Facebook Live-Streaming: How It Affect the Purchase Intention of Generation Y in Malaysia?

to create their profiles and post freely. In 2016, Facebook launched a new livestreaming feature (Johnson et al., 2018). The ease of use and easy access have made Facebook Live streaming one of the most popular social media tools businesses use to market their products to Generation Y consumers (Lu et al., 2020). Using this live-streaming feature, sellers can use actual video to showcase products and communicate with potential buyers. Consumers can share their views and post comments in real-time (Sun et al., 2019). Livestream viewers can interact and exchange information in contrast to marketers’ suggestions and feedback (Li & Ku, 2018). In the USA, consumer purchases driven by social media totalled over $22 billion in 2019 and is projected to rise to over $84 billion in 2024 (Phaneuf, 2021). Facebook Live is different from other forms of e-commerce as viewers’ experiences play an essential role (Cheng et al., 2019). Customers can now obtain more information when the presenter demonstrates the product during the Facebook Live streams. Research Problem The idea of livestreaming is new and progressing yet has received less attention in academia (Hou et al., 2020; Wang & Li, 2020), mainly on streaming broadcasting techniques. According to Yu et al. (2018), limited studies have investigated the influence of livestreaming on customer purchase intention. While livestreaming is found to have the ability to build customer interactions (Wongkitrungrueng & Assarut, 2018), it remains unclear whether it can be used to affect customer buying intentions (Sun et al., 2019). Furthermore, livestreaming has not been studied extensively (Sun et al., 2019), justifying the limited studies on how Facebook Live affects consumer purchase intention (Clement Addo et al., 2021; Zhang et al., 2020). According to Muda et al. (2016), Generation Y users process data from e-commerce websites five times faster than the older generations. Moreover, Generation Y regularly browses Facebook creating an opportunity for businesses to use Facebook Live streaming to communicate with them. Due to the increase in businesses that use Facebook Live streaming to sell their products, marketers must understand consumer behaviour, particularly on Facebook Live. Understanding the consumers’ behaviour in Facebook Live will enable the marketers to develop an effective strategy to influence their purchase intention.

Studies carried out by Zhu and Chen (2015) indicated that companies struggle to understand the social media marketing factors that drive consumers’ demands and needs. Previous studies have been heavily influenced by the Technology Acceptance Model (TAM) (Davis, 1986), ignoring other theories. This limitation constitutes the gap in understanding how different aspects of social media marketing can impact consumer purchase intentions and in what way. By identifying this gap, the factors affecting consumer purchase intention in Facebook Live streaming can be investigated. This study aimed to identify the influence of attitude towards broadcasters, interactions, peer influence, perceived risk and product quality towards the consumer purchase intentions on Facebook Live streaming. Research Scope The study focused on how Facebook Live streaming can influence online purchase intention of Generation Y consumers via a Google Forms survey on Facebook. LITERATURE REVIEW Social Media and Facebook Social media platforms such as Facebook, YouTube, Instagram, and TikTok are virtual communities or networks hosted on websites that connect millions of individuals worldwide (Sin et al., 2012). Social media users can communicate beyond geographic, political, and economic boundaries using social platforms available in several languages. Over two billion people use social media platforms worldwide and as mobile apps and social media platforms become increasingly popular, these numbers are only expected to rise. The number of active users on these social media sites will affect the size of the social media network. Although the principal value of these networks is linked to how often participants engage and interact with one another, for marketers, social media platforms enable the consumers to get to know the products by subscribing to their platform and allowing word-of-mouth transmission of information (Iblasi et al., 2016). According to Wang et al. (2020), social media are online social networking sites for individuals to interact and communicate with others, attracting billions of people. A growing body of literature indicates that social media helps entrepreneurs gain insight into clients or prospects, organise capital to promote their projects, and manage customer relationships after launching the company. Among Malaysian social media users, Facebook was the most popular platform (97.3%), followed by Instagram (56.1%) (MCMC Internet Survey, 2017). Additionally, 58% of social media users in Malaysia purchased products after watching branded social videos, whereas 88% of Malaysian internet users have visited the online retail stores on the web after visiting the store’s social media page (Kemp, 2019). Nevertheless, despite consumer’s high social media usage rates, businesses have been slow in adopting social media usage in Malaysia. In 2017, only 55% of companies in the Malaysian services sector were using social media. In contrast, other sectors such as manufacturing, agriculture, and construction had lower numbers of 33.5%, 29.7% and 19.5%, respectively (The Statista, 2020). Facebook Live Facebook Live has changed the traditional business model in different ways. First, in e-commence, consumers can only learn about sales items through pictures and text. Conversely, Facebook Live shopping enables a broadcaster or online seller to present their products in realtime videos. Furthermore, it can provide more product information and enable customers to consult with the broadcaster in the comment section (Wongkitrungrueng & Assarut, 2018). Facebook Live enables the customers to consult the seller when there are any problems, and the broadcaster can respond to customers’ queries and offer practical and customised customer support. In addition, previous studies have postulated that communication done using Facebook Live can significantly influence consumer purchasing behaviour (Chen et al., 2017; Kim & Park, 2013; Zhou et al., 2018). Moreover, customers can use the Facebook Live stream to ask questions on the spot and obtain immediate responses from the broadcaster, enhancing the relationship between customers and the company. Conversely, Facebook Live streams can aid new start-ups or home-based entrepreneurs to minimise the cost of advertising their products.

Theory of Planned Behaviour (TPB)

The Theory of Planned Behaviour (TPB) explained that attitude and subjective norms would impact intention, influencing behaviour. TPB is the continuation of the Theory of Rational Action (TRA) (Azjen, 1980) due to the manifest limitation of the former theory of voluntary behaviour. TRA argued that a person’s positive mindset is the behavioural intent and the thoughts of a person. Unlike the TRA, the TPB model offers a simplified description of the behavioural theory where an individual is expected to perform specific actions if that person regulates the activities. The TPB is the most crucial theory in social psychology for predicting human behaviour. The TPB indicated that attitudes, subjective norms, and perceived behavioural control influence the elements that affect consumer intentions, leading to influencing consumer buying behaviour (Yee & Mansori, 2016). Peripheral persuasion, which is a part of predicting human behaviour, is derived from the refined probability model is applied to the system. In this analysis, these predictive variables are considered the driving force for encouraging consumers to plan and make purchases. Therefore, understanding the motivations, behavioural patterns, subjective norms, perceived behavioural impact, and peripheral persuasion can help marketers better understand various customers’ behavioural aspects. Marketers can then create attractive marketing strategies that can inspire customers to purchase goods. Purchase Intention Purchase Intention is defined as the possibility to plan or request future purchases of a particular product or service (Cao et al., 2020). Additionally, consumers’ purchase intention reflects their aim or feeling to purchase the product or service. Consumers’ buying decisions may be affected when they read comments posted on Social Network Sites (SNSs). Consumers can click the “like” button, answer or share posts after watching videos posted on the SNSs. Studies believe that looking for and seeing content with higher volumes of resonance activity may lead to a greater intention to buy the product (Shang et al., 2017). Previous studies suggest that external and internal influences impact online purchase intention based on the consumer behaviour theory.

The internal factors that influence online purchase intention are gender, age, education, and economic status, whereas external factors include network infrastructure, reliability of trading, prices, and product quality. Previous research has examined the driving forces of intention to buy online. Study postulated that customers prefer to buy online when the online store offers better features, such as item catalogue, search machine, smart price comparison mediums, shopping carts, online payment instruments, and tracking system. In summary, technological characteristics, shopping situations, and product qualifications are vital in influencing online purchases. For e-vendors and companies to fascinate and retain customers, they need to understand the cognate value of these elements (Chen et al., 2010). Attitude towards the Broadcaster According to Das (2014), consumer attitude is seen as an indicator of how one likes or prefers conducting the behaviour. Attitude plays a central role in consumer study since it affects emotions, feelings, and, most importantly, the mechanism of consumer decision-making. Moreover, according to Matching and Test (2001), attitudes help consumers understand whether they are purchasing or not purchasing a specific product. Customers measure the efficacy of advertising campaigns to test advertising efforts before they are introduced to the market. Brand beliefs, evaluations, and purchasing intentions explain the three components of attitudes. According to Al-shaaban and Nguyen (2014), purchasing a product depends on the consumers’ attitude and personal desirability. The attitude towards a particular behaviour is based on perceptions and assumptions about potential consequences. This study investigated the attitude of viewers towards the Facebook Live broadcaster. Previous studies found that non-traditional celebrities and their association with products lead to a higher purchasing intention of the goods they endorse, as customers identify and want to copy them (Tran & Strutton, 2014). Nowadays, groups of young social media celebrities and broadcasters are generating demand for their brands while participating in semi-promotional activities as the products associated with these influencers are authentic and relatable. According to Hu and Chaudhry (2020), consumers will be more attached to the source of the service and more likely to use the service or products if they have a positive attitude towards the broadcaster, who is the salesperson on livestreams. Therefore, it is pertinent to study the consumers’ attitudes towards the broadcaster compared to the brand as it plays a pivotal role in interacting with the consumers (Hu & Chaudhry, 2020; Satchapappichit, 2013). Therefore, a hypothesis was postulated: H1: There is a significant relationship between attitude towards the Facebook broadcaster in Facebook Live streams and purchase intention. Interactivity Interactivity is a vital aspect of online business. It is defined as the degree to which consumers engage in social shopping activities, produce and share knowledge to gain acceptance within the online community (Steure, 1993). Interactivity involves the degree of salience in the engagement and interpersonal relationships between the seller and consumer are heightened by live broadcasting. This is beyond what is seen in traditional e-commerce and s-commerce transactions in which the seller’s personal information is barely or not visible. The increased level of social interaction and social presence generated by synchronised two-way contact between buyers and sellers and the display of other viewer comments will increase customers’ trust and reduce confusion. This will convince the consumer that the online seller is genuine, sociable, and understands their needs, convincing them to purchase the product (Wongkitrungrueng & Assarut, 2020). According to Tajvidi et al. (2017), interactivity is built via social action and feedback obtained during the forum or group sharing and suggestions sessions. These roles differentiate social commerce compared to other types of e-commerce. Furthermore, Tong (2017) stated that the prevalent feature in livestreaming is the interactivity between customers and sellers. Livestreaming customers will be able to vividly experience the broadcasters’ temperance, mental aura and body language while communicating directly with the broadcasters. The real-time interaction provides an urgent atmosphere giving customers a true sense of immediacy to obtain the products. This kind of urgency will directly enhance the customer purchase intention. Therefore, this study hypothesised that: H2: There is a significant relationship between the interactivity of Facebook Live streams and purchase intention.

Peer Influence

Based on the study carried out by Khan et al. (2016), the influence of peers is defined as the degree to which peers affect the attitudes, beliefs and behaviour of a person. It refers to the pressure imposed on an individual by a group of people of the same age group, sharing common interests, and belonging to the same social-cultural category, causing them to change their actions, attitudes, and values to comply with the group’s norms. Peer pressure arises when those involved in a popular activity do not support anyone outside the groups ’unique conduct and deliberately try to persuade the “outsider” to behave similarly. Peer pressure is often reflected as bullying, such as bullying a team member to purchase a smartphone to use the latest features (Zhou et al., 2019). According to Zhang et al. (2018), the influence of peers is influenced by the surrounding factors. The pinnacle of peer influence happens at fourteen years old during which the surrounding affects their life’s actions. Given the impact of peers on the intention to purchase, Yin et al. (2019) found that the degree of peer relationship between users has a significant effect on the influence. The closer the relationship between community members, the deeper the exchanges of information, resulting in greater peer influence. Hence, this study hypothesised that: H3: There is a significant relationship between peer influence on Facebook Live streams and purchase intention. Perceived Risk Perceived risk can be defined as the existence and amount of risk a customer perceives when evaluating a specific e-commerce purchasing decision. Customer have perceived risk because they cannot always be confident when purchasing (Cox & Rich, 1964). According to Cox and Rich (1964), perceived risks exist in electronic commerce. In marketing literature, perceived risk has been widely described. Seven types of risk are indicated: financial, performance (product), physical, psychological, social, time, and cost of opportunity. Two main types of risk dominate traditional shopping: financial and product

(Kaplan et al., 1974). However, in e-commerce, the information-based risk is associated with online conversations with online retailers such as recommendations, privacy and security. This research summarises perceived risk in online stores as a single construct with the four attributes (financial, product, physical, and security) where privacy is part of security. Previous research shows that consumers experienced a higher degree of risk when purchasing from a non-store outlet. In the online shopping environment, the internet shopper experiences the fear of failure when performing a particular online purchase. Consumers experience higher risks in online shopping than in traditional shops because they face more difficulty achieving their shopping goals in online settings (Chang & Tseng, 2013). Furthermore, studies carried out by Yin et al. (2019) noted that private security is a concern for online customers. Customers are concerned that their personal information such as contact information, bank card numbers, and other financial information will be misused or sold illegally. The increase in perceived risk would also affect the willingness of customers to purchase online. Hence, this study hypothesised that: H4: There is a significant relationship between perceived risk on Facebook Live stream and purchase intention. Product Quality Product quality is the ability of the product to perform its purpose (Foster and Johansyah, 2019). According to Taiwo et al. (2017), product quality is a technical component based on a product’s technical requirements. Correspondingly, Malaysian consumers commonly recognise product quality as a significant factor in purchasing intent, and they associate popular items with good quality (Hafifi, 2016). Previous studies carried out by Mohd et al. (2013) indicate that the quality of the product was determined by three characteristics: features, benefits, and image. In making judgement and selecting preferences, all three attributes were found to have influenced consumers’ behaviour. A feature of a product indicates a denotation of the characteristic attributes that describe a product or service. Recently, purchasing intention has become complex, and product quality plays a vital role in influencing purchase behaviour (Alam, 2020; Sandhu & Bala, 2011). If a product meets the standards, the customer will be satisfied and assume that the product is reasonable or even high quality. Conversely, the product is perceived as poor quality if the customer’s requirements are not met, implying that a product’s quality determines the product’s ability to satisfy the consumer’s needs and expectations (Ariffin et al., 2016). The findings from Kotler and Keller (2012) demonstrated that product attributes such as product quality, and price positively impacted purchasing intention. According to Aaker (1991), customers’ choices are influenced by their perception of quality, which stimulates purchase decisions, brand distinctiveness, brand extensions development and establishes the price. Hence, this study hypothesised that: H5: There is a significant relationship between product quality on Facebook Live streams and purchase intention. METHODOLOGY The TPB (Azjen, 1980) was referred to when forming the research model of this study. This study added the uniqueness of the Facebook Live streaming environment, product quality (Foster & Johansyah, 2019), and interactivity (Tong, 2017) to the existing TPB model. The independent variables of this study are attitude towards the Facebook Live streaming broadcasters, interactivity, peer influence, perceived risk, and product quality, whereas the dependent variable is purchase intention. Five hypotheses were postulated in this study: H1: There is a significant relationship between attitude towards the broadcaster in Facebook Live streams and purchase intention. H2: There is a significant relationship between interactivity in Facebook Live streams and purchase intention. H3: There is a significant relationship between peer influence in Facebook Live streams and purchase intention. H4: There is a significant relationship between perceived risk in Facebook Live streams and purchase intention. H5: There is a significant relationship between product quality in Facebook Live streams and purchase intention. Questionnaire Design and Measurement Scale The questionnaire was separated into two parts (Part A and Part B). Part A consists of the demographic information of the respondents such as gender, age, ethnicity, education background, occupation and monthly income.Part B is related to the dependent variable (purchase intention) and the independent variables (attitude towards the broadcaster, peer influence, interactivity, perceived risk, and product quality). An online questionnaire with a 5-point Likert scale adapted from previous studies and validated by the panels with experiences in online studies was used. The questionnaire consists of three items from Huang et al. (2010) for purchase intention and ten items from Moon et al. (2017) for attitude towards the broadcaster. Additionally, three items from Moshrefjavadi et al. (2012) were adapted to measure peer influence. Five items from Jeong and Hyun (2019) were adapted to measure interactivity, three items from Kim et al. (2008) were used to measure perceived risk, and five items from Zhao et al. (2021) was adapted to measure product quality. Sampling Plan This study focused on the influence of Facebook Live in influencing the purchase intention of Generation Y consumers. According to Yadav and Pathak (2017), Generation Y, also known as Millennials, is the first high-tech generation born from 1980 to 1999. In Malaysia, about 40% of the population belongs to Generation Y. Generation Y was targeted because they are familiar with the internet (Yin et al., 2020) and use social media frequently. As suggested by G-power, the research sample size for this study should have a minimum of 138 respondents, and all the participants must have a Facebook app on their smartphones, have previously watched Facebook Live streams. The questionnaires were randomly sent to the users’ accounts on Facebook, and the participants were encouraged to share the questionnaire with others. Convenient sampling and snowball sampling techniques were used to collect the data. Data Collection Method This research used the “Statistical Package for the Social Sciences” (SPSS) to analyse the collected data to investigate the Frequency,

Reliability Analysis, Mean Analysis, Pearson correlation, and Multiple Regression Analysis. Data Analysis Method The data were analysed using Statistical Package for the Social Sciences (SPSS). In addition, SPSS was also used to conduct several analyses such as Frequency Analysis, Reliability Analysis, Mean Analysis, Pearson correlation, and Multiple Regression Analysis.

RESULTS

A total of 200 online questionnaire data was collected through Facebook. The respondents consisted of 52% male and 48% females from Generation Y. All the users have Facebook accounts and have experience watching Facebook Live streams. Among the respondents, 48.5% watched Facebook Live streams at least 1 hour per week, 37% watched Facebook Live streams at least 2 hours per week, and 14.5% watched Facebook Live streams at least 3 hours per week. Reliability Analysis The Reliability Analysis method measures various widely used reliability measurements and provides data on the relationships between individual items on the scale. A reliability test value below 0.61 is considered unacceptable (Tavakol &Dennick, 2011). Based on the analysis done, peer influence obtained the highest Cronbach’s Alpha value at 0.985, followed by perceived risk (0.909), attitude (0.905), purchase intention (0.866), interactivity is 0.787, and product quality (0746). All variables had acceptable reliability. Pearson Correlation Pearson’s correlation was used to analyse the relationship between purchase intention and consumer attitude. The Pearson’s correlation value were categorized into three groups: 1(very strong positive relationship), -1 (very strong negative relationship), and 0 (no relationship at all). Table 1 shows the Pearson Correlations for all the variables in the study.

Based on the results, the significant value of attitude and purchase intention was 0.00, and the Pearson correlation value was 0.325, indicating a weak association between attitude and purchase intention. Additionally, the significant value of interactivity and purchase intention was 0.77, more than 0.05, indicating no association between interactivity and purchase intention. In addition, the significant value of peer influence was 0.000, with a Pearson value of 0.375 indicating a weak association between peer influence and purchase intention. The significant value of product quality and purchase intention was 0.000, with a Pearson value of 0.378 indicating a weak association between product quality and purchase intention. Lastly, the significant value of perceived risk was 0.255, and the correlation value was -0.081, indicating no relationship between purchase intentions and perceived risk. Multiple Linear Regressions Multiple Linear Regressions (MLR) helped to establish a connection between the independent and dependent variables. The results of the Regression Analysis of the factors that influence purchase intention are shown in Table 2. Regression Analysis was used to measure the relationships between independent variables (attitude, peer influence, interactivity, perceived risk, and product quality) and the dependent variable (purchase intention). Based on the results, the R square is 0.237. Consequently, the product quality, interactivity, perceived risk, attitude, and peer influence fit the regression model by 23.7%. Moreover, the p-value for attitude was 0.006, which is less than 0.05. Hence, hypothesis H1 was accepted. The p-value for peer influence was 0.03, which is less than 0.05. Hence hypothesis H2 was accepted. Alternatively, the p-value for interactivity is 0.402, which is more than 0.05, indicating that there was no relationship between interaction and purchase intention. Hence, hypothesis H3 was rejected. In addition, the p-value for perceived risk was 0.620, which is more than 0.05, indicating no relationship between perceived risk and purchase intention. Therefore, hypothesis H4 was rejected. Lastly, the p-value for product quality was 0.01, which is less than 0.05. Hence, hypothesis H5 was accepted. Based on the B value, the most significant influential variable was product quality (0.463), followed by attitude towards the broadcaster (0.276) and peer influence (0.209).

Accepted

a. Dependent Variable: PurMean b. R= 0.487, R Square= 0.237, sig= 0.000

Regression Analysis was used to measure the relationships between independent variables (attitude, peer influence, interactivity, perceived risk, and product quality) and the dependent variable (purchase intention). Based on the results, the R square is 0.237. Consequently, the product quality, interactivity, perceived risk, attitude, and peer influence fit the regression model by 23.7%. Moreover, the p-value for attitude was 0.006, which is less than 0.05. Hence, hypothesis H1 was accepted. The p-value for peer influence was 0.03, which is less than 0.05. Hence hypothesis H2 was accepted. Alternatively, the p-value for interactivity is 0.402, which is more than 0.05, indicating that there was no relationship between interaction and purchase intention. Hence, hypothesis H3 was rejected. In addition, the p-value for perceived risk was 0.620, which is more than 0.05, indicating no relationship between perceived risk and purchase intention. Therefore, hypothesis H4 was rejected. Lastly, the p-value for product quality was 0.01, which is less than 0.05. Hence, hypothesis H5 was accepted. Based on the B value, the most significant influential variable was product quality (0.463), followed by attitude towards the broadcaster (0.276) and peer influence (0.209). DISCUSSION AND CONCLUSION Discussion The study aimed to identify the factors affecting consumer purchase intention via Facebook Live streaming. Several studies have found a positive relationship between consumer attitude towards the broadcaster and buying intentions. According to Jennings and Seaman (1990), customers who emotionally judge online shopping behaviour are more likely to shape their buying intentions. Moreover, studies carried out by Qian and Li (2020) indicate that e-commerce broadcasters have more business expertise and have better market involvement than the viewers. Consequently, e-commerce broadcasters are often perceived to have more experience and integrity when introducing the products thus influencing the consumers’ purchase intention. Consumers would be more attached to the source of the service and more likely to use the service or purchase goods if they have a positive attitude toward the broadcaster (Hu & Chaudhry, 2020). This study revealed that there is no relationship between interactivity and purchase intention. This is supported by Patanasiri and Krairit (2019), indicating no significant effect between interactivity and consumers’ purchase intention. This implies that the streaming platform providing its customers with two-way contact tools will not affect users’ purchase intention. The interactivity between the broadcaster and the viewer serves as a good platform for the viewers to get needed information, clear purchasing doubts, and create curiosity towards the products (Patanasiri & Krairit, 2019). Based on the results, there is a significant relationship between peer influence and purchase intention. The results postulate that peer guidelines are practical because people tend to behave the same way as their peers to maintain the subjective standard. These findings are consistent with the findings from the previous research (Mohd Suki &Mohd Suki, 2019).

The study indicated no significant relationship between perceived risk and purchase intention. This study result is similar to Chen (2012), which showed no clear link between perceived risk and purchase intention. Due to the increase in popularity of online shopping, service providers have taken steps to reduce consumers’ risk perceptions by providing multi-channel payment options. As a result, more consumers are aware of the risk possibilities associated with Facebook Live shopping (Gan & Wang, 2017). Lastly, there is a significant relationship between product quality and purchase intention. This is supported by studies carried out by Usman and Permatasari (2020). In addition, according to Mirabi (2005), product quality is the most influential factor influencing consumers’ purchasing intention. Knowledge Implication There are insufficient data and information about Facebook Live streams in the previous studies. In addition, the rapid growth of livestream shopping has influenced the purchase intention of customers. Hence, this research provided valuable insight into the factors affecting customers’ purchase intention in Facebook Live streams. Managerial Implications Facebook Live has become a popular platform for businesses to promote and sell their products, while customers view livestreaming as an additional platform to purchase their products. This study gives businesses insight into better understanding the factors affecting consumers’ purchase intention on Facebook Live streaming. The study showed that product quality is the most influential factor affecting consumers’ purchase intention. Marketers should produce more quality and innovative products that suit the consumers and broadcasters should emphasise product quality on livestreams. Based on the study, consumers’ attitude towards the broadcaster is the second most influential factor. Therefore, the company must select an excellent broadcaster to introduce, demonstrate, and communicate with the consumers. This would positively affect the customers’ attitude towards the broadcaster when they are watching Facebook Live streams. Recently, Facebook Live streams have become a routine activity for some consumers. Therefore, broadcasters must create a delightful atmosphere to influence consumers positively.

This study would assist marketers to understand better how product quality, attitude towards the broadcaster and peer influence will affect consumers’ purchase intention. Furthermore, marketers can obtain insights into designing effective marketing strategies to increase consumer purchase intention when using Facebook Live streaming to sell their products. Limitation and Suggestions for Future Research There were several limitations in this study. Firstly, the study is limited to Generation Y respondents. Future studies should investigate different ethnicities and age groups such as Generation X, Baby Boomers, and Generation Z and compare the factors affecting consumers’ purchase intention on Facebook Live streams between the different age groups and ethnicities to expand the discussion range. In addition, this study only collected a small sample size of 200 respondents. Future studies can expand the study to include a more comprehensive sample size. The current study focuses only on five research variables. Future studies should include other factors such as product price, promotion, and perceived value to better comprehend consumers’ purchase intention. ACKNOWLEDGMENT This research received no specific grant from any funding agency in the public, commercial, or non-profit sectors. This paper was presented at the Conference on Managing Digital Industry, Technology, and Entrepreneurship (CoMDITE) on the 7th and 8th April 2021.