Live Streaming Shopping: Effects on Purchase Intentions among Malaysian Consumers

Live streaming shopping is revolutionizing the way that people shop, as it offers a convenient and interactive shopping experience allowing customers to have real-time access to products and services from their home. This has led to tremendous growth in the industry, with more and more retailers investing in live streaming shopping platforms to meet the needs of their customers. This study investigated the relationship between live streaming and purchase intentions in social commerce in Malaysia using the Theory of Planned Behavior (TPB). A survey was conducted among 200 Malaysian consumers who have experience with live-streaming. The results showed that while attitude does not significantly influ ence customers’ intention to purchase, subjective norms and perceived behavioral control are significant in influencing consumers’ intention to purchase through live -streaming. The findings provide insights on how marketers can increase the likelihood of customers making a purchase through live-streaming channels.


Introduction
As social media streaming has become more prevalent (Giertz et al., 2021), many vendors on e-commerce websites are using it as a way to boost their sales in Malaysia. This has produced a new type of online shopping called live-streaming shopping, which has been proven beneficial for several sellers in the digital commerce market (Lin et al., 2023). In Malaysia, live streaming has become increasingly popular over the past few years (Jacob, 2022). Malaysia is one of the leading countries in Southeast Asia in terms of the emergence and growth of the use of live-streaming technology (Chan et al., 2022).
In comparison to the traditional model of online shopping, which only produces pictures and text for customers to view, live streaming has revolutionized the shopping experience . Through this platform, streamers have the ability to show products being used in real-time videos . Consequently, customers are able to gain more comprehensive product information. In traditional social commerce, customers who have questions about products must go away from the product page in order to get in touch with the seller (Zhang & Benyoucef, 2016). Meanwhile, live-streaming shopping makes it possible for shoppers to communicate through a bullet screen and receive answers immediately with no delays . Furthermore, conventional social commerce does not often allow vendors to offer advice to customers in regard to products; this lack of physical interaction can lead customers to feel uncertain about the legitimacy of suppliers and thus make them more hesitant when it comes to online shopping (Sarker et al., 2019). Livestreaming shopping is an ideal way to address this issue. Through the chat feature, clients can pose queries and vendors can reply in real-time, providing tailored services and advice that may positively sway customer purchasing decisions (Gilbert, 2019).
To date, there has been a lack of research into live streaming as it is an emerging trend. Furthermore, despite the rising popularity of live-streaming shopping, few studies have explored how it affects customers' purchase intentions. While it has been established that live-streaming shopping fosters customer engagement (Wang & Wu, 2019;Yu & Zheng, 2021), it is still unclear whether increased engagement will lead to positive customer purchase intention in social commerce.
This paper investigates the relationship between live streaming and purchase intentions in social commerce in Malaysia. The Theory of Planned Behavior (TPB) has been used to explore the issue and develop a research model. The research model can provide an improved understanding of the impact of live streaming on purchase intentions in social commerce. The goal is to inform online vendors on e-commerce websites and other digital marketing platforms on the significance of live streaming as a tool to increase customer purchase intentions. Ultimately, by understanding the impact of live streaming on purchase intention, digital marketers and online retailers will be better able to optimize their strategies to drive sales.
The structure of this research paper is as follows: first, literature relevant to the research problem is discussed; followed by an overview of the theoretical framework and research model; next, a description of methodology and data sources; followed by a discussion of the results; and finally, summary and conclusions.

Literature Review Live Streaming
Live streaming is a type of video streaming technology that allows users to broadcast live content in real-time (Chen & Lin, 2018). It has become increasingly popular due to its ability to provide interactive experiences for viewers (Ham & Lee, 2020). With live streaming, users can watch events as they are happening, allowing them to engage with the presenters and ask questions or interact with others in the room (Guo et al., 2022). Popular use cases include webinars, virtual events, conferences, and more. Livestreaming offers a unique way to engage with an audience and can provide valuable insights into customer behavior, allowing businesses to better understand their customers' needs and preferences (Ho et al., 2022). With this information, companies can create more engaging experiences for their customers and optimize their products and services accordingly. As the demand for more interactive experiences continues to rise, live-streaming will only become more popular and businesses should look into ways to leverage this technology to better engage with their customers.
Live streaming is also becoming increasingly popular on social media platforms such as TikTok, YouTube, Facebook, and Instagram. By live-streaming directly to a platform's users, businesses can create more engaging experiences for their followers and provide real-time updates about their products and services (Thorburn, 2014). Additionally, by leveraging the power of social media influencers, businesses can reach a larger audience and increase brand awareness (Qiu et al., 2021). Livestreaming on social media is quickly becoming an essential strategy for modern marketing, as it allows businesses to interact directly with their customers in real time and create more personalized experiences (Woodcock & Johnson, 2019).
Live streaming shopping is an emerging social media platform with a high level of human-computer interaction (Lu & Chen, 2021). Prior studies on live streaming have mainly focused on video games and e-sports (Li et al., 2020). Nevertheless, newer studies are providing a new understanding of live-streaming shopping. Certain researchers have employed the perspectives of intrinsic and extrinsic motivation to investigate which factors can influence streamers' decision to broadcast (Li et al., 2021). Other scholars have studied the impact of design features (Xiao et al., 2022) and customers' perceptions (Ma, 2021) on their live-streaming use intention. Although there have been studies examining the customer purchase decision-making process (Park & Lin, 2020), these studies have not taken into account how live-streaming shopping has changed the way customers view products and shop.
Additionally, research on the impact of live-streaming shopping on customer buying behavior is scarce. Therefore, further research is needed to explore the influence of livestreaming shopping on customer purchase intentions. Understanding more about this issue could then provide insight into how companies can better employ and utilize the technology to their advantage. The effects of live streaming on customer purchase intention have yet to be thoroughly examined. Research has indicated that streaming broadcasting techniques have the potential to generate interactions between customers and sellers ; however, it is not clear whether this effect can be used to affect consumer buying habits. Few studies are exploring the impact of Facebook Live on customer purchase intention (Darlin Clement Addo et al., 2021) suggesting that more research is needed in this area.

Theory of Planned Behavior
The Theory of Planned Behaviour (TPB) builds on the earlier Theory of Reasoned Action (TRA), which suggested that people's beliefs and perceptions influence their behaviour (Icek Ajzen, 1991). TPB is an updated version, providing a more detailed explanation for voluntary behavior. It proposes that attitude and subjective norms affect one's intention and subsequent actions. This theory is widely used in social psychology to predict how people will act (Icek Ajzen, 2011).
The Theory of Planned Behavior (TPB) has been widely used in marketing research to understand customer behavior and purchase decisions. According to the TPB, customer purchasing intention is influenced by three components: attitude toward buying, subjective norms, and perceived behavioral control. Attitude towards buying refers to a customer's evaluation of a product, subjective norms refer to the influence of others on customer buying behavior, and perceived behavioral control refers to the customers' belief in their ability to buy the product. The TPB suggests that customers' attitude toward buying is influenced by their beliefs about the consequences of buying (positive or negative) and their evaluations of those consequences. Subjective norms are based on the customers' perceptions of whether people who are important to them would approve or disapprove of their buying, while perceived behavioral control is based on a customer's belief that they have the skills and resources necessary to buy the product.
The TPB has been used in a variety of different contexts, including the investigation of customer purchase intention in live streaming. Xu et al (2022) developed a model based on the theory of planned behavior (TPB) to understand and predict viewers' virtual gifting behaviors in online live streaming. The model was tested with data from 392 viewers who had prior experience in virtual gifting. Results showed that subjective norms both offline and online influenced attitudes towards virtual gifting and intention to engage in it. The model that was proposed predicted virtual gifting behavior well. However, more research is needed to explore how this model fits the online interaction context and explains if the same results can be replicated in the context of purchase intentions.  investigated determinants that influence Chinese flower and seedling family farms' adoption intention of live-streaming services in Shuyang, China. The results found that subjective norms and attitudes had a positive effect on their behavior. However, the context of the study was completely different because agricultural products have entirely different product lifecycles and buying behavior. Similarly,  applied TPB to investigate the influence of factors such as customer perception, marketing mix, content marketing, and influencer on consumers' behavioral intentions towards livestreaming services. The findings showed that these factors had a positive impact on perceived value and attitudes which in turn affected their behavior. However, more research needs to be done to explore the effects of live streaming on purchase intentions. Thus, the TPB can be applied in this study to investigate the effect of live streaming on the purchase intentions of customers in Malaysia.

Hypotheses Development
Customers' attitude toward a product is driven by their evaluation of the consequences of buying it (Ahn & Back, 2018). Thus, customers who have a positive attitude toward livestreaming shopping will be more likely to purchase products offered through this platform than those with a negative attitude. Live streaming shopping is a relatively new concept in Malaysia, and customers' attitudes towards it are likely to be shaped by their perceptions of its benefits. Therefore, customers who perceive live streaming as a convenient and engaging way of purchasing products will have higher purchase intentions than those who perceive it negatively.

H1: Customer's attitude is positively associated with purchase intention.
Subjective norms refer to the influence of social pressure on customers' decisions to buy a product (Othman & Rahman, 2014). Customers are more likely to purchase products that are seen as socially acceptable by those who matter to them (Noor et al., 2020). Therefore, customers who perceive live-streaming shopping to be socially accepted or approved of by their family and friends will have higher purchase intentions than those with an unfavorable perception. Live-streaming shopping is convenient and efficient, allowing customers to shop from the comfort of their own homes . It also allows customers to engage with influencers who can provide advice and product recommendations through live sessions (Addo et al., 2021). Thus, customers who perceive these benefits as socially acceptable will be more likely to purchase products via live streaming than those with an unfavorable subjective norm.

H2: Subjective norms is positively associated with purchase intention.
Perceived behavioral control refers to a customer's belief in their ability to buy the product (Icek Ajzen, 1985). Customers who believe that they have the skills and resources necessary to purchase products through live streaming will be more likely to do so than those with low levels of perceived behavioral control. Live streaming shopping requires customers to be familiar with the platform as well as the products and services being offered (Li et al., 2021). Therefore, customers who can navigate the live-streaming platform easily and have access to the necessary tools and technology will have higher purchase intentions than those with low levels of perceived behavioral control. Furthermore, customers who believe that they can effectively monitor their purchases through live streaming will be more likely to make purchases through this platform than those with low levels of perceived behavioral control.

H3:
Perceived behavioural control is positively associated with purchase intention. Figure 1 illustrated the relationship among the constructs of this study.

Figure 1 Conceptual Model
Methodology Data was collected using a self-administered survey method. In order to reach the target respondents, a purposive sampling technique was adopted. Two qualifying criterions were used to select the respondents, in which they must be more than 18 years old and have watched live streaming business session. 220 respondents answered the questionnaire but only 200 are valid for data analysis as the other respondents have never watched livestreaming activities.
The survey questionnaire was developed using items used by other authors. The items for attitude and subjective norms were adopted from Lee and Lee (2011), while the items for perceived behavioral control were adapted from (Yan, 2022). On the other hand, the items for intention were adopted from (Ajzen, 1988). Seven-point Likert scale was used to measure the independent and dependent variables, while dichotomous questions were included to capture demographic information of the respondents such as age, gender, education and occupation. The questionnaire was prepared in dual language which is Malay and English.
The data analysis was conducted using SPSS software version 24. To test the hypotheses, multiple regression analysis was used. In the case of this study which more than one independent variables which are attitude, subjective norms, and perceived behavioral control were tested to predict intention, a multiple regression analysis is considered suitable (Hair et al., 2013).
To test the internal consistency of items (Sekaran & Bougie, 2009) and to check whether items in the same dimension are measuring the same underlying construct (Pallant, 2005), a reliability analysis was performed. The result is presented in Table 1. Since Cronbach's Alpha of all the constructs is more than 0.70, the reliability is considered good. Then, a multiple regression analysis was performed to examine the relationship between the variables. The analysis showed that the R 2 for the model is 0.441, indicating that the independent variables tested in this study explained 44.1% of the dependent variable. The ANOVA result shows that the model is statistically significant with an F value of 53.418. Overall, from the three hypotheses proposed, two hypotheses are significant. The result shows that subjective norms and perceived behavioral control are significant in influencing consumers' intention to purchase via live-streaming. Subjective norms show the largest positive significant value (β = 0.559 p = 0.000), followed by perceived behavioral control (β = 0.213, p = 0.014). Thus, hypotheses 2, and 3 are supported.

Conclusion
The result of this study shows that the attitude of consumers towards live-streaming does not significantly influence their intention to buy products through live-streaming. However, subjective norms and perceived behavioral control are significant in influencing consumers' intention to purchase via live-streaming. The findings may be due to several factors including the collectivism culture of Malaysian which make them easily influenced by family and friends and the majority of respondents are young at the age 18-29 which usually are IT-savvy and more open towards new technology. Specifically, this study offers several theoretical and practical contributions. Theoretically, the study extends the applicability of TPB in the context of emerging shopping platform which is live streaming. Results of the study support the applicability of TPB construct specifically subjective norms and perceived behavioural control in explaining purchase intention in the live streaming setting. This reinforces the relevance of of these constructs within the TPB framework.
Practically, the study offers insights into the factors which could motivate customers to engage in live-streaming buying. It is suggested that marketers should focus on the importance of social influences and incorporate strategies that enhance positive subjective norms to drive purchase intention. On the other hand, the significant influence of perceived behavioural control shows the needs to to provide user-friendly and seemless shopping experience, reducing barriers and enhancing customers confidence towards the platform in order to increase purchase intention.
The limitations of this study include the use of a small sample size, which may limit the generalizability of the results. Additionally, since the data was collected from only one country, Malaysia, it is possible that other countries may have different results. Furthermore, this study did not consider multiple factors such as trust and personal values towards live streaming which may be influential in determining consumers' intention to purchase via livestreaming. Thus, it is recommended that future studies should include additional independent factors such as trust and personal values towards live streaming in order to gain a more comprehensive understanding of the phenomenon.