Factors Influencing Purchase Intention on Cake Product: Facebook Commerce Perspective

Factors Influencing Purchase Intention on Cake Product:  Facebook Commerce Perspective

Nadiah Nabilah Ibrahim¹ and  Faradewi Bee Binti A. Rahman^*2

¹Faculty of Hospitality and Tourism, City University Malaysia,

²Faculty of Hotel and Tourism Management, UiTM

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.814MG0051

Received: 04 December 2024; Accepted: 13 December 2024; Published: 14 January 2025

ABSTRACT

This research examines the variables affecting consumers’ intention to purchase cakes through the Facebook commerce platform. The study aims to identify factors that influence online consumer behavior in the cake market, focusing on Facebook as a primary e-commerce platform. By analyzing these factors, the research seeks to provide insights into the role of social media in shaping consumer purchasing decisions. Data were collected from 331 respondents through an online survey, providing a diverse and representative sample. The data were analyzed using descriptive analysis to identify trends, factor analysis to uncover underlying dimensions of the variables, and coefficient correlation to assess relationships between these factors and purchase intentions. This methodological approach ensures a comprehensive analysis of consumer behavior in Facebook commerce. The study indicates that performance expectancy, price value, habit, and trust positively influence consumers’ online purchase intentions. These factors enhance consumers’ confidence and willingness to buy cakes through Facebook. However, effort expectancy, facilitating conditions, social influence, and hedonic motivation do not significantly affect purchase intentions in this context, suggesting these factors may be less relevant for cake products on Facebook commerce platforms. These findings contribute to the body of knowledge on consumer behavior in social commerce, especially in Facebook-based e-commerce. They offer valuable theoretical insights into the factors influencing purchasing decisions and provide practical implications for marketers looking to optimize their online presence. By focusing on performance expectancy, price value, habit, and trust, businesses can tailor strategies to meet consumer expectations and increase purchase intentions. This study is one of the few that examines factors influencing consumer purchase intentions towards cake products through Facebook commerce. Its findings contribute to both practical and theoretical knowledge, providing unique insights into social media, consumer behavior, and e-commerce in the cake industry.

Keywords: Social Commerce, Facebook Commerce, Trust, Purchase Intention, UTAUT2.

INTRODUCTION

In contemporary times, a multitude of technologies have arisen and emerged. Concurrently, the internet has undergone substantial growth and transformation, leading to the development of innovative forms of communication technology. Through this technological advancement, users now can instantly exchange information with one another (Dash et al., 2021; Dwivedi et al., 2021; Helal, 2017).  Furthermore, the business landscape has undergone a significant shift owing to the rapid expansion of digital technologies, encompassing information technology (IT), mobile services, and cloud computing. Consequently, this surge has breathed new life into established enterprises, enabling them to not only endure but also adapt (Dwivedi et al., 2021; Nambisan, 2017; Mueller, 2019). Digital technology (DT) has emerged as a critical component in formulating a company’s business strategy, thereby facilitating its success and concurrently fostering entrepreneurial initiatives (Von Briel et al., 2018). Moreover, Low et al. (2020) have posited that a myriad of contemporary businesses have embraced digital marketing techniques, which inherently embody the pervasive influence of digital technology (DT).

According to Zahra et al. (2022) digital Technology (DT) can be in many forms such as digital products and services, digital platforms, digital tools, digital arts, digital entrepreneurship, or internet services. Moreover, digital technology (DT) also aids in creating and maintaining a new entrepreneurial ecosystem in which individuals with similar aims and motivation can interact with each other such as on LinkedIn (Zahra et al., 2022). Those entrepreneurial activities have resulted in significant changes such as online businesses being able to reach customers and stakeholders, reconnect with the customer, raise funds, use social media to outsource activities, and test the potential ideas (Zahra et al., 2022).

Digital marketing is a marketing activity undertaken via the internet, such as social media. This marketing method can increase sales volume while also being low-cost (Erlangga et al., 2021). By employing this method, it will be easier for potential customers to access product information, and they will be less likely to visit the store in order to do so. Nowadays, digital marketing is an efficient product or service marketing technique (Erlangga et al., 2021). One example of digital marketing is electronic commerce (e-commerce).  Electronic commerce (e-commerce) is the term that describes trading over the internet. Moreover, electronic commerce (e-commerce) involves the process of making financial transactions through the internet in exchange for products or services (Qin et al., 2014; Jackson, 2015; Vladimir, 1996). Hence, electronic commerce (e-commerce) has benefited both businesses and customers (Olatokun & Kebonye, 2010; Sadeeq et al., 2020; Wu, 2018). By using electronic commerce (e-commerce), there are lots of benefits gained by the customers such as there is no time and location restrictions to shopping, can get more information about the information, product versatility and availability, and can easily compare the price of the similar products from the different owners (Helal, 2017; Sadeeq et al., 2020).

Social media is a part of electronic commerce (e-commerce). The use of social media in the marketing of products and services is widespread. Companies and marketers utilize social media to advertise their products or services by leveraging the individuals who use it (Erlangga et.al, 2021). An online community, engagement, content sharing, accessibility, and legitimacy are five markers of social media marketing as stated by Erlangga et al. (2021). All five indicators have a role in establishing consumer trust, and they all relate to what potential customers desire. The internet and social media have grown ingrained in the lives of billions of people across the globe. This is evidenced by the latest estimate that 4.54 billion individuals use social media. Citizens’ lives have been enriched by the use of social media. Social media users are expected to reach 30.4 million in 2025 (Statista, 2021). Through social media, a company, a businessman, or a marketer may engage with potential customers, raise brand recognition, influence consumer attitudes, get feedback, and enhance products and services (Dwivedi et al., 2021).

On the consumer side, today’s potential purchasers or consumers are becoming smarter when it comes to making purchasing decisions based on what they see on the internet. Twitter, Facebook, Instagram, Spotify, Youtube, Telegram, and TikTok are examples of the internet or social media platforms that are commonly used in marketing (Erlangga et al., 2021). Based on Walsh (2022), not all businesses are suitable for all social media platforms. There are about more than 100 types of social media platforms (Santora, 2022). Despite all types of platforms, Facebook has become the most social media platform that has the most users in the world which is 2.9 billion users. Youtube places second with 2.2 billion users. WhatsApp and Instagram have 2 billion users followed by TikTok, Snapchat, Pinterest, Reddit, LinkedIn and Twitter (Walsh, 2022). According to Dwivedi et al. (2021), over 50 million businesses have Facebook profiles, and 88% of businesses utilise Twitter for marketing.

Past research found that Facebook is one of the most used social media marketing platforms among marketers worldwide. 93% of the marketers use Facebook in promoting their business and another 78% is on Instagram. The rising popularity of social media has influenced consumer purchasing behaviour (Baethge et al., 2016). GlobalStates (2022) found that Facebook has the highest users in Malaysia which is 78.95%. Twitter places second with only 6.61% users followed by Pinterest (4.17%), Instagram (4.14%), Youtube (4.01%) and Reddit (1.05%) (GlobalStates, 2022).  Today’s digital technology has made almost everything available online. Food businesses must be active on social media due to the ease of access to anything online (Sharma, 2023). Cornell University research found that chain restaurants use social media in 59% of cases and independent eateries in 79% (Sharma, 2023). Even though a sizable portion of bakeries and pastries are active on social media, many of them have trouble coming up with effective strategies to raise brand recognition. Also, social media makes it simple to find new clients. A study found that 61% of individuals learn about eateries, bakeries, pastries, and other grocery businesses via social media (Sharma, 2023).

Due to Covid-19, online consumer behaviour has changed, and firms must immediately adopt new tactics. The transmissions from Covid-19 have disseminated several topics and plotlines throughout the nation. In terms of online consumer behaviour by intention, these messages will affect consumer motivation, attitudes, and social media perception (Meja-Trejo, 2021). Nair and Vinod (2021) argued that it is critical to comprehend if social media influences consumers’ decisions about their food intake (Nair & Vinod, 2021). The food business is seen to be very important for digital marketing. Technology is advancing quickly, thus this area needs a lot of research (Bu et al., 2021). There is, however, very few research on digital marketing.

It was found that there is a gap in population since it was discovered that the Klang Valley has been the subject of prior research. The gap in previous research is concentrated across a wide area (Rahman et al., 2022). The location must be specified, and responses must be gathered from a specific place only. This is due to the possibility that the findings would differ from previous research. Moreover, there are also gap in concept since the earlier research also emphasises on different sorts of pastry and does not focus on specific social media platform (Rahman et al., 2022). Investigating customer purchasing patterns for particular bakery items, such cake products, might be an excellent opportunity.

LITERATURE REVIEW

Cake Industry

For the cake industry, the size of the worldwide cake market, which was estimated at USD 42.94 billion in 2019, is expected to increase at a CAGR of 3.3% from 2020 to 2027 (Market Search, 2020). Moreover, Birthdays, marriages, marriage anniversaries, Valentine’s Day, Christmas, the birthday of a pet, a work anniversary, Mother’s Day, and children’s day are just a few examples of the unique events that are increasingly being celebrated throughout the world in recent years. Customers are spending more money on occasion-specific cakes, which is broadening the market’s appeal (Market Search, 2020). In addition, the cake product has the largest share of 50.5% in 2019 and is expected to maintain its lead (Market Search, 2020).

Figure 1: Sales Value of bread, cake and other bakery products in Malaysia (2012 – 2019).

Sources: Hirschmann (2022)

From the figure 1, it indicates that the cake product is continuing to increase. There is a high potential for business growth primarily engaged in the cake product. In terms of company marketing, however, micro, small, and medium enterprises cannot compete with the branded cake sector. These renowned entrepreneurs fueled the majority of the cake market demand at the time. One likely explanation is that most businesses can invest heavily in their marketing campaigns (Kiumarsi et al., 2014). Cake market keeps increasing year by year and is expected to keep increasing. However, it is necessary to study the factors that contribute to the increasing cake market. It is also necessary whether social commerce contributes to the increase of the cake market.

Social Commerce

There are lots of benefits provided by social commerce to the business (Zafar et al., 2021). Social commerce is a subset of current electronic commerce (e-commerce) that makes use of social media to improve the overall business experience (Li, 2019). Social media such as Facebook, Instagram, Twitter and others have influenced the consumer to purchase products or services and force the company to do commercial and marketing activities on those platforms called social commerce (Liang & Turban, 2011; Xiang et al., 2016).

Because of its benefits, lots of companies nowadays have used social media as one of their marketing channels for micro, small and medium enterprises. They have used this platform to market their product and services (Carter, 2019; Fastenau, 2018; Yahia et al., 2018). As a result, when it comes to social commerce features, these websites may be divided into two categories. First, are electronic commerce (e-commerce) websites with social interaction features and second, as social networks with commercial features (Liao et al., 2022; Ng, 2013). Social commerce allows consumers to actively participate in marketing and selling products (Huang & Benyoucef, 2015; Shadkam & O’Hara, 2013; Shang & Bao, 2022). Social commerce is an online platform that enables the consumer to share information, experience and comment on the product and services (Baethge et al., 2016; Shadkam & O’Hara, 2013). There is a high chance for business growth especially in the pastry industry (Kiumarsi et al., 2014).

Facebook Commerce

Facebook has become one of the most popular social networks on both national and international level (Facebook, 2016). The appearance of Facebook has developed a new form of online business known as Facebook commerce (f-commerce). Facebook commerce (f-commerce) can be defined as selling of products through Facebook (Leong et al., 2018). Another definition of Facebook commerce (f-commerce) comes from the word electronic commerce (e-commerce), it is the use of Facebook platform to execute selling and buying processes (Leong et al., 2018). Facebook commerce (f-commerce) can be categorised into two categories (Leong et al., 2018).  The first categories consist of companies that are connected to Facebook pages (Leong et al., 2018). The second category consists of companies that are connected through fan pages (Leong et al., 2018). Electronic marketer has started to use Facebook commerce (f-commerce) to improve the buying experiences (Leong et al., 2018).  Electronic commerce (e-commerce) is significantly different from Facebook commerce (f-commerce). In Facebook commerce (f-commerce) the consumer has the control and shortens the gap between consumer and seller (Costantinides & Fountain, 2008). Moreover, Facebook commerce (f-commerce) focuses on social interaction like networking, collaborations and sharing (Huang & Benyoucef, 2013). Additionally, unlike electronic commerce (e-commerce), Facebook commerce (f-commerce) provides more social interaction online experiences where the sellers will assist the consumer in decision making (Leong et al., 2017).  While, an electronic commerce (e-commerce) the consumer usually individually interacts with the buyers with electronic commerce platform while (Costantinides & Fountain, 2008). Moreover, from a marketing perspective, electronic commerce (e-commerce) is focused on maximising the shopping efficiency (Huang & Benyoucef, 2013). In a system interaction context, electronic commerce (e-commerce) offers one-directional browsing where the information of the consumers will be shared to others (Leong et al., 2017).

Purchase Intention

Purchase intention is a fundamental factor in determining a company’s success in social commerce marketing, as it directly influences sales and conversions. It is defined as the likelihood that a consumer will purchase a product in the future (Khan, 2022), and it reflects the consumer’s decision-making process, shaped by both internal and external factors. Social commerce, a blend of social media and e-commerce, plays a vital role in influencing this intention by creating a more interactive and engaging environment for consumers to explore, engage with, and purchase products (Yadav and Rahman, 2017). Even when financial resources are limited, a variety of factors continue to influence a consumer’s purchase intention. One such factor is the willingness to explore alternative shopping channels, which is particularly relevant in social commerce. Consumers may be more inclined to use social media platforms to discover products, compare prices, or engage with user-generated content such as reviews and recommendations. This willingness to explore different channels is driven by the belief that social commerce provides access to more personalized, authentic, and often more cost-effective options, even for budget-conscious shoppers (Sembada and Koay, 2021). To understand the dynamics of consumer purchase intention more comprehensively, it is essential to integrate insights from the Unified Theory of Acceptance and Use of Technology (UTAUT), which explains the factors influencing technology adoption and usage. The UTAUT model identifies several key variables that can influence consumers’ intention to engage with new technologies, including social commerce platforms.

Underpinning Theory

Various theories have been proposed to explain individual behavior and technology adoption, each with its own focus and limitations. For instance, the Theory of Reasoned Action (TRA) primarily examines individual behavior (Rahman et al., 2022), focusing on attitudes and subjective norms that influence decisions. However, TRA is limited in its ability to account for technology-related behavior, as it does not specifically address how individuals interact with new technologies (Neo et al., 2017). In contrast, Social Cognitive Theory (SCT) emphasizes the role of social influence, reinforcement, and self-regulation in shaping behavior. It focuses on how consumers adjust their behavior through learning, control, and motivation to achieve goal-directed outcomes over time (Wayne, 2018). While SCT offers valuable insights into behavior modification, it is less focused on the technology adoption process itself. Similarly, the Technology Acceptance Model (TAM) seeks to explain how users accept and use technology, primarily considering factors like perceived ease of use and perceived usefulness (Rahman et al., 2022). TAM is widely applied to understand technology adoption but is limited in its ability to provide a comprehensive explanation of the broader decision-making process when consumers are faced with new technologies. The Theory of Planned Behavior (TPB) extends TRA by incorporating perceived behavioral control, which accounts for external factors that might influence a person’s ability to perform a behavior (La Barbera & Ajzen, 2021; Madden et al., 1992). TPB predicts a person’s intention to change behavior at a specific time and location, adding a layer of understanding regarding intentions to adopt or modify behaviors based on both internal and external factors.

Among these, the Unified Theory of Acceptance and Use of Technology (UTAUT) stands out as a more holistic and specific framework. Developed by Venkatesh et al. (2003), UTAUT integrates elements from eight different models to provide a comprehensive view of the factors influencing technology acceptance. It focuses on key factors like performance expectancy, effort expectancy, social influence, and facilitating conditions. Furthermore, the UTAUT2 model, an extension of UTAUT, was developed to address the variation in behavioral intention that previous models could not fully explain. UTAUT2 adds new dimensions, such as hedonic motivation, price value, and habit, improving the prediction of consumers’ intention to use technology (Venkatesh et al., 2016; Venkatesh, 2012).

Research Framework

Consequently, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)  is employed to inform the approach of this study. UTAUT2 was chosen because it examines both technological acceptability and utilisation (Venkatesh et al., 2012). UTAUT2 improves upon UTAUT by increasing the variance in behavioural intention explained (Huang and Kao, 2015; Venkatesh et al., 2016; Venkatesh et al., 2012). The key differences between UTAUT2 and UTAUT are that UTAUT2 identifies three additional factors from historical analysis regarding the general adoption and usage of technologies and consumer adoption. It also refines several existing relationships based on the initial UTAUT conceptualization and introduces new interrelationships (Venkatesh et al., 2012). Figure 2 explains the proposed research framework for this study.

Figure 2: Proposed research framework

Sources: Ahmed (2016 ) and Glennie (2010) and Rahman et al,(2022)and Venkatesh et al.(2012)

Hypotheses Development

Performance Expectancy and Purchase Intention

Performance expectancy is included in this study due to its strong  theoretical and empirical support in previous findings. It is explained as the extent to which consumers believe that a product or service enhances personal growth and job performance (Davis, 1989; Jogiyanto, 2008). Meanwhile Sudirman et al. (2021) describe it as belief that improved task performance of a product or service refers to the benefits users expect to gain from a product or service (Davis et al., 1992; Gatautis and Medziausiene, 2014). Suh and Han (2002) note that these benefits enhance online shopping performance. However, research shows that these effects may vary depending on the consumer’s familiarity with technology or shopping platforms (Lim & Ting, 2012; Escobar-Rodriguez & Carvajal-Trujillo, 2013). Although studies affirm the positive influence of performance expectancy on purchase intention, less attention has been given to emerging shopping channels, such as social commerce platforms. Additionally, some findings suggest that the influence of performance expectancy may diminish in markets where users prioritize emotional or experiential factors over task performance (Lim and Ting, 2012). Addressing these gaps, this study proposes hypothesis one (1):

H₁. Performance expectancy positively influences purchase intention.

Effort Expectancy and Purchase Intention

Effort expectancy refers to the perceived ease of use as experienced by the users. Venkatesh et al. (2003) defined it as the degree of ease associated with using Business-to-Consumer e-commerce websites, a concept later expanded by Venkatesh et al. (2012). It has been found that effort expectancy impacts women more than men (Sair and Danish, 2018). Additionally, Palau-Saumell et al. (2019) stated that effort expectancy is related to the intention of use, as consumers are more likely to intend to use technology when it is easy to search, choose, and saves time. The ease of access and exploration, according to Singh et al. (2017), can attract consumers to make purchase decisions. Oentario et al. (2017) further found that effort expectancy positively influences consumer attitudes.

While these studies establish a foundation for understanding effort expectancy, they do not fully account for its role in emerging contexts, such as social commerce platforms, where user interactions and expectations may differ significantly. For example, Sharma (2019) highlighted that pastry entrepreneurs can use social commerce to ensure their social media accounts are accurately created, completed, and regularly updated, including promotional content. Moreover, the relationship between effort expectancy and customers’ online purchase intentions has been supported by several studies (Martin and Herrero, 2012; Pascaul-Miguel et al., 2015). Singh et al. (2017) also found a positive relationship between effort expectancy and purchase intention. The relevance of this link suggests that simpler, easier-to-access designs make online shopping websites more user-friendly than complex, difficult-to-use systems. Given these gaps, this study seeks to examine the influence of effort expectancy on purchase intentions within the context of social commerce, particularly for small entrepreneurial businesses. By addressing these limitations, this research provides a more comprehensive understanding of how platform usability drives consumer decisions.  Therefore, this study proposes hypothesis two (2):

H₂. Effort expectancy positively influences purchase intention.

Social Influence and Purchase Intention

Social influence plays a significant role in shaping consumer perceptions and approaches to technology (Attuquayefio and Addo, 2014). One digital marketing method that leverages electronic word of mouth (eWOM) is the use of social media influencers (Zhou et al., 2021). A study conducted by Kim and Kim (2021) found influenced-product compatibility effectively enhances product sentiment and decreases advertisement acceptability by increasing purpose inferences. Social influence can impact individuals and shape collective behaviour (Lorenz et al., 2011). It can mould individual behaviour as people are easily influenced by their environment (Venkatesh and Davis, 2000). Venkatesh et al., (2012) also found that social influence impacts behavioural intention.

In terms of the promotion, social interactions are found to increase the consumer’s purchase intention toward deals recommended by their social network friends (Shin et al., 2011). These results suggest that social interactions between consumers and their social network community directly influence the intention to buy (Ng, 2013). The perception of familiarity and closeness with others increases with the number of social interactions a consumer has within the social network community (Ng, 2013). Therefore, a consumer’s intention to buy becomes more susceptible to community influence (Ng, 2013). While these findings are valuable, they often rely on generalized social network behaviours, overlooking the nuances of platform-specific interactions, content engagement styles, and cultural contexts that may influence social influence dynamics. Given these observations, the existing literature confirms the relationship between social influence and purchase intention but leaves questions about its applicability in emerging contexts such as social commerce among small businesses and niche markets. Understanding these dynamics is particularly relevant as consumers increasingly rely on community-driven trust to make purchasing decisions. Based on this argument, the study proposes hypothesis three (3):

H₃. Social influence positively influences purchase intention.

Facilitating Condition and Purchase Intention

Facilitating Conditions refer to the degree to which an individual believes that the institutional and technological infrastructure is in place to support the use of a system (Venkatesh et al., 2003). It can also be described as the external environment that helps individuals overcome obstacles and limitations when using new information technology (Gu et al., 2009). However, existing definitions tend to emphasize broad, technology-centric contexts and do not sufficiently consider specific setor or consumer-driven variations, particularly in emerging domains like social commerce.  According to Singh et al. (2017), consumers may view online shopping as trustworthy only when they recognise how beneficial it is to utilize external infrastructure, support, and environments. Other than that, Sudirman et al. (2021) further define facilitating conditions as the extent to which an individual believes the technological and organisational environment is designed to make using the software easier.

Pahnila et al. (2011) found that facilitating conditions directly affect behavioural intention. Additionally, a study by Harsono and Suryana (2014) found that facilitating conditions can influence the behavioural intention and usage behaviour. Another study highlighted that facilitating conditions, such as promotion, product diversity, and the ability to make purchases, play an important role in the preferences for social commerce marketing channels (Gul et al., 2017).  Nevertheless, such studies often fail to examine facilitating conditions in micro-level industries, such as cake and pastry businesses, where consumer preferences may differ from broader e-commerce patterns. Therefore, it is essential to study the influence of facilitating conditions on cake consumer purchase intention. Based on the review, the study proposes hypothesis four (4):

H₄. Facilitating conditions positively influence purchase intention.

Hedonic Motivation and Purchase Intention

Social media platforms are often described as new venues where individuals can seek pleasure and entertainment (Alalwan et al., 2017). Consumers are particularly attracted to social media marketing because of its appealing and creative nature (Wamba et al., 2017). Hedonic motivation may therefore play a significant role in shaping consumer responses  to perceptions of social media ads (Alalwan, 2018). According to Babin et al. (1994), hedonic motivation is important to consumers who are driven by hedonic values, as these customers enjoy the experience even without making a purchase, although such experiences can lead to a purchase. Hedonic principles include desires for entertainment and escapism (Babin et al., 1994).

Leong et al. (2018) noted that in Facebook commerce (f-commerce), customers may visit f-commerce stores online to purchase goods or services. However, individuals may simply enjoy browsing or have a pleasurable experience while engaging in the transaction (Leong et al., 2018). Ramayah and Ignatius (2003) also found that when consumers enjoy themselves, they are more satisfied with their online purchases and are more likely to engage in such activities. Based on the propositions, the study suggests hypothesis five (5):

H₅.  Hedonic motivation positively influences purchase intention.

Price Value and Purchase Intention

According to Venkatesh et al. (2012), price value is defined as the consumers’ cognitive evaluation of the trade-off between the perceived benefits of an application and its associated monetary costs. It can also be described as the psychological trade-off between the perceived advantages of the technology and its cost to consumers (Dodds et al., 1991).Kankanhalli et al. (2005) further define price value as the favourable outcome of a transaction, encompassing both extrinsic and intrinsic benefits.

Price value is often evaluated in conjunction with the quality of product or services to determine overall value (Zeithaml, 1988). Ajina (2019) highlights that price value is crucial from both economic and psychological perspectives and serves as a competitive advantage for businesses. It is also a key factor in studying consumer behaviour (Ajina, 2019). Sudirman et al. (2021) note that price value is considered favourable when the benefits of technology outweigh its costs, positively impacting purchase intention. Nair et al. (2015) found that price value influences consumers’ usage intentions. Li and Hitt (2010) suggest that perceived value from prior purchasing experiences can affect a consumer’s decision to repurchase online. Shang and Bao (2022) propose that high perceived value due to great quality and low pricing can facilitate repurchasing decisions for social commerce clients.  While the concept of price value is well-established, prior studies often focus on general technology adoption contexts rather than sector-specific environments, such as social commerce for food-related purchases. Based on this discussion, the study proposes hypothesis six (6):

H₆^. Price value positively influences purchase intention.

Habit and Purchase Intention

Habit is related directly to behaviour or behaviour intention (Venkatesh et al., 2012). There are several perspectives on the definition of habit (Chiu et al., 2012). For example, Traindis (1979) described it as situation-behaviour patterns that have evolved into automatic responses, frequently occurring without the individual being aware of them. Verplanken and Aarts (1999) defined it as a learned set of actions that have become automatic reactions to certain stimuli, useful in achieving particular goals or end states. It is also defined as the condition where an individual tends to perform behaviour automatically (Limayem et al., 2007). These definitions demonstrate that habits are deliberate, learned behaviours. Through regular use, habits become increasingly ingrained in procedural memory (Chiu et al., 2012).

Past studies have found that habit can influence both behavioural intention and usage intention, making it a crucial antecedent of consumer behaviour (Baptista and Oliveira, 2015). Moreover, according to Liao et al. (2006), a B2C website’s perceived utility, trustworthiness, and habit influence consumers’ intentions to continue using it. The concept of habit has also been used to describe how information systems are utilised in organisations. Furthermore, past research by Ghazali et al. (2018) found that with increased habit, attitudes as predictors of purchasing intentions became less important. Additionally, research has shown that behaviour is influenced by habit, which in turn predicts intentions (Carrus et al., 2008; Verplanken et al., 1998). This suggests that when a behaviour becomes habitual, consumers are more likely to engage in repetitive purchases or system usage. This study aims to address these gaps by examining habit in the context of social commerce for cake purchasing. It explores the role of platform familiarity, user convenience, and repeated exposure in fostering habitual behaviours that positively influence purchase intention.Therefore, based on the review, this study proposes hypothesis seven (7):

H₇^. Habits positively influence purchase intention.

Trust and Purchase Intention

Trust is a foundational element in consumer behaviour and purchasing decisions, particularly in online environments. Mayer et al. (1995) define trust as the willingness to rely on the actions of another party, rooted in the expectation that they will act in a reliable and beneficial manner. Trust is especially significant in transactions where uncertainty or risk exists, as it reduces perceived vulnerability and fosters confidence in exchanges (Chow and Shi, 2014). In the context of online purchases, trust plays a critical role in mitigating the risks associated with distance, anonymity, and lack of physical verification. Cho and Sagynov (2015) highlight that trust and confidence are prerequisites for consumers to engage in online purchasing, as they create a sense of security. Similarly, Pavlou (2003) argues that trust facilitates transactional exchanges by enabling consumers to believe in the reliability and integrity of online sellers and platforms.

In social commerce, trust becomes even more critical due to heightened uncertainty stemming from user-generated content, social interactions, and the lack of face-to-face engagement. Feathermann and Hajli (2015) emphasize that trust in social commerce platforms significantly reduces perceived risk and encourages purchasing behaviour. This is because social commerce integrates social media features such as recommendations, reviews, and user discussions that directly influence consumer perceptions of reliability.   Prior research indicates that a consumer’s decision to purchase online is largely dependent on trust (Hajli et al., 2017). Wang and Herrando (2019) further suggest that consumers are more likely to engage and make purchases when they trust the social media platforms they use. Institutional trust in e-commerce sites reduces perceived risk, influenced by the regulatory environment (Wang and Herrando, 2019). While prior studies establish the link between trust and purchase intention, context-specific insights remain under-explored, where limited studies address the role of trust in social commerce for cake purchasing, based on this argument, this study proposes hypothesis eight (8):

H₈.  Trust positively influences purchase intention.

Study Method

The primary objective of this research is to establish cause-and-effect relationships, as outlined in the research framework. To achieve this, a quantitative research approach is employed, which involves collecting data from a sample of the population and analyzing it statistically to generate usable insights. This study utilizes primary data collection methods to address specific research questions. A questionnaire is designed and distributed to respondents, using a non-probability sampling method due to the unknown probability of respondent selection and unequal chances of participation. Specifically, convenience sampling is used, as the survey is distributed online, allowing respondents to participate at their convenience. The total population for this study is 1.9 million (DOS, 2022). Based on Krejcie and Morgan’s (1970) recommendation, a minimum sample size of 384 is suitable for populations between 75,000 and 1,000,000. However, a G*Power analysis indicates that 160 respondents are sufficient for this research. Delice (2010) also supports that sample sizes between 30 and 500 are adequate for most studies. Furthermore, research by Tabachnick et al. (2007) and Wai et al. (2019) suggests that a sample of at least 300 is preferable for reliable measurements. Therefore, this study targeted a sample size of 330 respondents to achieve a 95 confidence level with a 5% margin of error. Ultimately, the study successfully obtained data from 331 respondents. Respondents for this study are selected based on their experience with purchasing cakes through Facebook. They must be at least 18 years old and have made a cake purchase via Facebook within the past six months. The survey is shared on Facebook, targeting users who have engaged with cake-related content, using hashtags like #cakeproduct, #cakeinKualaLumpur, and #kekmurah. Additionally, the survey is posted in multiple Facebook groups dedicated to Kuala Lumpur residents, reaching a broader audience.

Before data collection begins, a pre-test and pilot testing of the questionnaire are conducted to assess its validity and reliability. Supervisors and food technology experts review the questionnaire, and their feedback is used to refine the instrument. A pilot test, conducted with 10% of the target sample size, confirms that the reliability coefficient exceeds 0.90, indicating excellent reliability. This level of reliability is considered well above the acceptable threshold of 0.70 (Drummond, 2017). Data collection is conducted using the Google survey platform, with the questionnaire available in both Malay and English. The survey is distributed via social media to respondents who meet the eligibility criteria. This method ensures that participants who have purchased cakes via Facebook and have seen cake advertisements through the platform are included in the sample.  For data analysis, SPSS software is used. Descriptive statistics are employed to examine respondents’ demographic profiles and assess their acceptance and trust in Facebook commerce (f-commerce) related to cake purchases. In addition, correlational analysis is used to examine the relationships between key variables, such as consumer trust and their acceptance of Facebook commerce. The Pearson correlation coefficient will measure the strength and direction of these relationships. Additionally, multiple regression analysis is applied to investigate the influences and predictive relationships between various independent variables and the dependent variable.

FINDINGS

Demographic Profiles

From table 1 response rate finding, four hundred and sixty-six respondents (466) click on the Google form link before the screening process. The respondents were screened thrice. The first screening is to identify the respondents that are residing in Kuala Lumpur. Thirty-four (34) respondents were excluded as they are not from Kuala Lumpur. The second screening is to identify whether the respondents have received the cake advertisement through Facebook or not. Thirty-one (31) respondents were deleted as they did not meet the criteria. The last screening is to screen whether the respondents have purchased cake products through Facebook within six (6) months. From the result, seventy (70) respondents were excluded. After the screening process only three hundred and thirty-one (331) respondents meet the criteria.

Table 1 :Response Rate

The analysis reveals that 22.1% (n = 73) of respondents are male, while 77.9% (n = 258) are female. This indicates that the majority of those purchasing cake products through Facebook are female. Regarding age distribution, the largest group of respondents (42.9%, n = 142) are aged 25–29. This is followed by 12.4% (n = 41) aged 30-34 and 9.2% (n = 31) aged 50-54. Additionally, 9.1% (n = 30) are aged 18-24, while 7.3% (n = 24) fall within the 45-49 age group. Respondents aged 40-44 account for 6.0% (n = 20), and 2.7% (n = 9) are aged 55-59. The smallest proportion of respondents, 0.9% (n = 3), are aged 60 and above. These findings suggest that Facebook remains a relevant platform for purchasing cake products among younger and middle-aged consumers, even with the growing popularity of Instagram and TikTok commerce.

In terms of education, the majority of respondents are bachelor’s degree holders (46.8%, n = 155), followed by diploma holders (29.9%, n = 99). Master’s degree holders make up 11.8% (n = 39), while 10.9% (n = 36) are graduates from primary or secondary schools. The smallest proportion, 0.6% (n = 2), comprises respondents with doctorate degrees. This illustrates that most consumers purchasing pastry products through Facebook are individuals with undergraduate-level education.

For purchase frequency, 39% (n = 129) of respondents bought cakes twice within six months, while 37.8% (n = 125) made a single purchase. Respondents purchasing three times accounted for 19.9% (n = 66), while 2.7% (n = 9) made purchases four times within the same period. Only 0.3% (n = 1) purchased cakes five or more times. It can be concluded that the majority of consumers purchased cakes once or twice within six months.

Descriptive Analysis

Descriptive tests help answer questions about the data. There are three key metrics that offer details on the areas where distribution scores cluster. Mode score is the first. The easiest way to measure central tendencies is to use mode. Next is the median score. The middle scores of a distribution are known as the median. The last is the mean score. The most common measure of central tendency for continuous data is the mean (Abu-Bader, 2021). Variability measurements may be found under the descriptive test. Range is the initial indicator of variability. The range of scores spans both the minimum and maximum values in a given unit. The mean deviation is the next. The statistic used to describe how scores vary from the mean is called mean deviation. Moreover, variance measures how spread out a distribution scores from mean score. Last but not least is standard deviation. It is the most used measure and always reports along with mean scores. This measure shows how closely scores in a distribution cluster around the mean. It is recommended to report the mean score and standard deviation (Abu-Bader, 2021). Table 2 indicates the finding.

Table 2: Descriptive Analysis

The study examined eight independent variables to assess factors influencing consumers’ usage of Facebook for purchasing cake products. The first variable, performance expectancy, explored Facebook’s usefulness in finding cake products. The highest mean score was for time-saving benefits (M = 5.81, SD = 0.984), while the lowest was for increased chances of finding quality cakes (M = 5.73, SD = 0.986). Overall, respondents slightly agreed that Facebook enhances productivity in searching for cake products. The second variable, effort expectancy, focused on ease of use and interaction with entrepreneurs. Most respondents agreed it was easy to make online purchases using Facebook (M = 6.28, SD = 0.983). However, learning to use the platform received a slightly lower agreement (M = 6.20, SD = 1.024). Social influence, the third variable, evaluated external factors affecting purchase decisions. Recommendations from close relatives and friends were significant (M = 5.45, SD = 1.112), while influence from other individuals had a lower impact (M = 5.21, SD = 1.095).  The fourth variable, facilitating conditions, measured knowledge and platform compatibility. The highest mean score was for knowledge adequacy (M = 6.31, SD = 0.966), with compatibility scoring slightly lower (M = 6.27, SD = 1.017). Respondents agreed that Facebook aligns well with existing technologies they use. For hedonic motivation, respondents slightly agreed that using Facebook is enjoyable (M = 5.21, SD = 1.060). The lowest score indicated limited agreement about deriving pleasure from the platform (M = 5.14, SD = 1.087). Price value examined cost-related perceptions. Respondents found Facebook commerce offers reasonable prices (M = 5.79, SD = 1.050) but showed slightly lower agreement on saving money by comparing prices across sellers (M = 5.72, SD = 1.092). The seventh variable, habit, assessed behavioural norms. Respondents moderately agreed that using Facebook for purchases has become a habit (M = 5.23, SD = 1.152), though fewer considered it a norm (M = 5.11, SD = 1.189). Trust, the eighth variable, focused on credibility. Respondents agreed that entrepreneurs prioritize customer benefits (M = 6.18, SD = 1.008) but were less confident about entrepreneurs’ commitment to transactions (M = 6.10, SD = 1.080).  Lastly, purchase intention, the dependent variable, revealed moderate intentions to recommend Facebook for cake purchases (M = 5.91, SD = 1.079). However, fewer respondents expressed a consistent intention to use Facebook for future purchases (M = 5.67, SD = 1.122).

Measurement Model Assessment

Common Method Bias (CMB)

Common method bias (CMB) occurs when variation in responses is caused by the instrument rather than the true bias of the respondent that the instrument is trying to reveal. Using Harman’s single factor score, in which all things are fed into one common factor, is one of the easiest ways to assess CMB. A single factor’s overall variance should be less than 50% to indicate that CMB has little to no impact on your data (Mat Roni, 2016).

Table 3: Common Method Bias

Notes: <50%

From the result above (Table 3), it indicates that the single factor explained only 49.8% of the total variance. Hence, suggesting that the collected data is free from the threats of common method variances.

Factor Analysis

Reducing dimensionality, or factor analysis, is the process of reducing a large number of measured and observable variables to a smaller number of unobservable latent variables that share a common variance (Bartholomew et al., 2011). EFA is used by researchers to assess the number of variables’ influencing factors and to determine which variables “go together” (Yong & Pearce, 2013).

Part 1: Assessing the data and extracting the factors

To evaluate the appropriateness of the data for factor analysis, the correlation matrix, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and Bartlett’s Test of Sphericity were examined. These measures assess the strength of relationships among variables and determine whether the dataset is suitable for further multivariate analysis (Julie, 2005).

Correlation Matrix

The correlation matrix (Table 4) revealed that all variables had values greater than the threshold of 0.30, indicating sufficient intercorrelations among variables for factor analysis. This is a crucial preliminary step, as correlations below this value may suggest that certain variables are not related enough to contribute meaningfully to shared variance.

Table 4: Correlation Matrix

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

The overall KMO value of 0.961 (Table 5) suggests that the sample size is more than adequate for factor analysis. According to Kaiser (1974), values greater than 0.9 are considered “marvelous,” demonstrating the data’s high suitability for uncovering underlying factors (Hutcheson & Sofroniou, 1999).

Bartlett’s Test of Sphericity

Bartlett’s Test of Sphericity tested whether the correlation matrix significantly deviates from an identity matrix (where variables are uncorrelated). The test yielded a chi-square value of 17,877.015 with 903 degrees of freedom and a significance value of p = 0.000. Since p<0.05p < 0.05p<0.05, the null hypothesis (that the correlation matrix is an identity matrix) was rejected. This confirms that the dataset contains sufficient relationships to perform factor analysis.

The results from the correlation matrix, KMO measure, and Bartlett’s Test indicate that the data is suitable for factor analysis. The high intercorrelations and strong sampling adequacy suggest that meaningful factors can be extracted from the dataset for further interpretation and analysis.

Table 5: Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test

Notes: p-values < 0.05

In the second stages, the researcher chose variables with an eigenvalue of 1 or more to be extracted. The number of components that were really retrieved, whose total should equal the number of items submitted to factor analysis, is what is represented by eigenvalue. The presence of eigenvalues greater than one (1) is a condition for determining the number of components or factors indicated by a set of variables (Chetty, 2022). Table 6 shows that components 1 to 5 meet the criterion of eigenvalues of 1 or more with a total 78.670%.

Table 6: Total Variance Explained

Figure 3 Scree Plot

Based on figure 3, it was found that variance happens in component 1 to 5 which means that component above 1 will be retained. Therefore, researchers will retain components 1 to 5 and will go for further investigation in Part 2.

Part 2: Factor Rotation and Interpretation

All five components were interpreted using factor rotation, which, while not altering the original solution, adjusted the variance distribution across the components. For component one, the variance decreased from 48.854% to 29.594% after rotation. Component two’s variance shifted from 17.724% to 14.525%. The variance for component three increased from 5.391 percent to 13.564 percent. Component four’s variance changed from 2.923% to 12.156%, and component five’s variance decreased from 2.779% to 8.832%. Table 7 explained the  total variance.

Table 7: Total Variance

The next step researcher has used a rotated component matrix, the rotated component matrix was utilized to refine the analysis by reducing the number of variables heavily loading on each factor. Rotation aids in better understanding the factors without altering the overall results. Variables with values below 0.5 or not meeting predetermined criteria were considered for additional analysis (Chetty, 2022). In table 8 the matrix indicated that all items exceeded the minimum value of 0.5. However, only one item measuring price value loaded under factor five, leading to the removal of two items from the price value variable.

Table 8:Rotated Component Matrix

 Coefficient Correlation

The coefficient correlation test assesses the linear relationship between two variables, where a value of +1 indicates a strong positive correlation, -1 indicating a strong negative correlation, and 0 indicates no relationship (Lakshmi and Ramana, 2022). This test is used to determine the strength and direction of the relationship between independent and dependent variables.

Table 9: Coefficient Correlation and Significance Level

Notes: r- value= 0.1 < r < 0.3 = weak relationship, 0.3 < r < 0.5 = moderate relationship, 0.5 < r < 1 = strong relationship. Notes: p-value < 0.000; <0.050

Table 9 illustrates the coefficient correlation and significance level of all variables. These values assess the presence of a relationship between the independent and dependent variables, as well as the strength of the relationship between those two variables. For the first hypothesis (H₁), the test demonstrates a strong positive correlation between performance expectancy and purchase intention (r=0.727, p=0.000),  thereby supporting the hypothesis that performance expectancy significantly influences consumer purchase intention on Facebook. Similarly, the second hypothesis (H2) reveals a strong positive correlation between effort expectancy and purchase intention (r=0.555, p=0.000), indicating a substantial impact. The third hypothesis (H3) identifies a moderate positive relationship between social influence and purchase intention (r=0.463, p=0.000), confirming its significance.

Additionally, the fourth hypothesis (H4) shows a strong positive correlation between facilitating conditions and purchase intention (r=0.556, p=0.000). The fifth hypothesis (H5) also presents a strong positive correlation between hedonic motivation and purchase intention (r=0.550, p=0.000). The sixth hypothesis (H6) indicates a moderate positive relationship between price value and purchase intention (r=0.643, p=0.000), suggesting that price value can influence the consumer purchasing intention for cake products on the Facebook platform. Thereby supporting H6.

The seventh hypothesis (H7) establishes a strong positive relationship between habit and purchase intention (r=0.649, p=0.000), indicating that habit significantly influences consumer purchase intention, thus supporting H7. Finally, the eighth hypothesis (H8) confirms a strong positive relationship between trust and purchase intention (r=0.714, p=0.000). Overall, all hypotheses are supported, demonstrating positive relationships between the independent and dependent variables.

Coefficient of Determination (R²)

The coefficient of determination (R²) quantifies the proportion of variance in the dependent variable that is explained by the independent variables within the regression model. It is an indicator of how much explained variation is contained in the data. Therefore, the better the model describes the data, the greater the value of R². A poor relationship between the predictor variable(s) and the response variable is shown by extremely low values of R² (<0.5) (Di Bucchianico, 2008). Table 10 indicates that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit and trust can be 71.8% explain in the purchase intention. The Adjusted R² is 0.711, which adjusts for the number of predictors and still explains about 71.1 percent of the variance in purchase intention, reflecting a robust model even after adjustment. The results are strong, as the analysis explains a significant portion of the factors that influence purchase intention. The slight change in R² does not have much impact on the overall strength of the findings

Table 10: Coefficient of Determination (R²)

Multiple Regression

A single dependent variable and numerous independent variables can be analysed using the statistical approach known as multiple regression. Using independent variables whose values are known, multiple regression analysis aims to predict the value of the single dependent value (Moore et al., 2006).

Table 11: Multiple Regression Result

The table 11 above showed that the F-Value shows that the regression model is a good fit for the data as the F-Value is equal to 102.300. Moreover, table 11 also illustrated that the independent variables are statistically significant to the dependent variables, p < 0.0.5.

Standardized Coefficient Beta (β)

This test analyses the two variables’ data to determine whether or not there is a linear relationship between them. Correlation coefficients for non-normal distributions should be calculated using the data’s rankings rather than their actual values (Akoglu, 2018). In order to identify a relationship between two or more variables and test the hypothesis, one can utilise the coefficient correlation. Strong positive correlation between the variables is denoted by +1, whereas strong negative correlation is denoted by -1. variables that got 0 means there is no relationship between the two variables (Lakshmi & Ramana, 2022).

Table 12: Standardized Coefficient Beta (β) and Significance Level

 Notes: r- value= 0.1 < r < 0.3 = weak relationship, 0.3 < r < 0.5 = moderate relationship, 0.5 < r < 1 = strong relationship. Notes: p-value < 0.000; <0.050

Table 12  illustrates the correlation value and p-value of all variables. These values will show whether there is a relationship between independent variables and dependent variables and will show the strength of the relationship between those two variables. The first hypothesis (H₁) suggests a weak positive relationship between performance expectancy and purchase intention (β= 0.206, p= 0.000), thus supporting H₁. The second hypothesis (H₂) which explores the relationship between effort expectancy and purchase intention (β= 0.011, p= 0.884), is not supported by the data. Similarly, the third hypothesis (H₃) concerning social influence and purchase intention (β= -0.062, p= 0.158) is also unsupported. The fourth hypothesis (H₄), which examines the influence of facilitating conditions on purchase intention (β= -0.111, p= 0.181), shows no significant effect and therefore not supported. The fifth hypothesis (H₅) regarding the relationship between hedonic motivation and purchase intention (β= 0.042, p= 0.410) is also unsupported. Conversely, the sixth hypothesis (H₆) reveals a strong positive relationship between price value and purchase intention (β= 0.184, p= 000), supporting H₆. The seventh hypothesis (H₇) shows that habit has a strong positive relationship with purchase intention (β= 0.258, p= 0.000), thereby supporting H₇. Finally, the eighth hypothesis (H₈) demonstrates a strong positive relationship between trust and purchase intention (β= 0.472, p= 0.000), supporting H_8.

Study Implication

It was identified that there is limited research on the intentions of cake consumers to make purchases on Facebook. Conducting this study is essential to provide insights that can aid future cake entrepreneurs in effectively targeting customers and developing marketing strategies. The eight independent variables proposed in this study may offer valuable guidance to cake entrepreneurs seeking to enhance customer approval, usage, and trust within the context of Facebook commerce. By addressing these key factors, this study provides a significant theoretical contribution and practical value for academics and practitioners in the relevant field. This study has made significant contributions to both the theoretical and practical understanding of Facebook commerce, particularly in the context of cake consumers’ acceptance and usage.

Academic Perspectives

This study contributes to the academic understanding of consumer behavior in the context of Facebook commerce, particularly in the cake industry, by applying the UTAUT2 model to examine the factors influencing consumers’ purchase intentions. The research findings offer insights into the underlying theoretical mechanisms that drive consumer decisions when engaging with Facebook as a commercial platform. The UTAUT2 model, which includes factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, and trust, provides a comprehensive framework for understanding consumer behavior in digital environments. The results suggest that performance expectancy plays a significant role in consumers’ belief that Facebook commerce enhances their shopping experience. Consumers perceive Facebook as a useful tool that streamlines the search for and purchase of cake products, which aligns with existing theories on the utility of e-commerce platforms.

Effort expectancy, another key variable, indicates that ease of use is a crucial factor in determining consumer engagement with Facebook commerce. This aligns with the Technology Acceptance Model (TAM), which posits that perceived ease of use is a fundamental determinant of technology adoption. In this study, consumers reported that interactions with businesses on Facebook were simple and required minimal effort, thus supporting the hypothesis that ease of use drives consumer adoption.

The influence of social factors, specifically the role of family and friends, also emerged as a significant determinant of purchase behavior. The findings suggest that consumers are more likely to engage with Facebook commerce when they perceive that their social circle influences their purchasing decisions. This resonates with social influence theory, which posits that individuals’ behaviors are often shaped by the opinions and behaviors of others.

Facilitating conditions, which refer to the perceived resources and support available to consumers when using Facebook commerce, were also found to influence purchase intentions. Consumers’ familiarity with the platform and their comfort level with its use contributed to their likelihood of purchasing cake products. This supports the view that perceived access to necessary resources facilitates technology adoption.

Hedonic motivation was another factor that emerged as influential in shaping consumers’ purchase intentions. The findings suggest that consumers are more likely to engage with Facebook commerce if the experience is enjoyable, which is consistent with the concept of hedonic consumption in marketing literature. Consumers who derive pleasure from using Facebook are more likely to make purchases, highlighting the importance of a positive and enjoyable online shopping experience.

Price value, which reflects the relationship between price and perceived quality, was also identified as a significant factor in consumers’ decision-making process. The study suggests that consumers are sensitive to both the cost of cake products and the perceived value they receive in return. This finding aligns with pricing theory, which posits that consumers assess the overall value of a product based on both its price and the quality they expect.

Habit, as a factor influencing consumer behavior, was also found to play a role in purchase intentions. This aligns with theories of consumer behavior that suggest habitual usage of a platform increases the likelihood of repeated engagement. The familiarity of Facebook as a platform, coupled with its ease of use, has led to habitual purchasing behaviors among consumers, which supports the notion of consumer loyalty in digital commerce.

Finally, trust emerged as a critical determinant in consumers’ willingness to engage in online transactions. The findings highlight the importance of building trust between consumers and businesses on Facebook, which is consistent with the literature on e-commerce trust. As consumers become increasingly wary of online fraud, the establishment of trust through transparent and reliable business practices is essential for fostering consumer confidence and encouraging repeat purchases. The findings contribute to the broader literature on technology acceptance and e-commerce, offering new insights into the role of social media platforms in shaping consumer purchase intentions.

Practical Perspectives

From a practical perspective, the findings of this study offer valuable insights for cake entrepreneurs utilizing Facebook commerce. The research identifies key factors that influence consumer purchase intentions, providing actionable recommendations for improving business strategies and enhancing consumer engagement on the platform. Based on the result, the consumers believe that consumer hedonic motivation can influence their purchase intention. Therefore, entrepreneurs can take this initiative to make their advertisement more engaging, fun and entertaining. Moreover, cake entrepreneurs should also ensure the consumer purchase experience is entertaining. Other than that, the cake entrepreneurs also need to make sure that the purchase procedure is simple and straightforward for customers of all ages to understand. This is because from the finding, the majority of the consumers purchased products from the Facebook platform because it is easy and effortless.

Furthermore, finding of this study can assist cake entrepreneurs in creating more effective Facebook content and communication strategies. The Cake entrepreneurs must adhere to current trends while creating Facebook posts. If a product or marketing follows current trends, it tends to attract greater attention. Furthermore, Facebook material needs to be engaging in order to get more young people to purchase the cake products.  Next, for the communication strategies, consumers tend to become more wary of making an online purchase as fraud concerns become more common. Therefore, in order to gain consumers’ confidence, cake entrepreneurs should communicate with them effectively and strategically.

Additionally, there are a lot of competitors in the cake industry due to the large number of new cake entrepreneurs. Moreover, many cake entrepreneurs now use the internet to advertise and sell their products due to the development of technology and the rise in social media usage. However, each social media platform has a unique set of driving forces that might affect the consumer’s intention to purchase. Therefore, based on the criteria that have been investigated, this study can assist future cake entrepreneurs in strategically marketing and selling their goods using the Facebook platform.

The recent investigation has shown that another crucial factor is price value. Therefore, cake business owners must work harder to improve the quality and quantity of the information provided. Moreover, complete and recent information covering all of the product aspects including prices, discount and others should be taken into account for Any Facebook advertisement messages. Additionally, the value proposition of any advertised items should also be emphasized in advertisements. Any advertising message in this situation needs to engage the consumer on an emotional and cognitive level. Furthermore, in order to draw consumer attention, advertisements should also promote low prices or products with higher quality.

LIMITATION AND RECOMMENDATION OF THE STUDY

There are several limitations in the research conducted. One primary limitation is the geographic focus of the study. While this research successfully identified the key factors influencing consumers’ purchase intentions through Facebook, it focused solely on consumers residing in Kuala Lumpur. The findings are therefore representative of how urban residents in Kuala Lumpur respond to products and services. It is recommended that future studies extend their focus to consumers in other states, as the results may differ due to regional variations in consumer behavior. Expanding the scope of the research could also help cake entrepreneurs in other states develop more region-specific marketing strategies.

Another limitation of the study is its exclusive focus on Facebook commerce. As a result, the findings are most relevant to business owners using Facebook to influence consumer purchase intentions for cake products. Future research could explore consumers’ purchase intentions across other social media platforms. This would provide valuable insights for cake entrepreneurs who use platforms other than Facebook, as each platform has unique features that could impact consumer behavior and purchase intentions.

CONCLUSION

Due to the widespread impact of COVID-19, there has been a significant shift towards online purchasing since 2020. This shift has prompted cake business owners to increasingly utilize social media platforms for advertising and marketing purposes. While Facebook stands as one of the pioneering social media platforms, its popularity as a preferred social network has dwindled. Nonetheless, despite its diminished user base, there remains a cohort of business owners who effectively advertise and sell cakes on this platform. Therefore, understanding the key factors influencing consumer purchase intention, as tested in previous research, can offer valuable insights to entrepreneurs on Facebook, enabling them to conduct more effective promotional campaigns that resonate with potential consumers. With the wealth of data now available to cake business owners on the Facebook platform, there is a newfound capacity to craft more compelling promotions and advertisements tailored to the interests and preferences of Facebook users. Moreover, empirical evidence supports the continued relevance of Facebook commerce across all age groups. This study has played a pivotal role in pinpointing the variables that play a crucial role in influencing consumer decisions to purchase cake products on Facebook.

ACKNOWLEDGMENT

The work described in this study was funded by the Universiti Teknologi MARA Selangor, DUCS-FAkulti, 600-UITMSEL (PI.5/4) (101/2022)

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