Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3, 1189-1201 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 15 January 2025; Revised: 28 February 2025; Accepted: 5 March 2025; Published: 14 March 2025 * Correspondence: aduche@ucsm.edu.pe Predictors of purchase intention mediated by customer satisfaction Aleixandre Brian Duche-Pérez1*, Olger, Albino Gutiérrez-Aguilar1, Manuel Edmundo Hillpa-Zuñiga1, Antonio Miguel Escobar-Juárez1, Ángela Valeria Mya Pazo Romero1, Lourdes Cecilia García-Toledo1, Ivonne Virginia Flores-Vilca1 1Universidad Católica de Santa María, Arequipa, Perú; aduche@ucsm.edu.pe (A.B.D.P.), ogutierrez@ucsm.edu.pe (O.A.G.A.), mhillpa@ucsm.edu.pe (M.E.H.Z.), aescobarj@ucsm.edu.pe (A.M.E.J.), apazo@ucsm.edu.pe (A.V.M.P.R.), lgarciat@ucsm.edu.pe (L.C.G.T.), ifloresv@ucsm.edu.pe (I.V.F.V.). Abstract: This study examines the factors influencing purchase intention, with a particular focus on the mediating role of customer satisfaction in consumer decision-making. Understanding these determinants is essential for businesses aiming to enhance consumer engagement and maintain a competitive market position. The research adopts a quantitative approach, utilizing data collected through structured surveys. Structural equation modeling (SEM) is employed to evaluate the relationships among key predictors, including perceived price, comfort, mood, and establishment innovation, as well as their impact on purchase intention. The findings reveal that customer satisfaction significantly mediates the relationship between perceived price, comfort, and purchase intention. Furthermore, mood and establishment innovation serve as moderating factors, reinforcing consumer engagement and fostering positive purchasing behavior. The study underscores the importance of these elements in shaping consumer preferences. To enhance purchase intention, businesses should prioritize customer satisfaction by refining pricing strategies, improving in-store comfort, and cultivating positive emotional experiences. The results offer valuable insights for marketers and business owners, emphasizing that strategic investments in customer experience, pricing, and innovation can effectively drive purchase intention and foster long-term customer loyalty. Keywords: Comfort, Mood, Customer satisfaction, Establishment innovation, Purchase intent, Perceived price. 1. Introduction In the competitive and dynamic landscape of consumer markets, understanding the determinants of purchase intention remains a critical area of research for businesses seeking to optimize their marketing strategies and enhance customer engagement. Prior studies have identified multiple factors influencing purchase behavior, with customer satisfaction emerging as a key mediating variable that shapes consumer decision-making [1, 2]. As businesses increasingly operate in digital and omnichannel environments, factors such as comfort, mood, establishment innovation, and perceived price have gained prominence in explaining consumer purchasing tendencies [3]. Customer satisfaction plays a central role in fostering consumer trust, enhancing brand loyalty, and encouraging repeat purchase behavior [4]. The interplay between customer experience and technological advancements, such as artificial intelligence (AI) and digital marketing, has reshaped the way consumers perceive and interact with brands [5]. Studies highlight that AI-driven customer service, electronic word-of-mouth (e-WOM), and personalized online experiences significantly contribute to satisfaction and purchase intent, demonstrating the growing importance of digital engagement in modern consumer behavior [3, 6]. 1190 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate Comfort (CM) has been recognized as a pivotal factor in shaping consumer perceptions of service quality, leading to greater satisfaction and stronger purchase intentions [2]. Establishments that prioritize customer comfort—whether through physical store ambiance, user-friendly online interfaces, or seamless omnichannel experiences—tend to foster greater consumer trust and engagement. Additionally, comfort indirectly influences purchase decisions by enhancing customer satisfaction, which, in turn, increases purchase intent [7]. Similarly, mood (ED) has been extensively examined as a psychological determinant of consumer behavior. Positive emotional states significantly enhance customer satisfaction and encourage purchasing decisions [8]. Emotional responses to retail environments, advertising content, and digital shopping experiences influence consumer decision-making processes, underscoring the necessity for businesses to integrate emotional marketing strategies [9]. Furthermore, research suggests that mood indirectly affects purchase intent by shaping customer satisfaction, reinforcing the mediating role of this variable [7]. Establishment innovation (IN) is another key driver of consumer satisfaction and purchase intention. Technological advancements, such as AI-powered recommendations, interactive digital storefronts, and live commerce platforms, have revolutionized the retail sector [8]. Innovations that improve service efficiency and personalization foster higher customer engagement and satisfaction, ultimately leading to increased purchase intentions [10]. Notably, the perceived value of these innovations varies across consumer segments, highlighting the need for tailored marketing approaches [11]. Purchase intention (IT) is a crucial indicator of consumer behavior, reflecting the likelihood of a customer completing a transaction based on various influencing factors. Prior research has consistently shown that purchase intention is shaped by a combination of emotional, cognitive, and contextual elements, including perceived value, brand trust, and service quality [7, 12]. Additionally, digital advancements, such as AI-powered customer interactions and seamless e-commerce experiences, have significantly enhanced purchase intent by improving customer engagement and satisfaction [4]. The mediating role of customer satisfaction further reinforces these relationships, as satisfied customers are more likely to translate their positive experiences into concrete purchasing decisions [11, 13]. Understanding the drivers of purchase intention allows businesses to refine their marketing strategies, optimize pricing models, and enhance customer retention in an increasingly competitive digital marketplace. The perceived price (PR) of products and services remains a crucial determinant of consumer decision-making. Studies indicate that fair pricing strategies, transparent cost structures, and perceived value significantly impact customer satisfaction and purchase intent [7, 14]. In both traditional and digital marketplaces, price perception influences consumer trust and brand loyalty, with competitive pricing strategies serving as a key differentiator in attracting and retaining customers [13]. Additionally, perceived price indirectly affects purchase intention through its impact on satisfaction, reinforcing its mediating role in consumer behavior [3]. Customer satisfaction (SA) mediates the relationships between these key predictors and purchase intention (IT), highlighting its importance as a central construct in consumer behavior research [4]. When customers experience higher levels of satisfaction, they are more likely to exhibit stronger purchase intentions and brand loyalty [12]. The mediating role of satisfaction is particularly evident in service-oriented industries, where factors such as trust, perceived quality, and brand reputation shape consumer decisions [15]. Given these insights, this study aims to empirically investigate the direct and indirect relationships between comfort, mood, establishment innovation, and perceived price on purchase intention, with customer satisfaction acting as a mediating variable. By integrating findings from research on consumer behavior, marketing strategies, and service quality, this study provides a comprehensive understanding of the mechanisms that drive purchase intention in contemporary consumer markets [5, 16]. 1191 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate Ultimately, this study contributes to the broader discourse on consumer decision-making and marketing effectiveness, offering valuable insights for businesses, policymakers, and researchers. Understanding the predictors of purchase intention mediated by customer satisfaction can inform strategic marketing initiatives, improve customer engagement strategies, and enhance overall business performance in both physical and digital retail environments. Based on these considerations, the following hypotheses are proposed: Direct Effect Hypotheses: • H1: There is a positive relationship between comfort (CM) and customer satisfaction (SA). • H2: Comfort (CM) has a positive impact on purchase intention (IT). • H3: Customer satisfaction (SA) positively influences purchase intention (IT). • H4: Innovations introduced by the establishment (IN) enhance customer satisfaction (SA). • H5: Innovations at the establishment (IN) foster greater purchase intention (IT). • H6: Mood (ED) positively contributes to customer satisfaction (SA). • H8: A positive mood (ED) increases purchase intention (IT). • H10: Perceived price (PR) is directly related to customer satisfaction (SA). • H11: Perceived price (PR) directly influences purchase intention (IT). Indirect or Mediating Effect Hypotheses: • H12: Comfort (CM) indirectly influences purchase intention (IT) through customer satisfaction (SA). • H13: Establishment innovation (IN) indirectly affects purchase intention (IT), mediated by customer satisfaction (SA). • H14: Mood (ED) indirectly modulates purchase intention (IT) through customer satisfaction (SA). • H15: Perceived price (PR) exerts an indirect influence on purchase intention (IT) via customer satisfaction (SA). 2. Materials and Methods For the study, online surveys were randomly distributed to a sample of 529 individuals who consented to participate in the research. The sample selection process ensured diversity in demographic characteristics, reflecting a broad representation of consumers in Arequipa, Peru. Both genders were included, with 204 men (39%) and 324 women (61%), ensuring gender balance and reducing biases related to consumer behavior patterns. The survey was conducted over a two-month period (April-May 2024) to capture a more comprehensive picture of purchase intent across different timeframes and potential external influences, such as economic fluctuations or seasonal promotions. Participants ranged in age from 18 to 55 years, with a mean age of 33.48 years and a standard deviation of 11.22, indicating a diverse age distribution. This range allowed the study to encompass a wide spectrum of consumer preferences and behaviors, from younger digital-native consumers to more experienced shoppers. Participants were recruited through social media advertisements, email invitations, and direct outreach via consumer networks, ensuring that respondents had prior exposure to online purchasing environments, which was a critical criterion for inclusion in the study. To maintain the study's validity and reliability, the survey instrument was specifically designed for this research and subjected to a rigorous validation process. The questionnaire underwent semantic and content validation by a panel of experts specializing in advertising, marketing, and consumer behavior to ensure clarity, relevance, and applicability to the study objectives. Pilot testing was conducted with a subset of 50 participants, allowing researchers to refine ambiguous wording, enhance question structure, and confirm that all constructs measured the intended latent variables effectively. The final instrument consisted of six latent variables, each measured using a 5-point Likert scale ranging from (1) "Strongly Disagree" to (5) "Strongly Agree." The distribution of items per construct was as follows: 1192 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate • Comfort (CM): 3 items, assessing the perceived convenience and ambiance of the shopping environment. • Mood (ED): 4 items, capturing the emotional state of the consumer during the purchase experience. • Establishment Innovation (IN): 3 items, measuring the impact of technological or service- related innovations introduced by the retailer. • Purchase Intent (IT): 3 items, evaluating the likelihood of completing a purchase. • Perceived Price (PR): 4 items, analyzing consumer perceptions of pricing fairness and affordability. • Customer Satisfaction (SA): 3 items, assessing the overall satisfaction derived from the shopping experience. For the statistical processing of the data, covariance-based structural equation modeling (CB-SEM) was employed, which efficiently complements partial least squares structural equation modeling (PLS- SEM). According to Sarstedt, et al. [17] both approaches are complementary in SEM applications and offer a robust framework for analyzing complex relationships between latent variables. The dual application of CB-SEM and PLS-SEM provided a more comprehensive analysis, allowing researchers to assess both measurement model validity and structural model relationships with greater accuracy. To ensure the rigor and reproducibility of the results, a systematic and structured approach to data classification and analysis was adopted. The statistical software Jamovi (v.2.5.6.0) and Smart PLS (v.4.1.0.8) were used to clean, process, and analyze the dataset, ensuring that the assumptions of structural equation modeling were met. Multi-group analysis (MGA) was performed to explore potential differences in purchase intent based on gender and age segments. Additionally, confirmatory factor analysis (CFA) was conducted to validate the measurement model, ensuring that each latent variable exhibited high internal consistency and construct validity. The methodological approach employed in this study was designed to provide robust empirical evidence regarding the predictors of purchase intent mediated by customer satisfaction. By integrating advanced statistical techniques with a well-validated survey instrument, the research offers valuable insights into the interplay of emotional, environmental, and economic factors influencing consumer decision-making. The findings are expected to contribute both to academic literature and practical applications in digital marketing, retail strategies, and consumer experience enhancement. 3. Results Table 1 presents the reliability indices for the study variables, including Cronbach's alpha and hierarchical omega, both of which indicate excellent internal consistency in the scales used. The variables exhibit high reliability, with Comfort (CM) showing a Cronbach's alpha of 0.74, Purchase Intention (IT) at 0.82, Perceived Price (PR) at 0.89, Establishment Innovation (IN) at 0.77, Customer Satisfaction (SA) at 0.88, and Mood (ED) at 0.91. Additionally, the Average Variance Extracted (AVE) values exceed the 0.50 threshold, except for CM, which falls slightly below this limit. This finding suggests that the constructs effectively capture item variance beyond measurement error. Overall, the results confirm the suitability of the scales for accurately and consistently measuring the latent variables involved in the study: 1193 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate Table 1. Reliability indices. Variable α ω₁ ω₂ ω₃ AVE CM 0.74 0.75 0.75 0.75 0.50 IT 0.82 0.82 0.82 0.82 0.60 PR 0.89 0.90 0.90 0.90 0.69 IN 0.77 0.78 0.78 0.78 0.54 SA 0.88 0.89 0.89 0.89 0.72 ED 0.91 0.91 0.91 0.90 0.71 In the Exploratory Factor Analysis (EFA), the following criteria were applied: the minimum residual extraction method with Oblimin rotation, and the number of factors was determined using parallel analysis. The results indicated a satisfactory fit for the six-factor model, with a Kaiser-Meyer- Olkin (KMO) measure of 0.900. To assess the model's stability, a Confirmatory Factor Analysis (CFA) was conducted, providing additional fit indices to compare the theoretical and observed matrices. The results included a Root Mean Square Error of Approximation (RMSEA) of 0.07, a Standardized Root Mean Square Residual (SRMR) of 0.04, a Comparative Fit Index (CFI) of 0.94, a Tucker-Lewis Index (TLI) of 0.93, and a Goodness of Fit Index (GFI) of 0.97, all of which were considered satisfactory. Table 2 presents the factor loadings obtained using SmartPLS, indicating the strength of the relationship between each item and its respective latent construct. Notably, the items CM1, CM2, and CM3 for "Comfort" exhibited strong factor loadings of 0.818, 0.848, and 0.819, respectively, underscoring the reliability of the construct. Similarly, the "Mood" items ED1 to ED4, with loadings ranging from 0.835 to 0.919, effectively captured respondents' emotional and psychological states. For "Establishment Innovation", items IN1 through IN3 demonstrated loadings between 0.816 and 0.877, with IN3 being the most representative. The "Purchase Intention" construct was robustly measured by IT1, IT2, and IT3, with loadings of 0.850, 0.857, and 0.877, respectively. In the case of "Perceived Price", items PR1 to PR4 exhibited high loadings, particularly PR2, which had the highest at 0.928, accurately capturing consumer price perceptions. Finally, items SA1, SA2, and SA3 reliably measured "Customer Satisfaction," with SA2 standing out with a loading of 0.923. These high factor loadings indicate strong convergent validity for each construct, confirming that the items effectively represent their theoretical dimensions. This provides a solid foundation for further analysis and conclusions. 1194 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate Table 2. External Loads - Smart PLS. Comfort (CM) State of mind (ED) Establishment Innovation (IN) Purchase intention (IT) Perceived Price (PR) Customer satisfaction (SA) CM1 0.818 CM2 0.848 CM3 0.819 ED1 0.892 ED2 0.919 ED3 0.882 ED4 0.835 IN1 0.816 IN2 0.828 IN3 0.877 IT1 0.850 IT2 0.857 IT3 0.877 PR1 0.823 PR2 0.928 PR3 0.858 PR4 0.905 SA1 0.893 SA2 0.923 SA3 0.881 Table 3 presents the construct validity and reliability metrics of the study. It includes each construct's Average Variance Extracted (AVE), standardized and unstandardized Cronbach's alpha, and composite reliability. The Comfort (CM) construct has a standardized Cronbach's alpha of 0.744 and an unstandardized value of 0.743, showing minimal variation. It also exhibits a composite reliability of 0.748 and an AVE of 0.502, indicating sufficient reliability with marginal convergent validity. Customer Satisfaction (SA) demonstrates high reliability, with Cronbach's alpha values of 0.883, a composite reliability of 0.846, and an AVE of 0.647, suggesting strong internal consistency and good construct validity. Establishment Innovation (IN) presents Cronbach's alpha values around 0.773, with composite reliability also at 0.773 and an AVE of 0.542, reflecting adequate reliability and convergent validity. Mood (ED) exhibits high reliability, with Cronbach's alpha values of 0.896, the highest composite reliability at 0.901, and an AVE of 0.754, indicating superior measurement precision. Perceived Price (PR) also demonstrates high reliability, with Cronbach’s alpha values of 0.895, a composite reliability of 0.897, and an AVE of 0.687, confirming strong consistency and validity. Finally, Purchase Intention (IT) reports Cronbach’s alpha values of 0.820, a composite reliability of 0.803, and an AVE of 0.578, denoting good reliability and acceptable validity. These metrics collectively attest to the robust reliability and validity of the constructs used in the study, ensuring that the measurements accurately reflect the theoretical concepts they are intended to represent. 1195 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate Table 3. Construct reliability and validity. Cronbach's alpha (standardized) Cronbach's alpha (unstandardized) Composite reliability (rho_c) Average variance extracted (AVE) Confort (CM) 0.744 0.743 0.748 0.502 Customer satisfaction (SA) 0.883 0.883 0.846 0.647 Establishment innovation (IN) 0.773 0.771 0.773 0.542 Mood (ED) 0.896 0.896 0.901 0.754 Perceived price (PR) 0.895 0.895 0.897 0.687 Purchase intent (IT) 0.820 0.820 0.803 0.578 Purchase intent (IT) 0.820 0.820 0.803 0.578 Table 3 demonstrates the discriminant validity of the study's constructs using the Heterotrait- Monotrait (HTMT) ratio criterion, effectively confirming that respondents perceive each construct as distinct and unrelated to others. The results indicate good discriminant validity between Comfort (CM) and all other constructs, as evidenced by HTMT values of 0.513 for Customer Satisfaction (SA) and 0.595 for Establishment Innovation (IN). However, lower ratios were observed between Comfort (CM) and Perceived Price (PR), as well as between Comfort and Purchase Intention (IT). While Customer Satisfaction (SA) remains distinct from most constructs, it exhibits a closer relationship with Mood (ED), as indicated by an HTMT value of 0.766. Similarly, the low HTMT values between Mood (ED) and Perceived Price (0.214), as well as between Perceived Price and Purchase Intention (0.646), strongly support discriminant validity, confirming that each construct captures a unique aspect of the study. Overall, these results validate the measurement model by ensuring that each construct shares less variance with other constructs than with itself, reinforcing their distinct conceptual identities within the research framework. Table 4. Discriminant validity Heterotrait Criterion - Monotrait –HTMT. (CM) (SA) (IN) (ED) Confort (CM) Customer satisfaction (SA) 0.513 Establishment innovation (IN) 0.595 0.688 Mood (ED) 0.539 0.766 0.592 Perceived price (PR) 0.274 0.426 0.466 0.214 Purchase intent (IT) 0.441 0.596 0.483 0.445 Figure 1 presents the results of the covariance-based structural equation modeling (CB-SEM) analysis conducted using SmartPLS. The model reveals that Comfort (CM) has a relatively low influence on Customer Satisfaction (SA), with a coefficient of 0.095, indicating that while comfort contributes to satisfaction, it is not the primary determinant. In contrast, Mood (ED) exerts a much stronger influence on Customer Satisfaction (SA), with a coefficient of 0.643, suggesting that customers' emotional state is a crucial factor in their perception of satisfaction. The path coefficient from Establishment Innovation (IN) to Customer Satisfaction (SA) is 0.314, indicating that establishment innovations have a moderate impact on customer satisfaction with the service or product. Similarly, Perceived Price (PR) has a coefficient of 0.272, highlighting that price perception also plays a significant role in overall customer satisfaction. Customer Satisfaction (SA) emerges as a key mediator between the independent variables and Purchase Intention (IT), with a substantial coefficient of 0.564 from SA to IT. This finding underscores the critical role of customer satisfaction in predicting purchase intention, emphasizing the importance of effectively managing customer perceptions and experiences to encourage purchasing behavior. 1196 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate Regarding the coefficient of determination (R²), the model explains 59.5% of the variance in Customer Satisfaction (SA) (R² = 0.595) and 31.8% of the variance in Purchase Intention (IT) (R² = 0.318). These values indicate that while the model captures a substantial proportion of the variability in both dependent variables, other external factors may also influence customer satisfaction and purchase intention. Figure 1. Model CBSEM – SmartPLS. Table 5 presents the total direct effects (standardized) of various con-structs within a research study, detailing the mean values, standard deviations, T-values, and P-values for each hypothesis tested. Notably: • H1 and H2 assess the effects of Comfort (CM) on Customer Satisfaction (SA) and Purchase Intent (IT), respectively. They show relatively low direct effects with P-values above 0.05, suggesting these relationships are not statistically significant. • H3, examining the effect of Customer Satisfaction (SA) on Purchase In-tent (IT), shows a strong direct effect of 0.564, with a highly significant T-value of 13.132, indicating a solid and statistically significant relationship. • H4 and H5 relate to Establishment Innovation (IN) influencing Customer Satisfaction (SA) and Purchase Intent (IT), with both hypotheses showing statistically significant effects and robust T-values, underscoring the substantial and enlightening impact of innovation on customer outcomes. 1197 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate • H6 and H8 evaluate the impact of Mood (ED) on Customer Satisfaction (SA) and Purchase Intent (IT). Both effects are considerable and statistically significant, particularly the strong influence of Mood on Customer Satisfaction. • H10 and H11 explore the effects of Perceived Price (PR) on Customer Satisfaction and Purchase Intent. Both hypotheses show statistically significant impacts, with moderate to strong effects, highlighting the critical and awareness-inducing role of price perception in shaping customer attitudes and behaviours. Overall, the table effectively demonstrates the varied strength and significance of relationships between different constructs, providing valuable insights into how mood, innovation, and price perceptions drive customer satisfaction and purchase intentions in the market context. Table 5. Total direct effects (standardized) – Mean, STDEV, T values, p values. (O) (M) (STDEV) (|O/STDEV|) P_values H1 Confort (CM) → Customer satisfaction (SA) 0.095 0.092 0.057 1.665 0.096 H2 Confort (CM) → Purchase intent (IT) 0.054 0.053 0.034 1.606 0.109 H3 Customer satisfaction (SA) → Purchase intent (IT) 0.564 0.566 0.043 13.132 0.000 H4 Establishment innovation (IN) → Customer satisfaction (SA) 0.314 0.314 0.071 4.439 0.000 H5 Establishment innovation (IN) → Purchase intent (IT) 0.177 0.177 0.041 4.289 0.000 H6 Mood (ED) → Customer satisfaction (SA) 0.643 0.641 0.055 11.604 0.000 H8 Mood (ED) → Purchase intent (IT) 0.363 0.363 0.040 8.998 0.000 H10 Perceived price (PR) → Customer satisfaction (SA) 0.272 0.270 0.049 5.510 0.000 H11 Perceived price (PR) → Purchase intent (IT) 0.153 0.154 0.035 4.422 0.000 The study's Table 6 looks at the total indirect effects and looks at how customer satisfaction (SA) affects the connections between different factors like comfort, establishment innovation, mood, and perceived price and purchase intent (IT). The results underscore the significance of customer satisfaction as a mediator: • H12 (Comfort -> Customer Satisfaction -> Purchase Intent): This hypothesis tests the indirect effect of Comfort on Purchase Intent through Customer Satisfaction. The result shows a small effect size of 0.054, with a T-value of 1.606 and a P-value of 0.109, suggesting that the effect is not statistically significant. While Comfort may contribute to Customer Satisfaction, its indirect influence on Purchase Intent through this pathway is not robust enough to be considered significant in this context. • H13 (Establishment Innovation -> Customer Satisfaction -> Purchase Intent): The indirect effect of Establishment Innovation on Purchase Intent, through Customer Satisfaction, is statistically significant with an effect size of 0.177 and a T-value of 4.289. This highlights that innovations within an establishment significantly enhance customer satisfaction, positively influencing their purchase intentions. • H14 (Mood -> Customer Satisfaction -> Purchase Intent): This hypothesis shows a solid and significant indirect effect (0.363) with a high T-value of 8.998. It illustrates that Mood substantially impacts Customer Satisfaction, which subsequently leads to increased Purchase Intent. This result emphasises the critical role of emotional states in driving purchasing decisions through the mediating effect of satisfaction. • H15 (Perceived Price -> Customer Satisfaction -> Purchase Intent): The indirect effect here is also statistically significant, with a value of 0.153 and a T-value of 4.422. This suggests that how customers perceive the price influences their satisfaction levels, which in turn affects their intention to purchase. The effect, though moderate, is significant and indicates that price 1198 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate perception is an essential determinant of purchasing behaviour via the route of customer satisfaction. Overall, these findings affirm the central role of Customer Satisfaction as a mediator in the relationship between various factors and Purchase Intent. The results are particularly compelling for hypotheses involving Mood and Establishment Innovation, where Customer Satisfaction acts as a strong intermediary, enhancing the impact of these factors on Purchase Intent. This insight could be pivotal for businesses focussing on strategies, too. Table 6. Total indirect effects (standardized) – Mean, STDEV, T values, p values. (O) (M) (STDEV) (|O/STDEV|) P values H12 Confort (CM) -> Customer satisfaction (SA) -> Purchase intent (IT) 0.054 0.053 0.034 1.606 0.109 H13 Establishment innovation (IN) -> Customer satisfaction (SA) -> Purchase intent (IT) 0.177 0.177 0.041 4.289 0.000 H14 Mood (ED) -> Customer satisfaction (SA) -> Purchase intent (IT) 0.363 0.363 0.040 8.998 0.000 H15 Perceived price (PR) -> Customer satisfaction (SA) -> Purchase intent (IT) 0.153 0.154 0.035 4.422 0.000 4. Discussion The study results demonstrate that customer satisfaction (CS) plays a key role as a mediator in the relationship between various variables and purchase intention (PI). This finding highlights the importance of understanding how consumer experience and perception can transform external factors into concrete purchasing decisions. Previous literature has emphasized that customer satisfaction is a fundamental determinant in consumer behavior, acting as a bridge between external stimuli and the final conversion into sales. Recent studies have stressed that without a satisfactory experience, even the most attractive factors, such as price or innovation, may not translate into a significant purchase intention. To ensure the robustness of the proposed model, construct reliability was assessed using indices such as Cronbach's alpha and hierarchical omega, which yielded high values, indicating strong internal consistency in the scales used. This suggests that the measurements are stable and accurately reflect the concepts they aim to evaluate. These findings align with the study by Winarko, et al. [2] who found that perceived value and Marketing 4.0 factors significantly influence customer satisfaction and purchase intention in e- commerce. Similarly, Zhang and Cheng [1] reinforce the idea that information and communication technologies (ICT) play a central role in mediating the relationship between digital marketing and purchase intention. These results underline the necessity of having valid and reliable measurement instruments to obtain robust conclusions in studies related to consumer behavior. The satisfactory fit of the theoretical model was assessed through confirmatory factor analysis (CFA), with indicators such as RMSEA, SRMR, CFI, TLI, and GFI falling within acceptable ranges. These results indicate that the proposed theoretical structure is stable and adequately reflects the relationships between the analyzed variables. The findings obtained in this phase are consistent with the study by Hu, et al. [4] who applied a similar structural model to analyze the impact of realism in AI-based customer service agents and their relationship with customer satisfaction and repurchase intention. This reinforces the validity of the model and the applicability of its conclusions to similar contexts, especially in digital environments where customer interaction is mediated by emerging technologies. One of the most relevant findings of the study is that customer satisfaction is positioned as a key predictor of purchase intention, with a coefficient of 0.564 (p < 0.001). This result is consistent with the research by Khatoon, et al. [5] in the Qatari banking sector, where customer satisfaction significantly 1199 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate mediated the relationship between e-banking service quality and purchase intention. This reinforces the idea that customer satisfaction depends not only on tangible factors such as price or product quality but also on the overall perceived experience. Innovation in commercial establishments showed a positive impact on both customer satisfaction (coefficient of 0.314, p < 0.001) and purchase intention (coefficient of 0.177, p < 0.001). These findings align with the study by Nguyen, et al. [7] who identified that perceived value, brand identity, and brand integrity directly influence customer satisfaction in e-commerce platforms. This indicates that innovation, whether in terms of products, services, or shopping experiences, can generate a more positive perception among customers and increase their willingness to purchase. Another significant finding is the impact of consumer mood on customer satisfaction (coefficient of 0.643, p < 0.001) and purchase intention (coefficient of 0.363, p < 0.001). These results support the findings of Riswanto, et al. [6] who demonstrated that visual attention in digital advertisements influences consumer satisfaction and purchase intention. This highlights the importance of designing marketing strategies that evoke positive emotions in customers, increasing their engagement and purchase likelihood. Price perception also plays a crucial role, as it impacts both customer satisfaction (coefficient of 0.272, p < 0.001) and purchase intention (coefficient of 0.153, p < 0.001). This finding is consistent with the research by Bueno and Gallego [18] who found that e-WOM (electronic word-of-mouth) and service quality influence customer satisfaction and purchase intention in C2C platforms. These results suggest that pricing strategies should consider not only market competitiveness but also the subjective perception of value that customers receive in exchange for their purchase. The indirect effects obtained in the study reinforce the mediating role of customer satisfaction in the relationship between other variables and purchase intention. Some of the most relevant findings include: • Establishment innovation → Customer satisfaction → Purchase intention (indirect effect of 0.177, p < 0.001), which aligns with the findings of Mtotywa and Kekana [16] on the mediation of satisfaction in the relationship between e-service quality and purchase intention. • Mood → Customer satisfaction → Purchase intention (indirect effect of 0.363, p < 0.001), supporting the findings of Duong, et al. [3] regarding the influence of digital content on consumer experience. • Price perception → Customer satisfaction → Purchase intention (indirect effect of 0.153, p < 0.001), aligning with the research by Dash, et al. [13] on the relationship between brand identity and customer satisfaction. These findings confirm that enhancing the customer experience not only directly influences satisfaction but also mediates the impact of other key factors on purchase intention. 5. Conclusion The research provides a comprehensive analysis of the mediating role of Customer Satisfaction in influencing Purchase Intent through various predictors such as Comfort, Establishment Innovation, Mood, and Perceived Price. Here are the key conclusions drawn from the study: • Customer Satisfaction as a Central Mediator: The findings underscore the pivotal role of Customer Satisfaction in mediating the effects of different factors on Purchase Intent. This mediator is crucial in transforming how various aspects of the business environment, such as comfort and innovation, translate into consumer purchasing decisions. • Impact of Establishment Innovation and Mood: Among the factors tested, Establishment Innovation and Mood had the most significant indirect effects on Purchase Intent via Customer Satisfaction. This indicates that businesses that innovate and effectively manage the consumer environment can enhance customer satisfaction, significantly boosting Purchase Intent. Similarly, a positive mood dramatically enhances customer satisfaction, leading to higher 1200 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1189-1201, 2025 DOI: 10.55214/25768484.v9i3.5446 © 2025 by the authors; licensee Learning Gate Purchase Intent and highlighting the importance of emotional engagement in marketing strategies. • Moderate Influence of Perceived Price: The study also found that Perceived Price impacts Purchase Intent through Customer Satisfaction. This relationship suggests that fair pricing strategies that align with customer perceptions can enhance satisfaction and encourage purchases. • Limited Role of Comfort: Comfort showed a minimal and statistically insignificant indirect effect on Purchase Intent through Customer Satisfaction. This suggests that while comfort is essential for achieving customer satisfaction, its role in directly enhancing Purchase Intent might require reinforcement with other compelling marketing or service elements. For businesses, focusing on innovative strategies that enhance customer satisfaction could be more effective in boosting purchase intent than merely enhancing physical comfort. Additionally, managing consumer emotions and aligning price perceptions with customer expectations is crucial for converting satisfaction into actual sales. Future studies could explore other potential mediators between these variables, such as brand loyalty or service quality, and investigate the impact of demographic factors on these relationships to tailor marketing strategies more effectively to target audiences. In conclusion, this research emphasises that while Customer Satisfaction is an essential mediator in driving Purchase Intent, the impact of underlying factors like Mood and Innovation is significant and should be leveraged in strategic planning and operational adjustments. This aligns with the broader marketing focus on creating positive customer experiences as a cornerstone for business success. Transparency: The authors confirm 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] C. Zhang and P. 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