Factors influencing online shopping behavior of young consumers through live streaming commerce on Tiktok shop in Hanoi

Abstract

This study investigates the factors influencing online impulse buying behavior during livestream commerce on TikTok Shop, emphasizing both perceived value and consumer responses. Guided by the Stimulus-Organism-Response (SOR) framework, the research explores key external factors - Suggestions and Availability, Stream Content Quality, Social Reference, Pre-Livestream Advertising, and Long Livestreams - alongside intrinsic factors such as Openness, Impulsiveness, and Perceived Value. Data collected from young consumers (aged 18 - 35) were analyzed using reliability testing, exploratory factor analysis (EFA), Pearson correlation, and multiple linear regression. Results indicate that suggestions and availability, livestream content quality, pre-livestream advertising, long livestreams, and social reference significantly drive online impulse buying behavior on TikTok Shop. Additionally, demographic analysis reveals that younger consumers are more prone to impulse purchases in this context. The study offers practical recommendations for e-commerce businesses, highlighting the importance of personalized marketing strategies, engaging livestream content, and innovative customer interaction to stimulate impulse buying and improve customer satisfaction. These findings deepen the understanding of consumer behavior in the rapidly growing livestream commerce sector.

Keywords: online shopping behavior, young consumers, live streaming commerce, Tiktok shop, impulse buying behavior.

1. Introduction

The emergence of the 4.0 technology revolution has transformed consumer behaviors, with online shopping becoming an integral part of daily life, especially in Vietnam's dynamic and youthful market. The rise of e-commerce, driven by convenience and increasing purchasing power, has reshaped traditional retail models. Vietnam is currently ranked among the top five countries with the fastest e-commerce growth rates globally, maintaining an annual growth rate of 16-20% even during the COVID-19 pandemic. The 2023 Vietnam E-commerce White Paper [1] highlights significant growth, with 61 million online consumers and an average annual expenditure per person of $336, underlining the robust potential of this market.

The competitive landscape of Vietnam’s e-commerce is currently dominated by five major platforms: Shopee, Lazada, Tiki, Sendo, and the emerging TikTok Shop. Since its entry into the market in 2022, TikTok Shop has achieved impressive growth, disrupting the market share of established platforms. In Q3/2023, TikTok Shop reached a market share of 16%, climbing to second place among e-commerce platforms in Vietnam. Its integration of “Shoppertainment”—a blend of shopping and entertainment—has proven effective in attracting young consumers through engaging livestream commerce. This innovative approach has shifted the traditional shopping experience into an interactive model, allowing sellers to better connect with customers. [2]

Young consumers aged 18-35, who account for 63% of e-commerce users in Vietnam, represent the driving force behind the sector's growth. This demographic prioritizes convenience, modern technological applications, and engaging experiences. TikTok Shop has successfully capitalized on these preferences, leveraging its strengths in short-form video content and creative marketing strategies. [1]

Despite the significant rise of livestream commerce, limited research has been conducted to understand its specific impact on consumer behavior. This study aims to explore the online shopping behaviors of young consumers in Hanoi through TikTok Shop’s livestream commerce. By employing the Structural Equation Modeling (SEM) method and analyzing data from 412 respondents, the research provides insights into factors influencing purchase intentions. The findings aim to assist businesses in enhancing their sales strategies and optimizing operational costs in this rapidly evolving e-commerce ecosystem.

2. Literature review

The study focuses on the theoretical foundations and research models applied, including concepts related to online shopping behavior and the application of the Stimulus-Organism-Response (S-O-R) model in the study. These concepts are interpreted based on reputable academic sources, clarifying the Stimulus (external factors), Organism (internal states), and Response (consumer behavior) in the decision-making process.

The evaluation of previous studies utilizing the S-O-R model to analyze shopping behavior across various contexts. However, influencing factors often differ across cultures and platforms. For instance, international studies emphasize entertainment value and promotional strategies, while in Vietnam, customer interaction and trust are typically prioritized. Factors such as content distribution algorithms, personalized experiences, and real-time interaction remain underexplored, particularly in the context of TikTok Shop.

Several key related studies are highlighted, such as the research by Nguyễn Hoài Nam (2023) [3], which identified that factors like interaction, visual content, entertainment value, and professionalism in livestreams significantly influence purchase intention. Similarly, the study by Sandra Miranda et al. (2024) [4] emphasized the role of direct communication, promotional strategies, and scarcity in driving impulsive buying behavior.

However, research gaps [5] [6] [7] [8] [9] [10] [11] [12] [13]still exist, especially in analyzing the shopping behavior of young consumers on the TikTok Shop platform in Vietnam. Factors such as entertainment value, promotions, and content algorithms require further investigation to better understand their impact on purchase intentions in the local context.

3. Methodology

3.1. Proposed research model

Based on the literature review and the hypotheses explored, the study proposes a model grounded in the Stimulus-Organism-Response (S-O-R) framework to examine the factors influencing the online shopping behavior of young consumers through Live Streaming Commerce on TikTok Shop (Figure 1).

Figure 1. Proposed research model

(Source: Proposed by author)

Research methodology

Data was collected from November 2024 to January 2024, involving 412 participants in Hanoi, focusing on young consumers aged 18–35. The data was gathered using a convenience sampling method and a detailed survey questionnaire based on a 5-point Likert scale. The hypotheses were tested using SPSS 26 software through methods such as Cronbach's Alpha reliability analysis, Exploratory Factor Analysis (EFA), Pearson correlation, and linear regression analysis.

4. Results and discussion

4.1. Descriptive statistics of the survey sample:

A total of 412 valid responses were collected from the online survey. The majority of respondents were aged 18–24 (81.07%), mostly students (78.40%), and earned less than 5 million VND/month (68.93%).

4.2. Reliability testing of scales (Cronbach's Alpha):

All measurement scales achieved good reliability, with Cronbach's Alpha values within the range of 0.6–0.95. No observed variables had a corrected item-total correlation below 0.3 and đều nhỏ hơn giá trị Cronbach’s Alpha chung.

4.3. Exploratory Factor Analysis (EFA):

The KMO and Bartlett’s test results met the requirements, with total variance explained exceeding 50%, ensuring both convergence and discriminant validity of the measurement scales.

4.4. Linear regression analysis:

In phase 1, factors positively influencing impulse buying behavior during livestreams included Suggestions & Availability (SA), Stream Content Quality (SCL), Pre-Livestream Advertising (PLA), Social Reference (SNR), and Long Livestreams (LLS), with SNR having the strongest impact.

Phase 1 Regression Equation:

PVL=0,106 + 0,064*SA + 0,177*SCL.pla + 0,458*SNR + 0,112*LLS

In phase 2, the key factors influencing customer responses in livestreams were Openness (OPN), Impulsiveness (IMP), and Perceived Value (PVL), with PVL being the most significant factor.

Phase 2 Regression Equation:

RSL = 0,139 + 0,203*OPN + 0,338*IMP + 0,415*PVL

Figure 2. Research model after testing

4.5. Testing differences among demographic groups:

No significant differences were found in online shopping behavior via livestreams between gender or occupational groups.

However, younger age groups (18–24) exhibited a higher tendency to engage in livestream shopping compared to older groups (30–35).

5. Conclusions and suggestions

Conclusions

Based on the synthesized results, The study identified critical factors such as Suggestions and Availability, Stream Content Quality, Social Reference, Pre-Livestream Advertising, and Long Livestreams as the main drivers of perceived value and impulsive purchasing behavior. Social Reference emerged as the most impactful factor, emphasizing the importance of social proof in online shopping.

The impact of demographic factors, including age and occupation, on shopping behavior was found to be minimal. Although younger age groups (18–29 years old) are more engaged in live streaming commerce, the overall differences across age, gender, and occupation groups did not significantly influence the results, suggesting that live streaming commerce appeals broadly across these categories.

Suggestions

Enhance Product Suggestions and Availability

Businesses/sellers should utilize AI and machine learning to provide personalized product recommendations based on customer behavior, preferences, and past purchases on TikTok Shop. Create diverse and high-quality product categories, combined with innovative presentation tools to increase appeal and attract customers when viewing products on the platform.

Improve  Live Stream Content Quality

A key approach to both communicate and enhance the quality of live stream content is the streamer —the person guiding customers during live streams. Collaborating with influential individuals or those with expertise can increase engagement and encourage customers to make quick purchase decisions. Additionally, using high-definition video and audio equipment, along with interactive elements such as live polls and flash discounts, can stimulate customers to make immediate purchases. Segmenting the live stream into product categories can also help businesses effectively target customers’ specific preferences.

Leverage Social References

As mentioned above, collaborating with influential individuals and Key Opinion Leaders (KOLs) can attract a certain audience due to the appeal of celebrities. Moreover, incorporating product reviews, feedback, and customer ratings during live streams can build credibility. Adding games/programs that encourage customer groups to share their experiences socially during live streams can foster participation and engagement.

Implement Pre-Live Stream Advertising

To attract more viewers, businesses should focus on strategic pre-live stream campaigns. This can include countdown campaigns, incentives for early subscribers, and comprehensive advertising efforts across both online and offline channels to generate excitement and anticipation.

Optimize Long Live Stream Sessions

By dividing live streams into segments based on product categories or interactive activities, businesses can sustain viewer interest and encourage purchases at different stages of the session. Limited-time promotions during these segments can further drive immediate purchases.

Focus on Psychological Engagement

Businesses should emphasize emotion-driven campaigns such as flash sales and gamified shopping experiences to stimulate impulsive buying behavior. Demonstrating product benefits through live demonstrations and storytelling can enhance the perceived value of their offerings.

Improve Service Quality

Ensuring platform reliability, fast delivery, effective return policies, and comprehensive customer service can enhance the overall shopping experience. Businesses should listen to customer feedback on product and service quality to regularly improve and deliver a better user experience. Providing flexible payment options, such as installment plans, can further reduce barriers to purchases.

Leverage Data Analytics

E-commerce platforms should use data analytics to track customer behavior, predict trends, and optimize their strategies. Continuous feedback analysis can help businesses adapt and respond quickly to changing consumer demands.

The suggestions above provide businesses/sellers with an overview of the potential in their services and limitations to address, helping to optimize their strategies to attract a larger audience while leveraging the platform's unique features to drive business growth.

References:

 Các yếu tố ảnh hưởng đến hành vi mua sắm trực tuyến của người tiêu dùng trẻ tại Hà Nội khi xem livestream bán hàng trực tiếp trên nền tảng mua sắm trực tuyến Tiktok Shop

Hoàng Minh Phương¹

TS. Nguyễn Thị Vũ Khuyên¹

¹Trường Kinh tế, Đại học Bách khoa Hà Nội

Tóm tắt:

Nghiên cứu này đánh giá tác động của các yếu tố ảnh hưởng đến hành vi mua hàng trực tuyến bộc phát trong quá trình xem livestream bán hàng trực tiếp trên nền tảng mua sắm trực tuyến Tiktok Shop. Trong đó, nghiên cứu tập trung đánh giá giá trị nhận thức và phản ứng từ phía người tiêu dùng. Dựa trên mô hình Kích thích (Stimulus) - Chủ thể (Organism) - Phản ứng (Response), hay còn gọi là mô hình SOR, nghiên cứu này đánh giá tác động của các yếu tố bên ngoài chủ chốt, gồm: Sự gợi ý và Tính sẵn có, Chất lượng nội dung livsstream, Tham chiếu xã hội, Quảng cáo trước khi livestream, và Livestream kéo dài, cùng với đó là các yếu tố nội tại, gồm Tính cởi mở, Tính bốc đồng, và Giá trị nhận thức.

Trong nghiên cứu này, dữ liệu thu thập được từ những người tiêu dùng trẻ tuổi (tuổi từ 18 đến 35) đã được phân tích thông qua việc kiểm thử độ tin cậy, phân tích nhân tố khám phá (EFA), tương quan Pearson và hồi quy tuyến tính bội. Kết quả chỉ ra rằng các yếu tố, gồm: Sự gợi ý và Tính sẵn có, Chất lượng nội dung livsstream, Quảng cáo trước khi livestream, Livestream kéo dài, và Tham chiếu xã hội, có tác động đáng kể đến hành vi mua hàng trực tuyến bộc phá của người tiêu dùng khi xem livestream bán hàng trực tiếp trên Tiktok Shop.

Ngoài ra, phân tích nhân khẩu học cho thấy rằng những người tiêu dùng trẻ tuổi có xu hướng mua hàng bốc phát nhiều hơn. Thông qua các kết quả trên, nghiên cứu đưa ra một số khuyến nghị cho các doanh nghiệp thương mại điện tử, nhất là về các chiến lược tiếp thị được cá nhân hóa, nâng cao chất lượng nội dung livestream, và tương tác sáng tạo với khách hàng để kích thích hành vi mua hàng bộc phát cũng như cải thiện sự hài lỏng của khách hàng. Những kết quả của nghiên cứu này kỳ vọng sẽ giúp cung cấp thông tin sâu hơn về hành vi của người tiêu dùng trong bối cảnh hoạt động livestream bán hàng đang phát triển nhanh chóng.

Từ khoá: hành vi mua sắm trực tuyến, người tiêu dùng trẻ, livestream bán hàng, cửa hàng trên Tiktok, hành vi mua hàng theo cảm tính.

[Tạp chí Công Thương - Các kết quả nghiên cứu khoa học và ứng dụng công nghệ, Số 1 tháng 1 năm 2025]