Cross-border online shopping: United States consumers’ intention to shop on Korean sites

Heesoon Yang

doi:10.2224/sbp.14981

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Published: Nov 5, 2025

DOI: https://doi.org/10.2224/sbp.14981

Issue: Vol. 53 No. 11 (2025)

Keywords:

cross-border e-commerce, country image, product image, perceived enjoyment, shopping intention

Article Highlights

A positive country image significantly enhanced the product image of Korean cosmetics, indicating that emotional perceptions of a country had a greater influence than cognitive factors in shaping consumer attitudes.

Unlike traditional factors, such as perceived ease of use and usefulness, perceived enjoyment on cross-border online platforms had a significant impact on consumers’ attitude and purchase intention, emphasizing the importance of creating pleasurable shopping experiences for global consumers.

I integrated country image and online transaction dynamics in this research, expanding understanding of factors influencing cross-border e-commerce, and highlighting the need for future studies to explore the varying effects of country image across different product categories.

Heesoon Yang

Department of Fashion and Textiles, Sangmyung University

Cite this article: Yang, H. (2025). Cross-border online shopping: United States consumers’ intention to shop on Korean sites. Social Behavior and Personality: An international journal, 53(11), e14981.

Abstract

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This study investigated the factors driving the growth of cross-border e-commerce, emphasizing the relationship between country image and online transactions. I developed a model that integrated these two dimensions, and the results demonstrated that country image significantly affected product image, particularly in the context of cross-border online shopping for Korean cosmetics. A positive country image enhanced consumers’ product perception, with the affective dimension being more influential than the cognitive dimension for consumers from the United States. Additionally, the perceived ease of use and usefulness of e-commerce sites did not significantly affect consumer attitudes toward cross-border shopping. However, perceived enjoyment played a crucial role in shaping consumers’ attitude and purchase intention. Ultimately, this research contributes to understanding purchasing behaviors in cross-border e-commerce settings by integrating country image and site dynamics, providing directions for further exploration across product categories.

Cross-border e-commerce has been defined as the act of purchasing goods through online retailers located in another country (Huang & Chang, 2019). The increasing integration of the internet, mobile devices, and advancements in international logistics have led to the diminishing significance of national borders in commerce. This trend has contributed to an increase in direct overseas purchases and cross-border shopping among consumers who prioritize informed and rational purchasing decisions on a global scale.

Cross-border e-commerce involves transactions of goods and services between businesses and consumers, executed directly via online platforms, such as business-to-consumer models, or through intermediaries like business-to-business-to-consumer platforms, thus facilitating international trade (Giuffrida et al., 2017; Mensah et al., 2020). The expansion of cross-border e-commerce, driven by the growth of the global online retail sector, presents numerous opportunities for both consumers and businesses (Giuffrida et al., 2017; Huang & Chang, 2019; Mensah et al., 2020). For businesses, this trend offers the opportunity to access broader markets and reach global consumers, while for consumers, it expands access to a wider array of products, enhancing their ability to satisfy diverse consumer needs.

E-commerce sales in the United States (US) were projected to continue growing robustly, reaching 23.6% of total retail sales by the end of 2025, up from 11.0% in 2019 (Davidkhanian, 2021). Further, revenue in the US e-commerce market was forecasted to increase by USD 657.8 billion (+53.79%) between 2024 and 2029, reaching USD 1.9 trillion in 2029, marking a new peak (Statista Research Department, 2024). As a result, the US represents a major global consumer market, making it an attractive target for countries around the world, particularly as it serves as a destination for indirect exports via online platforms.

In 2020, despite the challenges posed by the COVID-19 pandemic, South Korea’s global cosmetics exports reached USD 7.28 billion, marking a 16.1% increase from the previous year, positioning Korea as the third-largest exporter of cosmetics globally, behind France and the US (Shim, 2021). Among the exported products, Korean cosmetics shipped to the US amounted to USD 640 million, ranking as the third-largest importer of Korean cosmetics, alongside China and Japan. Notably, Olive Young, a prominent Korean beauty retailer, launched an online shopping platform for global consumers in June 2019, focusing on the US, the world’s largest cosmetics market, as its initial target. In the latter half of 2020, Olive Young’s sales in the US surged by 1,000% compared to the same period in the previous year, with the US and Canada accounting for over 80% of these sales (Oh, 2021). Consequently, the US has become a critical market for Korean cosmetics, and cross-border e-commerce has emerged as an increasingly vital channel for exporting Korean beauty products.

Despite the growth of cross-border e-commerce, there remains a notable gap in research regarding the factors that drive consumers to engage in cross-border e-commerce transactions (Huang & Chang, 2019). With the ongoing globalization of markets, businesses and marketers are keen to understand how consumers in target countries perceive imported products, and which factors influence these perceptions (Klein et al., 1998). Thus, this study examined the role of country image in influencing cross-border e-commerce and sought to identify key characteristics of overseas e-commerce platforms that impact consumer purchasing behaviors. The findings will provide valuable insights for e-commerce companies seeking to attract international consumers and improve the service quality of their platforms.

In the context of cross-border e-commerce, various factors can influence consumer purchasing behavior. I focused on two primary factors: country image and the characteristics of online platforms. Country image serves as an external cue when consumers evaluate product quality, influencing purchasing decisions and reducing perceived risks associated with uncertainty (Hong & Wyer, 1989). Additionally, due to the complexities of navigating international platforms compared to domestic channels, platform characteristics can significantly impact consumer behavior. Therefore, I empirically analyzed the direct purchase intention of US consumers toward Korean cosmetics within the cross-border e-commerce context. This offers both academic and practical implications, contributing to the development of distribution channel strategies for international companies targeting the US market.

Country Image and Product Image

One of the key factors influencing consumers’ decision-making processes when evaluating and purchasing foreign products is the image of the product’s country of origin (Kaynak et al., 2000; Martin & Eroglu, 1993; Rha et al., 2012; K. Roth & Diamantopoulos, 2009). Country image has been defined as consumers’ perception of a particular country, shaped by various factors such as its “representative products, economic and political maturity, historical events and relationships, traditions, industrialization and the degree of technological virtuosity” (Bannister & Saunders, 1978, p. 562). Despite its significance, prior research has suggested that country image is a multidimensional construct (Lopez et al., 2011).

The conceptualization of country image can be classified into two perspectives (B. Jin et al., 2019; B. E. Jin et al., 2020). The first perspective defines country image as comprising macro- and microcountry dimensions (Magnusson et al., 2014; Pappu et al., 2007). The macrocountry image is the overall set of descriptive, speculative, and informational beliefs that people hold about a particular country, and the microcountry image specifically pertains to their perception of products manufactured in that country. The second perspective conceptualizes country image as comprising cognitive and affective elements (K. Roth & Diamantopoulos, 2009). The cognitive country image refers to the state of technological or economic development, and the affective country image reflects consumer preferences for the country’s culture (Kaynak et al., 2000; K. Roth & Diamantopoulos, 2009; Wang et al., 2012). These two perspectives of a country’s image exert distinctive influences on consumer behavior and should be considered separately.

Furthermore, Lee and Ganesh (1999) suggested that while consumers may hold a positive perception of a country’s products, they may have a negative overall perception of the country, including aspects such as its politics, economy, and people. Therefore, these authors argued for a clear distinction between overall country image and product image. According to Lee and Ganesh’s perspective, overall country image is a cognitive and affective network (Askegaard & Ger, 1998; Verlegh et al., 2005), whereas county image and product image are independent, albeit related, concepts (Lee & Ganesh, 1999). Cognitive country image refers to the generalized image created by products representing the country, and also to economic and political maturity, tradition, and the degree of industrialization (K. Roth & Diamantopoulos, 2009). Cognitive attributes are known to influence assessments of product quality, including reliability and durability (Verlegh & Steenkamp, 1999). In contrast, the affective country image is associated with the consumer’s emotional response to a country, and links its products to symbolic benefits such as social status (K. Roth & Diamantopoulos, 2009; Verlegh & Steenkamp, 1999).

Moreover, Wang et al. (2012) examined the relationship between country image and product image by measuring product image. Although a country’s image is partially formed through consumers’ previous experience with products made in that country, the general image of a country is distinct from products related to that particular country (Pappu et al., 2007). Country image and product image also influence each other, and they can affect consumers’ evaluation of product quality and, ultimately, affect consumer purchase behaviors (Samiee, 2010; Wang et al., 2012).

Product image refers to consumers’ overall perceptions or beliefs regarding a specific product category from a particular country (Nagashima, 1977; Parameswaran & Pisharodi, 1994; M. Roth & Romeo, 1992), and acts as a major criterion when consumers select foreign products while having little knowledge about said product (Han, 1989). Although country image and product image influence each other, the recent popularity of Korean cosmetics in the US could be due to the Korean wave phenomenon (Yang et al., 2020). Given this context, I expected that country image would influence the product image of Korean cosmetics. Accordingly, I proposed the following hypotheses:

Hypothesis 1a: Affective country image will positively influence the product image of Korean cosmetics.

Hypothesis 1b: Cognitive country image will positively influence the product image of Korean cosmetics.

Product Image and Cross-Border Online Shopping Intention

In previous research on country image, the product image of a specific country has sometimes been defined as the country image at the product level (Bilkey & Nes, 1982; Han 1989; Nagashima, 1977; M. Roth & Romeo, 1992). However, other researchers have emphasized the need to distinguish country image from product image to better understand their relationship and respective influences (Parameswaran & Pisharodi, 1994; Parameswaran & Yaprak, 1987). As mentioned by Lee and Ganesh (1999), when consumers purchase foreign products, their attitude toward the product and purchase intention are both influenced by the product image. Since product image serves as a key evaluation criterion, particularly when consumers have limited knowledge about a foreign product, it plays a more significant role in shaping purchasing attitudes than does country image (Han, 1989). Further, cognitive country image, which includes technical and economic image, influences purchase intention through product image, and affective country image, which is an emotional response to the country or its people, also affects purchase intention via product image (Wang et al., 2012). I expected that product image would influence consumers’ attitude and purchase intention in the context of cross-border e-commerce; thus, I proposed the following hypotheses:

Hypothesis 2: The product image of Korean cosmetics will positively influence consumers’ attitude toward cross-border online shopping.

Hypothesis 3: The product image of Korean cosmetics will positively influence cross-border online shopping intention for Korean cosmetics.

The Technology Acceptance Model in Cross-Border Online Shopping

Cross-border online shopping refers to the purchase of global products through overseas, rather than domestic, sites (Huang & Chang, 2019). Consequently, consumers may encounter greater difficulties when using cross-border shopping platforms, as they must navigate a foreign website rather than a familiar domestic one. To better understand consumer behavior toward purchasing global products using overseas sites, this study applied the technology acceptance model (TAM; Davis et al., 1989) to the context of cross-border e-commerce.

The TAM has been widely used to identify key determinants of technology adoption (Manis & Choi, 2019). According to the TAM, the adoption of technology (actual system use) is determined by behavioral intention, which is influenced by attitude and perceived usefulness. Additionally, attitude is determined by both perceived usefulness and perceived ease of use (Davis et al., 1989). Perceived usefulness refers to the degree to which an individual believes that a particular skill can enhance productivity or improve performance on a specific task (Davis et al., 1989). For consumers, perceived usefulness is associated with the functional consequences of technology use, and can be expressed as the perceived likelihood that a technology can help perform a certain task (Ferreira et al., 2014). Perceived ease of use reflects an individual’s perception of how easy is the task of learning to use a piece of technology (Davis, 1989). Stronger perceptions that a piece of technology or product is easy to use indicate greater acceptance of said technology (Hwang et al., 2016). As critical variables influencing technology adoption, perceived usefulness and perceived ease of use have been widely applied in research on information technology acceptance. Further, prior studies have demonstrated that perceived usefulness has a significant and positive effect on consumers’ attitude toward adopting new technology (e.g., Ferreira et al., 2014). On the basis of the above research findings, I proposed the following hypotheses:

Hypothesis 4a: Perceived ease of use will positively influence consumers’ attitude toward cross-border shopping.

Hypothesis 4b: Perceived usefulness will positively influence consumers’ attitude toward cross-border shopping.

Hypothesis 4c: Perceived enjoyment will positively influence consumers’ attitude toward cross-border shopping.

Hypothesis 5: Perceived ease of use will positively influence the perceived usefulness of cross-border shopping.

Hypothesis 6: Consumers’ attitude toward cross-border shopping will positively influence their cross-border online shopping intention.

The formulated research model based on the proposed hypotheses is shown in Figure 1.

Figure 1. Research Model

Method

Participants and Procedure

This research was conducted with the approval and supervision of Sangmyung University Institutional Review Board (Approval No. IRB-SMU-S-2020-4-003). I used a questionnaire to collect empirical data on how individuals respond to online shopping stimuli, specifically targeting US consumers engaged in cross-border e-commerce. Data collection was conducted using Amazon Mechanical Turk (https://www.mturk.com). Respondents were instructed to browse the designated online shopping platform, Olive Young (https://global.oliveyoung.com), for at least 10 minutes, under the assumption that they were considering purchasing cosmetics. Filtering questions were added to verify that respondents had properly browsed the site, and those who did not respond correctly were excluded from the final analysis. As a result, 478 valid responses were included in the final dataset. The demographic characteristics of the participants are presented in Table 1.

Table 1. Demographic Characteristics

Measures

The questionnaire was composed of items in five dimensions: country image (affective, cognitive), product image, perceived qualities of cross-border online site (ease of use, usefulness, enjoyment), consumers’ attitude toward using cross-border online shopping, cross-border online shopping intentions for Korean cosmetics, and demographic information. All items, except for demographics, were assessed using a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree.

Affective country image and cognitive country image were measured using a nine-item scale developed by Wang et al. (2012). The measure of product image was also adapted from a five-item scale developed by Wang et al. (2012). The dimensions of perceived ease of use and perceived usefulness were measured using six items each from a scale developed by Gefen et al. (2003). The four items measuring perceived enjoyment were adopted from Plotkina and Saurel (2019). Finally, the dimensions of attitude toward cross-border online shopping and cross-border online shopping intention for Korean cosmetics were measured using three items each from van der Heijden et al. (2003). All items are listed in Table 2 below.

Data Analysis

I conducted frequency and reliability analyses using SPSS 27.0, and structural equation modeling using SmartPLS 4.0.

Results

Validity and Reliability of the Measurement Model

Table 2 presents the results of the reliability analysis. Surpassing the commonly accepted thresholds, Cronbach’s alpha values of each construct exceeded .80 (Hair et al., 2017) and composite reliability values were above .90 (Anderson & Gerbing, 1988; Chin, 1998; Hair et al. 2017). Therefore, the measurement model showed satisfactory reliability.

Factor loadings and average variance extracted (AVE) values were used to assess the convergent validity of the measurement model. According to Hulland (1999), factor loadings should exceed .70 and items with loadings below .40 should be removed. The factor loadings in this study ranged from .78 to .95, indicating satisfactory convergent validity. Additionally, I conducted a bootstrapping analysis to evaluate the significance of the outer measurement model. All items were found to be significant, and AVE values were above the threshold of .50. Therefore, the convergent validity of the measurement model was confirmed (see Table 2).

Table 2. Confirmatory Factor Analysis Results

Note. AVE = average variance extracted; CR = composite reliability.

Table 3 presents this study’s assessment of discriminant validity. Discriminant validity of the measurement model was confirmed, as the square root of the AVE for each construct was greater than the correlation coefficients between the constructs (Fornell & Larcker, 1981).

Table 3. Discriminant Validity

Note. Values on the diagonal are the square root of the average variance extracted; off-diagonal values are correlation coefficients.

The structural model was evaluated based on the recommendations of Hair et al., (2013), which involved examining R² values, β coefficients, and t values derived from a bootstrapping procedure with 5,000 resamples. In addition to these fundamental metrics, I assessed the predictive relevance (Q²) and effect size (f²). The R² values were as follows: attitude = .579, online shopping intention = .779, product image = .608, and perceived usefulness = .735. Correspondingly, the Q² values were as follows: attitude = .494, online shopping intention = .677, product image = .436, and perceived usefulness = .487. Effect sizes (f²) were assessed based on Cohen’s (1992) guidelines, which define small, medium, and large effects as .02, .15, and .35, respectively. The results of the effect-size analysis are summarized in Table 4.

The results of the hypothesis tests are detailed below and shown in Table 4. First, I examined the relationships among the variables. Affective country image (β = .56, p < .001) and cognitive country image (β = .17, p < .05) had a significant impact on the product image of Korean cosmetics, thus supporting Hypotheses 1a and 1b. Product image also demonstrated a direct effect on both attitude toward cross-border shopping (β = .29, p < .001), and cross-border online shopping intention for Korean cosmetics (β = .20, p < .001), thus supporting Hypotheses 2 and 3. However, perceived ease of use and perceived usefulness did not significantly influence attitude toward the cross-border site, while perceived enjoyment (β = .47, p < .001) positively impacted attitudes toward the cross-border site. Consequently, Hypotheses 4a and 4b were not supported, while Hypothesis 4c was supported. Perceived ease of use (β = .86, p < .001) significantly affected perceived usefulness, thereby supporting Hypothesis 5. Last, attitude toward cross-border shopping (β = .74, p < .05) positively influenced the intention to engage in cross-border online shopping for Korean cosmetics, thereby supporting Hypothesis 6.

Table 4. Structural Estimates (Hypothesis Testing)

Note. f² = effect size.

* p < .05. *** p < .001.

Discussion

This study explored the factors influencing the continuous growth of cross-border e-commerce. To achieve this, I analyzed key elements that facilitate the revitalization of cross-border e-commerce. The results verified the applicability of a model that integrates both cross-border transactions and online transactions, employing country image for the former and the TAM for the latter. The findings led me to form two primary conclusions.

First, the analysis results revealed that country image significantly impacted product image. Specifically, country image played a crucial role in shaping the product image of Korean cosmetics. This finding corresponds with those of previous studies (Li et al., 2014; Wang et al., 2012) that examined the influence of country image on product image. This halo effect was reaffirmed, demonstrating that a positive country image associated with South Korea enhanced consumers’ product image perception. My findings are consistent with those of Han (1989), who claimed that country image influences beliefs and ultimately affects brand attitude. Moreover, I found that affective country image had a greater influence on product image than did cognitive country image. While this is consistent with the findings of Li et al. (2014), it does not align with those of Wang et al. (2012). Both Li et al. and Wang et al. conducted their studies without specifying a particular product category, whereas in the present study I focused on Korean cosmetics as a defined category. Consequently, the results suggest that, in the context of cosmetics, affective country image has a stronger impact on product image than it does on cognitive country image. This indicates that for US consumers, emotional responses toward South Korea and its people are more vital in shaping their perceptions of product image. These findings further suggest that the extent to which country image influences product image may vary depending on the product category. Thus, I recommend leveraging country image in alignment with the specific product category. Additionally, product image was found to have a direct influence on cross-border online shopping intention, significantly impacting this intention through attitude toward cross-border shopping. Ultimately, product image played a crucial role in shaping cross-border shopping intentions, which is consistent with the findings of Lee and Ganesh (1998).

Second, the perceived ease of use and perceived usefulness of cross-border e-commerce sites did not significantly affect consumer attitudes toward cross-border shopping. In contrast, perceived enjoyment demonstrated a substantial effect on attitude. Early applications of the TAM in technology acceptance research primarily focused on traditional variables, such as perceived usefulness and perceived ease of use (Hwang et al., 2016). With the recognition that intrinsic forms of motivation, such as perceived enjoyment, alongside extrinsic types of motivation can enhance the behavioral intention toward technology acceptance (Davis et al. 1992), perceived enjoyment has emerged as a pivotal antecedent in the adoption of technology. Furthermore, I found that attitude significantly influenced cross-border online shopping intention, emphasizing the importance of pleasurable experiences on cross-border e-commerce platforms as a crucial factor in influencing behavioral intention. This aligns with recent research highlighting the significance of pleasure in the extended TAM framework (Beck & Crié, 2018; Choi & Kim, 2016). The lack of impact from perceived ease of use or perceived usefulness, which are traditionally regarded as critical variables in cross-border e-commerce studies, suggests that usability in cross-border transactions has reached a standardized level. In other words, barriers to site accessibility in cross-border transactions appear to have diminished. Therefore, it is essential to find ways to enhance the enjoyment of global consumers using cross-border online platforms.

Theoretical and Practical Implications

The academic implications of this study are noteworthy. First, this research provides a comprehensive perspective on the factors influencing global consumers’ purchasing behavior in cross-border e-commerce by simultaneously considering country image and platform-related factors. While numerous studies have examined the influence of country image on foreign product purchases (Bilkey & Nes, 1982; B. Jin et al., 2019; B. E. Jin et al., 2020; Li et al., 2014; Pappu et al., 2007), and several have applied the TAM to cross-border e-commerce (Huang & Chang, 2019; Hwang et al., 2016), research integrating these two dimensions remains limited. Given that cross-border e-commerce involves purchasing foreign products through international sites, it is crucial to consider these two factors together. This study contributes by analyzing and integrating both aspects, thus expanding research related to cross-border e-commerce.

Second, my findings underscore the importance of country image in shaping consumer purchasing behavior within the context of cross-border e-commerce. Country image remains a critical factor in global product purchases through cross-border e-commerce platforms. While previous studies have primarily emphasized the cognitive dimension of country image (Li et al., 2014; Peterson & Jolibert, 1995), this study has revealed that the affective dimension significantly influences the product image of cosmetics. This suggests that, particularly in the case of experience goods (e.g., cosmetics), the emotional aspects of the country are more significant than are cognitive elements. Moreover, the impact of country image on product image may vary based on the product type. This aligns with the findings of Li et al. (2014), who demonstrated that affective country image directly impacts the product image of experience goods. Consequently, future research could examine the varying effects of country image across different product categories to better understand these dynamics.

Limitations and Future Research

Despite its meaningful findings, this study has several limitations that offer opportunities for future research. First, the sample was limited to U.S. consumers, which may constrain the generalizability of the results. Future studies could conduct cross-cultural comparisons among consumers from different countries to verify whether the observed relationships hold across diverse cultural contexts. Second, this study focused solely on Korean cosmetics as a representative product category. As the influence of country image may differ depending on product type—particularly between utilitarian and hedonic goods—future research could investigate other categories such as electronics, fashion apparel, or food products to provide a broader understanding of cross-border e-commerce behavior. Finally, the study focused primarily on perceptual and attitudinal factors; however, emerging variables such as trust in global platforms, digital payment confidence, or the role of artificial-intelligence-driven recommendation systems could be incorporated into future models. Exploring these technological and psychological factors in combination with country image will further enrich understanding of cross-border online shopping behavior.

References

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Askegaard, S., & Ger, G. (1998). Product-country images: Towards a contextualized approach. European Advances in Consumer Research, 3(1), 50–58.

Bannister, J. P., & Saunders, J. A. (1978). UK consumers’ attitudes towards imports: The measurement of national stereotype image. European Journal of Marketing, 12(8), 562–570. https://doi.org/10.1108/EUM0000000004982

Beck, M., & Crié, D. (2018). I virtually try it…I want it! Virtual Fitting Room: A tool to increase on-line and off-line exploratory behavior, patronage and purchase intentions. Journal of Retailing and Consumer Services, 40, 279–286. https://doi.org/10.1016/j.jretconser.2016.08.006

Bilkey, W. J., & Nes, E. (1982). Country-of-origin effects on product evaluations. Journal of International Business Studies, 13(1), 89–100. https://doi.org/10.1057/palgrave.jibs.8490539

Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), 7–16.

Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777–786. https://doi.org/10.1016/j.chb.2016.06.007

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037//0033-2909.112.1.155

Davidkhanian, S. (2021). US e-commerce forecast 2021. Emarketer. https://www.emarketer.com/content/us-ecommerce-forecast-2021

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

Ferreira, J. B., da Rocha, A., & da Silva, J. F. (2014). Impacts of technology readiness on emotions and cognition in Brazil. Journal of Business Research, 67(5), 865–873. https://doi.org/10.1016/j.jbusres.2013.07.005

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

Giuffrida, M., Mangiaracina, R., Perego, A., & Tumino, A. (2017). Cross-border B2C e-commerce to Greater China and the role of logistics: A literature review. International Journal of Physical Distribution and Logistics Management, 47(9), 772–795. https://doi.org/10.1108/IJPDLM-08-2016-0241

Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.

Hair, J. F., Jr., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Han, C. M. (1989). Country image: Halo or summary construct? Journal of Marketing Research, 26(2), 222–229. https://doi.org/10.1177/002224378902600208

Hong, S.-T., & Wyer, R. S., Jr. (1989). Effects of country-of-origin and product-attribute information on product evaluation: An information processing perspective. Journal of Consumer Research, 16(2), 175–187. https://doi.org/10.1086/209206

Huang, S.-L., & Chang, Y.-C. (2019). Cross-border e-commerce: Consumers’ intention to shop on foreign websites. Internet Research, 29(6), 1256–1279. https://doi.org/10.1108/INTR-11-2017-0428

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7

Hwang, C., Chung, T.-L., & Sanders, E. A. (2016). Attitudes and purchase intentions for smart clothing: Examining U.S. consumers’ functional, expressive, and aesthetic needs for solar-powered clothing. Clothing and Textiles Research Journal, 34(3), 207–222. https://doi.org/10.1177/0887302X16646447

Jin, B., Yang, H., & Kim, N. (2019). Prototypical brands and cultural influences: Enhancing a country’s image via the marketing of its products. Management Decision, 57(11), 3159–3176. https://doi.org/10.1108/MD-01-2018-0057

Jin, B. E., Kim, N. L., Yang, H., & Jung, M. (2020). Effect of country image and materialism on the quality evaluation of Korean products: Empirical findings from four countries with varying economic development status. Asia Pacific Journal of Marketing and Logistics, 32(2), 386–405. https://doi.org/10.1108/APJML-11-2018-0456

Kaynak, E., Kucukemiroglu, O., & Hyder, A. S. (2000). Consumers’ country-of-origin (COO) perceptions of imported products in a homogeneous less-developed country. European Journal of Marketing, 34(9/10), 1221–1241. https://doi.org/10.1108/03090560010342610

Klein, J. G., Ettenson, R., & Morris, M. D. (1998). The animosity model of foreign product purchase: An empirical test in the People’s Republic of China. Journal of Marketing, 62(1), 89–100. https://doi.org/10.1177/002224299806200108

Lee, D., & Ganesh, G. (1999). Effects of partitioned country image in the context of brand image and familiarity: A categorization theory perspective. International Marketing Review, 16(1), 18–41. https://doi.org/10.1108/02651339910257610

Li, D., Wang, C. L., Jiang, Y., Barnes, R. B., & Zhang, H. (2014). The asymmetric influence of cognitive and affective country image on rational and experiential purchases. European Journal of Marketing, 48(11/12), 2153–2175. https://doi.org/10.1108/EJM-09-2012-0505

Lopez, C., Gotsi, M., & Andriopoulos, C. (2011). Conceptualising the influence of corporate image on country image. European Journal of Marketing, 45(11/12), 1601–1641. https://doi.org/10.1108/03090561111167315

Magnusson, P., Krishnan, V., Westjohn, S. A., & Zdravkovic, S. (2014). The spillover effects of prototype brand transgressions on country image and related brands. Journal of International Marketing, 22(1), 21–38. https://doi.org/10.1509/jim.13.0068

Manis, K. T., & Choi, D. (2019). The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research, 100, 503–513. https://doi.org/10.1016/j.jbusres.2018.10.021

Martin, I. M., & Eroglu, S. (1993). Measuring a multi-dimensional construct: Country image. Journal of Business Research, 28(3), 191–210. https://doi.org/10.1016/0148-2963(93)90047-S

Mensah, I. K., Zeng, G., & Luo, C. (2020). The impact of national culture dimensions on the adoption of cross-border e-commerce: A comparative study. International Journal of Information Systems in the Service Sector, 12(4), 91–112. https://doi.org/10.4018/IJISSS.2020100105

Nagashima, A. (1977). A comparative “made in” product image survey among Japanese businessmen. Journal of Marketing, 41(3), 95–100. https://doi.org/10.2307/1250943

Oh, J.-E. (2021). CJ Olive Young Global Mall sales jump 1000%, “Korean iHerb” [In Korean]. Money Today. https://news.mt.co.kr/mtview.php?no=2021010711460435359

Pappu, R., Quester, P. G., & Cooksey, R. W. (2007). Country image and consumer-based brand equity: Relationships and implications for international marketing. Journal of International Business Studies, 38(5), 726–745. https://doi.org/10.1057/palgrave.jibs.8400293

Parameswaran, R., & Pisharodi, R. M. (1994). Facets of country of origin image: An empirical assessment. Journal of Advertising, 23(1), 43–56. https://doi.org/10.1080/00913367.1994.10673430

Parameswaran, R., & Yaprak, A. (1987). A cross-national comparison of consumer research measures. Journal of International Business Studies, 18(1), 35–49. https://doi.org/10.1057/palgrave.jibs.8490398

Peterson, R. A., & Jolibert, A. J. P. (1995). A meta-analysis of country-of-origin effects. Journal of International Business Studies, 26, 883–900. https://doi.org/10.1057/palgrave.jibs.8490824

Plotkina, D., & Saurel, H. (2019). Me or just like me? The role of virtual try-on and physical appearance in apparel m-retailing. Journal of Retailing and Consumer Services, 51, 362–377. https://doi.org/10.1016/j.jretconser.2019.07.002

Rha, J. Y., Han, G. H., Lee, J. M., Yoon, J. S., & Lee, H. R. (2012). An exploratory study on the country of origin image of baby products [In Korean]. Journal of Consumer Policy Studies, 42, 73–99. https://doi.org/10.15723/jcps..42.201208.73

Roth, K. P., & Diamantopoulos, A. (2009). Advancing the country image construct. Journal of Business Research, 62(7), 726–740. https://doi.org/10.1016/j.jbusres.2008.05.014

Roth, M. S., & Romeo, J. B. (1992). Matching product category and country image perceptions: A framework for managing country-of-origin effects. Journal of International Business Studies, 23(3), 477–497. https://doi.org/10.1057/palgrave.jibs.8490276

Samiee, S. (2010). Advancing the country image construct – A commentary essay. Journal of Business Research, 63(4), 442–445. https://doi.org/10.1016/j.jbusres.2008.12.012

Shim, W.-H. (2021). South Korea’s exports of cosmetics products ranked third in the world last year. The Korea Herald. https://www.koreaherald.com/view.php?ud=20210621000834

Statista Research Department. (2024). Revenue of the e-commerce industry in the U.S. 2019–2029. https://www.statista.com/statistics/272391/us-retail-e-commerce-sales-forecast/

van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: Contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48. https://doi.org/10.1057/palgrave.ejis.3000445

Verlegh, P. W. J., & Steenkamp, J.-B. E. M. (1999). A review and meta-analysis of country-of-origin research. Journal of Economic Psychology, 20(5), 521–546. https://doi.org/10.1016/S0167-4870(99)00023-9

Verlegh, P. W. J., Steenkamp, J.-B. E. M., & Meulenberg, M. T. G. (2005). Country-of-origin effects in consumer processing of advertising claims. International Journal of Research in Marketing, 22(2), 127–139. https://doi.org/10.1016/j.ijresmar.2004.05.003

Wang, C. L., Li, D., Barnes, B. R., & Ahn, J. (2012). Country image, product image and consumer purchase intention: Evidence from an emerging economy. International Business Review, 21(6), 1041–1051. https://doi.org/10.1016/j.ibusrev.2011.11.010

Yang, H., Jin, B. E., & Jung, M. (2020). The influence of country image, the Korean wave, and website characteristics on cross-border online shopping intentions for Korean cosmetics: Focusing on US and Chinese consumers. International Journal of Costume and Fashion, 20(2), 38–49. https://doi.org/10.7233/ijcf.2020.20.2.038