Determinants of repurchase intention in E-commerce and the moderating role of risks: an emerging digital economy perspective

Over the past decade, online shopping has experienced significant growth across various business segments. As a result, brand interaction over digital platforms has become the new norm (Dash et al., 2023; Kotler et al., 2016). In addition, the pandemic has prompted a shift to online platforms in recent years, resulting in a proliferation of online marketplaces. One of the most significant segments in online marketplaces is branded personal accessories. Watches, bags, jewelry, wallets, and eyewear, among other items, comprise this segment. Over the years, online shopping for these products has grown manifold. Heavy discounts and ease of buying attracted customers. Worldwide, the accessories market generated US$551.10 billion in revenue in 2023 and is expected to grow at a rate of 4.2% annually over the next four years (Statista, 2023). The online market leads with 33.1% of total revenue (Statista, 2023). However, too many choices spoiled the customers' thought process, and repurchase intention was challenging to apprehend. Every seller wanted to retain the customers even at an extra cost. Assessment of purchase intention has given way to repurchase intention, especially in online markets, which is now the new focus for marketers. Various factors shape customers' online repurchase intention (ORPI). The most critical influencers are online customer experience (OCE) (Holmlund et al., 2020; Wereda and Wozniak, 2019; Sundstrom et al., 2019), customer satisfaction (CS) (Dash et al., 2023; Zihayat, 2021), and online purchase behavior (OPB) (Sundstrom et al., 2019; Zarei et al., 2019). However, recent experiences with online purchases have highlighted a few risks. The risks in online shopping can be broadly divided into three types: product risk (Anser et al., 2020; Bhatnagar and Ghose, 2004), financial risk (Montford et al., 2019; Forsythe et al., 2006), and privacy risk (Oghazi, 2020; Garbarino and Strahilevitz, 2004). While the experience, satisfaction, and previous purchases positively shaped the intention to make online repurchases, these risks created a hostile zone of influence.

The top twenty emerging economies accounted for 34% of the world's nominal GDP and 46% in purchasing power parity terms (Duttagupta and Pazarbasioglu, 2021; IMF). The study focused on emerging economies with similar features, such as market and government reforms, closing infrastructure gaps, and expanding social security nets. In emerging economies, sellers and buyers are rapidly transitioning to online mode. We selected India and Saudi Arabia due to the following reasons. Both countries fit the characteristics mentioned above. As of 2022, Saudi Arabia and India are leading the charts in GDP growth rates among large emerging economies (Arab News, 2023). Both countries have a growing e-commerce industry, and this industry is also expanding rapidly in the online environment. A global e-commerce portal was selected for this study, and data availability was a crucial factor. Hence, the availability of the portal in both countries and the emerging economy status influenced the decision. Therefore, it has become essential to understand and assess the factors shaping online repurchase intention in an emerging economy context.

Considering the developments mentioned above, we raised specific research questions for this study and tried to answer them in the following sections:

The rest of the study is divided into the following sections. Section 2 reviews the relevant literature, theoretical background, hypotheses framing, and an integrated conceptual model. Section 3 provides the methodology (data, sample, instruments) adopted. Results and discussions follow it. Finally, the study concludes with implications and future directions for the researchers.

Existing theories on purchase intention, purchase behavior, repurchase intention, and technology adoption influence this study. For example, the Theory of Planned Behavior (TPB) is employed in numerous studies to assess customer satisfaction, online customer experience, online purchase behavior, and online repurchase intention (Sun et al., 2022; Loh and Hassan, 2022; Javed and Wu, 2020). In addition, the Technology Acceptance Model (TAM) is also used to assess online customer experience and repurchase intention in recent literary works (Shaker et al., 2023; Oliveira et al., 2023; Aparicio et al., 2021; Foroudi et al., 2018). Similarly, the Unified Theory of Acceptance and Use of Technology (UTAUT I and II) (Miao et al., 2022; Jebarajakirthy et al., 2021; Tamilmani et al., 2019) is adapted to assess the relationships between the proposed constructs of this study. Stimulus Organism Response (SOR) model is also used by researchers in recent works (Butt and Muhammed, 2025; Irimia-Diéguez et al., 2025). From a cross-national e-commerce perspective, San Martin et al. (2011) tried to evaluate the dual effect of perceived risks in a mediating role. Although we have not based our study on a specific theory, it is influenced by TPB with modifications to better understand repurchase intentions in the online context. Table 1 presents a selection of literary works and the motivation for this study. Existing literature focused on different aspects of repurchase intention. We included crucial and empirically proven determinants and added the risks as moderators in a cross-national and online context. This study explored various antecedents of online repurchase intention. Additionally, the impact of satisfaction, experience, and purchase behavior was also evaluated. Next, the mediating role of online purchase behavior in the relationship between experience and satisfaction, as well as its impact on repurchase intention, was evaluated. Finally, the moderating role of the three types of risks was also assessed.

The online customer experience (OCE) refers to the interaction, feelings, and emotions that customers experience while interacting with a company during an online purchase (Martin et al., 2015; Ma et al., 2022). This internal feeling defines his future course of action (Anshu et al., 2022; Homburg et al., 2017; Gentile et al., 2007). Customer satisfaction (CS) results from a person's accumulated experiences when comparing their performance with expectations (Agag et al., 2024). Although different evaluations and metrics have gained popularity over time, customer satisfaction measuring has become a fairly common technique in marketing (Bayraktar et al., 2012; Zihayat, 2021). Online purchase behavior (OPB) refers to the process of buying goods and services online. During the process, customers define their needs (Lo et al., 2020). It is further supported by information available on the internet regarding his search for a product across various e-markets. Online repurchase intention (ORPI) refers to an individual's intention to purchase the same product or service from the same company in a digital environment (Bayraktar et al., 2012). It is primarily based on his previous organizational experience (Liao et al., 2017). The development of online repurchase intention is incorporated when the customer finds value in the product.

Product risk (PDR) refers to the discrepancy between the product specifications/features claimed in the advertisement and the delivered product, which leads the customer to feel cheated (Cox et al., 2006). Also, when the virtual image of the product differs from the physical product in the online environment. As a result, the consumer restrains himself from buying the product online (Bhatnagar et al., 2000). Therefore, it can be elucidated that product risk is the most crucial factor contributing to not purchasing the product online (Anser et al., 2020). Losing money while buying online can be a financial risk (FNR) (Dabrynin and Zhang, 2019). The cybercrime rate has also increased with the increased digitized transactions. Hence, customers are discouraged from purchasing online due to fears of hacking, particularly when using plastic money for digital transactions (Montford et al., 2019). Emerging economies are undergoing a transition, and their digital infrastructure is evolving. A lack of consumer education and awareness exacerbates the problem. Phishing is a prevalent issue in emerging economies. Privacy and personal information loss while purchasing online is referred to as privacy risk (PVR). When buying online, consumers share their personal information on the internet. Information related to credit cards, date of birth, age, and mobile number is shared. As a result, the data gets stolen, leading to privacy risks (Garbarino and Strahilevitz, 2004; Oghazi, 2020). This factor alone has many severe repercussions for consumers.

The e-commerce business has experienced significant growth across nations in recent years. Therefore, it becomes essential for companies to enhance customer experience to retain the maximum number of customers (Lo et al., 2020). The effective use of digitization and the development of trust between a company and its customers can improve the customer experience (Sundstrom et al., 2019). Studies have emphasized the importance of a high-quality customer experience in raising the profitability and sustainability of the company (González et al., 2021). A positive online customer experience will significantly impact customers' online purchasing behavior, thereby increasing the profitability of companies (Bhattacharya et al., 2019). In other words, altering the customer experience can influence online purchasing behavior. Therefore, to study the relationship between the online customer experience and online purchase behavior, the following hypothesis has been formulated:

The quality of the product, ease of use of the website, and the services offered define the customer experience. A positive customer experience drives them to make repeat purchases (Kuo and Cheng, 2022). Customer experience has become an element of immense importance, gaining a status of integrity over the last 2 decades and playing a vital role in the success of businesses (Sundstrom et al., 2019). It is an essential area where companies need help to achieve an acceptable level of performance. A positive customer experience provides an added advantage to companies, thereby increasing their growth and profitability. In addition, it motivates customers to develop their online repurchase intention (Anshu et al., 2022; Giese and Cote, 2000). Studies have demonstrated a positive and significant relationship between online customer experience and their intention to repurchase. To explore it, the following hypothesis has been proposed:

Customer satisfaction is essential in marketing (Giese and Cote, 2000). It includes the customer fulfillment response after consuming the product. Customer satisfaction is a crucial factor influencing online purchase behavior (Zihayat et al., 2021). Therefore, it has a significant impact on online purchase behavior. Studies have demonstrated a significant correlation between customer satisfaction and online purchasing behavior. Hence, the following hypothesis has been proposed:

Customer satisfaction refers to the contentment felt by the customer after consuming a product or service (Wu et al., 2014). Ease of use, security, easy access to helpful information, hassle-free ordering, and customization are some factors that contribute to customer satisfaction. It, in turn, leads to an online repurchase intention. Studies have shown a significant relationship between customer satisfaction and online repurchase intention. Numerous empirical studies have shown that repeated customer purchases are often driven by customer satisfaction (Brendemühl and Schaarschmidt, 2020). Furthermore, it is worth emphasizing that customer satisfaction is the driving force behind online repurchase intention (Zihayat et al., 2021). Hence, we proposed the following:

Online purchase behavior is a decision-making process that involves a series of consecutive decisions. Consumers surf the internet according to their needs (Sundstrom et al., 2019). The quality of services provided by online staff, structured websites, and customization all contribute to the online purchasing behavior of consumers (Lo et al., 2020). Positive online purchase behavior influences customers' online repurchase intentions (Wei and Ho, 2019; Zarei et al., 2019). Few studies have highlighted the relationship between online purchase behavior and online repurchase intention, motivating us to explore this further. Hence, the following hypothesis was proposed:

The internal feeling generated by the online purchase process defines the future course of action. The gratification level is built on positive or negative experiences (Lo et al., 2020). The customer's online purchase behavior is significantly influenced by these experiences, which have a substantial impact on their online repurchase intention (Zeithaml et al., 2011). Therefore, businesses persistently strive to provide their customers with enchanting and high-quality customer experiences to influence their purchase behavior for online transactions. It, in turn, would grossly affect their intention to repurchase. Customer satisfaction occurs when a customer feels satisfied with the quality of the product or services. It is found within his expectation level in terms of performance drawn by him with similar products online (Goić et al., 2021). It is an essential ingredient significantly affecting the customer's online purchase behavior, further impacting their repurchase intention. Consumers were satisfied with their online shopping experience, which positively influenced their online purchase behavior (Wei and Ho, 2019). Further, positive online purchase behavior generates the thrust for online repurchase intention.

Hence, we proposed the following hypotheses:

When purchasing a product online, customers rely on the vendor's product information (Dabrynin and Zhang, 2019; Chopdar et al., 2022). Therefore, product risk is a significant risk factor that can significantly impact the relationship between online purchase behavior and customer repurchase intentions when buying online (Mortimer et al., 2020). Furthermore, the customer expected the product to be of good quality, with a suitable color, shape, and outlook. Thus, consumers may perceive a product risk that significantly redefines the relationship between online purchase behavior and their repurchase intention (Dai et al., 2014). Sometimes, customers may be required to pay more than the product's actual price, which can lead to financial risk (Bhukya and Singh, 2015; Patel et al., 2023). Therefore, financial risk influences the relationship between online purchase behavior and online repurchase intention (Chang, 2021). A perceived high financial risk has a significant impact on customers' online purchasing behavior. Therefore, it redefines the relationship between online purchase behavior and customer repurchase intention (Goić et al., 2021). With the mushrooming growth of e-retailing companies worldwide, the privacy risk is heightened. As a result, the customers feel wary about trusting so many of them with lucrative offers (Goić et al., 2021). Given the privacy risk, the relationship between online purchase behavior and repurchase intention undergoes a drastic change (Dai et al., 2014). Additionally, while sharing their debit and credit card information, concerns about hacking persist in their minds when shopping online (Chen and Guo, 2020). Therefore, the privacy risk significantly impacts the relationship between online purchase behavior and customer repurchase intentions when buying online. Hence, the following hypotheses were proposed.

The above-discussed hypotheses were integrated and presented as a conceptual model in Figure 1.

The study focused on online shoppers and their intention to repurchase, so a global e-commerce portal was selected as the platform for this purpose. We focused on customers who have recently made online purchases on this portal. As the study narrowed the segment to branded personal accessories, we specified the sampling frame as customers who had purchased branded personal accessories within the last three months. All the constructions were adapted from established scales and modified per this study's sector and objectives. Hence, a hybrid sampling design was employed, incorporating both purposive and stratified methods (Malhotra et al., 2006). Mixed modes of data collection should be undertaken for better results (Robson, 2011; Hair et al., 2010; Malhotra et al., 2006). Nearly 10% of the data was collected offline (personal meetings), and the rest was collected online. The details of the respondents are provided in Table 2.

For the sample, customers from two countries, India and Saudi Arabia, were considered. Both countries are emerging digital economies, and the mentioned e-commerce portal is available. More than two thousand users of the e-commerce portal were invited to participate in the survey. A detailed, structured questionnaire was developed for the survey. After the socio-demographic questions, screening questions were administered to select the suitable sample according to the earlier specifications. A seven-point Likert scale was used to finalize the measurement items under the constructs (details in Table 3). The questionnaire was distributed via chat apps, social media, and networking sites. We finalized 504 out of approximately 700 returned responses. It was conducted based on two parameters: manual checking of engagement levels and the absence of missing data (Hair et al., 2017; Malhotra et al., 2006).

Seven factors or constructs were used in this study. Online Customer Experience (OCE) and Customer Satisfaction (CS) were independent constructs. In addition, two dependent constructs were taken: Online Purchase Behavior (OPB) and Online Repurchase Intention (ORPI). OPB also serves as a mediator between OCE and ORPI, as well as between CS and ORPI. Three types of risks were taken as the moderators of the influence of OPB on ORPI. These were Product Risk (PDR), Financial Risk (FNR), and Privacy Risk (PVR). Table 3 lists all the constructs and their corresponding items.

All variables under the mentioned constructs were pooled, and an exploratory factor analysis (EFA) was conducted. Nine items were removed due to reliability and validity issues. The remaining twenty-five items were finally considered. EFA extracted seven factors, accounting for more than 77% of the variance. Confirmatory factor analysis (CFA) was undertaken to confirm the findings of EFA. The lowest factor loading was 0.78 (Table 3), reflecting the validity of the constructs (Dash and Paul, 2021; Hair et al., 2010). Tables 3 and 4 provide the results for reliability, discriminant, and convergent validity measures (Dash and Paul, 2021; Chakraborty et al., 2021; Henseler et al., 2015; Dogra et al., 2023; Hair et al., 2010; Malhotra et al., 2006), which meet the threshold levels.

The seven-construct model's CFA fit measures indicated excellent levels of fit. Cmin/df was 2.87. GFI and AGFI were 0.9 and 0.88, respectively. SRMR was 0.04, RMSEA was 0.06, TLI was 0.94, NFI was 0.93, and CFI was 0.95, which confirmed the same.

CMV is natural if the study is empirical and all the measures include the same participants. However, there are many contradictory views about testing it. Two major schools of thought held differing opinions on the matter. One group advocated for a procedural adjustment, while the other recommended an unrelated marker variable approach as a statistical measure (Podsakoff et al., 2024; Dash et al., 2024). The objectives and basic instructions were available to the respondents. All the items were taken from established scales and multiple sources. Through Harman's single-factor test, a significant portion of the variance, approximately 33%, was accounted for in the data. However, the value was below 50%, the threshold value. The possibility of CMV was negated after adopting both practices. In addition, normality and multicollinearity checks were conducted along with variance inflation factor (VIF) tests. Skewness and kurtosis values were within the threshold values, ranging from −1 to +1 and from −2 to +2, respectively. VIF values were within the threshold values of 1–5 (Pahari et al., 2024; Chakraborty et al., 2024).

Smart PLS 3.3.3 (Ringle et al., 2015) was used to analyze five direct hypotheses: H1: H3, two mediation relationships: H4(a): H4(b), and three moderation hypotheses: H5(a): H5(c). Essential statistical tools such as R² (0.29 and 0.61) values for the model for predictability assessment; standardized regression (path) coefficients (β); t-values and p-values for the significance level (Dash et al., 2021; Hair et al., 2010; Malhotra et al., 2006) were used to assess the model (Figure 2).

Table 5 depicts the results of these hypotheses. Except for H2(b), the other four hypotheses were accepted because the impacts were significant and positive.

This section presents the results of three sets of hypotheses that were tested. The first set presents the direct effect of online customer experience on online purchase behavior and online repurchase intention, as well as the relationship between customer satisfaction and online purchase behavior and online repurchase intention. The last set examines the impact of online purchase behavior on online repurchase intention. The findings showed that all had a significant impact except for customer satisfaction on online repurchase intention. Further, the findings confirmed that Online customer experience significantly impacted online purchase behavior [H1(a)]. It aligned with the previous studies (Bhattacharya et al., 2019; González et al., 2021). Improving trust and digitization, and providing quality products, impacts online purchase behavior.

It was found that the online customer experience significantly impacted online repurchase intention [H1(b)]. Therefore, it can be established that this finding is consistent with previous studies (Chen and Yang, 2021; Rose et al., 2011). In addition, the ease of use of the website and its privacy policies significantly impacted the online customer experience, which in turn affected online repurchase intention. Growth and profitability are direct outcomes of a positive customer experience (Anshu et al., 2022; Giese and Cote, 2000), which benefits providers who pass it back to customers in the form of discounts and cashback. It brings customers back to the site.

The analysis also confirmed the significant impact of customer satisfaction on online purchase behavior [H2(a)], aligned with the previous studies (Zihayat, 2021). Satisfaction with service quality, product quality, and experiences with front-line employees all impact customer satisfaction, which in turn influences online purchase behavior (Anshu et al., 2022; Giese and Cote, 2000). However, contrary to our expectations, it was found that customer satisfaction did not significantly impact online repurchase intention [H2(b)]. It could be due to other factors that dominate online repurchase intention. Information searching costs (Wu et al., 2014), which involve customers in co-creating a delightful Online Customer Experience, and delivery services (Javed and Wu, 2020) are some of the other factors that impact online repurchase intention. However, these findings may also be specific to our study, and more in-depth research is required to make any generalizations. Further, the study confirmed the significant impact of online purchase behavior on Online repurchase intention [H3]. In addition, the ease of using the internet from anywhere, with broader product choices and easy price comparisons, impacts online purchase behavior, further influencing online repurchase intention. Prior online purchase behavior influences the consumer's future intentions (Wei and Ho, 2019; Zarei et al., 2019).

The second set of hypotheses presented the results of the mediation effect, which are significant (Table 6). The results indicate that Online Purchase Behavior partially mediates the association between Online Customer Experience and Online Repurchase Intention [H4(a)]. Additionally, Online Purchase Behavior fully mediates the association between Customer Satisfaction and Online Repurchase Intention [H4(b)]. Accordingly, when customers have an excellent online experience during the purchasing process, their online purchase behavior positively influences their intention to repurchase. Similarly, satisfied customers exhibit positive online purchase behavior, which in turn impacts their online repurchase intention. These findings agree with previous studies on online customer experience (Anshu et al., 2022) and customer satisfaction (Tueanrat et al., 2021).

The third set of hypotheses tested the moderation effects (Table 7). Our result rejected the moderation effect of Product Risk on the association between Online Purchase Behavior and Online Repurchase Intention [H5(a)]. Additionally, the moderating effect of Privacy Risk is rejected on the association between Online Purchase Behavior and Online Repurchase Intention [H5(c)]. Product-related risks in e-commerce can be broadly categorized into three key areas: product quality, customer satisfaction, and legal compliance. Although our study found that product risk had no moderating effect, the reasons for this might lie in other factors. The probable cause could be the growth of the e-commerce business. The demand for online products is increasing; companies are placing greater importance on satisfying customers by providing high-quality products and services (Tueanrat et al., 2021). On the other hand, it could be due to other dominating factors of the product impacting online purchase behavior and online repurchase intention. For example, the stigma-related risk perception for a product, rich contextual product display (González et al., 2021), and online consumer reviews are some of the critical factors impacting online purchase behavior. Geographical and demographic limitations may have played a role, which should be explored in future studies.

Similarly, other essential factors might play a dominant role in privacy risk. For example, trust in the vendor leads consumers to accept any associated risks with a transaction; higher brand credibility reduces users' perception of privacy risks (Jain et al., 2022). Further, the company knows that violating privacy risks leads to distrust of their websites. Moreover, the company will take a considerable amount of time to rebuild that trust and integrity. However, it is also possible that the results are relevant to the geographical area of our study, which warrants further exploration.

In comparison, Financial Risk moderates the association between Online Purchase Behavior and Online Repurchase Intention [H5(b)]. FNR dampens the positive relationship between OPB and ORPI (−0.14**) (Table 7). The results suggest that financial risk is a crucial factor in influencing customers' online purchase behavior and online repurchase intentions. Credit card fraud, financial loss, and hidden transaction costs impact online purchase behavior and repurchase intention (Bhukya and Singh, 2015). Additionally, to the best of the authors' knowledge, fewer studies have examined this relationship, which warrants further exploration.

This study offers valuable suggestions for global managers and marketers across all types of service industries, particularly in emerging economies. Our findings suggest that a satisfied customer and positive online customer experience lead to online purchase behavior and, in many cases, to online repurchase intentions. Therefore, detailed information about the product or brand should be provided so that comparisons can be made among different brands and products of the company. It also enhances the ease of price comparison. Further, the ease of using the app to secure transactions will help customers buy products online from a particular brand. Once the relationship is established, the customer can purchase additional services from the specific brand, rather than switching to other brands. Another crucial implication of this study is the reduction of risks in online purchases.

Although our research has specifically identified financial risk as the primary dampener, the other two risks also threaten consumers' repurchase intentions. We have assessed the customers with a few statements. Nevertheless, the following two statements highlighted the concerns: “Online transactions might not be secure while purchasing online”; “Failed transactions cause considerable trouble to get the money back. ” Therefore, the implications for marketers can be outlined below. First, transactions must be highly secure, utilizing the highest level of encryption and automation. Customers must be adequately informed about this to enhance trust. In emerging economies, both the issues mentioned above thrive due to a lack of advanced security (which is still evolving) and a lack of consumer awareness and education. Second, refunds must be quick and hassle-free for customers. Transparency and regular information or updates are essential for online players in both processes. In emerging economies, the inadequate execution of grievance redressal mechanisms and a lack of transparency exacerbate the problem. It will bring down the anxiety level of the customers and, if done correctly, turn them into advocates.

Although the impact of Product risk (PDR) was not found to moderate the repurchase intention, it is indeed a serious risk in the online context. It is the mismatch between the product specifications/features claimed in the advertisement and the product delivered (Cox et al., 2006). In the online context, the gap between virtual images and physical products fuels the risk of receiving an inferior product. The lack of moderation's impact might be due to various reasons, such as an enhanced virtual experience in pre-purchase service encounters that reassures the consumer about the physical product. A guaranteed return policy with no-questions-asked clauses boosts consumers' confidence in the product before they purchase online, significantly enhancing their repurchase intention. All global online e-commerce platforms are striving to ensure that post-purchase customer service for delivered products remains consistent with pre-purchase service. It encourages the consumer to make future purchases. In addition, the loss of privacy and personal information during online purchasing is referred to as privacy risk (PVR). Consumers are prone to lose private information due to a lack of secure online purchase environments (Garbarino and Strahilevitz, 2004; Oghazi, 2020). We did not find its impact as a moderator. There might be various reasons, such as enhanced security protocols by the regulators for e-commerce sites. Awareness among consumers over the years, combined with learning from past mistakes, has also reduced unforced errors. The regulators and providers also provide education to customers. Data privacy and cybersecurity have become the focus of online businesses, and consumers have better access to updated information due to the availability of modern technologies and applications.

This study also has several implications for theory. The antecedents of online repurchase intention were identified rigorously. Prioritization of the same can be helpful for the research community. Understanding the same in specific industry, economy, and cultural contexts is equally crucial. This study has provided the impact of risks in the online context. It represents a significant step in enriching the existing literature. The same can be tested and replicated in different industries and economic contexts. An extensive model was used to assess the relationships between the constructs. The study finds the mediating role of OPB to be significant. It has several implications for the researchers. We have demonstrated that actual behavior is more influential than intention in shaping future intentions. All intention-based studies can take cues and extend the frameworks with appropriate consequences and the role of actual past behaviors. Out of the three risks, FNR dampens the positive impact of OPB on ORPI. The general perception across the samples was to become more aware of the financial risks of online purchases. With new business models, new types of risks are emerging, and our study can provide insights on how to handle them positively. This model attempts to provide and test a complex model with data from multiple groups. It enhances the existing frameworks in this domain. This study also enriches the TPB, TAM, UTAUT, and other frameworks by including the risks in the model. The items and scale utilize modified statements under the industry context requirements. Many items were altered in the online context as well. The scale provides researchers with opportunities for future advanced work. We also have a short but crucial implication for the researchers who use SEM to analyze models. Discriminant validity of the constructs must be established to assess the measures more effectively. We have used the heterotrait-monotrait (HTMT) model (Henseler et al., 2015) as a higher boundary measure to achieve this goal. It is strongly recommended that this tool be used to assess the constructs before running the relationship models.

Although the study has made its best effort, there are a few limitations as well. First, the current framework does not explore cultural contexts. Second, the control effects of socio-demographic variables, such as age, gender, and education, are not assessed. Even their roles as moderators are not examined. Third, we have just focused on three types of risks. However, many unexplored risks might have a significant effect on the current proposed model. Fourth, our entire focus was on emerging economies. It can be expanded to include the developed economies. More than two countries can be involved for better generalization. Fifth, we restricted ourselves to the proposed hypotheses and did not explore the interaction effects between the risks. Sixth, qualitative data were not included in the current framework, which might have provided a better understanding of the relationships. To address these limitations, the following subsection offers future directions for researchers.

The proposed model can be expanded to include additional mediators and moderators. Additionally, other potential influences of ORPI can be explored. Cultural contexts should be explored, especially comparative assessments based on Hofstede's cultural dimensions (Soares et al., 2007) (and other frameworks). As we have not assessed the role of socio-demographic factors as moderators or control variables, it is another direction that future researchers can explore. Future studies can focus on the differences between user groups, such as young vs old, male vs female, and low-income vs high-income. Further insights and comparisons with existing studies in developed markets can be explored. A few additional risks can be explored, in addition to the three mentioned risks. Dedicated research on the risks and other related dimensions should be attempted. The interaction effect among the risks can be explored to gain a deeper understanding of the overall impact. This study uses the framework for branded personal accessories. Other segments can be examined, and comparative conclusions can be drawn. We recommend adopting the proven model for other segments, such as apparel, luxury items, and specific services, among others. This study has included only quantitative data, providing scope for including qualitative data in the future. The mixed-methods approach is best for exploring an upgraded and expanded model. We suggest using PLS-MGA (multi-group analysis) and prioritizing influencers through an Importance-Performance Map Analysis (IPMA). A bibliometric approach coupled with structural equation modeling is the best way to study the impacts.