







































Asian Themes in Social Sciences Research 
ISSN: 2578-5516 

Vol. 3, No. 1, pp. 1-10 
2019 

DOI: 10.33094/journal.139.2019.31.1.10 
© 2019 by the authors; licensee Online Academic Press, USA 

1 
© 2019 by the authors; licensee Online Academic Press, USA 

  
 

 
 
 
 

The Influence of Social Commerce Factors on Customer 
Intention to Purchase 
 
 

Abdul Kadir Othman1* --- Lailatul Faizah Abu Hassan2 --- Muhammad Iskandar Hamzah3 --- 
Amirun Razin Razali4 --- Mohamad Amir Shah Saim5 --- Mohd Safwan Ramli6 --- 
Muhammad Amir Osman7 --- Mohamad Amirul Anbia Azhar8  
 
1,2,3,4,5,6,7,8Faculty of Business Management, Universiti Teknologi MARA 40450 Shah Alam, Selangor, Malaysia. 

  
 

 
Abstract 

The rapid development in social commerce creates a new opportunity for businesses. With the 
use of social technologies they are able to create an environment that is on a socially interactive 
platform.  These social bonds can drive online social support in e-commerce, the result is 

shown by trusts created and increased intention to use social commerce.  This article will 
examine the factors that triggered consumers’ intention to purchase using social commerce. A 
total of 184 respondents were chosen for this study to investigate factors that might contribute 
to customers’ purchasing intention.  The findings provide a better understanding on factors 
that contribute to intention to purchase, which comprise shopping enjoyment, perceived 
usefulness, trust, security, perceived quality, perceived value, ease of use and transaction 
convenience. It also suggests that online firms should consider these contextual factors in order 
to facilitate consumers' adoption behavior. 
 
 

Keywords: Ease of use, Security, Trust, Perceived value, Purchase intention. 
Licensed:  This work is licensed under a Creative Commons Attribution 4.0 License.  
Funding: This study received no financial support. 
Competing Interests: The authors declare that they have no competing interests. 

Acknowledgement: The authors would like to acknowledge the contribution of those involved in the preparation of this 
paper. 

 
 
1. Introduction 

The internet population has grown tremendously since its commercial introduction in 1991. The past 
decade has seen tremendous growth in retail transactions using electronic commerce platforms. Information 
technology and online platforms have stimulated impulse purchasing behavior by increasing consumer access 
to products and service. This is partly due to the easy process of purchasing and payment (Chen, Su and 
Widjaja, 2016).  

Social commerce provides consumers the opportunity to shop online and consequentially creates 
customers’ intention to purchase via click-and-mortar platforms. Social commerce is defined as “any 
commercial activities facilitated by or conducted through the broad social media and Web 2.0 tools in 
consumers’ online shopping process or business’ interactions with their customers” (Lin, Li and Wang, 2017). 
It is common for social commerce platforms to utilize sophisticated computer algorithms that could 
understand and respond to customers’ behavioral patterns. This allows companies to offer the right products 
that match the exact needs of customers through electronic channels. Social  commerce (s-commerce) is rapidly 
expanding worldwide, and it has various models including flash sales, group buying, social shopping, purchase 
sharing and personal shoppers (Kim and Eastin, 2011). Among these models, the most popular type of social 
commerce is group buying. The internet and social networking sites or SNS (Facebook, YouTube, and 

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Twitter) are evolving day by day. In tandem with the rapid growth of online SNS users, the number of online 
suppliers is also expanding to cater the needs of the users. 

Due to easy accessibility of the internet, customers feel free to create and share information regarding 
brands and products on social commerce and thus create value (Yadav, 2015). Currently, social commerce is 
rapidly gaining acceptance among consumers in developing countries including Malaysia, as a platform to 
shop online. The upward trend is expected to continue in the future. This positive outlook is mainly driven by 
several pull factors such as convenience, speed of transaction and accessibility.  Other than that, consumers are 
increasingly shifting their focus from desktop computer to mobile platforms. The availability of various types 
of shopping applications and mobile payment systems means that there are a lot of options that customers can 
choose from. Luan and Teo (2009) claimed that the acceptance of new technology influences an individual’s 
attitude and perception which result in adoption of new technology.  Nevertheless, one of the main problems 
with social commerce is that certain people might feel insecure in accepting attractive deals offered in social 
media. Besides, not much is known on the factors that influence purchase intention over social commerce 
platforms within the context of Malaysian consumers. This is owing to the fact that some research has shown 
that the variance explained in behavioral tendency of using social commerce is still considered low (Hew, et al., 
2016; Ng, 2013). Therefore, this paper is meant to identify factors that contribute to social commerce adoption 
to facilitate online purchase intention among Malaysian consumers. 
 

2. Literature Review 
2.1. Customer Online Purchase Intention 

Online customer purchase intention can be characterized as the client’s willingness to pay for a certain 
product after evaluating the available choices, and the risks and benefits of each of the options. According to 
George (2004) customers are willing to search, select and purchase products via the Internet but the 
advertisement must be creative enough to attract customers. This includes sharing of positive testimonies and 
user experiences by social media influencers and online celebrities. Organizations need to meet the requests of 
the buyers in order for purchase intention to build (Fortsythe and Shi, 2003). The online purchase is the result 
of purchase intention (Lee and Lee, 2015). Online purchase intention is a noteworthy indicator of actual 
purchase (Pavlou, 2003). Customers' genuine purchasing conduct is dichotomous on the grounds that shoppers 
either need to buy or not buy the product. In addition, online purchase goal is influenced by the buyers' 
assurance to buy from a web based business (Salisbury, Pearson, Pearson and Miller, 2001; Ling, Chai and 
Phew, 2010). 
 
2.2. Shopping Enjoyment 

The first factor that is expected to influence customers’ online purchase intention is shopping enjoyment. 
Potential announcement of internet shopping comes from substantial online shopping knowledge. “Online 
shopping is a wilful and hedonic action, besides clients take an interest since they are characteristically 
spurred" (Shen, 2012). Consumers who appreciate shopping accomplish joy from shopping and investing 
energy perusing for items (Seock and Bailey, 2008).  

The feeling of delight felt while going to a site can expand the online purchase expect ation among 
purchasers by adding a charming shopping background, henceforth it is of extraordinary significance for 
organizations working on the web to perceive the effect of pleasure the site has to offer. According to Kim and 
Niehm (2009) a few items require a more top to bottom depiction and consequently it is proposed that 3D 
virtual models could be utilized. That sort of innovation on a site may prompt a more elevated amount of 
shopping delight among the buyers which at that point will upgrade the customers’ purchase expectations. 

 
2.3. Perceived Usefulness 

The second factor that affect customers’ online purchase intention is perceived usefulness of the online 
store. In the late 1990s to the early 2000s, buying goods online could be regarded as a static and transactional 
affair. A decade later saw online commerce platforms started to embed dynamic and user-generated contents 
that allow users to rate their products and provide feedback in a transparent process. Consumers find 
themselves to be increasingly connected and engaged with each other by sharing their purchase and 
ownership experiences, which are highly useful for potential buyers to make the right purchase decisions. 
Since the perception of other user reviews can potentially shape consumers’ expectation, the use of dynamic 
and shared content in social commerce platforms is expected to benefit them in assessing the values and risks 
associated with the particular product (Plotkina and Munzel, 2016). Perceived usefulness is one of the key 
variables in the classical Technology Acceptance Model (TAM) framework (Davis, 1989; Venkatesh, et al., 
2003). Within the Malaysian context, perceived usefulness was found to influence consumers’ intention to use 
mobile social commerce (Hew, et al., 2016) and purchase via social networking sites (Sin, Nor and Al-Agaga, 
2012; Leng et al., 2011).  

 
2.4. Trust 

The third factor that is expected to contribute to customers’ online purchase intention is trust. One of the 
most challenging issues of social network is building trust among users before they make purchases from the 



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seller. According to Hajli (2015) to build customer trust for consumers is crucial. There are countless online 
sellers appearing every day advertising their products. An accurate website and frequent interaction enhances 
confidence toward the seller. Consumers gain trust and confidence by current customer reviews, ratings, and 
recommendations shared in the forums and communities. If the recommendations are positive, the level of 
trust would be higher towards the vendors. Trust is the willingness to accept the unpredictable consequences 
brought about by the third party behaviors.   

The context of trust is divided into three.  First is the ability of vendors to portray their capability to 
deliver according to customer expectations. Second is the integrity to meet and produce an output that 
satisfies customers. Lastly is benevolence which is empathy towards customers to fulfil their extraordinary 
expectations of the organization. According to Wright (2016) creating trust among consumers is the ultimate 
objective of social network provided by the organization as well as establishing a personal interaction and face 
to face contact. In a situation where the level of trust is high in the intention of purchase in social network, the 
consequences of perceived risk will be reduced by buying online. 

 
2.5. Security 

The fourth factor that is expected to influence customers’ online purchase intention is security. When 
discussing online security, there are a lot of assumptions among users regarding their reluctance to share their 
personal information and use electronic transaction to avoid any misuse of information.  In order to capture 
consumer confidence on security, sellers must provide secure approach that governs data protection such as 
identity verification and security policy. When sellers have implemented all possible security measures, 
consumers will have more confidence and make repeat purchases from online sellers, and if there is any issue 
such as late delivery or faulty product the sellers should provide a return and cash back guarantee (Ponte, 
Carvajal-Trujillo and Escobar-Rodríguez, 2015). 

 
2.6. Perceived Quality and Value 

The fifth factor that might affect customers’ online purchase intention is perceived quality and value. Yen 
and Lu (2008) mentioned that customer expectation of the seller's service quality can be defined as the 
importance of the quality, including responsiveness, fulfilment of transaction, contact with seller and after 
sales service. There is some agreement to that statement where some studies on the effects of product quality, 
service quality and price fairness on customer loyalty found that  service quality provides added value and leads 
to customer loyalty through customer satisfaction (Giovanis, Athanasopoulou and Tsoukatos, 2015). Good 
product and service quality are very important in today’s competitive environment where many sellers offer 
the same type of service and product. Therefore, the first impression to customers on product and serv ice 
quality is crucial in building a long term relationship with them.  

Tahir and Guru (2017) mentioned that perceived product quality and perceived fair price affect perceived 
value equally whereby perceived value in turn, strongly affect online purchase intention. This relates to 
purchasing behavior where most customers will look up for the price tag first to ensure price reasonability. 
Tahir and Guru (2017) also added that perceived value is a measure between the quality of the product once 
customers really received the item and the price they actually paid. It can be concluded that, customer 
satisfaction is affected by the price perception, whereby it is an indirect outcome of the perception of price 
fairness (Bamfield, 2013).  

 
2.7. Ease of use and Transaction Convenience 

The sixth factor that might affect customers’ online purchase intention is ease of use and transaction 
convenience. Social commerce enables people to have the convenience to shop online. According to Jiang and 
Rosenbloom (2005) among the technology characteristics, website performance, product information, product 
selection, ease of ordering, and shipping/handling have positive influences on customer perception of shopping 
convenience. Previous studies (Gupta and Kim, 2010) supported the idea that customers consider convenience 
as critical in adopting or conducting online shopping. Widely accepted literature suggests that the likelihood 
of customers to accept or use new technology will increase when the technology is considered easy to use, 
useful and convenient (Devaraj, et al., 2002).  

According to Assael (2005) this is because people nowadays are living in an era of hectic and busy 
working lifestyles, and thus it has become very difficult for most  people to go shopping outside their home. 
Moreover, based on a survey of over 5000 internet users, Assael (2005) suggested that heavy users of the 
internet are somewhat younger, growing up in the age of technology and taking advantage of it, and more 
likely to be workaholics and working more than 50 hours a week. In the same study, Assael (2005) also 
mentioned that heavy internet users are categorized as a multitasking group that tends to do more jobs or 
activities and seek to do more than they currently have on their plate. They may be “time st arved’’ and are 
constantly exploring ways to reduce the time taken to complete various tasks to manage their busy schedules 
(Vijayasarathy, 2004). According to Poon (2008) the convenience of online shopping is that consumers are free 
to shop at different web sites and they are able to switch from one website to another with just a click. 

The ease of access or accessibility refers to the degree to which a customer believes that accessing the 
internet via a mobile phone will be of little effort. In computer-related research, many studies have 



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investigated ease of use but not ease of access. Several studies showed that users’ perception on ease of use 
significantly influences users’ intrinsic motivation (enjoyment or playfulness) (Hsu and Lu, 2007).  

Mobile shopping service (MSS) is easier to access on the mobile internet and would be less intimidating to 
customers. Therefore, ease of access is expected to have a positive impact on customer’s perception of 
enjoyment in mobile shopping context. Ease of use has been shown to play an important role in determining 
the intention to use (Mao, et al., 2005; Pijpers and van Montfort, 2005). Davis (1989) identified that freedom of 
effort in using a system is an important predictor for its adoption and subsequent usefulness. Much research 
has confirmed that the construct of freedom from effort has a strong effect on usefulness (Igbaria, et al., 1997; 
Luarn and Lin, 2005; McFarland and Hamilton, 2006). Very little research has looked into the effect of ease of 
access or accessibility from which an individual obtains a connection to mobile internet and their intention to 
purchase.   
 

3. Conceptual Framework 
 

 
Figure-1. Conceptual Framework of the Study 

 
Figure 1 above shows the conceptual framework of the study that explains the connection between each 

independent variable (social commerce factors) and the dependent variable (customer intention to purchase).  
 
3.1. Hypotheses 

Below are the research hypotheses highlighting the relationship between social commerce factors and 
customer intention to purchase. 
H1: There is a significant relationship between shopping enjoyment and customers’ online purchase intention  
H2: There is a significant relationship between perceived usefulness and customers’ online purchase intention 
H3: There is a significant relationship between trust and customers’ online purchase intention 
H4: There is a significant relationship between perceived value and customers’ online purchase intention 
H5: There is a significant relationship between ease of use and customers’ online purchase intention 
H6: There is a significant relationship between security and customers’ online purchase intention 
H7: There is a significant relationship between perceived quality and customers’ online purchase intention 
H8: There is a significant relationship between transaction convenience and customers’ online purchase 
intention 
 

4. Methodology 
The purpose of this study is to investigate the relationship between social commerce factors and 

customers’ online purchase intention.  This section includes research design, the instrument used for data 
collection, population and sample and data analysis. 
 
4.1. Research Design  

The research used non-experimental research approach that is a correlational research design.  
Correlational research involves the study of the relationship that exists between two or more variables 
without attempting to manipulate them and describe the degree to which those variables are related to each 
other.  According to Salkind (2013) correlational research is used to identify how one or more variables relate 
to one another. 
 
4.2. Research instrument 

During the data collection period, the instrument used was a questionnaire. They were distributed 
directly to each of the respondents involved in this study. The questionnaire was designed precisely to answer 
the research question.  The questionnaire was adapted from Lin (2017) to meet the requirements and verified 
by Associate Professor Dr Abdul Kadir Othman, a lecturer from Universiti Teknologi MARA, Shah Alam. 
 
 

 



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5. Results and Discussion  
5.1. Response Rate 

In this study, a total of 184 questionnaires were distributed via Google Form.  The questionnaires were 
sent via Facebook and WhatsApp application. The distribution and collection of responses took one week to 
complete.  There were 184 respondents from various ages and from different educational backgrounds. 
 

Table-1. Respondents’ Profile. 

Variable Description Frequency Percentage 
Gender Male 90 48.9 

 Female 94 51.1 
Age 20 - 30 Years old 152 82.6 

 31 - 40 Years old 16 8.7 
 41 - 50 Years old 12 6.5 
 51 Years old and above 4 2.2 

Education SPM or below 16 8.7 
 Diploma 42 22.8 
 Bachelor’s degree 102 55.4 

 Master’s degree 24 13.0 
 Doctorate 0 0 

Marital Status Single 138 75.0 
 Married 46 25.0 

 
Table-2. The results of Factor Analysis of the Independent Variables. 

 Component  

 1 2 3 4 5 
PV2 .882     

PQ2 .830     
PV3 .776     

PQ4 .773     
PQ3 .703     
PV4 .689     

PV1 .688     
PQ1 .677     

EO4  .871    
EO1  .760    
EO3  .725    

EO2  .666    
TC2  .617    

TC3  .609    
S3   .912   
S2   .884   

S4   .879   
S1   .776   

T2    .863  
T1    .772  
T3    .727  

SE2     .817 

SE3     .737 

SE1     .732 
% variance explained (76.626%) 23.273 16.471 15.668 10.904 10.611 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.  .880 

Bartlett's Test of Sphericity 
Approx. Chi-Square  4161.238 

df  267 

Sig.  .000 

MSA  
 .814-

.929 
 

Table 1 shows that the number of female respondents was slightly higher than male respondents which 
were represented by 94 (51.1%) female and 90 (48.9%) male respondents. In terms of age, Table 1 shows that 
the majority of the respondents were in the age range of 20-30 years old comprising 152 (82.6%) respondents. 
 This was followed by those between 31-40 years old at a total of 16 (8.7%) respondents and those who were 



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between 41-50 years old at 12 (6.5%) respondents. The remaining four (2.2%) respondents were 51 years old 
and above. In terms of educational background, it was found that majority of the respondents possessed 
bachelor degree qualification which summed up to 102 (55.4%) respondents, followed by those who had 
Diploma qualification which comprised 42 (22.8%) respondents. Meanwhile, 16 (8.7%) respondents had SPM 
qualification.  In addition, 24 (22.8%) respondents had Master’s Degree.   

Table 2 shows the principle component factor analysis conducted in order to test the factor structure of 
the social commerce factors.  The analyzed data signified a promising result where it had contributed to the 
creation of the five factor structure and in this case, it explained 76.626% of the total variance. The KMO 
measure of sampling adequacy projected a value of 0.880 which gave a clear indication that the items were 
interrelated. Apart from that, the Bartlett’s Test of Sphericity offered a significant value in the sense that the 
Approx. Chi-Square value was to 4161.238, p<. 001.  Hence, the analyzed results show the significance of the 
correlation matrix for factor analysis to be conducted. 

Initially, there were eight independent variables but due to the similarities of the variables, the data had 
been merged into smaller number of variables. Perceived quality was merged with perceived value to become 
perceived quality and value. Ease of use was merged with transaction convenience and became ease of use and 
convenience.  Perceived usefulness was removed due to high cross loadings. Other variables such as security, 
trust and shopping enjoyment remained as originally conceptualized. 
 

Table-3. The Result of Factor Analysis of the Dependent Variables. 
 Component 

1 

PI3 .868 
PI2 .780 

PI4 .753 
PI1 .667 

% variance explained 59.387 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .673 

Bartlett's Test of Sphericity 

Approx. Chi-Square 220.657 

df 6 
Sig. .000 

MSA  .652-.623 

 
Table 3 shows the result of factor analysis for the dependent variable that is customers’ online purchase 

intention. The result shows that the KMO value of .673 is sufficient for factor analysis to be conducted. 
Similarly, the MSA values between .652 and .623 show that the sampling is adequate for each item in the 
factor. Looking at the loadings of items, they are higher than .05 , which indicate strong relationship between 
each item and the underlying factor. All four items were retained to represent  purchase intention.  
 

Table-4. The Results of Correlations and Reliability Analyses. 

No Variables Mean SD 1 2 3 4 5 
1 Perceived Quality Value 3.51 .70 (.941)     

2 Ease of use convenience 3.81 .73 .719** (.911)    
3 Security 2.34 .97 .478** .295** (.938)   

4 Trust 3.20 .90 .485** .467** .540** (.866)  
5 Enjoyment 3.66 .88 .516** .573** .365** .410** (.805) 
6 Purchase Intention 3.55 .73 .665**. .676** .253** .446** .578** 

                   Notes: **. Correlation is significant at the 0.01 level (1 -tailed). N=184. Cronbach alphas in parentheses along the diagonal . 

 
Table 4 shows the correlation analysis between social commerce and purchase intention. There was a 

positive relationship between independent variables and dependent variables. The results show that the 
highest correlation is between purchase intention and ease of use (r=.676; p<01), followed by the correlation 
between perceived quality and value and purchase intention (r=665; p<01).  Security has the lowest 
correlation with purchase intention (r=.253; p<.01). The results indicate the existence of concurrent validity of 
the factors measuring social commerce and online purchase intention. The correlation between one 
independent variable with another independent variable ranges from low (r=.295; p<.01) for ease of 
use/convenience and security, to high correlation for ease of use and convenience and perceived quality and 
value (r=.719; p<.01), indicating convergent validity of the factors constituting social commerce. All items are 
relible to measure the intended variables, which can be seen from the Cronbach’s alphas that range from .805 
to .958. 

 
 
 
 



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Table-5. The Results of Multiple Regression Analysis. 

Variables Standardized Beta 

Perceive Quality and Value .365** 
Ease of Use and Convenience .257** 

Trust .135** 

Security -.160 
Enjoyment .245** 

R .760 
R2 .578 

F value 48.728 

Sig. F value .000 
Durbin Watson 2.196 

                Notes: **. Significant at the 0.01 level . 
 

Table 5 shows the result of regression analysis which explains the percentage of variance in the dependent 
variable (customers’ online purchase intention), which was explained by the social commerce factors. The R 
squared value of .578 indicates that 57.8% of the variance was explained. The F value is significant showing 
that the regression model is good at explaining the dependent variable. Durbin Watson value of 2.196 denotes 
the absence of auto correlation in the model. Looking at the contribution of the individual independent 
variable in explaining customers’ online purchase intention, four out of five factors are significant. The 

strongest predictor is perceived quality and value (β=.365; p<.01).  It is followed by ease of use and 

convenience (β=.257; p<.01), enjoyment (β=.245; p<.01), and trust (β=.135; p<.01). Interestingly, security has 

no significant influence on purchase intention (β=-.160; p>.05).   
 

6. Discussion 
This study has several key findings; first, the results confirm the significant positive impact of perceived 

quality and value, ease of use and convenience, enjoyment and trust on purchase intention in the context of 
social commerce.  A substantial degree of quality, convenience in using the social commerce, enjoyment and 
trust are needed by online buyers before making purchase decisions.   

Thus, this study upholds the claim that these factors are important in social commerce and it is extended 
to the context of e-commerce.  Second, the results did not support the hypothesis on the role of security in 
relation to purchase intention.  Although studies have claimed that security is the most pertinent factor in 
affecting purchase intention, this present study failed to provide the evidence required to support it.  In social 
commerce, where the exchange of sensitive information is not required, the aspect of security has become 
irrelevant.  

Customers use social commerce to get the required product information, reviews and feedback before 
advancing to the next stage of purchasing the product. Therefore, the finding is well-justified. Finally, by 
looking into buyers’ behavior in online commerce marketplace, it was learned that social commerce poses the 
biggest effect that influences purchase intention that deserves more attention in future studies. Social influence 
in purchase intention has been overlooked or simplified in prior literature; however, the mushrooming of social 
commerce has called for more attention to the social perspective of purchase intention.  
 

7. Implications of the Study 
This study is expected to provide theoretical, practical, methodological implications for further studies. 

These five aspects are expected to provide a scientific understanding of related responsibilities in an effort to 
develop theories according to the field of study which are the responsibility of the researcher. Moreover, the 
implications of this study are also expected to provide recommendations to marketers regarding efforts that 
should be linked with the studied problems. 

Theoretically, this research provides the foundation or framework depicting the influence of social 
commerce factors on customers’ intention to purchase online. It is based on the uniqueness in this research 
that gives a different perspective from previous studies. The uniqueness can be seen from the identified 
variables that have been modelled and adjusted to research setting in Malaysia. Furthermore, this research is 
also expected to be discussed further, so that it can be developed and tested in different research settings. 

Practically, this study is valuable for marketers because it is expected to provide insights associated with 
the concept of social commerce factors toward customers’ intention to purchase online. Understanding the 
concept of social commerce factors can provide a broader perspective on the purchase intention that can be 
used to design strategies to increase sales for online retailers. In addition, this study can be used as a reference 
for online marketers to customize the marketing strategies according to what customers want. It is expected 
that this study could enrich theoretical understanding on the role of social commerce.  

 
 
 



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Online retailers or business owners should take proper steps in investing some of their capital to enhance 
their services for example in website performance, products information, product selection, ease of ordering, 
and shipping/handling which are related to quality, ease of use or convenience, enjoyment and trust in 
creating purchase intention, so that they can attract more customers to engage in online purchase and 
subsequently generate more income. Furthermore, the experience of customers while interacting with retailers 
must be desirable so that they will be excited in prolonging the relationship with the retailers.  
 

8. Limitations and Suggestions for Future Research 
While this study contributes to extending the body of social commerce on purchase intention research, 

several limitations should be recognized. First, the sample was limited to university students; thus the results 
may not represent the Malaysian population in general.  

The majority of the respondents consisted of Universiti Teknologi MARA students of the Faculty of 
Business and Management located in Shah Alam, therefore the results may not characterize individuals from 
diverse backgrounds and other specific regions of the country. Extending the study to other regions of 
Malaysia would contribute greatly to the understanding of consumer intentions to use social comme rce while 
shopping for fashion products.  

Second, the present study focused on an investigation of millennial consumers’ perceived ease of use of 
social commerce, usefulness, and enjoyment, which influenced their attitudes and intentions to use social 
commerce when shopping for fashion products. A majority of the participants were 20 to 30 years of age 
(82.6%), whereby the lifestyle of the participants and their previous experiences with social media may affect 
the findings of this study.  

Third, this study examined the relationships among perceived ease of use, usefulness, enjoyment, trust, 
and customer purchase intention to shopping for fashion products. The focus on shopping for fashion products 
affects the generalizability of the results on other product  categories. To increase the validity of the model, 
future research should be conducted for other product categories. Further investigation is needed to explore 
other factors (e.g., perceived risks and ease of use) that affect product purchase through social commerce. 
 

9. Conclusion 
The general purpose of this study was to identify the social commerce factors affecting customers’ online 

purchase intention. The conclusion deliberates the overall overview of the findings discussed in the results. 
The result of the study shed insights on online purchasing intention; specifically, the significant key factors 
affecting customers’ online purchase intention.  

The findings contributed to the conclusion that perceived quality/ value, ease of use/ convenience, trust 
and enjoyment are the significant factors that affect customer’s intention to purchase. It can be explained that 
millennials especially are more concerned about purchasing products in an efficient and timely manner with 
the least effort.  However, security gave a non-significant result in the study.  

This can be explained given that social commerce is in it preliminary phase of online shopping where the 
exchange of sensitive or confidential information has yet to happen. Although non-significant, the role of 
security on social commerce is well-explained. Finally, it is expected that this research may provide 
information that would be useful to online retailers and marketers, to customers and also large organisations.  
 

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