In this section, descriptive statistics and cross-correlation coefficients provide a basic understanding of the data and the relationships between variables. Descriptive statistics summarise the data, while cross-correlation coefficients help determine the strength and direction of relationships between variables. On the other hand, tests of linearity, normality, and homoscedasticity ensure the 1289 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate accuracy of the statistical model, as the relationship between variables is verified; normality evaluates the distribution of the data, and symmetry ensures constant variation across data points. Evaluation of the measurement model also focuses on the reliability and validity of the tools used in the study, ensuring that the constructs are measured accurately and that the results are robust and valid for explaining the mental image of a product and its influence on purchase and repeat purchase decisions by Saudi consumers. 4.1.1. Analysis of Structural Equation In the present research, a model is used to show the relationship between the six variables of mental image: product aesthetics (PA), self-image congruence (SIC), perceived quality (PQ), exposure to advertising (ETA), purchasing decision (PD) and repeat purchasing behavior (RPB). PA, SIC, PQ, and ETA are the independent variables, PD is the mediating variable, and RPB is the dependent variable. Each questionnaire question was considered one variable measure, which increased the number of measurement variables to the high 44. Instead, factor analysis was applied to find the definite number of factors in the questionnaire used as measurement variables. Table 2 represents the factor rotation for each question in each factor after the rotation. Factors are labelled as Loading_1 to Loading_5. The highest load factor in each row indicates which factor the question is related to. Tenenbaum’s Goodness- of-Fit (GOF) statistic is critical for evaluating how well a model fits the data. In the current context, where the GOF registers at 0.615, it signifies a noteworthy level of model fitness. This assessment aligns with established benchmarks: a value surpassing 0.1 denotes a discernible effect size, indicative of a model exhibiting a degree of proficiency in capturing the underlying patterns within the dataset. Consequently, the model demonstrates a commendable degree of explanatory power commensurate with the expectations set forth by prevailing standards in Table 2. Normalized structure loadings and cross- loadings for various constructs are illustrated. Despite these exclusions, the remaining loadings offer valuable insights into the correlation between observed variables and their respective constructs. Adjustments have been made to cross-loadings to ensure they exceed the threshold value, enhancing the specificity of each variable to its intended construct. This meticulous approach aims to refine the understanding of how observed variables contribute to defining their constructs while upholding the integrity of the measurement model. 4.1.2. Convergent Validity Convergent validity determines instrument items that can be used as indicators of overall latent variables. The results of this test are measured based on the value of the construct indicator's loading factor (outer loading). The following convergent validity test results are presented in Table 2, which also shows that all outer loading factors have values greater than 0.5. This measurement can be concluded to have met the requirements of convergent validity. The convergent validity of the measurement model using reflective indicators is assessed based on the outer loading factor of the indicators that measure the construct. In this study, there are five constructs with several indicators ranging from 4 to 9 indicators with a scale of 1 to 5. If the correlation coefficient is equal to 0.3 or greater (no less than 0.3), then the instrument is declared valid and invalid if the correlation coefficient is smaller than 0.3. 1290 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate Table 2. Factor rotation for each question. Variables Loading_1 Loading_2 Loading_3 Loading_4 Loading_5 Status PA1 0.803 0.125 0.441 0.785 0.667 Valid PA2 0.824 0.104 0.541 0.676 0.785 Valid PA3 0.691 0.201 0.551 0.789 0.815 Valid PA4 0.785 0.132 0.553 0.811 0.889 Valid PA5 0.676 0.245 0.614 0.701 0.899 Valid SIC1 0.789 0.812 0.412 0.735 0.845 Valid SIC2 0.811 0.214 0.447 0.788 0.787 Valid SIC3 0.701 0.325 0.621 0.812 0.785 Valid SIC4 0.735 0.441 0.785 0.899 0.888 Valid SIC5 0.788 0.541 0.888 0.845 0.895 Valid PQ1 0.812 0.551 0.895 0.214 0.898 Valid PQ2 0.899 0.553 0.898 0.325 0.781 Valid PQ3 0.845 0.614 0.781 0.441 0.447 Valid PQ4 0.787 0.412 0.802 0.541 0.621 Valid PQ5 0.799 0.447 0.881 0.551 0.811 Valid ETA1 0.691 0.621 0.788 0.553 0.701 Valid ETA2 0.667 0.811 0.812 0.614 0.735 Valid ETA3 0.785 0.701 0.899 0.412 0.788 Valid ETA4 0.815 0.735 0.845 0.447 0.812 Valid ETA5 0.889 0.788 0.787 0.621 0.899 Valid PD1 0.899 0.812 0.799 0.811 0.845 Valid PD2 0.845 0.899 0.691 0.691 0.787 Valid PD3 0.787 0.845 0.815 0.815 0.799 Valid PD4 0.785 0.787 0.889 0.889 0.691 Valid PD5 0.888 0.799 0.899 0.899 0.815 Valid RPB1 0.895 0.691 0.845 0.845 0.654 Valid RPB2 0.898 0.815 0.787 0.787 0.898 Valid RPB3 0.781 0.889 0.785 0.785 0.781 Valid RPB4 0.802 0.899 0.898 0.898 0.802 Valid RPB5 0.881 0.845 0.781 0.781 0.881 Valid In Figure 1, PA, SIC, PQ, and ETA are represented by dark yellow ellipses to show that they are the independent variables used by the researcher in the study. In contrast, PD is represented by a pale- yellow ellipse, which indicates the mediating variable used by the researchers. RPB is a dependent variable and is shown with a black ellipse. Coefficients discovered in a factor pattern or structure matrix represent factor loadings. The first matrix contains regression coefficients that multiply standard components to predict seen variables, also known as manifest variables. In contrast, the second matrix contains product-moment correlation coefficients between common factors and observed variables. Factor loadings are part of the outcome of factor analysis, which acts as a data reduction method to explain the relationships between observed variables using fewer components. 4.1.3. Reliability Statistics Before starting the statistical analysis, testing the hypotheses, and estimating the measurement model, the respondents’ data and suitability for estimation and testing must be examined. Evaluation of the measurement model also focuses on the reliability and validity of the tools used in the study, ensuring that the constructs are measured accurately and that the results are solid and valid for explaining the impact of the mental image of the product on the purchase and repurchase decisions of Saudi consumers. On the other hand, tests of linearity, normality, and homoscedasticity ensure the accuracy of the statistical model, as the relationship between variables is verified, normality evaluates the distribution of the data, and symmetry ensures constant variation across data points. We also used 1291 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate cross-correlation coefficients, which provide a basic understanding of the data and the relationships between the variables to be estimated, tested, and measured. Table 3. Reliability Statistics. Constructs PA SIC PQ ETA PD RPB Cronbach’s Alpha Mean SD PA 1 0.81 3.23 0.841 SIC 0.525* 1 0.82 3.18 0.801 PQ 0.627* 0.497* 1 0.86 3.25 0.783 ETA 0.593* 0.502* 0.672* 1 0.79 3.42 0.776 PD 0.661* 0.593* 0.525* 0.662* 1 0.84 3.51 0.771 RPB 0.547* 0.661* 0.627* 0.597* 0.681* 1 0.81 3.61 0.767 In this study, the researcher used a five-point Likert scale. The factor loadings of each construct's items exceed the prescribed limit of 0.70. This indicates that all the statements clearly explain their respective theoretical assumed constructs. Moreover, it was specified by researchers to achieve the objectives of their study. We notice from Table 3. It was found that product aesthetics (M= 3.21, SD= 0.84), self-image congruence (M= 3.17, SD= 0.80), perceived quality (M= 3.23, SD= 0.78), exposure to advertising (M= 3.41, SD= 0.77), purchase decision, (M= 3.52, SD= 0.77), and repeated purchasing behavior (M= 3.59, SD= 0.80) ranked first with a high level. The results indicated that the intercorrelation values between the mental image of a product were less than 0.80. The mean values of all the items in the questionnaire of each construct are more than 3; it indicates the positive response of perceived aesthetics, self-image congruence, perceived quality, exposure to advertising, purchase decision, and repeated purchasing behavior. The construct is declared reliable if it has a composite reliability value above 0.70 and Cronbach's alpha above 0.60. From the results of the AMOS output, the above constructs have composite reliability values of above 0.70. So, it can be concluded that the construct is reliable. Figure 2. Measurement model from AMOS. 1292 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate 4.1.4. Evaluating the Measurement Model The structural model in AMOS was evaluated by using (Rho-A, AVE, MSV, √AVE, IC, and CR) for the dependent variable and the value of the loading’s coefficient for the independent variables and then assessed for its significance based on the t-statistic value of each path. Table 4 shows that the item loadings ranged between 0.675 and 0.795, which exceeded the least threshold of 0.50 to keep the factors [67]. The results show that the average variance extracted (AVE) exceeded the maximum shared variance (MSV) and that the square root of AVE (√AVE) was more significant than the correlation coefficients between the rest of the research constructs. Moreover, the AVE values were bigger than 0.50, which is considered the minimum acceptable AVE limit [68, 69]. Hence, the measurement model had a convergent validity [70, 71]. Therefore, the measurement model could be considered to have discriminant validity [72, 73]. Regarding reliability, Cronbach's alpha (IC) values ranged between 0.735 and 0.861. Also, McDonald's omega (CR) values exceeded 0.70, considered this index's lowest value. Subsequently, the model is used to examine the mental image of a product that influences purchase and repurchase decisions among Saudi consumers [74]. Besides ensuring the validity and reliability of the measurement model, a confirming factor analysis (CFA) was conducted to test the suitability of the model used to test hypotheses through construction validity indicators. The rho-a value establishes that the “internal consistency” was also greater than 0.7. Therefore, the convergent validity of the constructs was proven [75]. Table 4. Evaluating measurement model (SEM by AMOS). Constructs Items AVE MSV √AVE IC CR Loadings Rho-A PA PA1 0.567 0.594 0.771 0.825 0.877 0.803 0.817 PA2 0.824 0.819 PA3 0.691 0.821 PA4 0.785 0.845 PA5 0.676 0.841 SIC SIC1 0.574 0.555 0.774 0.854 0.834 0.789 0.788 SIC2 0.811 0.779 SIC3 0.701 0.787 SIC4 0.735 0.778 SIC5 0.788 0.797 PQ PQ1 0.523 0.574 0.758 0.824 0.823 0.812 0.815 PQ2 0.899 0.814 PQ3 0.845 0.825 PQ4 0.787 0.812 PQ5 0.799 0.811 ETA ETA1 0.591 0.578 0.851 0.853 0.811 0.691 0.882 ETA2 0.667 0.889 ETA3 0.785 0.876 ETA4 0.815 0.824 ETA5 0.889 0.886 PD PD1 0.555 0.562 0.885 0.874 0.825 0.899 0.814 PD2 0.845 0.825 PD3 0.787 0.812 PD4 0.785 0.811 PD5 0.888 0.802 RPB RPB1 0.552 0.565 0.824 0.825 0.801 0.895 0.876 RPB2 0.898 0.824 RPB3 0.781 0.886 RPB4 0.802 0.821 RPB5 0.881 0.882 1293 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate 4.1.5. Discriminant Validity The discriminant validity is often assessed by comparing √AVE for each construct with the correlations between constructs. Suppose √the AVE for each construct is greater than the correlation between that construct and any other construct in the model. In that case, discriminant validity is established, indicating that the construct measures a distinct and unique aspect of the studied phenomenon. Table 5 presents correlations among latent variables alongside the square roots of their (AVEs, offering insights into the interrelations and reliability of the measurement model. Regarding the correlations among latent variables, (PA): perceived aesthetics, exhibits varying degrees of association with other constructs; (SIC): self-image congruence correlation with (PQ): perceived quality suggests a potential link between (EA): exposure to advertising and (PD): purchase decision with (RPB): repeated purchasing behavior. Additionally, the square roots of AVEs indicate the extent to which indicators within each latent variable explain variance; for example, the square root of the AVE for mental image of a product at 0.755 implies that approximately 65.4% of the variance within the construct is accounted for by its indicators (PA): perceived aesthetics, (SIC): self-image congruence, (PQ): perceived quality, (EA): exposure to advertising, (PD): purchase decision, and (RPB): repeated purchasing behavior. These findings enhance the understanding of construct relationships and the robustness of the measurement model, informing further analysis and interpretation of the data. Table 5. Correlation coefficients for study variables. Variables PA SIC PQ ETA PD RPB Discriminant Validity PA 0.755 0.654 0.385 0.504 0.561 0.581 Valid SIC 0.652 0.625 0.612 0.657 0.652 0.557 Valid PQ 0.504 0.652 0.704 0.575 0.504 0.578 Valid ETA 0.657 0.561 0.585 0.561 0.657 0.707 Valid PD 0.575 0.652 0.618 0.652 0.575 0.578 Valid RPB 0.523 0.653 0.701 0581 0652 0.515 Valid Note: The √AVE is shown on the diagonal. 4.2. Hypothesis Testing The data processing conducted by the researcher can be used to answer the hypotheses of this study. Hypothesis testing in this study was conducted by looking at the t-value and p-value. The research hypothesis can be accepted if the p-value <0.05 and the calculated t-value is positive. The following are the results obtained from the hypothesis testing through the inner model. What is this? Sing structural equation modelling in AMOS. Table 6. Hypothesis testing. Hypothesis Effects T P-Value H1 The aesthetics of product design make purchases more likely and increase the likelihood of repeat purchases. 3.272 0.011 H2 Self-image congruence with the product affects purchase decisions significantly and encourages repeat purchases. 3.196 0.015 H3 Perceived quality decides to purchase more likely and encourages repeat purchasing behaviour. 3.102 0.015 H4 Exposure to advertising positively impacts purchasing decisions and enhances repeat purchasing behaviour. 3.244 0.013 H5 A positive purchase decision strongly influences repeat purchase behaviour. 3.235 0.011 Table 6 shows that hypotheses H1, H2, H3, H4, and H5 were supported and that the attractiveness of product design facilitates purchase decisions and increases the likelihood of repeat purchases (t-value = 3.272, and p < 0.001). Self-image congruence with the product significantly influences purchase decisions (t-value = 3.196, p < 0.001). Perceived product quality enhances the purchase decision and 1294 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate encourages consumers to repeat purchasing behavior (t-value = 3.102, and p < 0.001). Exposure to advertising contributes positively to purchase decisions, supporting the continuity of repeat purchases (t-value = 3.244, and p < 0.001). A favorable purchase decision is strongly associated with increased repeat purchase behavior (t-value = 3.235, and p < 0.001). The examination of relationships between the mental image of a product and its influence on purchasing and repeat purchasing decisions by Saudi consumers yielded intriguing findings. A significant positive correlation emerged between product aesthetics (PA), self-image congruence (SIC), perceived quality (PQ), exposure to advertising (ETA), purchasing decision (PD), and repeat purchasing behaviour (RPB), indicating that a favorable brand image aligns with increased intent to purchase. Surprisingly, this link was significant while purchasing decision (PD) exhibited a notable connection with repeat purchase behaviour (RPB). However, when purchasing decision (PD) mediated the relationship between repeat purchase behaviour (RPB) and perceived aesthetics (PA), self-image congruence (SIC), perceived quality (PQ), exposure to advertising (ETA), a significant association emerged, suggesting that the mental image of a product and its influence on purchase and repeat purchase decisions by Saudi consumers. Figure 3. Structural equation modelling results (SEM) for the hypothesized mediation model. 5. Discussion and Conclusion The foregoing analysis provides critical insights into the effect of a product’s mental image on initial purchasing and repeated purchasing behaviour by Saudi consumers. Product aesthetics or visual appeal, elf-image congruence, perceived quality, and exposure to advertising have significant influence here, which reinforces previous work on aesthetics [26, 28] product reliability and functionality [42, 76] and the role of consistent advertising in generating positive product images [25, 59]. Product quality is essential for cars and smartphones [45]. The findings for sel-image congruences align with prior research on luxury products and personal electronics [33-35]. The results also confirmed the mediating role of purchasing decisions in the relationship between these independent variables and repeat purchasing behaviour. Customers who develop strong positive feelings towards a product after an initial purchase are more likely to buy the same product again, which corroborates other research on the link between brand preferences and repeat purchases [10]. These findings present valuable insights for businesses involved in the Saudi market. Enhancing a product's visual appeal, making it fit with potential customers’ personal and cultural identities, ensuring product quality, and being consistent with advertising can all be effective in fostering positive purchasing decisions and enhancing subsequent purchasing [3, 8]. 6. Limitations and Future Research Directions Despite its comprehensive nature and large sample size, this study has several limitations. First, it only considered people in the Kingdom of Saudi Arabia, so the findings may not be applicable in other 1295 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate cultural contexts or regions. The country is experiencing rapid changes in its economy and society as a result of Vision 2030. Future research could, therefore, explore the role of mental product images in different cultural contexts, thus validating or contrasting with the findings of this study. Second, the data used was from self-completed questionnaires, which can be subject to poor recall and social desirability bias. Even though the survey was clearly anonymous, participants may give answers that they believe to be socially acceptable instead of being honest. Therefore, Future research could use more objective measures, including actual purchase data and customer loyalty programs to track repeat purchases. Third, the study considers the cross-sectional relationship between mental image and consumer behavior. However, perceptions and behavior change over time as markets evolve, branding strategies change, and broader social factors develop. Longitudinal studies would give more profound insights into the formation of product images and how they influence purchasing behaviour. Finally, the study explored key internal variables but did not examine external factors such as economic conditions, technology, or the behaviour of market competitors, which all significantly affect consumer activities. Future research could incorporate these variables into the conceptual model to give a more comprehensive picture of how mental product images affect consumer behaviour in varied market conditions. However, despite these limitations, the present study does provide valuable insights into how mental images of products affect consumers' decisions about purchasing. Future research that addresses these limitations can, therefore, develop a more comprehensive picture of the complex relationship between consumers’ perceptions of products and their market behaviour. Transparency: The author confirms that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] V. A. Zeithaml, M. J. Bitner, and D. D. 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Questionnaire Constructs, Scales, and Sources for Survey Questions Demographic Information Age Under 18 (prepared by the author, 2025) 18–25 26–35 36–45 Over 45 Gender Male Female Monthly income Under 5,000 SAR 5,000–10,000 SAR 10,000–15,000 SAR Over 15,000 SAR What products do you buy? Electronics Fashion Automotive Household Goods Other Perceived Aesthetics PA1 I am attracted to products with appealing designs and aesthetics. Li and Li [77] and Toufani, et al. [78] PA2 The visual design of a product strongly influences my buying decision. PA3 I prefer products that have a modern and visually pleasing appearance. PA4 A product's aesthetics are more important to me than functionality. PA5 Products with attractive designs make me more willing to consider a purchase. Self-Image Congruence SIC1 I feel a stronger connection to products that align with my image. Sirgy [20] and Aguirre-Rodriguez, et al. [79] SIC2 I am more likely to buy products that reflect my personality or lifestyle. SIC3 I prefer to purchase products that help me express my identity. SIC4 I am more inclined to purchase a product that aligns with my self-image. SIC5 I choose products that reinforce the image I want to present to others. Perceived Quality PQ1 I am willing to pay more for a product that I perceive as high- quality. Zeithaml [76] and Dodds, et al. [80] PQ2 I consider a product’s reputation for quality when making purchase decisions. PQ3 Product quality is the most important factor in my purchase decision. PQ4 Higher-priced products are generally of better quality. PQ5 I am more likely to purchase a product if I perceive it to be durable and well-made. Exposure to Advertising EA1 I trust products that are heavily advertised more than those that are not. Zhao, et al. [81] EA2 Advertising helps me form a positive mental image of the product. EA3 I often decide to purchase a product after seeing repeated advertisements. EA4 I am likelier to try new products if exposed to persuasive advertising. EA5 Advertisements significantly influence my perception of a product’s value. Purchase Decision PD1 A product’s aesthetics play a crucial role in purchasing it. Zeithaml [76] and 1300 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 2: 1283-1300, 2025 DOI: 10.55214/25768484.v9i2.4750 © 2025 by the author; licensee Learning Gate PD2 I often make purchasing decisions based on my mental image of the product. Sirgy [20] PD3 If a product aligns with my self-image, I will purchase it immediately. PD4 I tend to buy products that I perceive as high quality, even without trying them. PD5 Advertising strongly influences my decision to buy a product. Repeated Purchasing Behavior RPB1 If I have a positive mental image of a product, I will likely repurchase it. Bloch [82] and Sirgy [20] RPB2 I often repurchase products that reflect my self-image. RPB3 My satisfaction with the quality of a product leads me to repurchase it. RPB4 I will likely repurchase a product if it meets the expectations created by advertising. RPB5 A product's appearance makes me more likely to buy it multiple times. Appendix 2: Constructs scale The constructs and scales of the study questionnaire were drawn from well-established theoretical foundations and empirical studies. For perceived aesthetics, items were based on research emphasizing the importance of product design, visual appeal, and consumer preference for modern, attractive designs as key drivers of purchase consideration [77, 78]. The self-image congruence scale was developed from studies focusing on the fit between products and a consumer's self-concept, lifestyle, and expression of identity [20, 79]. Measures of perceived quality were based on the importance of perceived quality, product reputation, durability, and price as determinants of purchasing decisions [76, 80]. For advertising exposure, the scale is derived from research emphasizing the role of advertising in shaping mental images, trust, and perception of product value, with the effect of repeated exposure on purchase intention [81]. The purchase decision scale integrates insights linking aesthetics, self-image fit, perceived quality, and advertising as key factors influencing consumer decisions [20, 76]. Finally, the repeat purchase behavior scale captures the influence of positive mental images, self-image alignment, and satisfaction with product quality on the likelihood of repurchase [20, 82]. These metrics ensure the validity of the data and its alignment with contemporary marketing and consumer behavior research.