Cognitive Dissonance in Online Shopping Experiences and Impulsive Buying Among E-Commerce Users in Indonesia

International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4943 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 Cognitive Dissonance in Online Shopping Experiences and Impulsive Buying Among E-Commerce Users in Indonesia Bayu Cunda Satria1, Retno Tanding Suryandari2 1,2Master of Management, Sebelas Maret University, Indonesia ABSTRACT: This study aims to analyze the influence of Online Customer Shopping Experience on Impulsive Buying and Cognitive Dissonance in the context of e-commerce use in Indonesia. Based on data from 420 respondents, it was found that the most widely used e-commerce platforms are Tokopedia, TikTok Shop, and Shopee. The majority of respondents purchase products such as clothing, accessories, as well as beauty and personal care products, which shows the dominance of lifestyle needs in online shopping behavior. The results of the analysis show that the online shopping experience has a significant effect on impulse purchases, which can then trigger post-purchase cognitive dissonance. In addition, it was found that Impulsive Buying also mediates the relationship between Online Customer Shopping Experience and Cognitive Dissonance. Thus, an engaging and emotional shopping experience not only encourages spontaneous purchases, but also contributes to dissatisfaction if the purchase results are not as expected. These findings provide important implications for e-commerce service providers to design shopping experiences that are not only visually appealing and promotional, but also able to meet consumer expectations to reduce the potential for cognitive dissonance. KEYWORDS: Cognitive Dissonance, e-commerce, Customer Experience, Impulsive Buying, Online Customer Shopping Experience. I. INTRODUCTION Consumer purchasing behavior in Indonesia has undergone significant changes with the increasing ease of acquiring goods and services both offline and online (Junejo, 2023; Murdiana et al., 2024). The digital era has fueled the global popularity of online shopping (Dr. B. K. Singh, 2022; Virmani et al., 2023), with the number of e-commerce users in Indonesia reaching 178.94 million in 2022 and projected to rise to 244.67 million by 2027 (dataindonesia.id). The Financial Services Authority (OJK) reports that 88.1% of internet users in Indonesia have utilized e-commerce services (Mahmood, 2016), underscoring the centrality of ecommerce in consumer behavior. Post-pandemic, both millennials and older generations have demonstrated a consistent increase in online shopping due to the availability of essential goods online (Firmandani et al., 2021; Lestari, 2019; Murdiana et al., 2024; Suryadi et al., 2022; Wijaya et al., 2024). The most frequently purchased items by Indonesian consumers include fashion (68%), gadgets (44%), electronics (35%), and other products such as food and cosmetics (We Are Social; Suryadi et al., 2022). Both online and offline shopping experiences influence consumer purchasing behavior, particularly impulsive buying, which occurs spontaneously without prior planning (Rook, 1987; Baumeister, 2002; Verplanken & Herabadi, 2001). Platforms such as Shopee, Tokopedia, Lazada, and TikTok Shop compete by innovating and introducing new features to enhance consumer experiences (Alam et al., 2020; Imtiaz Ali et al., 2018; Lim et al., 2016; Qin et al., 2020). Events such as “Harbolnas” (National Online Shopping Day) and “date-based” promotions (11.11, 12.12) have stimulated impulsive buying, with transactions reaching IDR 25.7 trillion in 2023 (idEA; kontan.id), and the FMCG sector is expected to grow by 13.9% during Harbolnas 2024 (Handriani et al., 2024; Putri, 2023; Saputra et al., 2024; Wilson & Christella, 2019). Preferences for online shopping are driven by free shipping promotions (54.9%), coupons/discounts, customer testimonials (52%), ease of checkout (45%), store reputation (40.1%), return policies (28.8%), and cash-on-delivery options (28.1%) (We Are Social, 2023). Other contributing factors include time savings, price comparisons, discounts, and product variety (goodstats.com). However, risks such as fraud, product discrepancies, delivery delays, and data breaches—as seen in the Tokopedia and Bhinneka cases (2020)—erode consumer trust (Pavlou, 2003; Fauzi et al., 2024; Putra et al., 2022; Wu et al., 2020; Zhang & Yu, 2020). Such risks may trigger cognitive dissonance, defined as the psychological discomfort experienced after making a purchase (Festinger, 1957; Cooper & Carlsmith, 2001; Wahyuni et al., 2021). International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4944 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 Cognitive dissonance arises when purchases conflict with consumers’ values or needs (Brehm, 1956; Singh, 2012; Akram et al., 2021; Yap & Gaur, 2014). A Slickdeals survey found that 74% of online shoppers in Indonesia regretted their purchases; the main reasons were goods not matching their price (39%), unused items (34%), and overspending (32%). Psychological factors such as perception, motivation, learning, beliefs, and attitudes play a dominant role in digital consumer behavior (Chowdhury, 2023; Handriani et al., 2024; Islam et al., 2019; Panjaitan et al., 2018; Solomon, 2013; Fernandez-Lores et al., 2024; Priya & Sharma, 2023; Abigail et al., 2024; Haqiqi, 2019). E-commerce platforms provide information that shapes both rational and impulsive purchase decisions, yet such information is often excessive or misleading, leading to consumer confusion (Blagoeva et al., 2023; Kim et al., 2008; Kumar et al., 2017; Petcharat & Leelasantitham, 2021; Salam et al., 2003; He et al., 2022; Lee et al., 2006). Perceptions of information overload positively influence consumers’ intention to repurchase online through impulsive buying and cognitive dissonance (Akram et al., 2021; C. Liao et al., 2017; Setyani et al., 2019). Understanding these risks and adopting preventive measures—such as product research, scrutinizing seller reputation and reviews, choosing trustworthy platforms, and utilizing reliable customer service—can help consumers make more prudent purchase decisions and reduce the likelihood of cognitive dissonance in the future. This study aims to examine consumers’ regret experiences resulting from impulsive purchases and their impact on future online shopping behavior. II. LITERATURE REVIEW Customer Experience (CX) and Online Customer's Shopping Experience (OCSE) CX encompasses all customer interactions with the company before and after the purchase and involves the emotional aspects that affect satisfaction and loyalty (Lemon & Verhoef, 2016). The Schmitt model identifies five key dimensions of customer experience—sensory, emotional, cognitive, social, and functional—that companies can manage to create positive experiences (Schmitt, 1999). In online shopping, the convenience of 24-hour service, fast delivery, and complete product information have a positive impact on satisfaction (Perera & Sachitra, 2019), while site quality such as design, functionality, and usability are significant predictors of satisfaction (Deyalage & Kulathunga, 2019). OCSE encompasses cognitive, emotional, and behavioral interactions when shopping digitally (Rose et al., 2012). Site design, ease of navigation, transaction security, and interactivity affect satisfaction and loyalty (Mclean & Wilson, 2016). Positive OCSEs increase trust, purchases, and long-term relationships (Bleier et al., 2019). Important factors in CX–OCSE include intuitive interface design and UX (Hassanein & Head, 2007), trust and security of transactions (Mccole, 2004), and personalization of product recommendations (Arora et al., 2008; Schiffman & Kanuk, 2008). Impulsive Buying Impulse purchases are influenced by internal factors such as personality and emotions as well as external factors such as promotion and product placement (Stern, 1962). Stern classifies four types: pure impulse (spontaneous), reminder impulse (remembering previous needs) (Hubert & Griffiths, 2018), suggestion impulse (due to product quality/benefits) (Nurlinda et al., 2020; Park et al., 2012), and planned impulse (a combination of plan and spontaneity) (Badgaiyan & Verma, 2015). Pure Impulsive Buying Impulse shopping is higher online than offline (Virmani et al., 2023; Fernando-Lores et al., 2024; Rundle-Thiele et al., 2013; Vicdan et al., 2007). Online consumers tend to be unplanned and find it difficult to control the buying impulse (Gong et al., 2020; Rook & Fisher, 1995; Savastano et al., 2024; Xiang et al., 2016; Singh et al., 2023) as well as having a strong emotional drive to buy (Ampadu et al., 2022; Koufaris et al., 2002; Parboteeah et al., 2009). Pure impulsive buying decisions are spontaneous without considering the consequences and are triggered by situational temptations that are difficult to control (Utama et al., 2021; Verplanken & Herabadi, 2001a; Hassan et al., 2016; Akram et al., 2018). This temptation can result in a strong emotional attachment to the product (Gulfraz et al., 2022; Park et al., 2012; Smoke, n.d.; Spiteri Cornish, 2020). Impulsive buying includes affective (pleasure, joy, guilt) and cognitive (absence of planning, elaborative thinking) aspects (Rook & Fisher, 1995; Dittmar, 2005; Beatty & Ferrell, 1998; Sharma et al., 2010; Sohn & Lee, 2017; Verplanken & Herabadi, 2001). Website design and layout factors can increase impulse purchase intent (Chen et al., 2017), as well as consumer trust in the platform (Gong et al., 2020). The analysis of Redine et al. (2023) identified five categories of online impulsive drivers: individual factors (Parboteeah et al., 2009; Khan et al., 2016), product factors, website factors (Vicdan et al., 2007), social factors, and situational factors. Attractive visual displays (Chen International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4945 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 & Yao, 2018) as well as credible positive reviews increase impulse purchases (Huang et al., 2023; Lina & Ahluwalia, 2021; Xu et al., 2020). Cognitive Dissonance Cognitive dissonance arises when behaviors or opinions are inconsistent and give rise to emotional discomfort (Hawkins & Mothersbaugh, 2016; Cooper & Carlsmith, 2015; Festinger, 1957; Morvan, 2017; Harmon-Jones & Mills, 2019). The greater the dissonance, the stronger the motivation to reduce it (Brehm, 1956; Harmon-Jones & Mills, 2019; Weingarten & Lagerkvist, 2023). Festinger (1957) stated that dissonance occurs due to inappropriate relationships between cognition, which is a motivating factor in itself. Individuals tend to justify decisions even if they differ from their beliefs (Tavris & Aronson, 2007; Aronson, 1999). These inconsistencies create psychological tension and encourage the search for consistency (Devine et al., 1999; Harmon-Jones, 2012; Steele, 1988). Indicators include emotional discomfort, changes in attitudes, behavioral rationalization, and information avoidance. In the context of purchase, dissonance occurs after purchase if the product does not meet expectations or is considered the wrong choice (Sweeney & Soutar, 2001; Li & Choudhury, 2021; Nam, 2023). Consumers then reevaluate or use strategies to reduce nonconformity (Festinger, 1957 in Barta et al., 2023; Chatterjee et al., 2023; Rees et al., 2015; Chung & Cheng, 2018; Draycott & Dabbs, 1998). Conceptual Framework Figure 1. Research Concept Model (Sources: Schmitt, 1999; Lemon & Verhoef, 2016; Hawkins–Stern, 1962; Festinger, 1957). III. METHODOLOGY This study uses a quantitative approach with descriptive and explanatory designs to test the hypotheses developed by the researcher. The research design serves as a blueprint for data collection, measurement, and analysis (Sekaran & Bougie, 2016). The quantitative approach was chosen because it aims to test theories with statistical procedures (Kusumastuti, 2020). The variables tested included Online Customer Shopping Experience (OCSE) as an independent variable (X), Impulsive Buying (IB) as a mediating variable (M), and Cognitive Dissonance (CD) as a dependent variable (Y). OCSE indicators refer to sensory, emotional, cognitive, social, functional experiences (Lemon & Verhoef, 2016; Schmitt, 1999) as well as online shopping experiences, including interface design, trust, security, and personalization (Bleier et al., 2019; Pandey & Chawla, 2018; Hassanein & Head, 2007; McCole, 2004; Arora et al., 2008; Schiffman, 2008). IB is measured based on aspects of spontaneous buying, less attention to consequences, and triggered by situational factors (Verplanken & Herabadi, 2009; Hassan & Shiu, 2016; Khan & Dhar, 2006). CD is measured through emotional discomfort, attitudinal changes, behavioral rationalization, and information avoidance (Festinger, 1957; 1962). The study population was e-commerce users who had made impulse purchases. The sampling technique used is convenience sampling because the population is not known for sure (Etikan et al., 2016). Based on the basic rules of the Partial Least Squares (PLS) method, the minimum number of samples is determined to be 5–10 times the number of indicators (Hair et al., 2011; Kock & Hadaya, 2018). With 42 indicators, the number of samples was set at 420 respondents. Respondents were selected based on the following criteria: (1) active e-commerce users over the age of 16 and (2) having made impulse purchases in the last six months. Data was collected through an online survey using a Google Form questionnaire after respondents expressed their willingness to participate. This study uses primary data from questionnaires and secondary data from documents or literature studies. Variables are measured on a Likert scale of 1–5 (strongly disagree – strongly agree). International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4946 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 Data analysis uses Structural Equation Modeling–Partial Least Squares (SEM-PLS) because of its ability to handle complex models with relatively small samples (Hair Jr et al., 2017; Henseler et al., 2016). The analysis included a descriptive test to describe the characteristics of the data, a validity test (Ghozali, 2009) using factor analysis and AVE (>0.5), a reliability test using Cronbach's Alpha and Composite Reliability (>0.7) (Priyatno, 2018; Suharsaputra, 2014; Hair Jr et al., 2014), as well as hypothesis testing and mediation through bootstrapping methods to determine the significance of direct and indirect influences (Zhao et al., 2010; Hair et al., 2017). IV. RESULTS AND DISCUSSION Respondent Characteristics This study involved 420 respondents using e-commerce in Indonesia. The results showed a significant correlation between impulsive buying 4946 ehaviour and the appearance of regret and dissatisfaction after a purchase. Around 71% of respondents (300 people) admitted to making impulse purchases often or occasionally, with the main triggers being discounts/promos (187 respondents), followed by attractive advertisements (58) and product availability (37). After impulse purchases, 54% of respondents (225) stated often/sometimes regretted; 51% (216) admit their purchases are influenced by negative emotions such as stress or dissatisfaction. Dissatisfaction with the product is quite dominant (190 sometimes dissatisfied; 72 often dissatisfied), mainly due to inappropriate quality (131) and price considered too high (101); Major/moderate regret was reported by 68% of respondents (286). In terms of demographics, the majority of respondents were male (59.5%; 249 people) and were in the age range of 22–27 years (51.9%; 218 people). The geographical distribution is quite wide: the most from Bandung (111), Jakarta (76), and other categories (212), strengthening the validity of the data (Raharjo, 2020). Product preferences showed the dominance of clothing and accessories (331 respondents), followed by beauty/personal care products (189), electronics (122), and home appliances (113). These findings show that Indonesian consumers not only use ecommerce for basic needs, but also to support lifestyle and personal comfort; The purchase of electronic and household products indicates trust in online platforms in providing high-value goods. Outer Model Analysis Validity Test The high outer loading indicates a lot of similarities in the construct. The minimum value of outer loading is 0.7 (Hair et al., 2022). The following are the results of the outer loading test which can be seen in Table 1. Table 1. Outer Loading Cognitive Dissonance Impulsive Buying Online Customer Shopping Experience Impulsive Buying x Online Customer Shopping Experience CD-1 0.791 CD-12 0.802 CD-13 0.831 CD-14 0.807 CD-15 0.787 CD-16 0.808 CD-2 0.795 CD-3 0.805 CD-4 0.806 CD-6 0.804 CD-7 0.818 CD-8 0.82 CD-9 0.811 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4947 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 Convergent validity refers to the extent to which a construct is able to measure each of its indicators. Convergent validity testing can be performed by evaluating the Average Variance Extracted (AVE). According to Hair et al. (2022), when the AVE value is greater than 0.5, the construct is able to explain more than 50% of the indicator’s variations. Table 2. AVE Construct Average variance extracted (AVE) Cognitive Dissonance 0.65 Impulsive Buying 0.609 Online Customer Shopping Experience 0.638 Cognitive Dissonance Impulsive Buying Online Customer Shopping Experience Impulsive Buying x Online Customer Shopping Experience IB-1 0.783 IB-2 0.783 IB-3 0.784 IB-4 0.792 IB-6 0.786 IB-7 0.767 IB-8 0.767 OCSE-1 0.818 OCSE-10 0.783 OCSE-12 0.785 OCSE-13 0.8 OCSE-14 0.789 OCSE-15 0.801 OCSE-16 0.803 OCSE-17 0.778 OCSE-18 0.797 OCSE-2 0.78 OCSE-3 0.802 OCSE-5 0.803 OCSE-7 0.82 OCSE-8 0.833 OCSE-9 0.788 Impulsive Buying x Online Customer Shopping Experience 1 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4948 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 All constructs in this model have qualified convergent validity with AVE values above 0.5. Cognitive Dissonance has an AVE of 0.65, Impulsive Buying of 0.609, and an Online Customer Shopping Experience of 0.638. This shows that each construct is able to explain more than 50% of the variance of the indicators used to measure it. The next criterion that needs to be considered is the value of cross loading. According to this criterion, the outer loading of an indicator on the related construct must be greater than the cross loading on the other construct. The value of the loading factor can be seen in Table 3. Table 3. Cross Loading Cognitive Dissonance Impulsive Buying Online Customer Shopping Experience Impulsive Buying x Online Customer Shopping Experience CD-1 0.791 0.514 0.377 -0.171 CD-12 0.802 0.523 0.431 -0.177 CD-13 0.831 0.528 0.404 -0.169 CD-14 0.807 0.512 0.407 -0.153 CD-15 0.787 0.515 0.432 -0.17 CD-16 0.808 0.476 0.417 -0.108 CD-2 0.795 0.485 0.411 -0.19 CD-3 0.805 0.497 0.391 -0.143 CD-4 0.806 0.491 0.388 -0.123 CD-6 0.804 0.514 0.42 -0.152 CD-7 0.818 0.491 0.447 -0.144 CD-8 0.82 0.518 0.411 -0.179 CD-9 0.811 0.503 0.453 -0.17 IB-1 0.443 0.783 0.491 -0.354 IB-2 0.484 0.783 0.473 -0.355 IB-3 0.479 0.784 0.494 -0.378 IB-4 0.53 0.792 0.537 -0.379 IB-6 0.509 0.786 0.532 -0.359 IB-7 0.498 0.767 0.489 -0.41 IB-8 0.473 0.767 0.488 -0.336 OCSE-1 0.446 0.498 0.818 -0.475 OCSE-10 0.368 0.54 0.783 -0.507 OCSE-12 0.389 0.504 0.785 -0.471 OCSE-13 0.386 0.555 0.8 -0.475 OCSE-14 0.451 0.531 0.789 -0.484 OCSE-15 0.376 0.519 0.801 -0.467 OCSE-16 0.373 0.507 0.803 -0.484 OCSE-17 0.387 0.526 0.778 -0.484 OCSE-18 0.44 0.502 0.797 -0.446 OCSE-2 0.408 0.487 0.78 -0.494 OCSE-3 0.419 0.536 0.802 -0.473 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4949 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 Cognitive Dissonance Impulsive Buying Online Customer Shopping Experience Impulsive Buying x Online Customer Shopping Experience OCSE-5 0.414 0.495 0.803 -0.488 OCSE-7 0.444 0.506 0.82 -0.46 OCSE-8 0.449 0.511 0.833 -0.465 OCSE-9 0.405 0.483 0.788 -0.462 Impulsive Buying x Online Customer Shopping Experience -0.196 -0.471 -0.595 1 Based on Table 3, it can be stated that the value of each of the outer loading is higher than the cross loading in the other constructs. Another important criterion to consider in discriminant validity is the heterotrait monotrait ratio (HTMT). HTMT is the mean of the entire relationship between the cross-construct indicators. According to (Hair et al., 2022), the maximum value of HTMT correlation is 0.9. HTMT correlation values of more than 0.9 indicate a lack of discriminant validity. Table 4. Heterotrait Monotrait Ration (HTMT) Cognitive Dissonance Impulsive Buying Online Customer Shopping Experience Impulsive Buying x Online Customer Shopping Experience Cognitive Dissonance Impulsive Buying 0.677 Online Customer Shopping Experience 0.536 0.693 Impulsive Buying x Online Customer Shopping Experience 0.2 0.498 0.608 Based on Table 4, there is no HTMT correlation value greater than 0.9. The value has met the HTMT criteria and has met the discriminant validity test. Reliability Test The next test that needs to be done on the outer model is the internal consistency reliability test. This test was carried out through Cronbach alpha and composite reliability values.The Cronbach alpha value describes the Cronbach correlation in a construct, while the composite reliability looks at the difference in the outer loading of the Cronbach Alpha. Hair et al. (2022) stated that the Cronbach alpha and composite reliability values received must be more than 0.7 (Hair et al., 2022). Table 5. Composite Reliability Construct Cronbach’s alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) Cognitive Dissonance 0.955 0.955 0.96 0.65 Impulsive Buying 0.893 0.894 0.916 0.609 Online Customer Shopping Experience 0.959 0.96 0.964 0.638 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4950 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 The test results in table 5 show that all latent variables meet the reliability test criteria. This is based on the Cronbach alpha value and composite reliability of all latent variables with values above 0.7. Therefore, all latent variables are declared reliable after meeting all measurement criteria. Inner Model Analysis The next evaluation that is carried out when the model measurement is declared valid and reliable is the Structural Model Assessment or commonly called the internal model evaluation. According to (Hair et al., 2022), the evaluation of the inner model is carried out with several tests, such as collinearity, significance and relevance of model relationships, Model’s Explanatory Power, and Model’s Predictive Power which will be discussed below. Test R Square (R2) Table 6 shows the results of the test analysis of the R-Square value. Table 6. R-Square Test (R2) R-square R-square adjusted Cognitive Dissonance 0.452 0.449 Impulsive Buying 0.413 0.412 The R Square value for Cognitive Dissonance of 0.452 indicates that 45.2% variability in cognitive dissonance can be explained by Impulsive Buying, Online Customer Shopping Experience, and their interactions. While the R Square value for Impulsive Buying of 0.413 indicates that 41.3% of the variability in impulsive 4950 ehaviour is explained by the online shopping experience. The Adjusted R Square, which is not much different, indicates that this model is stable and quite powerful. Assess the structural model for collinearity issues (VIF) Collinearity is a condition in which two or more (independent) predictor variables in a model have a high linear relationship, meaning they are highly correlated with each other. The collinearity test can be done by looking at the VIF value. If the value is VIF<5, then the model is fit and can be continued in the next analysis. The results of the VIF value test can be seen in Table 7 below. Table 7. Cholinity Test VIVID Impulsive Buying -> Cognitive Dissonance 1.74 Online Customer Shopping Experience -> Cognitive Dissonance 2.098 Online Customer Shopping Experience -> Impulsive Buying 1 Impulsive Buying x Online Customer Shopping Experience -> Cognitive Dissonance 1.582 It can be seen in Table 7 above that the VIF value between the research variables has met the test limit, which is < 5. From the internal model testing , it was found that the model in general is quite good. Path Analysis At this stage, the test is carried out by looking at the path coefficient value and the t-value. A path coefficient value close to 1 indicates a positive relationship and conversely, a value close to 0 indicates a weak relationship in the model structure. Furthermore, the value t indicates the significance of a relationship between variables at a given error level. In this study, the researcher used a significance level error of 5% which means that the t-value must be greater than 1.96. The following are the (Hair et al., 2022) path coefficient and t values shown in Table 8. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4951 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 Table 8. Path Coefficient Value Original Sample Estimate Standard Deviation (STDEV) T-Statistics P-Values Impulsive Buying -> Cognitive Dissonance 0.542 0.048 11.373 0.000 Online Customer Shopping Experience -> Cognitive Dissonance 0.312 0.06 5.175 0.000 Online Customer Shopping Experience -> Impulsive Buying 0.643 0.037 17.144 0.000 Impulsive Buying x Online Customer Shopping Experience -> Cognitive Dissonance 0.187 0.044 4.228 0.000 Impulsive Buying had a significant effect on Cognitive Dissonance with a coefficient of 0.542, a T-value of 11.373, and a p-value of 0.000, which suggests that impulsive buying 4951 ehaviour can increase the cognitive dissonance felt by consumers after shopping. Online Customer Shopping Experience also has a direct effect on Cognitive Dissonance with a coefficient of 0.312, a Tvalue of 5.175, and a p-value of 0.000. In addition, Online Customer Shopping Experience also has a significant influence on Impulsive Buying with a coefficient of 0.643, a T-value of 17.144, and a p-value of 0.000, which indicates that the online shopping experience felt by consumers encourages impulse purchases. The interaction between Impulsive Buying and Online Customer Shopping Experience as a moderation construct also had a significant effect on Cognitive Dissonance (coefficient 0.187, T = 4.228, p = 0.000), which showed the presence of a moderation effect in the relationship. Furthermore, it is the path coefficient and t value values with indirect influence shown in the following Table 9. Table 9. Indirect Effect Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Online Customer Shopping Experience -> Impulsive Buying -> Cognitive Dissonance 0.348 0.349 0.036 9.752 0.000 Online Customer Shopping Experience also has an indirect influence on Cognitive Dissonance through Impulsive Buying with a coefficient of 0.348, a T value of 9.752, and a p-value of 0.000. This means that most of the influence of the online shopping experience on cognitive dissonance occurs indirectly through impulse buying 4951 ehaviour. Mediation Test Table 10. Mediation Test Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Online Customer Shopping Experience -> Impulsive Buying - > Cognitive Dissonance 0.348 0.349 0.036 9.752 0.000 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4958 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 81. Lin, S.-W., Lo, L. Y.-S., & Chen, Y.-J. (2024). Unpacking Mobile Website Aesthetics and Its Effect: A Case of Mcommerce Website Offering Search and Experience Goods. Journal of Organizational Computing and Electronic Commerce. 82. Lina, L. F., & Ahluwalia, L. (2021). Customers' impulse buying in social commerce: The role of flow experience in personalized advertising. Journal of Maranatha Management, 21(1). 83. Lv, J., & Liu, X. (2022). The Impact of Information Overload of E-Commerce Platform on Consumer Return Intention: Considering the Moderating Role of Perceived Environmental Effectiveness. International Journal of Environmental Research and Public Health, 19(13). https://doi.org/10.3390/ijerph19138060 84. Mahmood, S. M. F. (2016). e-Commerce, Online Shopping and Customer Satisfaction: An Empirical Study on eCommerce System in Dhaka. International Journal of Entrepreneurship and Development Studies (IJEDS) 4(3) 2016, 323–338 E-COMMERCE, 3(2018), 323–338. https://www.researchgate.net/publication/322791494%0AE-COMMERCE, 85. Martin, L. M., Espel-Huynh, H. M., Marando-Blanck, S., Evans, B. C., Forman, E. M., Butryn, M. L., Baer, R. A., Wolever, R. Q., & Herbert, J. D. (2017). Trusting homeostatic cues versus accepting hedonic cues: A randomized controlled trial comparing two distinct mindfulness-based intervention components. Journal of Contextual Behavioral Science, 6(4), 409– 417. https://doi.org/10.1016/j.jcbs.2017.09.002 86. Matzler, K., & Sauerwein, E. (2002). The factor structure of customer satisfaction: An empirical test of the importance grid and the penalty-reward-contrast analysis. International Journal of Service Industry Management, 13(4), 314–332. https://doi.org/10.1108/09564230210445078 87. Mccole, P. (2004). Refocusing marketing to reflect practice: The changing role of marketing for business. In Marketing Intelligence & Planning (Vol. 22, Issue 5, pp. 531–539). https://doi.org/10.1108/02634500410551914 88. Mclean, G., & Wilson, A. (2016). Evolving the online customer experience... Is there a role for online customer support? 89. Mittal, A. (2013). E-commerce: It's Impact on consumer Behavior. In Global Journal of Management and Business Studies (Vol. 3, Issue 2). http://www.ripublication.com/gjmbs.htm 90. Morvan, C.; O. A. (2017). An Analysis of Leon Festinger's A Theory of Cognitive Dissonance (1st ed.) (1st ed.). Macat Library. 91. Murdiana, R., Abdinagoro, S. B., Setiawati, S., Lubis, A. L. P., & Rosalina, R. (2024). Online Shopping Culture And Internet Generation Consumer Behavior In Indonesia. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 1(1), 576–586. https://doi.org/10.21009/isc-beam.011.40 92. Nam, S. (2023). Predicting Tourist Revisit Intention by Applying the Extended Model of Goal-Directed Behaviour and Cognitive Dissonance Theory. Journal of Tourism and Services, 14(26), 57–72. https://doi.org/10.29036/jots.v14i26.453 93. Nurlinda, R., Christina, D., Arjuna Utara No, J., Kepa, D., Kb Jeruk, K., West Jakarta, K., & Special Capital Jakarta, D. (2020). THE ROLE OF POSITIVE EMOTION AS A MEDIATOR OF HEDONIC SHOPPING AND SHOPPING LIFESTYLE TOWARDS IMPULSE BUYING ON LAZADA under a Creative Commons Attribution (CC-BY-NC-SA) 4.0 license. Journal of Management and Business Research (JRMB) Faculty of Economics UNIAT, 5(1). http://jrmb.ejournal-feuniat.net/index.php/JRMB/article/view/380 94. Nyrhinen, J., Sirola, A., Koskelainen, T., Munnukka, J., & Wilska, T. (2024). Computers in Human Behavior Online antecedents for young consumers' impulse buying behavior. Computers in Human Behavior, 153(November 2022), 108129. https://doi.org/10.1016/j.chb.2023.108129 95. Otterbring, T., Bhatnagar, R., Samuelsson, P., & Borau, S. (2021). Positive gender congruency effects on shopper responses: Field evidence from a gender egalitarian culture. Journal of Retailing and Consumer Services, 63(June), 102738. https://doi.org/10.1016/j.jretconser.2021.102738 96. Pandey, S., & Chawla, D. (2018). Online customer experience (OCE) in clothing e-retail: Exploring OCE dimensions and their impact on satisfaction and loyalty – Does gender matter? International Journal of Retail and Distribution Management, 46(3), 323–346. https://doi.org/10.1108/IJRDM-01-2017-0005 97. Panjaitan, S. P., Usage, P., Paylater, S., Promo, D., & Marpaung, N. (n.d.). HARBOLNAS (NATIONAL ONLINE SHOPPING DAY) AGAINST IMPULSE BUYING OF SHOPEE USERS IN THE CITY OF MEDAN. Journal of Creative Economy and Digital Business Management, 1(3), 479–496. https://transpublika.co.id/ojs/index.php/JEKOMBITAL International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4959 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 98. Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009a). The influence of website characteristics on a consumer's urge to buy impulsively. Information Systems Research, 20(1), 60–78. https://doi.org/10.1287/isre.1070.0157 99. Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009b). The influence of website characteristics on a consumer's urge to buy impulsively. Information Systems Research, 20(1), 60–78. https://doi.org/10.1287/isre.1070.0157 100. Park, E. J., Kim, E. Y., Funches, V. M., & Foxx, W. (2012). Apparel product attributes, web browsing, and e-impulse buying on shopping websites. Journal of Business Research, 65(11), 1583–1589. https://doi.org/10.1016/J.JBUSRES.2011.02.043 101. Pavlou, P. A. (2003a). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275 102. Pavlou, P. A. (2003b). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275 103. Perera, G. A. B. S., & Sachitra, K. M. V. (2019). Customer Satisfaction towards Online Shopping in Sri Lanka: Moderating Effect of Income Level. Asian Journal of Advanced Research and Reports, 1–10. https://doi.org/10.9734/ajarr/2019/v6i230149 104. Petcharat, T., & Leelasantitham, A. (2021). A retentive consumer behavior assessment model of the online purchase decision-making process. Heliyon, 7(10). https://doi.org/10.1016/j.heliyon.2021.e08169 105. Priya, B., & Sharma, V. (2023). Exploring users' adoption intentions of intelligent virtual assistants in financial services: An anthropomorphic perspectives and socio-psychological perspectives. Computers in Human Behavior, 148. https://doi.org/10.1016/j.chb.2023.107912 106. Priyatno, D. (2018). Independent Learning Data Analysis with SPSS. Mediakom. 107. Program, N. P., Management, S., Pgri, S., & Jombang, D. (2022). THE INFLUENCE OF CUSTOMER EXPERIENCE ON WORD OF MOUTH MEDIATED BY CUSTOMER SATISFACTION (Study on Consumers of Bank Syariah Mandiri Jombang Branch). Journal of Management and Applied Accounting (JIMAT), 13(1). 108. Qin, X., Liu, Z., & Tian, L. (2020). The strategic analysis of logistics service sharing in an e-commerce platform. Omega (United Kingdom), 92, 102153. https://doi.org/10.1016/j.omega.2019.102153 109. Raden Vina Iskandya Putri1, T. A. R. (2023). THE EFFECT OF THE USE OF SHOPEE PAYLATER AND THE HARBOLNAS PROMO (NATIONAL ONLINE SHOPPING DAY) ON THE IMPULSE BUYING OF SHOPEE USERS IN MEDAN CITY. The Role of Customer Satisfaction in Mediating the Influence of Customer Relationship Marketing on Customer Loyalty, 2(3), 310–324. https://bnr.bg/post/101787017/bsp-za-balgaria-e-pod-nomer-1-v-buletinata-za-votagerb-s-nomer-2-pp-db-s-nomer-12 110. Redine, A., Deshpande, S., Jebarajakirthy, C., & Surachartkumtonkun, J. (2023). Impulse buying: A systematic literature review and future research directions. In International Journal of Consumer Studies (Vol. 47, Issue 1, pp. 3–41). John Wiley and Sons Inc. https://doi.org/10.1111/ijcs.12862 111. Rees, J. H., Klug, S., & Bamberg, S. (2015). Guilty conscience: motivating pro-environmental behavior by inducing negative moral emotions. Climatic Change, 130(3), 439–452. https://doi.org/10.1007/s10584-014-1278-x 112. Ronaldo Yolanda Putra, Syifa Nurul Fadillah, Annisa Afifah Nugroho, & Ifta Firdausa Nuzula. (2022). Analysis of Factors Contributing to Impulse Buying Behavior of E-Commerce Users. Journal of Management Studies and Development, 1(01), 48–67. https://doi.org/10.56741/jmsd.v1i01.43 113. Rook, D. W. (n.d.). The Buying Impulse. 114. Rook, D. W. (1987). The buying impulse. Journal of Consumer Research. Journal of Consumer Research, 14(2), 189–199. 115. Rook, D. W., & Fisher, R. J. (1995). Normative Influences on Impulsive Buying Behavior. 116. Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online Customer Experience in e-Retailing: An empirical model of Antecedents and Outcomes. Journal of Retailing, 88(2), 308–322. https://doi.org/10.1016/j.jretai.2012.03.001 117. Rundle-Thiele, S., Russell-Bennett, R., Leo, C., & Dietrich, T. (2013). Moderating teen drinking: Combining social marketing and education. Health Education, 113(5), 392–406. https://doi.org/10.1108/HE-07-2012-0041 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4960 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 118. Salam, A. F., Rao, H. R., & Pegels, C. C. (2003). Consumer-perceived risk in e-commerce transactions. www.penton.com 119. Saputra, D., Abigail, J., Sari, V. P., & Saputra, D. (2024). ScienceDirect ScienceDirect The Influence of Product Quality and Online Shopping Experience ( OSE ) on Consumer Loyalty Through Customer Satisfaction on The Influence of Product Quality and Online Shopping Experience Local Skincare Products ( OSE ) on Con. Procedia Computer Science, 234(2023), 537–544. https://doi.org/10.1016/j.procs.2024.03.037 120. Savastano, M., Anagnoste, S., Biclesanu, I., & Amendola, C. (2024). The impact of e-commerce platforms' quality on customer satisfaction and repurchase intention in post COVID-19 settings. TQM Journal. https://doi.org/10.1108/TQM04-2024-0143 121. Schiffman, L., & Kanuk, L. L. (2008). Consumer Behavior (7th ed.). 122. Schmitt, B. (1999). Experiential Marketing. Journal of Marketing Management, 15(1–3), 53–67. https://doi.org/10.1362/026725799784870496 123. Sekaran, U., & Bougie, R. (2016). Research Methods for Business: A Skills Development Approach. (Vol. 7). Wiley & Sons, West Sussex. 124. Setyani, V., Zhu, Y. Q., Hidayanto, A. N., Sandhyaduhita, P. I., & Hsiao, B. (2019). Exploring the psychological mechanisms from personalized advertisements to urge to buy impulsively on social media. International Journal of Information Management, 48, 96–107. https://doi.org/10.1016/j.ijinfomgt.2019.01.007 125. Sharma, A., Iyer, G. R., Mehrotra, A., & Krishnan, R. (2010). Sustainability and business-to-business marketing: A framework and implications. Industrial Marketing Management, 39(2), 330–341. https://doi.org/10.1016/J.INDMARMAN.2008.11.005 126. Sharma, P., Sivakumaran, B., & Marshall, R. (2010). Impulse buying and variety seeking: A trait-correlates perspective. Journal of Business Research, 63(3), 276–283. https://doi.org/10.1016/j.jbusres.2009.03.013 127. Singh, Dr. B. K. (2022). CONSUMER BEHAVIOR IN ONLINE VS. OFFLINE SHOPPING: A CASE STUDY. 11, 2258– 2268. 128. Singh, P., Sharma, B. K., Arora, L., & Bhatt, V. (2023). Measuring social media impact on Impulse Buying Behavior. Cogent Business and Management, 10(3). https://doi.org/10.1080/23311975.2023.2262371 129. SIVAKUMAR, V., & MOHAN, R. (2020). Sustainable solid waste management in leather and textile industry: Leather & textile waste fibre-polymer composite and nanocomposite - overview and review. In Textile and Leather Review (Vol. 3, Issue 2, pp. 54–63). Seniko studio Ltd. https://doi.org/10.31881/TLR.2020.04 130. Sohn, H. K., & Lee, T. J. (2017). Tourists' impulse buying behavior at duty-free shops: the moderating effects of time pressure and shopping involvement. Journal of Travel and Tourism Marketing, 34(3), 341–356. https://doi.org/10.1080/10548408.2016.1170650 131. Solomon, M. R.; B. G. A. S. H. M. K. (2013). Consumer Behaviour. Pearson Education Limited. 132. Spiteri Cornish, L. (2020). Why did I buy this? Consumers' post-impulse-consumption experience and its impact on the propensity for future impulse buying behaviour. Journal of Consumer Behavior, 19(1), 36–46. https://doi.org/10.1002/cb.1792 133. Steele, C. M. (1988). The psychology of self-affirmation: Sustaining the integrity of the self. Advances in Experimental Social Psychology, 21. 134. Stern, H. (n.d.). The Significance of Impulse Buying Today. 135. Suharsaputra, U. (2014). Quantitative, Qualitative, and Action Research Methodology. (2nd ed.). Aditama Review. 136. Sundström, M., Hjelm-Lidholm, S., & Radon, A. (2019). Clicking the boredom away – Exploring impulse fashion buying behavior online. Journal of Retailing and Consumer Services, 47, 150–156. https://doi.org/10.1016/J.JRETCONSER.2018.11.006 137. Suryadi, N., Anggraeni, R., Ariyanto, D., & Fajrul Islam, M. (2022). The effect of the COVID-19 pandemic on consumer behaviors: A study on gen-z in Indonesia. International Journal of Research in Business and Social Science (2147– 4478), 10(8), 313–318. https://doi.org/10.20525/ijrbs.v10i8.1490 138. Svobodová, Z., & Rajchlová, J. (2020). Strategic behavior of e-commerce businesses in online industry of electronics from a customer perspective. Administrative Sciences, 10(4). https://doi.org/10.3390/admsci10040078 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4961 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 139. Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220. https://doi.org/10.1016/S0022-4359(01)00041-0 140. Szymanski, D. M., Hise, R. T., & Al, M. (2000). e-Satisfaction: An Initial Examination. Journal of Retailing, 76(3), 309– 322. 141. Tavris, Carol., & Aronson, Elliot. (2007). Self-justification in public and private spheres. The General Psychologist, 42, 4–7. 142. Utama, A., Sawitri, H. S. R., Haryanto, B., & Wahyudi, L. (2021). Impulse Buying: The Influence of Impulse Buying Tendency, Urge to Buy and Gender on Impulse Buying of the Retail Customers. Journal of Distribution Science, 19(7), 101–111. https://doi.org/10.15722/jds.19.7.202107.101 143. Verhagen, T., & Van Dolen, W. (2011). The influence of online store beliefs on consumer online impulse buying: A model and empirical application. Information and Management, 48(8), 320–327. https://doi.org/10.1016/j.im.2011.08.001 144. Verplanken, B. A. S., & Herabadi, A. (2001). Individual Differences in Impulse Buying Tendencies: Feeling and No Thinking. 83(May), 71–83. 145. Verplanken, B., & Herabadi, A. (2001a). Individual differences in impulse buying tendency: Feeling and no thinking. European Journal of Personality, 15(1 SUPPL.). https://doi.org/10.1002/per.423 146. Verplanken, B., & Herabadi, A. (2001b). Individual differences in impulse buying tendency: Feeling and no thinking. European Journal of Personality, 15(1 SUPPL.). https://doi.org/10.1002/per.423 147. Vicdan, H., Chapa, S., & de Los Santos, G. (2007). Understanding compulsive buyers' online shopping incidence: a closer look at the effects of sales promotions and bargains on Hispanic Americans. Journal of Customer Behaviour, 6(1), 57–74. https://doi.org/10.1362/147539207x198374 148. Virmani, N., Sharma, S., Kumar, A., & Luthra, S. (2023a). Adoption of industry 4.0 evidence in emerging economy: Behavioral reasoning theory perspective. Technological Forecasting and Social Change, 188 (November 2022), 122317. https://doi.org/10.1016/j.techfore.2023.122317 149. Virmani, N., Sharma, S., Kumar, A., & Luthra, S. (2023b). Adoption of industry 4.0 evidence in emerging economy: Behavioral reasoning theory perspective. Technological Forecasting and Social Change, 188. https://doi.org/10.1016/j.techfore.2023.122317 150. Wahyuni, S., Lukman, & Fakhri, N. (2021). The Effect of Cognitive Effort on Cognitive Dissonance After Online Purchases. Journal Of Art, Humanity And Social Studies, 1(6), 63–69. 151. Weingarten, N., & Lagerkvist, C. J. (2023). Can images and textual information lead to meat avoidance? The mediating role of cognitive dissonance. Food Quality and Preference, 104. https://doi.org/10.1016/j.foodqual.2022.104747 152. Wesely, P. M. (2012). Learner Attitudes, Perceptions, and Beliefs in Language Learning. Foreign Language Annals, 45(S1), 98–117. https://doi.org/10.111/j.1944-9720.2012.01181.x 153. Wijaya, L., Meng, C. K., Tunku, U., Rahman, A., Ng, K. C., & Valerie, J. (2024). Towards Sustainable E-Commerce: Understanding Post-Pandemic Online Shopping Intentions in Indonesia. Journal of System and Management Sciences, July. https://doi.org/10.33168/jsms.2024.0813 154. Wilson, N., & Christella, R. (2019). An Empirical Research of Factors Affecting Customer Satisfaction: A Case of the Indonesian E-Commerce Industry. DeReMa (Development Research of Management): Journal of Management, 14(1), 21. https://doi.org/10.19166/derema.v14i1.1108 155. Wilson, N., Ken, & Tan, P. H. P. (2019). The effect of website design quality and service quality on repurchase intention in the E-commerce industry: A cross-continental analysis. . Gadjah Mada International Journal of Business, 21(2), 187– 222. 156. Wu, I. L., Chiu, M. L., & Chen, K. W. (2020). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52. https://doi.org/10.1016/j.ijinfomgt.2020.102099 157. Xiang, L., Zheng, X., Lee, M. K. O., & Zhao, D. (2016). Exploring consumers' impulse buying behavior on social commerce platform: The role of parasocial interaction. International Journal of Information Management, 36(3), 333–347. https://doi.org/10.1016/j.ijinfomgt.2015.11.002 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 10 October 2025 DOI: 10.47191/ijcsrr/V8-i10-08, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 4962 *Corresponding Author: Bayu Cunda Satria Volume 08 Issue 10 October 2025 Available at: www.ijcsrr.org Page No. 4943-4962 158. Xu, H., Zhang, K. Z. K., & Zhao, S. J. (2020a). A dual systems model of online impulse buying. Industrial Management and Data Systems, 120(5), 845–861. https://doi.org/10.1108/IMDS-04-2019-0214 159. Xu, H., Zhang, K. Z., & Zhao, S. J. (2020b). A dual systems model of online impulse buying. Industrial Management & Data Systems, 120(5), 845–861. 160. Xu, X., Liu, J., & Cai, R. (2022). How do customers navigate perceived inappropriateness of collective emotion in group service recovery? An application of cognitive dissonance theory. Tourism Management, 93, 104615. https://doi.org/10.1016/J.TOURMAN.2022.104615 161. Yap, S. F., & Gaur, S. S. (2014). Consumer Dissonance in the Context of Online Consumer Behavior: A Review and Research Agenda. Journal of Internet Commerce, 13(2), 116–137. https://doi.org/10.1080/15332861.2014.934647 162. Youn, S., & Faber, R. J. (2000). Impulse Buying: Its Relation to Personality Traits and Cues. Advances in Consumer Research, 27, 179–185. 163. Zara, I., Velicu, B. C., Munthiu, M., & Tuta, M. (2012). Using analytics for understanding the consumer online. . Anal. It would be economical. Timisoara, 18, 788–793. 164. Zhang, X., & Yu, X. (2020). The Impact of Perceived Risk on Consumers' Cross-Platform Buying Behavior. Frontiers in Psychology, 11(October). https://doi.org/10.3389/fpsyg.2020.592246 Cite this Article: Satria, B.C., Suryandari, R.T. (2025). Cognitive Dissonance in Online Shopping Experiences and Impulsive Buying Among E-Commerce Users in Indonesia. International Journal of Current Science Research and Review, 8(10), pp. 4943-4962. DOI: https://doi.org/10.47191/ijcsrr/V8-i10-08