Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7, 754-766 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 7 May 2025; Revised: 10 June 2025; Accepted: 15 June 2025; Published: 9 July 2025 * Correspondence: phamngockhanh@iuh.edu.vn The impact of return policies on consumers' online shopping intentions in Ho Chi Minh city, Vietnam Le Van Son1, Pham Ngoc Khanh2* 1Vietnam Women’s Academy, Hanoi City, Vietnam; Sonlv@vwa.edu.vn (L.V.S.). 2Industrial University of Ho Chi Minh City, Vietnam; phamngockhanh@iuh.edu.vn (P.N.K.). Abstract: In the context of continuous and robust development of e-commerce, businesses are adjusting their return policies to meet the growing consumer demand, thereby increasing trust and enhancing the online shopping experience. The author of the study titled “The Impact of Return Policies on Consumers' Online Shopping Intentions in Ho Chi Minh City, Vietnam” aims to identify reimbursement factors that influence online shopping intentions through the mediating role of perceived service quality. The study measures the impact of these factors and offers governance implications to help businesses and retailers improve consumers' online shopping intentions. The research employs both qualitative and quantitative methods, collecting 303 valid questionnaires, which are analyzed using Structural Equation Modeling (SEM). The findings indicate that four factors positively influence consumers' online shopping intentions through the mediating role of intermediaries, listed in order: (i) reimbursement effort, (ii) reimbursement cost, (iii) exchange, and (iv) reimbursement period. Keywords: Online shopping, Purchase intent, SEM, The perceived quality of service, Tolerant return policy. 1. Introduction In the context of global economic integration, along with the explosive development of information technology, Vietnam and the world are experiencing the Covid-19 pandemic that has affected all aspects of life and social activities. This affects many different economic sectors and industries. However, the e-commerce sector alone has grown exponentially during this period and created a breakthrough for the digital transformation process. According to the 2022 report by Google, Temasek and Bain & Company focusing on 6 countries: Indonesia, Malaysia, Singapore, Philippines, Thailand and Vietnam, more than 20 million people started using online platforms for the first time in 2022, bringing the total number of internet users in the region from 360 million in 2019 to 460 million in 2022 [1]. In 2023, the size of Vietnam's retail e-commerce market is expected to reach 20.5 billion USD, an increase of about 4 billion USD (equivalent to 25%) compared to 2022. According to Ministry of Industry and Trade of Vietnam [1] Vietnam's e-commerce growth rate is ranked among the top 10 countries with the fastest e-commerce growth rate in the world. However, when making purchases on online social platforms, e-commerce platforms cannot directly check the quality of goods and products. Therefore, they are still hesitant and worried when buying goods through cyberspace. According to the Vietnam E-commerce Association [2] of the Vietnam E- commerce Association [2], the reason why consumers are not ready to buy goods on online platforms is because consumers do not have confidence in the store (41%) and it is difficult to check the quality (33%). Therefore, businesses and retailers have policies for joint inspection and return of goods, refunds to help consumers feel secure and at the same time protect their rights when shopping online [3]. Currently, studies on return policy factors affecting consumers' purchase intentions have been of interest to researchers [4-6]. However, the research context of foreign studies is different from that of 755 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate Vietnam, so there will also be differences in research results. In Vietnam, most studies only focus on factors affecting purchase intentions such as reference group opinions, perceived behavioral control, electronic word of mouth, etc. In addition, in Vietnam, there are some published studies such as the study of Thang [7] and Niem and Quang [8] studied the return policy factor on online shopping intention. However, there are few in-depth studies on how return policy factors affect consumers' online shopping intention in the current context of e-commerce in Vietnam. Therefore, more research is needed to better understand the impact of return policy on online shopping consumers. Therefore, the objective of this paper is to identify the return factors that influence online shopping intention through the mediating role of perceived service quality, measure the influence of the factors, and propose managerial implications to help businesses and retailers improve consumers' online shopping intention. 2. Literature Review 2.1. Return Policy Reimbursement policy is one of the after-sales services provided by retailers. Retailers allow consumers who are not satisfied with the product experience to receive a refund if the consumer returns the product within a certain period of time. In the process of online shopping, the biggest concern and worry of consumers is the ability to return. This is an important factor when making a purchase, as consumers expect assurance and flexibility in their shopping experience [8]. 2.2. Purchase Intention Purchase intention is a complex process and purchase intention is one of two factors that determine consumer purchasing behavior Ajzen [9]. Delafrooz, et al. [10] argued that the strength of consumers' intention to perform a specific behavior when shopping online. Mirabi, et al. [11] argued that consumers' purchase intention is directly related to their attitudes, perceptions and behaviors. In addition, Quan, et al. [12] argued that “the higher the purchase intention, the higher the likelihood of purchasing behavior and vice versa". 2.3. Theory of Reasoned Action The Theory of Reasoned Action (TRA) was published by Fishbein and Ajzen [13] a research model on consumer behavior and is considered the best theoretical model to predict consumer behavior. The model includes behavioral tendencies, attitudes and subjective norms. In the TRA model, consumers' beliefs about product attributes about the product will affect their attitudes. Attitudes do not directly affect purchase behavior, but attitudes directly affect behavioral tendencies. According to Fishbein and Ajzen [13] subjective norm is defined as an individual consumer's perception of the influences from family, friends and social relationships such as colleagues and the public on whether or not that individual should perform the behavior. 2.4. Theory of Planned Behavior The theory of planned behavior inherits two factors, behavioral attitude and subjective norm, from the TRA. In addition, Ajzen [9] added a new factor, perceived behavioral control. The theory of planned behavior has three factors affecting behavioral intention: behavioral attitude, subjective norm, and perceived behavioral control. And the factor "perceived behavioral control is considered a factor affecting behavioral intention as well as a factor affecting actual consumption behavior" [14]. 2.5. Signaling Theory According to Spence [15] signaling theory comes from the field of economics related to information data in the context of information asymmetry in the market. Signaling theory is widely used in the fields of economics and marketing to explain the impact of information asymmetry in many different contexts [16] and Al-Adwan, et al. [17]. In the purchasing process, consumers lack a 756 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate lot of information about the product, so it is difficult for them to distinguish which is a reputable retailer so that consumers can trust the retailer about the product they intend to buy. Therefore, signaling theory is used to explain the signals that e-retailers provide to consumers to support them in the purchasing process [17]. 2.6. Previous Studies For foreign studies, Janakiraman, et al. [4] studied the impact of return policy on purchase intention through the following aspects: time leniency, monetary leniency, effort leniency, scope leniency, and exchange leniency. Wang, et al. [18] investigated the relationship between consumers' repurchase intention on different aspects of e-retailer's product return process such as leniency, fairness, and quality of return process. Abdulla, et al. [19] studied how different return policies differ in leniency based on five aspects: monetary, time, effort, scope, and exchange. For domestic research, studies affect consumers' purchase intentions through many different aspects: According to Quan, et al. [12] factors affecting purchase intentions include: usefulness, ease of use, popularity, support policies, and safety. Research by Pham, et al. [20] identifies factors in sales services to enhance customer satisfaction, trust, and repurchase intentions such as: product return service, product exchange service, and after-sales service to measure sales services in online shopping. 3. Research Hypotheses and Models 3.1. Research hypothesis 3.1.1. Reimbursement Period (TG) Retailers on online platforms, e-commerce platforms often specify a return period for their return policies. Return policies that stipulate a longer return period are considered more [4]. Currently, on major e-commerce platforms, the return period of “7 days for non-defective return” has become the standard [6]. Return policies that stipulate a longer return period are considered more lenient [4]. According to Heiman, et al. [21] the Reimbursement period required for consumers to check product attributes and evaluate product quality to meet consumer needs is an important variable. Based on the above, the author proposes hypothesis H1: Hypothesis H1: Reimbursement period has a positive impact on the perceived quality of service. 3.1.2. Reimbursement Cost (CP) Bower and Maxham III [22] define cost fairness in returns policies as the extent to which consumers believe that the outcome of a return policy, whether charged or not, is fair. A free return can be understood as a return where the consumer receives a full refund and the consumer pays nothing [23, 24]. However, consumers may have a stronger response to a return policy that involves a fee [22]. Therefore, free returns provide an opportunity for impulsive buying because consumers are not exposed to risk at the beginning of their purchase. Based on this, the author proposes hypothesis H2: Hypothesis H2: Reimbursement cost has a positive impact on the perceived quality of service. 3.1.3. Reimbursement Effort (NL) According to Janakiraman, et al. [4] the effort required by consumers to make a return varies. When consumers receive a product that is not as expected and feel dissatisfied, consumers perceive that complaining is not worth the effort and they often choose other ways to resolve the issue [25]. The study by Janakiraman and Ordóñez [26] on return effort and return time gives the same view. Wood [27] shows the influence of return time and effort on how consumers actually make return decisions. The author proposes hypothesis H3: Hypothesis H3: Reimbursement effort has a positive impact on the perceived quality of service. 757 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate 3.1.4. Exchange (TD) According to Janakiraman, et al. [4] exchange is a refund in exchange for a returned item. When making a product return, consumers usually receive some form of refund from the retailer in exchange for the returned product. Janakiraman, et al. [4] show that lenient exchange is when retailers condition the return of the product to the consumer by refunding the amount of money. The authors propose hypothesis H4: Hypothesis H4: Exchange has a positive impact on the perceived quality of service. 3.1.5. The Perceived Quality of Service (CLDV) Service quality is a comprehensive assessment of service performance, compared to consumers' general expectations of what the service will deliver [19, 28]. Perceived service quality is one of the essential factors that create trust in online shopping Al-Dweeri, et al. [29]; Hsu, et al. [30]. Ibrahim, et al. [31] found that perceived service quality affects online purchase intention. Based on that, the author proposes hypothesis H5: Hypothesis H5: The perceived quality of service has a positive impact on online shopping intention. 3.2. Research Model From the literature review, theoretical basis above, and the specificity of online shopping return policies, the author decided to propose a research model (Figure 1). Figure 1. Research model. 4. Research Methodology 4.1. Qualitative and Quantitative Research Methods 4.1.1. Qualitative Research Methods The author conducted in-depth interviews and focus group discussions with 10 experts in the field of e-commerce business in Ho Chi Minh City (HCMC) to explore the impact of return policy on consumers' online shopping intention; and recalibrated the scale of factors in the research model to suit the practical context in HCMC. Before collecting information, the research team conducted a pilot survey of 5 questionnaires for consumers who have been shopping online on platforms such as: website, shopee, lazada, tiki, tiktok to check the logic of the questionnaire and will adjust and supplement it to suit the research purpose. After that, the author conducted direct interviews with 308 consumers to collect data for quantitative analysis. 758 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate 4.1.2. Quantitative Research Methods The information was collected and processed through SPSS 27.0 and AMOS 20.0 software. The preliminary research was conducted by sending a survey questionnaire to 59 consumers. After collecting 50 valid responses, the author coded and analyzed the data using SPSS 27.0 software to test the reliability of Cronbach's Alpha (CA). After that, the author began to do formal quantitative research. The author analyzed descriptive statistics of the survey sample, variables and tested CA to assess the reliability of the scale; analyzed the EFA exploratory factor with Bartlett and KMO tests; used AMOS software to analyze the CFA confirmatory factor. Analyzed the SEM linear structure model, tested the influence of factors and evaluated the impact of return policy on online shopping intention. Thereby, the author drew conclusions and proposed managerial implications to help businesses and retailers improve consumers' online shopping intention. 4.2. Survey Form According to Hair, et al. [32] be able to conduct exploratory factor analysis, it is necessary to collect data with a sample size of 5:1 observation ratio for each measurement variable, which means that for every 1 measurement variable, at least 5 observations are needed. In the research model, there are a total of 21 observed variables, so the minimum sample size needed is 105 observations (n=5*21=105). To increase the reliability of the study, the author plans to survey 308 consumers. The survey sampling method was convenient but based on characteristics such as: gender, age, income, occupation, and region to ensure that the selected survey sample is the most representative. The results obtained 303 valid survey questionnaires after screening and were used to analyze and officially evaluate the impact of return policy on online shopping intention. 4.3. Build a Scale The study used a Likert scale with 5 levels: (1) Strongly disagree, (2) Disagree, (3) Neutral, (4) Agree, and (5) Strongly agree to measure the impact of return policy on online shopping intention. 5. Research Results 5.1. Descriptive Statistics of the Survey Sample Table 1 shows that, in terms of gender, female accounts for the highest proportion (51.2%) and other genders account for the lowest proportion (1.3%); in terms of age, from 18 to 24 years old accounts for the highest proportion (58.7%) and over 35 years old accounts for the lowest proportion (8.7%); in terms of income, under 3 million VND accounts for the highest proportion (39.6%) and income over 15 million VND accounts for the lowest proportion (17.2%); in terms of shopping experience, from 1 year to 2 years accounts for the highest proportion (40.6%) and under 1 year accounts for the lowest proportion (19.5%); in terms of shopping frequency, once every three months accounts for the highest proportion (10.9%) and once a day accounts for the lowest proportion (6.3%); in terms of monthly shopping expenditure, under 1 million VND accounts for the highest proportion and over 3 million VND accounts for the lowest proportion (14.1%). 759 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate Table 1. Survey sample characteristics. Details Categories Frequency Percent (%) Gender Male 144 47.5 Female 155 51.2 Other 4 1.3 Total 303 100 Age Under 18 years old 28 9.2 18 - 24 years old 178 58.7 25 - 34 years old 71 23.4 Over 34 years old 26 8.7 Total 303 100 Income Under 3 million VND 120 39.6 3 - 5 million VND 53 17.5 6 – 15 million VND 78 25.7 Over 15 million VND 52 17.2 Total 303 100 Shopping experience Under 1 year 59 19.5 1 - 2 years 123 40.6 Over 2 years 121 39.9 Total 303 100 Purchase frequency Once a day 19 6.3 Once a week 87 28.7 A few times a week 68 22.4 Once a month 73 24.1 Once every three months 33 10.9 Once a year 23 7.6 Total 303 100 Monthly spending on online shopping Under 3 million VND 175 57.8 1 - 3 million VND 85 28.1 Over 3 million VND 43 14.1 Total 303 100 5.2. Evaluate the Reliability of the Scale Table 2 show that the CA coefficient of the remaining observed variables is > 0.6 and the total variable correlation coefficient > 0.3, so the remaining observed variables are all reliable and tested in the next step. Table 2. Results of reliability assessment. Variables Cronbach's Alpha Corrected item-total Correlation Reimbursement period (TG) 0.802 0.683 – 0.773 Reimbursement cost (CP) 0.849 0.799 – 0.817 Reimbursement effort (NL) 0.805 0.737 – 0.787 Exchange (TD) 0.815 0.734 – 0.764 The Perceived Quality of Service (CLDV) 0.898 0.854 – 0.876 Online Shopping Intention (YD) 0.841 0.734 – 0.804 5.3. Exploratory Factor Analysis (EFA) KMO = 0.768 > 0.5 in Table 3 indicates the reliability of the study's factors. According to Bartlett's test, the independent factors are statistically significant (Sig = 0.000 < 0.05). The total variance retrieved = 69.751% indicates that 69.751% of the data variability can be explained by independent causes. The convergence of four independent components is indicated by the fourth factor, which yields the Eigenvalues = 1.832 > 1. The four independent factors that are represented by the observed variables all have load factors more than 0.5, indicating that the variables are significant and 760 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate accurately reflect the elements they represent. Table 3. Results of exploratory factor analysis of independent variables. KMO and Bartlett’s test KMO 0.768 Bartlett’s test 0.000 Eigenvalues 1.832 % of total variance 69.751% KMO = 0.841 > 0.5 in Table 4 indicates the reliability of the study's factors. When Bartlett's test reveals intermediate variables that are statistically significant, Sig = 0.000 < 0.05. One intermediary component accounts for 76.672% of the data variability, according to the total variance retrieved = 76.672%. The convergence of one intermediate factor is indicated by the first factor for Eigenvalues = 3.067 > 1, which ends at the first factor. All of the loading factors for the observed variables that represent the intermediate factor are greater than 0.8, indicating that they are significant and accurately reflect the component they represent. Four of the intermediate factor's observed variables are kept after EFA testing. Table 4. Results of exploratory factor analysis of intermediate variables. KMO and Bartlett’s test KMO 0.841 Bartlett’s test 0.000 Eigenvalues 3.067 % of total variance 76.672% KMO = 0.719 > 0.5 in Table 5 indicates the reliability of the study's factors. When Bartlett's test reveals statistically significant dependencies, Sig = 0.000 < 0.05. Dependent factors account for 76.243% of the data variability, as indicated by the total variance retrieved = 76.243%. The convergence of one dependent component is indicated by the first factor for Eigenvalues = 2.287 > 1, which ends at the first factor. All of the dependent factors' observed variables have loading factors greater than 0.8, indicating that they are significant and accurately reflect the component they represent. Three of the dependent factor's observed variables are kept after EFA testing. Table 5. Results of exploratory factor analysis of dependent variables. KMO and Bartlett’s test KMO 0.719 Bartlett’s test 0.000 Eigenvalues 2.287 % of total variance 76.243% Thus, the results of EFA with scales on the impact of return policy on consumers' online shopping intention show high reliability. 761 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate 5.4. Confirmatory Factor Analysis (CFA) Figure 2. Results of Confirmatory Factor Analysis (CFA). The results of CFA analysis of the scales in Figure 2 show that the model fits the research data: Chi-square = 243.563, df = 174, P = 0.000, Chi-square/df = 1.400 < 3, GFI = 0.932, TLI = 0.971, CFI = 0.976 > 0.90, RMSEA = 0.036 < 0.05. The values all meet the requirements from acceptable to very good. The data are eligible for further analysis. The standardized regression coefficients of the observed variables in the model are all greater than 0.5 and the P value = 0.000 < 0.05 is statistically 762 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate significant, so the model achieves convergence value. Table 6. Summary of scale test results. Variables Cronbach’s Alpha CR AVE Exchange (TD) 0.802 0.819 0.601 The Perceived Quality of Service (CLDV) 0.849 0.899 0.690 Reimbursement Cost (CP) 0.805 0.849 0.584 Reimbursement Effort (NL) 0.815 0.808 0.514 Reimbursement Period (TG) 0.898 0.820 0.603 Online Shopping Intention (YD) 0.841 0.845 0.645 Table 6 shows that the average variance extracted (AVE) of the factors is larger than 0.5, the composite reliability coefficient (CR) of the six factors is greater than 0.7, and all of the CA scale values are above the 0.7 threshold. Therefore, it is concluded that the scales all have good reliability. Thus, the data are eligible for further analysis and the model fits the research data well. 5.5. Structural Equation Modeling (SEM) Analysis 5.5.1. Assessment of SEM Model Fit Figure 3. Results of the SEM Model Analysis 763 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate The estimated results of SEM show that the Chi-square index = 301.964, df = 178, P = 0.000, Chi- square/df = 1.696 < 3, GFI = 0.918, TLI = 0.949, CFI = 0.956 > 0.90, and RMSEA = 0.048 < 0.05. The indexes all satisfy the conditions, so the model fits the research data perfectly (Figure 3). 5.5.2. Testing the Research Model Using Bootstrap Table 7. Bootstrap Analysis Results. Relationship SE SE-SE Mean Bias SE-Bias C.R TG  CLDV 0.070 0.002 0.200 0.002 0.002 1 CP  CLDV 0.076 0.002 0.237 -0.004 0.002 -2 NL  CLDV 0.085 0.002 0.273 0.002 0.003 0.67 TD  CLDV 0.080 0.002 0.263 0.003 0.003 0.5 CLDV  YD 0.071 0.002 0.473 -0.002 0.002 -1 In Table 7, the estimated difference appears but has a small and stable value. Therefore, it can be concluded that the estimates in the SEM model used to test the model are reliable. 5.5.3. Testing Research Hypotheses Using SEM In Table 8, the P coefficient is less than 0.05, showing that the relationship between the hypotheses in the research model is statistically significant and the hypotheses in the research model from H1 to H5 are all accepted. Table 8. Results of testing the SEM Relationship Standardized Path Coefficient S.E. C.R. P Result TG  CLDV 0.179 0.070 2.830 0.005 Supported CP  CLDV 0.213 0.071 3.379 0.000 Supported NL  CLDV 0.223 0.078 3.489 0.000 Supported TD  CLDV 0.199 0.085 3.082 0.002 Supported CLDV  YD 0.500 0.063 7.576 0.000 Supported 6. Discussions The research results show that the factor of Reimbursemen Effort (NL) is the factor that has the strongest impact on perceived service quality and online shopping intention of consumers. The p-value is less than 0.05, so this hypothesis is accepted with a standardized coefficient of 0.223, proving that the relationship between perceived service quality and online shopping intention with refund effort is in the same direction. This result is in agreement with the studies of Janakiraman, et al. [4] and Abdulla, et al. [19]. The factor Reimbursemen Cost (CP) is the factor with the second highest level of impact on perceived service quality and online shopping intention of consumers. This hypothesis is accepted because the p-value is less than 0.05 with the standardized coefficient of 0.213, indicating that the relationship between perceived service quality and online shopping intention with return cost is in the same direction. This result is consistent with the studies of Janakiraman, et al. [4]; Wang, et al. [18] and Abdulla, et al. [19]. The Exchange factor (TD), the third most influential factor on perceived service quality and online shopping intention of consumers. This hypothesis is accepted because the p-value is less than 0.05 with a standardized coefficient of 0.199, indicating that the relationship between perceived service quality and online shopping intention with exchange is positive. This result is in line with the studies of Janakiraman, et al. [4] and Abdulla, et al. [19]. The factor Reimbursement Period (TG), the factor has the weakest impact on perceived service quality and online shopping intention of consumers. This hypothesis is accepted because the p-value is 764 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate less than 0.05 with a standardized coefficient of 0.17, proving that the relationship between perceived service quality and online shopping intention with return time is in the same direction. This result is consistent with the studies of Janakiraman, et al. [4] and Abdulla, et al. [19]. The mediator Perceived Quality of Service (CLDV) has an influence on consumers’ online shopping intention. This hypothesis is accepted because the p-value is less than 0.05 with a coefficient of 0.500, indicating that the relationship between perceived service quality and online shopping intention is positive. The results are in line with Wang, et al. [18]; Shao, et al. [6] and Abdulla, et al. [19]. However, the results of this study have some differences compared to previous studies. In the study of Abdulla, et al. [19] the Reimbursemen Cost factor is the strongest factor in influencing online shopping intention through the mediating role of perceived service quality. 7. Conclusions and Implications 7.1. Conclusions The study on the impact of return policy on online shopping intention of consumers in HCMC has solved the following objectives: identifying factors affecting perceived service quality and online shopping intention of consumers; measuring the impact of factors on perceived service quality and online shopping intention of consumers; evaluating the mediating effect of perceived service quality in return policy on online shopping intention; and proposing some managerial implications to help businesses and retailers improve online shopping intention of consumers. The study found that the factors affecting online shopping intention through the mediating role of perceived service quality include 4 factors arranged in descending order: (i) Reimbursement effort (standardized coefficient is 0.223); (ii) Reimbursement cost (0.213); (iii) Exchange (0.199); and (iv) Reimbursement time (0.179). The factor with the strongest impact on online shopping intention is perceived service quality (standardized coefficient is 0.500). On that basis, the author proposes managerial implications arranged according to the priority of influence of factors affecting consumers' online shopping intention, in which perceived service quality plays a mediating role. 7.2. Implications Firstly, the Perceived Quality of Service: businesses and retailers need to develop strategies to improve the perceived service quality of consumers by developing branding strategies, reasonable return policies that ensure compliance with the terms of the policy to ensure consumer trust. Businesses and retailers should improve their staff, invest in, train and coach their staff in the process of supporting and advising customers when they encounter product problems during the shopping process. Secondly, the Reimbursement Effort factor: businesses need to create easy return steps that do not require much effort from consumers such as: easy form filling, consumers need to fill in information and images related to the product they want to return. Businesses coordinate the shipping department to the customer's address to receive the returned product without the customer having to go to the product return location. Thirdly, the Reimbursement Cost factor: businesses come up with cost strategies to enhance consumers' online shopping intentions and perceived quality: (i) businesses come up with strategies to subsidize return shipping costs to increase consumers' online shopping intentions; and (ii) businesses limit penalties when customers return products. Fourthly, the Exchange factor: businesses and retailers need to establish policies on exchanges between consumers such as: (i) businesses and retailers establish refund policies when customers return products such as 100% refund in case of defective products from businesses and retailers. In case of products from consumers, businesses and retailers will consider and handle them most appropriately based on the condition of the product that the consumer returns; and (2) businesses and retailers send customers similar products with the same value as the returned product, helping to increase online 765 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate shopping intentions from consumers. Fifthly, the Reimbursemen Time factor: businesses and retailers need to provide a suitable return period for consumers: (i) Set a 7-day return period for clothing, cosmetics, etc.; (ii) 30 days for household items and large-ticket items such as watches, phones, etc. so that consumers can feel secure in preparing to return the goods. 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] Ministry of Industry and Trade of Vietnam, "E-commerce and digital economy trends in the ASEAN region," 2023. Retrieved: https://moit.gov.vn/khoa-hoc-va-cong-nghe/xu-huong-thuong-mai-dien-tu-va-kinh-te-so-trong-khu- vuc-asean.html. 2023. [2] Vietnam E-commerce Association, "Vietnam E-commerce report 2023," 2023. Retrieved: http://ebi.vecom.vn/Bao- Cao/Noi-dung-bao-cao/49/Bao-cao-Chi-so-Thuong-mai-dien-tu-Viet-nam-2023.aspx. 2023. [3] VnExpress, "Online shoppers benefit from return and refund policies," 2024. Retrieved: https://vnexpress.net/nguoi- mua-hang-online-huong-loi-nho-chinh-sach-tra-hang-hoan-tien-4702138.html. 2024. [4] N. Janakiraman, H. A. Syrdal, and R. Freling, "The effect of return policy leniency on consumer purchase and return decisions: A meta-analytic review," Journal of Retailing, vol. 92, no. 2, pp. 226-235, 2016. https://doi.org/10.1016/j.jretai.2015.11.002 [5] M. Rokonuzzaman, P. Iyer, and A. Harun, "Return policy, No joke: An investigation into the impact of a retailer's return policy on consumers' decision making," Journal of Retailing and Consumer Services, vol. 59, p. 102346, 2021. [6] B. Shao, Z. Cheng, L. Wan, and J. Yue, "The impact of cross border E-tailer's return policy on consumer's purchase intention," Journal of Retailing and Consumer Services, vol. 59, p. 102367, 2021. https://doi.org/10.1016/j.jretconser.2020.102367 [7] H. N. Thang, "Factors affecting online shopping intention of Vietnamese consumers: An extension of the theory of planned behavior," VNU Journal of Economics and Business, vol. 32, no. 4, pp. 21-28, 2016. [8] N. X. Niem and B. V. Quang, "Research on factors affecting online shopping behavior on Facebook of consumers in Ho Chi Minh City," Journal of Science and Technology - Industrial University of Ho Chi Minh City, vol. 36, no. 6, pp. 1-10, 2018. [9] I. Ajzen, "The theory of planned behavior," Organizational Behavior and Human Decision Processes, vol. 50, no. 2, pp. 179-211, 1991. https://doi.org/10.1016/0749-5978(91)90020-T [10] N. Delafrooz, L. Paim, and A. Khatibi, "A research modeling to understand online shopping intention," Australian Journal of Basic and Applied Sciences, vol. 5, no. 5, pp. 70-77, 2011. [11] V. Mirabi, H. Akbariyeh, and H. Tahmasebifard, "A study of factors affecting on customers purchase intention," Journal of Multidisciplinary Engineering Science and Technology, vol. 2, no. 1, pp. 28–33, 2015. [12] N. H. Quan, T. T. Hanh, D. N. Anh, N. L. M. Hanh, H. T. Linh, and N. D. L. Nhi, "The impact of augmented reality technology on consumers' online purchase intention," Hue University Journal of Science: Economics and Development, vol. 132, pp. 19-38, 2023. [13] M. Fishbein and I. Ajzen, Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley, 1975. [14] H. T. Hung, H. T. T. Quyen, and H. T. Nhi, "Factors affecting green consumption behavior of consumers in Hue City," Hue University Journal of Science: Economics and Development, vol. 127, no. 5A, pp. 199–212, 2018. [15] A. M. Spence, "Market signaling: Informational transfer in hiring and related screening processes," 1974. [16] B. Lu and Z. Chen, "Live streaming commerce and consumers’ purchase intention: An uncertainty reduction perspective," Information & Management, vol. 58, no. 7, p. 103509, 2021. https://doi.org/10.1016/j.im.2021.103509 [17] A. S. Al-Adwan, M. K. Alrousan, H. Yaseen, A. M. Alkufahy, and M. Alsoud, "Boosting online purchase intention in high-uncertainty-avoidance societies: A signaling theory approach," Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 3, p. 136, 2022. https://doi.org/10.3390/joitmc8030136 [18] Y. Wang, J. Anderson, S.-J. Joo, and J. R. Huscroft, "The leniency of return policy and consumers’ repurchase intention in online retailing," Industrial Management & Data Systems, vol. 120, no. 1, pp. 21-39, 2020. https://creativecommons.org/licenses/by/4.0/ https://moit.gov.vn/khoa-hoc-va-cong-nghe/xu-huong-thuong-mai-dien-tu-va-kinh-te-so-trong-khu-vuc-asean.html https://moit.gov.vn/khoa-hoc-va-cong-nghe/xu-huong-thuong-mai-dien-tu-va-kinh-te-so-trong-khu-vuc-asean.html http://ebi.vecom.vn/Bao-Cao/Noi-dung-bao-cao/49/Bao-cao-Chi-so-Thuong-mai-dien-tu-Viet-nam-2023.aspx http://ebi.vecom.vn/Bao-Cao/Noi-dung-bao-cao/49/Bao-cao-Chi-so-Thuong-mai-dien-tu-Viet-nam-2023.aspx https://vnexpress.net/nguoi-mua-hang-online-huong-loi-nho-chinh-sach-tra-hang-hoan-tien-4702138.html https://vnexpress.net/nguoi-mua-hang-online-huong-loi-nho-chinh-sach-tra-hang-hoan-tien-4702138.html https://doi.org/10.1016/j.jretai.2015.11.002 https://doi.org/10.1016/j.jretconser.2020.102367 https://doi.org/10.1016/0749-5978(91)90020-T https://doi.org/10.1016/j.im.2021.103509 https://doi.org/10.3390/joitmc8030136 766 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 7: 754-766, 2025 DOI: 10.55214/25768484.v9i7.8723 © 2025 by the authors; licensee Learning Gate [19] H. Abdulla, J. D. Abbey, and M. Ketzenberg, "How consumers value retailer's return policy leniency levers: An empirical investigation," Production and Operations Management, vol. 31, no. 4, pp. 1719-1733, 2022. [20] Q. T. Pham, X. P. Tran, S. Misra, R. Maskeliūnas, and R. Damaševičius, "Relationship between convenience, perceived value, and repurchase intention in online shopping in Vietnam," Sustainability, vol. 10, no. 1, p. 156, 2018. https://doi.org/10.3390/su10010156 [21] A. Heiman, B. McWilliams, J. Zhao, and D. Zilberman, "Valuation and management of money-back guarantee options," Journal of Retailing, vol. 78, no. 3, pp. 193-205, 2002. https://doi.org/10.1016/S0022-4359(02)00065-9 [22] A. B. Bower and J. G. Maxham III, "Return shipping policies of online retailers: Normative assumptions and the long-term consequences of fee and free returns," Journal of Marketing, vol. 76, no. 5, pp. 110-124, 2012. [23] B. Lantz and K. Hjort, "Real e-customer behavioural responses to free delivery and free returns," Electronic Commerce Research, vol. 13, pp. 183-198, 2013. [24] X. Su, "Consumer returns policies and supply chain performance," Manufacturing & Service Operations Management, vol. 11, no. 4, pp. 595-612, 2009. [25] J. W. Huppertz, "Firms' complaint handling policies and consumer complaint voicing," Journal of Consumer Marketing, vol. 24, no. 7, pp. 428-437, 2007. https://doi.org/10.1108/07363760710834843 [26] N. Janakiraman and L. Ordóñez, "Effect of effort and deadlines on consumer product returns," Journal of Consumer Psychology, vol. 22, no. 2, pp. 260-271, 2012. https://doi.org/10.1016/j.jcps.2011.05.002 [27] S. L. Wood, "Remote purchase environments: The influence of return policy leniency on two-stage decision processes," Journal of Marketing Research, vol. 38, no. 2, pp. 157-169, 2001. https://doi.org/10.1509/jmkr.38.2.157.18847 [28] A. Parasuraman, V. A. Zeithaml, and L. L. Berry, "SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality," Journal of Retailing, vol. 64, no. 1, pp. 12-40, 1988. [29] R. M. Al-Dweeri, A. Ruiz Moreno, F. J. L. Montes, Z. M. Obeidat, and K. M. Al-dwairi, "The effect of e-service quality on Jordanian student’s e-loyalty: an empirical study in online retailing," Industrial Management & Data Systems, vol. 119, no. 4, pp. 902-923, 2019. [30] C.-L. Hsu, M.-C. Chen, and V. Kumar, "How social shopping retains customers? Capturing the essence of website quality and relationship quality," Total Quality Management & Business Excellence, vol. 29, no. 1-2, pp. 161-184, 2018. [31] A. M. M. Ibrahim, M. S. H. Hassan, and S. Yusuf, Factors determining e-shopping compliance by Nigerians. In Advanced methodologies and technologies in digital marketing and entrepreneurship. Hershey, PA: IGI Global, 2019. [32] J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, and R. L. Tatham, Multivariate data analysis, 5th ed. Upper Saddle River, NJ: Prentice Hall, 1998. https://doi.org/10.3390/su10010156 https://doi.org/10.1016/S0022-4359(02)00065-9 https://doi.org/10.1108/07363760710834843 https://doi.org/10.1016/j.jcps.2011.05.002 https://doi.org/10.1509/jmkr.38.2.157.18847