1. Introduction
In recent years, the global e-commerce market has rapidly grown, particularly focusing on Chinese platforms like AliExpress, Temu, and SHEIN, which have expanded their global user bases and influenced consumer behavior. The e-commerce market share is rising both domestically and internationally, with an international CAGR of 15.1% and a domestic CAGR of 14.6% from 2018 to 2023 (FKI, 2024). Google Trends analysis shows that Shein has rapidly increased its global presence since 2020, while AliExpress has seen a slowdown. Temu, launched in 2022, has experienced steady growth. In the domestic market, AliExpress has shown consistent growth, with Shein gradually increasing since 2020. Temu has also rapidly grown in Korea, mirroring its international performance. Notably, interest in AliExpress is significant in Korea and abroad, while Temu and Shein are gaining more global attention than in Korea, as indicated by Google Trends data.
In the existing research, it is evident that the numbers of research on both domestic and international e-commerce industry are not enough(Choi, Sung Won, 2022; Nam, HyungJeong, Yeonjin Cho, 2020; Yeonjin Cho, Nam, HyungJeong, 2021), and studies focusing on Temu and Shein, which were launched in 2022 and 2023, are relatively scarce compared to the studies about AliExpress(Son, Kyung Wook, 2024; Go, Eun Seo, 2024). At this point when Chinese E-commerce industry is rapidly growing in the market, it’s significantly meaningful to examine the consumer perceptions, in both domestic and international ways. In recent times, there has been substantial research on big data analytics, which is being applied across various industries. Prior studies have also explored the use of big data in the e-commerce industry(Lee, Eunji, 2022; Zhu, Tai Lin, 2020; Go, Eun Seo, 2024). There is a lot of interest in AliExpress in korea and abroad, and Temu and Shein are getting more interest from the international market than from Korea using Google trends. Domestic and foreign market share of e-commerce is increasing. Temu recently surpassed Shein and AliExpress. Especially, Temu experiencing has rapidly grown. In the mobile app market, the number of new installations for AliExpress and Coupang is steadily increasing, with Temu showing rapid growth. Given these trends, this study focuses on analyzing the consumer perceptions of Temu, Shein, and AliExpress. The research aims to explore the perceptions of domestic and international customers in the e-commerce industry. It will also compare these perceptions to identify key differences and similarities.
2. Literature Review
2.1 E-commerce Industry
Chinese e-commerce platforms have experienced rapid growth and have had a significant impact on the global economy. The Chinese e-commerce market, which began in the early 2000s, expanded dramatically with the emergence of major platforms like Alibaba Group, JD.com, and Pinduoduo. China has become the largest e-commerce market in the world, and platforms such as Alibaba's AliExpress, Pinduoduo's Temu, and Shein have demonstrated significant international competitiveness. AliExpress, founded in 2010, is Alibaba Group's largest international e-commerce platform. In 2012, it transitioned from a B2B (Business to Business) model to a B2C (Business to Consumer) model and began its global expansion. AliExpress has gained popularity worldwide by offering low prices and a wide range of products. After entering the Korean market in 2018, the platform further strengthened its presence by announcing a 100 billion-won investment in 2023. AliExpress has also seen remarkable growth in the U.S., with order volumes increasing by 60% year-over-year in the fourth quarter of 2023.
Temu, a subsidiary of Pinduoduo, has grown rapidly since its launch in September 2022. Within four months of its launch, it became the most downloaded app in the U.S., and by March 2023, it had reached 50 million downloads. Temu provides a wide variety of products at competitive prices and quickly expanded to 12 countries. It entered the Korean market on July 24, 2023, and by October 2023, became the most popular shopping app in Korea due to a surge in new users. Shein, founded in 2008, originally started as an overseas direct-purchase shopping mall focused on wedding dresses. However, in 2012, Shein shifted its strategy to selling excess fashion products to overseas markets. In 2014, Shein launched its website and app for global markets, expanding particularly in Europe and the U.S. The company entered the Indian market in 2018, where its sales grew 16-fold over five years. By 2023, Shein had become a global leader in fast fashion, gaining widespread popularity. The rapid growth of these platforms is attributed to China's advanced e-commerce infrastructure and innovative logistics systems. China has gained a competitive edge in the global e-commerce market through highly developed digital payment systems, logistics networks, and data analytics technology. Additionally, the platforms' focus on low prices, fast shipping, and mobile-centered shopping environments has drawn positive consumer responses, contributing to their success.
Existing research on Chinese e-commerce platforms has primarily focused on analyzing the successful global expansion and marketing strategies of platforms such as AliExpress. Domestic Consumers' Perceptions of Chinese E-Commerce (Hyeyoung Joo, Byoungbu Yoo, 2024) analyzes consumer perceptions of AliExpress in South Korea, focusing on its aggressive marketing strategies and rapid market share expansion. Additionally, An Analysis of Topic Modeling of Chinese E-Commerce Issues in Korea Using Text Mining (Hyeyoung Joo, Byoungbu Yoo, 2024) employs text mining techniques to examine the social attitudes and perceptions of Korean consumers toward Chinese e-commerce platforms. A Study on Consumer Perception of E-Commerce Using Big Data* (Go, Eun Seo, 2024) compares consumer perceptions of e-commerce in China and Korea through big data analysis, exploring differences in consumer preferences and market environments between the two countries. The Logistics Strategies of China's Cross-Border E-Commerce Platforms: A Case Study of AliExpress and Joybuy (Jiang Xue, Seung-chul Kim, 2019) compares and analyzes the logistics strategies of major Chinese platforms like AliExpress and JD.com, explaining the strengths and differences of each system.
However, research on Temu and Shein remains relatively scarce. These two platforms have experienced rapid growth, with Temu quickly establishing itself in the South Korean market in 2023, while Shein has already solidified its position as a global fast-fashion brand. In this context, studying the rapid growth and global expansion of Temu and Shein is highly significant. Therefore, this study focuses on comparing and analyzing the perceptions of domestic and international consumers regarding the three major Chinese e-commerce platforms: AliExpress, Temu, and Shein. By analyzing the growth strategies of each platform and their competitiveness in the global market, this study aims to provide strategic insights that can help domestic e-commerce companies secure a competitive edge in the international market. Given the lack of comprehensive research on the rapid growth of emerging platforms like Temu and Shein, this study holds important temporal relevance.
2.2 Big Data
In the Fourth Industrial Revolution, attention has shifted to big data, with research focusing on data analysis (Lee Eun-ji et al., 2022). This research enables both structured and unstructured text analysis through natural language processing, facilitating the extraction of diverse information and understanding relationships among data. Big data helps manage the vast amounts of real-time data generated, increasing interest in its potential value (Kang Jung-mook, 2015). It allows for the classification and organization of data, leading to the discovery of meaningful insights (Oh, 2020). Thus, big data is a valuable technique for analyzing the context of text materials in research. Definitions of big data vary among scholars. Generally, it refers to vast digital data generated quickly, encompassing both structured and unstructured forms like text and video, often exceeding terabytes in volume. McKinsey (2011) defines it as data that exceeds the capabilities of standard database software for collection, storage, management, and analysis. IDC (2011) describes big data as next-generation technology designed for cost-effective extraction of value through high-speed data capture from large volumes of diverse data. Scholars also highlight three main characteristics of big data: Volume (amount), Variety (diversity), and Velocity (speed) (Kim Sung-hyun et al., 2017; Wang et al., 2018). Kim Gye-soo (2015) adds that big data represents a new information environment defined by these attributes, along with Value and Complexity. The market for big data is rapidly expanding, with Kim Kang-won (2017) predicting over 20% annual growth starting in 2016.
Among various types of big data analysis, Text mining techniques are being extensively researched. Text mining is a type of data mining that involves extracting implicit information from data in the form of text. It is a method for extracting information from unstructured data, such as text, using natural language processing techniques (Oh Chang-seok et al., 2016). As interest in big data has grown and the volume of text data has increased, the demand for technologies dealing with unstructured data has become increasingly important (Park Jin-seok, 2021). Text data can be considered unstructured data composed of natural language, and as computers process text data, there is a growing need to represent text as structured data. Text mining technology is essential to perform this function (Cho Tae-ho, 2001). Generally, the text mining process follows the typical steps of 'unstructured information collection → preprocessing → information extraction → information analysis,' where useful information is extracted using mathematical models or algorithms during the information extraction phase (Oh Chang-seok et al., 2016). Big data analysis, unlike traditional research methods, is a highly useful analytical technique for identifying various opinions and perceptions by objectively inferring diverse meanings about social phenomena based on large volumes of data through text mining techniques.
Recently, due to the growing importance of big data and social media, various studies have emerged. The collected big data can be analyzed through text mining, a scientific and objective research methodology, and social network analysis can be applied to key keywords to identify the relationships between words and the overall meaning structure, thereby deriving implications for customer perceptions. For example, Joo Tae-rim (2020) conducted research on the brand image of Xiaomi as perceived by Korean consumers using semantic network analysis. Wang A-kyung (2020) studied the characteristics of consumers using direct purchase services from China through big data analysis. Son Kyung-wook (2024) focused on case studies of global expansion of Chinese e-commerce companies, specifically AliExpress and Temu. There have been studies on research trends, such as analysis of trends in predictions and management related to artificial intelligence (Jeong Ye-eun, Kim Yong-soo, 2023) and analysis of research trends on Quality 4.0 using text mining (Kim Min-jun, 2023). Additionally, there was research on quality issues in the defense C5ISR sector using big data analysis and predictive modeling (Heo Hyeong-jo et al., 2023). Furthermore, Go Eun-seo (2024) studied consumer perceptions of e-commerce in China and domestically using big data. In previous big data analyses, text analysis and network analysis were conducted. However, in this study, both N-gram and Concor analysis were performed not only through Textom analysis but also visualized using UCINET6, and implications were derived. Notably, in existing e-commerce big data analyses, research has typically focused on either domestic or international markets, or compared only one or two companies. This study is significant in that it individually analyzes three e-commerce companies, drawing valuable insights from the comparison. Research on this topic remains relatively scarce compared to other studies. Therefore, this study aims to investigate the e-commerce industry as perceived by both domestic and international customers, focusing on AliExpress, Temu, and Shein, using big data analysis methods such as text mining based on social media data.
3. Research Method
3.1 Research Topic
This study aims to conduct a thorough analysis of both domestic and international customer perceptions of three major e-commerce companies: AliExpress, Temu, and Shein. By examining customer-generated content on social media platforms, the study seeks to identify and compare how these companies are perceived across different cultural and geographical contexts. Understanding these perceptions will provide valuable insights into the factors influencing customer behavior and decision-making in the global e-commerce landscape. Previous research has highlighted the presence of significant biases in consumer perceptions of Chinese products.
Hyeon Hyo-won et al. (2017) observed that Chinese goods are often associated with low pricing and perceived lower quality, leading to apprehension among consumers regarding their reliability and value. This perception creates a barrier to purchasing, with social risks, such as concerns over product quality and post-purchase issues, emerging as key factors that affect consumer confidence and behavior. These biases can significantly impact e-commerce companies from China, even when they offer competitive prices and vast product selections. Joo Tae-rim (2020) used semantic network analysis to investigate the perceptions of Xiaomi in South Korea, noting the positive attributes that the brand holds in the eyes of Korean consumers but also the need for deeper cross-cultural research to understand its reception in other countries. This study emphasized the importance of expanding research beyond local markets to gain a comprehensive view of brand perception and its global evolution. Similarly, Go Eun-seo (2024) stressed the importance of ongoing comparative studies of customer perceptions of Chinese e-commerce platforms in both domestic (Korean) and international contexts. This research found that despite the growing global influence of Chinese e-commerce companies, there remain significant differences in how these platforms are perceived in different regions, influenced by local market characteristics, cultural attitudes, and consumer experiences.
Building on these previous studies, the present research aims to extend the analysis by focusing specifically on AliExpress, Temu, and Shein—three rapidly growing e-commerce giants that have expanded globally, including into the Korean market. The study will explore both the positive and negative perceptions surrounding these platforms and how various factors such as pricing strategies, product variety, customer service, and brand image affect consumer trust and purchase decisions in different countries. Through comparative analysis, this research aims to uncover key differences in customer attitudes between domestic and international consumers, providing insights that can help these companies tailor their strategies to meet diverse consumer expectations and improve their global competitiveness. In addition, this study aims to identify the underlying causes of the perceptual differences between domestic and international customers. By examining these insights, the research will contribute to the growing body of knowledge in the field of e-commerce, offering practical recommendations for e-commerce platforms looking to expand or solidify their position in international markets. Based on this, the present study has established the following Research Topics:
RQ1. What are the perceptions of international customers in the e-commerce industry?
RQ2. What are the perceptions of domestic customers in the e-commerce industry?
RQ3. How can the perceptions of domestic and international customers in the e-commerce industry be compared?
3.2 Research Method
3.2.1 Data Collection and Data Cleaning
The data collection for this study focused on capturing relevant text data related to the three e-commerce platforms—AliExpress, Temu, and Shein—over the period from January 1, 2023, to August 31, 2024, covering their launch periods. For this purpose, targeted keywords, including ‘AliExpress(알리익스프레스)’, ‘Temu(테무)’, and ‘Shein(쉬인)’, were used. Data was gathered from two primary online sources, Naver and Google, utilizing the web-crawling tool TEXTOM. After initial data collection, further processing was conducted using NetMiner, an automated text analysis program that leverages natural language processing (NLP) functions. This program structured the collected text data into an organized format suitable for advanced analysis (as referenced in Noh Hee-kyung, Choi Hee-geun, 2019). The volume of collected data, including the total instances and data size from each platform, is detailed in the following sections. This clear organization ensures comprehensive data coverage and enables structured analysis of customer perceptions across these platforms.
3.2.2 Methods of Analysis
The data analysis methods are as follows: First, data was extracted from social media using TEXTOM. Second, the data underwent a preprocessing stage. Common data cleaning methods included removing duplicate posts, promotional content, and irrelevant posts to the research, as well as standardizing similar terms, removing stop words, stemming, eliminating punctuation and numbers, and removing extra spaces. Additionally, nouns and adjectives were extracted and utilized for analysis according to the purpose of the study. Third, based on the final selected data, which was collected and refined, text mining analysis was conducted. This included word frequency analysis, TF-IDF(Term Frequency-Inverse Document Frequency) analysis, and N-Gram analysis. After extracting the top 10 texts, CONCOR analysis was performed using UCINET 6.0 (Nam Joon-ho, 2016; Myung Min-sik, 2023; TEXTOM, 2020). After downloading the word frequency matrix from Textom, create an attribute file in UCINET 6.0 using the Matrix Editor. Then, run “Visualize Network” with NetDraw to connect nodes and conduct network analysis. Finally, sentiment analysis was conducted by displaying the distribution of emotional vocabulary and extracting positive and negative words to study customer perceptions.
4. Results of Analysis
The data used in this study was collected using Textom, focusing on social media content related to 'AliExpress,' 'Temu,' and 'Shein,' covering the period from January 1, 2023, to August 31, 2024. The target companies are the Chinese e-commerce firms AliExpress, Temu, and Shein. From the collected data, duplicate entries, promotional posts, and content deemed irrelevant to the purpose of this study were removed. After the data cleansing process, a total of 31,953 posts for AliExpress, 39,734 posts for Temu, and 46,214 posts for Shein were analyzed domestically, while internationally, 4,815 posts for AliExpress, 5,331 posts for Temu, and 6,340 posts for Shein were used. Text preprocessing was performed on the collected data. Nouns and adjectives were extracted from the initially processed words, and the top 25 words were identified based on their frequency in the cleaned data.
The following are the results for word frequency and TF-IDF in the domestic context. The TF-IDF index was calculated for the top 100 keywords based on their frequency, to determine the weights of the words. A higher TF-IDF value is interpreted as indicating that the word is more significant within the document.
The results of the TF-IDF analysis are presented below, showing similar findings. A comparison of AliExpress, Temu, and Shein revealed differences in specific keywords. For AliExpress, the word frequency analysis identified “code” and “product,” while the TF-IDF analysis highlighted “customs,” “promotion,” “Taobao,” and “recommendation.” For Temu, the word frequency analysis found “price,” “America,” and “product,” whereas TF-IDF revealed “app,” “review,” “AliExpress,” and “free.” In the case of Shein, the word frequency analysis showed “online,” while TF-IDF included “fashion,” “product,” “shopping mall,” and “brand.” Notably, “Korea” emerged among the top results, indicating a significant distinction.
The results of the keyword analysis and TF-IDF analysis conducted internationally are presented as follows. The results of the word frequency analysis and TF-IDF analysis are presented below, and the findings are similarly derived.
The word frequency and TF-IDF analyses are presented below, showing similar findings. Comparing AliExpress, Temu, and Shein reveals differences in specific keywords. For AliExpress, the word frequency analysis showed “shipping” and “shopping” ranking high, along with “seller,” “Alibaba,” “click,” “dropshipping,” and competitors “Amazon” and “Temu.” The TF-IDF results also highlighted "review" and “return.” In Temu's case, distinct keywords included “facebook,” “friend,” “invitation,” “shop,” “haul,” and “discount.” For Shein, the analysis identified keywords like “clothing,” “sale,” “style,” “outfit,” “retailer,” “brand,” and “collection,” indicating notable differences.
N-gram is a statistical language analysis model that extracts N consecutive elements from a string, calculating the frequency of words that co-occur consecutively within a sentence. The type of N-gram depends on how many words the sentence is divided into, with Textom using an N-2 Bigram model. High frequencies of word pairs, like word1 and word2, indicate that they often appear together. The N-gram visualization clearly presents these relationships, showing similarities in both domestic and international contexts for AliExpress, and highlighting connections related to its business and marketing strategies.
In the domestic N-gram analysis for Temu, terms such as “online shopping mall” and competitors (including Shein and AliExpress) were identified. A notable distinction was the presence of connections related to claims, returns, and carcinogenic substances. In the international context, similar connections were identified, including marketing-related terms such as “Facebook”, “coupon”, “code”, “invitation”, and “haul”. Keywords like “world” and “power” stood out, indicating an intention to establish a presence in the global market. For Shein, there were associations related to China and fashion platforms, as well as connections to Korea and its competitors. In the international context, there were relatively many connections with terms like “fast fashion” and “clothing,” along with associations related to “kid,” “outfit,” and “photo.” CONCOR (Convergence of Iterated Correlation) analysis identifies structural equivalence relationships within a network, showing that nodes occupy similar positions. This study applied CONCOR analysis to semantic network results, resulting in visualizations for both domestic and international contexts, allowing researchers to assign group names based on associated keywords.
Examining the clusters derived from the visualization, AliExpress in the domestic context was identified with five clusters based on the associated keywords. In the international context, three clusters were identified. For Temu, four clusters were identified in the domestic context and three clusters in the international context. For Shein, four groups were identified in the domestic context and three groups in the international context.
Sentiment analysis was conducted using the Textom program, showing the distribution of sentiment lexicons. In TEXTOM, a sentiment lexicon, which was developed in-house, is used to analyze emotions. This lexicon includes three positive words—interest, favorability, and joy—and six negative words—pain, sadness, anger, fear, surprise, and rejection. Each sentiment was standardized according to intensity and scored on a 7-point scale. Based on this, the most frequently favored words and sentiment-specific words were identified with high sentiment intensity and ratios. For AliExpress, in the domestic context, positive words accounted for 73.41%, with keywords like ‘recommended,’ ‘convenient,’ and ‘decent.’ Negative words made up 26.59%, with rejection at 15.12% being the highest, and keywords like ‘difficult’ ‘burdensome,’ and ‘awkward’ were noted. Internationally, positive words were 61.39%, with keywords including ‘fan,’ ‘top,’ ‘welcome,’ ‘growth,’ and negative words were 38.61%, with keywords like ‘dispute,’ ‘problem,’ ‘scam,’ and ‘error.’ For Temu, in the domestic context, positive words were 69.05%, with ‘recommended,’ ‘decent,’ and ‘growing’ as key terms. Negative words were 30.95%, with terms like ‘difficult,’ ‘flaw,’ ‘challenging,’ and ‘awkward.’ Internationally, positive sentiment was 75.02%, with keywords like ‘gift,’ ‘help,’ ‘comfort,’ and ‘love.’ Negative words accounted for 24.98%, with keywords including ‘lawsuit,’ ‘scam,’ and ‘dupe.’ For Shein, domestically, positive sentiment was 64.17%, with keywords such as ‘recommended,’ ‘lovely,’ and ‘growing.’ Negative sentiment was 35.83%, with keywords like ‘dislike’ and ‘severe.’ Internationally, positive words made up 62.01%, with terms like 'inspiration,' 'top,' 'love,' 'likes,' and 'fun.' Negative sentiment was 37.99%, with keywords such as ‘infringement,’ ‘lawsuit,’ ‘problem,’ and ‘fraud.’
5. Conclusion
This study aimed to provide strategic insights into domestic and international consumer perceptions of Chinese e-commerce companies—AliExpress, Temu, and Shein—through text analysis. The main findings include the following: Consumer perceptions of these companies were explored via big data analysis, with data collected from January 1, 2023, to August 31, 2024, using Textom. A text-cleaning process was conducted on the unstructured data, followed by text mining to calculate word frequency and identify key terms. These terms were converted into matrix data and analyzed using Ucinet 6.0 for network centrality and structural equivalence through CONCOR analysis, with results visualized in NetDraw. Finally, a sentiment analysis was performed to examine positive and negative perceptions, which were also visualized for better understanding.
RQ1: What are the perceptions of international customers in the e-commerce industry? First, text analysis identified key terms like “AliExpress,” “Temu,” “Shein,” “shipping,” “platform,” and “China,” indicating international customers’ awareness of these companies. Second, TF-IDF analysis confirmed similar findings to the word frequency analysis but revealed notable keywords such as “customs clearance,” “promotion,” “Taobao,” “recommendation,” “app,” “review,” “fashion,” “shopping mall,” and “brand,” highlighting specific concerns and interests. Third, N-gram analysis showed strong associations among terms like “AliExpress,” “promotion,” “code,” and “purchase,” with “shopping,” “Shein,” and “AliExpress” being highly relevant for Temu and Shein. Fourth, CONCOR analysis clustered AliExpress around company definition, market environment, and customer acquisition strategies, while Temu was linked to data privacy management and order platforms, and Shein was categorized by company definition, competitors, and marketing strategies. Finally, sentiment analysis indicated a predominance of positive language, with the ranking of positive word frequency being AliExpress > Temu > Shein.
RQ2: What are the perceptions of domestic customers in the e-commerce industry? First, text analysis revealed similar results to the international analysis but included additional keywords like “app,” “link,” “code,” “fashion,” “haul,” and “woman,” suggesting that domestic customers recognize the characteristics of companies like AliExpress, Temu, and Shein, albeit with less specificity. Second, the TF-IDF analysis was consistent with keyword analysis but highlighted differences in terms such as “click,” “dropshipping,” “review,” “return,” “friend,” “invitation,” “outfit,” and “collection,” indicating varied interactions with these platforms. Third, N-gram analysis showed high relevance for AliExpress with keywords like “link,” “click,” and “hipping,” while Temu was associated with “app,” and Shein with “haul,” “fashion,” and “Shein.” Fourth, CONCOR analysis differentiated the companies: AliExpress was linked to corporate definition and customer strategy, Temu focused on corporate introduction and customer acquisition strategies, and Shein was characterized by its fast-fashion identity and customer complaints. Finally, sentiment analysis indicated that positive sentiments were more frequent, with Temu perceived most positively, followed by Shein and then AliExpress.
RQ3: How can the perceptions of domestic and international customers in the e-commerce industry be compared? Both domestic and international customers recognize Chinese e-commerce companies, but international customers have a clearer understanding of each company’s distinct characteristics, particularly with Shein being associated with “fashion” and “woman.” Both markets utilize customer acquisition strategies, but international markets engage more actively, as seen with Temu’s friend invitations and AliExpress’s dropshipping. Domestic strategies tend to focus on promotional codes and discounts, while international markets offer more personalized activities; for instance, Temu promotes a user-friendly app and free shipping, and Shein targets younger audiences through influencer marketing. Domestic companies prioritize market share and customer acquisition, whereas international companies also consider market environment, competitors, and customer complaints. Overall, there is a noticeable increase in positive attitudes toward Chinese companies, indicating potential for further growth if they continue to emphasize quality, reliability, and value.
The academic implications of this study are significant as they highlight how consumer perceptions of rapidly growing Chinese e-commerce companies shape the market, enhancing our understanding of the e-commerce landscape from a consumer-centric perspective. Additionally, the research provides valuable cross-cultural comparisons, identifying differences in consumer behavior and market strategies across regions, thus offering insights into global market dynamics. Furthermore, by utilizing text analysis rather than simple quantitative methods, the study uncovers richer insights into consumer sentiments and trends, laying a foundation for further research in e-commerce. Based on these academic insights, practical implications can be drawn. First, while AliExpress has been relatively more successful in its domestic entry, it must sustain customer acquisition by maintaining its domestic strategies while adapting to the foreign market strategies of competitors. Second, Temu should maintain its aggressive marketing tactics while also focusing on improving product quality and customer service, especially in the mid- to low-cost product segment, to succeed both domestically and internationally. Lastly, Shein needs to enhance its domestic brand awareness and adopt foreign marketing strategies. Although it is a relatively late entrant and less recognized domestically, It has a clear corporate identity and successfully attracts younger consumers abroad. It should replicate this success at home by prioritizing convenient services and improving its brand image.
This study has several limitations. First, efforts are needed to generalize the research findings. Conducting actual opinion surveys, such as questionnaires, or examining consumer perceptions and case studies from various countries would be meaningful for comparison. Second, since the study primarily focused on external customers, it is necessary to explore the effects of platforms or actual marketing strategies from the perspective of the companies involved. Third, there were limitations in data collection and analysis using the Textom platform. While the ability to collect data from both domestic and international sources through the same channel is noteworthy, the limitations of the specific social media and SNS platforms available on Textom indicate that expanding these sources could yield more comprehensive insights. Therefore, conducting various analyses based on the provided data could reveal additional implications and findings.
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