The Effect of Promotional Strategies on Sales in Live Streaming Commerce: An Empirical Study in Malaysia

The Effect of Promotional Strategies on Sales in Live Streaming Commerce: An Empirical Study in Malaysia

*Mohd Syafiq Md. Taib¹, Muhammad Najwan Haqimi Muhamad Najed¹, Mohd Fazli Mohd Sam¹, Siti Nur Aisyah Alias¹, Nurul Hasyimah Mohamed¹, Fadhlur Rahim Azmi²

¹Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka, Malaysia

²Faculty of Business and Management, Kampus Bandaraya Melaka, Universiti Teknologi MARA, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90400173

Received: 28 March 2025; Accepted: 04 April 2025; Published: 05 May 2025

ABSTRACT

Live streaming has emerged as a powerful e-commerce tool, enabling businesses to engage with customers in real time and drive sales through interactive content. This study investigates the impact of key promotional strategies—namely promotional offer type, live host characteristics, and post timing—on sales performance during live streaming events in Malaysia. Utilizing a quantitative research design, data were collected through a structured questionnaire distributed to respondents who have experienced live shopping. The study employed descriptive statistics, correlation analysis, and multiple regression to examine the relationships between the independent variables and sales outcomes.

Findings revealed that all three promotional strategies significantly and positively influenced sales. Promotional offer type, live host characteristics, and post timing each played an important role in shaping consumer purchase decisions. These results underscore the importance of well-designed promotions, engaging hosts, and strategic timing in maximizing live commerce success. The study offers valuable insights for marketers and businesses seeking to optimize their live streaming strategies to boost sales and enhance e-commerce performance.

Keywords: Live Streaming Commerce, Promotional Strategies, Sales Performance, E-Commerce, Marketing, Malaysia

INTRODUCTION

The rise of live streaming as a sales channel has transformed the digital commerce landscape, merging entertainment with real-time shopping experiences. Unlike conventional e-commerce platforms, live streaming offers businesses the opportunity to interact directly with consumers, creating a sense of urgency and personal connection that can significantly influence purchase decisions. In markets such as China, live commerce has become a mainstream sales strategy, with platforms like Taobao Live reporting billions in annual revenue (McKinsey & Company, 2021). This phenomenon is gaining traction in Southeast Asia, particularly in Malaysia, where live streaming commerce is increasingly utilized by SMEs to drive online sales.

In the Malaysian context, the growth of live streaming as a sales medium was accelerated by the COVID-19 pandemic, which shifted consumer behavior toward digital channels. Promotional strategies implemented during live streams, such as flash sales, limited-time discounts, and influencer marketing, have proven effective in capturing buyer interest and prompting immediate purchases. However, questions remain regarding the optimal design and deployment of such strategies to consistently boost sales outcomes.

This study seeks to empirically examine how specific promotional strategies, namely promotional offer type, live host characteristics, and post timing, affect sales performance in live streaming commerce. Understanding these relationships is crucial for marketers and digital entrepreneurs aiming to maximize the return on investment (ROI) of their live streaming campaigns.

LITERATURE REVIEW

Promotional Offer Type

Promotional offers, such as discounts, giveaways, and limited-time deals, are commonly employed in live commerce to stimulate urgency and drive consumer action. Chen et al. (2023) warn against over-reliance on discounting, as it may diminish brand value and attract price-sensitive, non-loyal customers. However, when used strategically, promotional offers can enhance perceived value and trigger impulse buying behavior (Kim & Ko, 2021). Effective promotions in live streaming should not only offer price reductions but also foster a sense of exclusivity and reward for viewers, thereby influencing immediate sales outcomes (Lee et al., 2023).

Live Host Characteristics

The role of the live stream host is critical in shaping viewer perception and influencing buying behavior. Trustworthiness, charisma, and product knowledge are among the traits that positively impact consumer confidence and decision-making (Sun et al., 2019). According to Park and Lin (2023), hosts who appear relatable and authentic tend to build stronger rapport with audiences, which can translate into higher conversion rates. In Malaysia, Bondarenko (2023) found that effective hosts not only entertain but also serve as key persuaders, significantly affecting sales performance during live sessions.

Post Timing

Timing is a frequently overlooked yet important factor in the success of promotional content. While most studies focus on social media engagement timing (Liu et al., 2020; Kumar et al., 2021), their findings are relevant to live streaming as well. Audiences tend to be more active during lunch hours and evenings, suggesting that well-timed streams can increase exposure and potential for purchase. Park et al. (2022) add that convenience and scheduling significantly affect participation and shopping behavior, making post timing a variable worth exploring in relation to sales performance.

Live Streaming Commerce

Live streaming commerce, often referred to as “live commerce,” combines live video with real-time product marketing and sales, creating an interactive shopping experience that mirrors in-store engagement (Wongkitrungrueng & Assarut, 2020). First popularized in China, this model has rapidly expanded across global markets, with platforms like Facebook Live, TikTok Shop, and Instagram Live enabling sellers to connect directly with audiences. Unlike static online shopping, live streaming commerce provides immediacy, human interaction, and entertainment, all of which contribute to higher consumer engagement and potential impulse purchases (McKinsey & Company, 2021).

The strength of live streaming lies in its ability to integrate demonstration, social proof, and promotion in a single event. According to Chen et al. (2023), the real-time nature of live streaming creates a sense of urgency, encouraging faster purchase decisions. Moreover, viewers can interact through chats, reactions, and questions, fostering a sense of community and enhancing trust in both the seller and the product.

Sales in Live Streaming Commerce

Sales performance is one of the key metrics of success in live streaming commerce. Research shows that the interactive format of live streaming can significantly influence purchasing behavior (Cai & Wohn, 2019). Real-time interaction between host and viewers has been found to enhance customer trust, product credibility, and perceived value—all of which are strong drivers of sales (Park & Lin, 2023). Flash deals, limited-time offers, and exclusive discounts offered during live streams have also been shown to boost conversion rates (Zhao et al., 2021).

Moreover, the emotional engagement fostered through live sessions plays a pivotal role. According to Sun et al. (2019), emotional appeal, storytelling, and host charisma contribute to buyer enthusiasm, increasing the likelihood of purchase. In the Malaysian context, live streaming has become especially popular among SMEs due to its low cost and high reach, enabling even small businesses to generate substantial sales without the need for traditional brick-and-mortar infrastructure (Euromonitor International, 2022).

Research Framework

The research framework for this study is designed to explore the relationships between various independent variables and the dependent variable, focusing on how specific promotional strategies influence sales performance in live streaming commerce. This framework is structured into three key components: the theoretical framework, the conceptual framework, and the hypotheses development.

Theoretical Framework

This study is grounded in two well-established behavioral theories: Uses and Gratifications Theory (UGT) and the Elaboration Likelihood Model (ELM). These theories provide a foundation for understanding consumer behavior in the context of digital and interactive media such as live commerce.

Uses and Gratifications Theory (UGT), proposed by Katz, Blumler, and Gurevitch (1974), emphasizes the active role of consumers in selecting media content to fulfill specific psychological or social needs. In the context of live streaming commerce, consumers engage with content not only for informational purposes but also for entertainment, interaction, and emotional satisfaction. Promotional strategies that align with these needs—such as engaging hosts, attractive time-limited offers, and well-timed broadcasts—are likely to enhance viewer satisfaction and influence purchase decisions.

Elaboration Likelihood Model (ELM), introduced by Petty and Cacioppo (1986), explains how individuals process persuasive messages through two cognitive routes: the central route, which involves careful evaluation of message content (e.g., product quality or discount value), and the peripheral route, which relies on external cues such as the communicator’s credibility or emotional appeal (e.g., host appearance, tone, and delivery). This study applies ELM by viewing promotional offer type as a central cue and live host characteristics and post timing as peripheral cues that influence consumer decisions.

Together, these theories help explain how consumers cognitively and emotionally respond to promotional strategies during live streaming sessions, ultimately affecting sales performance.

Conceptual Framework

Based on the theoretical foundations and a review of relevant literature, the conceptual framework for this study outlines the relationships between three independent variables –promotional offer type, live host characteristics, and post timing – and one dependent variable; sales performance.

Figure 3.1: Conceptual framework

Hypotheses

The following hypotheses are developed based on a synthesis of theoretical foundations (Uses and Gratifications Theory, Elaboration Likelihood Model) and existing empirical literature in the domain of live streaming commerce and consumer behavior.

H1: There is a positive and significant relationship between promotional offer type and sales in live streaming commerce.

Promotional offers are among the most widely used strategies in live commerce due to their ability to create a sense of urgency and exclusivity (Kim & Ko, 2021). Flash sales, vouchers, time-limited discounts, and bundle deals are not only cost-saving mechanisms but also psychological triggers that prompt impulse buying behavior. According to the Elaboration Likelihood Model (Petty & Cacioppo, 1986), these offers serve as central cues that appeal to consumers’ rational evaluations of value. Studies such as Chen et al. (2023) and Zhao et al. (2021) have shown that effective promotional offers increase conversion rates and purchase intent during live broadcasts. Hence, it is hypothesized that:

H1: Promotional offer type positively influences sales in live streaming commerce.

H2: There is a positive and significant relationship between live host characteristics and sales in live streaming commerce.

The host of a live stream plays a pivotal role in shaping audience engagement and trust. Traits such as enthusiasm, credibility, relatability, and expertise can significantly affect how viewers perceive the product and the brand (Sun et al., 2019). From the perspective of Uses and Gratifications Theory (UGT), viewers are not only looking for product information but also entertainment and social connection—needs that a compelling host can fulfill. Park and Lin (2023) found that hosts with higher social attractiveness and persuasive skills are more effective in driving real-time purchase behavior. Thus, the following hypothesis is proposed:

H2: Live host characteristics positively influence sales in live streaming commerce.

H3: There is a positive and significant relationship between post timing and sales in live streaming commerce.

Timing plays a strategic role in determining the effectiveness of live commerce promotions. Broadcasting during high-traffic periods such as lunch breaks, evenings, or weekends, increases the likelihood that a larger audience will be exposed to the content (Liu et al., 2020). Moreover, well-timed posts that align with consumer online behavior patterns can enhance visibility and engagement. Although timing is often considered a logistical factor, its impact on exposure and attention makes it a relevant element in the decision-making process. Park et al. (2022) argue that the right timing can amplify the effectiveness of both message delivery and promotional strategies. Therefore, the following hypothesis is proposed:

H3: Post timing positively influences sales in live streaming commerce.

RESEARCH METHODOLOGY

Research Design

This study adopts a quantitative research design to examine the relationship between promotional strategies and sales performance in live streaming commerce. A cross-sectional survey method was employed to collect primary data from respondents who have experience with live shopping events. The use of structured questionnaires allows for the objective measurement of respondents’ perceptions regarding various promotional strategies and their influence on purchasing behavior. Statistical analysis was conducted using SPSS Version 26, incorporating descriptive statistics, Pearson correlation, and multiple regression techniques to test the study’s hypotheses.

Population and Sampling

The study’s population comprises consumers in Malacca, Malaysia, who have participated in live streaming shopping sessions. A non-probability purposive sampling method was used to select participants who are familiar with live commerce platforms such as Facebook Live, TikTok Shop, and Instagram Live. A total of 251 valid responses were collected, ensuring a sufficient sample size for regression analysis. The sample includes diverse demographic profiles in terms of age, gender, and occupation.

Variables and Measurement

Independent Variables:

• Promotional Offer Type: Refers to discounts, time-limited deals, and exclusive bundles presented during live streams.
• Live Host Characteristics: Encompasses the charisma, credibility, and engagement ability of the live host.
• Post Timing: Refers to the specific time the live session is scheduled and how it aligns with audience availability.

Each variable was measured using five items on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).

Dependent Variable:

• Sales Performance: Measured by participants’ self-reported likelihood of purchasing and their actual past purchasing behavior during live streams.

Instrumentation

A structured questionnaire was developed and distributed via Google Forms. The questionnaire was divided into two sections: (1) demographic data, and (2) items related to the study’s variables. Content validity was ensured by adapting items from previous validated studies (e.g., Chen et al., 2023; Sun et al., 2019). A pilot test involving 30 respondents yielded a Cronbach’s Alpha above 0.7 for all constructs, confirming internal consistency.

Data Collection Procedure

Data were collected over a period of two months in late 2024. Respondents were recruited via online platforms, including Facebook groups and e-commerce community forums. Participation was voluntary and anonymous, with respondents providing informed consent. The survey link was distributed digitally, ensuring broad reach and convenience.

Data Analysis Techniques

The collected data were analyzed using SPSS. The following statistical methods were applied:

• Descriptive Analysis: To summarize demographic characteristics and variable distributions.
• Reliability Analysis: Using Cronbach’s Alpha to assess internal consistency of scales.
• Pearson Correlation: To identify the strength and direction of relationships between variables.
• Multiple Regression Analysis: To evaluate the predictive power of promotional strategies on sales performance.

FINDINGS

Overview

This chapter presents the analysis of data collected from 251 respondents. The analysis includes descriptive statistics, reliability testing, correlation analysis, and multiple regression to determine the effect of promotional strategies on sales in live streaming commerce.

Descriptive Statistics

Table 5.1 presents the mean scores and standard deviations for each independent variable. All items were measured on a 5-point Likert scale.

Table 5.1: Descriptive statistics

Respondents generally agreed that promotional strategies influenced their purchase decisions during live streams.

Reliability Analysis

Cronbach’s Alpha values for all constructs exceeded 0.70, indicating acceptable internal consistency.

Table 5.2: Reliability analysis

Pearson Correlation Analysis

Pearson’s correlation was used to examine the relationships between the three promotional strategy variables and sales performance.

Table 5.3: Pearson Correlation Analysis

All variables show a strong and statistically significant positive relationship with sales.

Multiple Regression Analysis

Multiple regression was used to assess the predictive power of the three independent variables on sales performance.

Model Summary

Table 5.4: Model Summary

The model explains 72.3% of the variance in sales performance, indicating a very good fit.

ANOVA

Table 5.6: ANOVA

The overall model is statistically significant.

Coeffcient Table

Table 5.7: Coeffcient table

Each promotional strategy variable contributes significantly and positively to sales performance.

Hypotheses Testing Summary

Based on the results of Pearson correlation and multiple regression analysis, all three hypotheses proposed in this study were supported. Table 5.8 below summarizes the outcome for each hypothesis.

Table 5.8: Hypothesis testing summary

These findings affirm that each of the promotional strategy components; offer type, host characteristics, and post timing, significantly and positively contributes to sales outcomes in the live streaming context.

DISCUSSION, IMPLICATIONS, AND CONCLUSION

Discussion

The findings of this study confirm that promotional strategies – namely promotional offer type, live host characteristics, and post timing, have a statistically significant and positive effect on sales performance in live streaming commerce. This aligns with prior literature (e.g., Kim & Ko, 2021; Sun et al., 2019) that highlights the importance of psychological triggers, trust-building mechanisms, and timely engagement in influencing consumer purchase decisions.

The strong impact of promotional offer type (β = 0.807) suggests that limited-time deals, exclusive bundles, and other pricing strategies effectively stimulate immediate purchasing behavior. This supports the notion of urgency and scarcity as persuasive elements in live stream environments (Chen et al., 2023).

Live host characteristics also emerged as a powerful predictor of sales (β = 0.815). Viewers are more likely to purchase when hosts demonstrate product expertise, authenticity, and charisma. This reflects the role of parasocial interaction in building trust and driving conversions, consistent with findings from Park and Lin (2023).

Finally, post timing (β = 0.749) significantly influences sales outcomes. This underscores the strategic value of scheduling live streams during peak viewing hours—typically lunch breaks and evenings—when viewers are most likely to engage and make purchases (Liu et al., 2020).

Theoretical Implications

This study contributes meaningfully to the growing body of literature on live streaming commerce by offering a theory-driven examination of how specific promotional strategies impact sales performance. By integrating Uses and Gratifications Theory (UGT) and the Elaboration Likelihood Model (ELM), the study provides a more holistic understanding of consumer decision-making in digital commerce environments.

The application of UGT demonstrates that consumers are not passive recipients of marketing content but are actively engaged in seeking out live content that satisfies specific needs, such as information, entertainment, and social interaction. Live promotional strategies that are aligned with these gratifications—especially charismatic hosts and time-limited offers—are more likely to stimulate engagement and conversion.

ELM enriches the analysis by distinguishing between logical (central) and emotional or situational (peripheral) cues. The identification of promotional offer type as a central cue and host characteristics and post timing as peripheral cues validates ELM in a live commerce setting, showing that both rational and emotional stimuli jointly influence purchase decisions. This dual-processing model offers a theoretical lens for future researchers and marketers to design campaigns that appeal to multiple aspects of consumer cognition and emotion.

Moreover, this study advances the conceptual clarity of promotional strategies by operationalizing them into discrete, measurable components—offer type, host quality, and timing—and empirically linking them to sales performance. This structured approach provides a replicable framework for further academic inquiry in the domains of digital marketing, technology management, and consumer behavior.

Practical Implications

The findings offer several actionable insights for e-commerce practitioners, marketers, and entrepreneurs, especially those leveraging live streaming as a sales channel:

1. Design Promotional Offers That Trigger Action

Discounted bundles, limited-time deals, and “only during this live session” offers were shown to significantly impact sales. Businesses should employ urgency-based messaging and exclusive live-only deals to create buying pressure.

2. Invest in Skilled and Charismatic Hosts

The live host plays a critical role in influencing consumer trust, attention, and willingness to buy. SMEs and large brands alike should consider training their live streamers in communication, product demonstration, and audience interaction skills to increase conversion rates.

3. Optimize Live Stream Timing

Sales performance is also affected by when live sessions are scheduled. Businesses should use data analytics to identify peak viewing hours (e.g., weekday evenings, lunchtime) and schedule streams accordingly to maximize reach and sales potential.

4. Build Integrated Strategies

Rather than relying on a single tactic, businesses should integrate all three components—promotional offers, host effectiveness, and timing—into a cohesive strategy to enhance the overall impact of their live selling efforts.

These insights are especially relevant for SMEs in Malaysia and Southeast Asia, where live streaming offers a cost-effective, high-engagement platform to drive online sales with minimal upfront investment.

Limitations and Future Research

While the study offers valuable contributions, it is not without limitations:

1. Geographic Limitation

The research was conducted in Malacca, Malaysia. While the insights are useful, they may not be fully generalizable to all Malaysian regions or other countries with different digital commerce cultures.

2. Self-Reported Data

The study relied on self-reported data, which is subject to biases such as social desirability and recall inaccuracy. Future studies could incorporate observational or behavioral data (e.g., platform sales analytics) for greater validity.

3. Cross-Sectional Design

The research employed a cross-sectional design, which limits the ability to infer causality. Longitudinal studies could better capture changing consumer behavior and promotional trends over time.

4. Product-Type Generalization

The study did not distinguish between different product categories (e.g., fashion, electronics, beauty). Future research could examine whether the impact of promotional strategies varies by product type.

5. Lack of Mediating or Moderating Variables

This study examined direct relationships only. Future research could explore mediating variables (e.g., trust, perceived value) or moderating factors (e.g., age, shopping frequency) to enrich the model.

Conclusion

This study empirically demonstrates that promotional offer type, live host characteristics, and post timing significantly and positively influence sales performance in live streaming commerce. By focusing exclusively on the sales outcome, the study offers clear insights into what drives actual purchase behavior in this fast-growing digital channel.

The integration of UGT and ELM provides a strong theoretical base to understand how consumers respond to different cues during live shopping events. Practically, the results offer a strategic guide for businesses to optimize their live streaming efforts by carefully crafting promotional strategies that are timely, engaging, and persuasive.

As live commerce continues to evolve and become a dominant form of online retail, understanding the psychological and strategic factors that drive sales becomes increasingly vital. This study contributes to that understanding and opens new avenues for both scholarly inquiry and practical innovation in digital sales.

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