Antecedents of Trust towards Purchasing Decision Via Social Commerce: Analytical Essay

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Abstract

Purpose – The development of social media has given rise to a new e-commerce paradigm called social commerce. Social commerce is a social interactions and user contributions to facilitate the online buying and selling of various products and services. Recent years have witnessed the rapid growth of social commerce in Malaysia, but this growth has involved a number of transaction-related issues. In particular, consumers’ trust has become a crucial factor in the success of social commerce firms, requiring these firms to make more effort to gain this trust. In this regard, this study identifies the key factors in social commerce that is influencing Malaysian consumers’ trust in social commerce. In addition, the study assesses the moderating effects of perceived risk on trust in seller that influence the purchase decision via social commerce. Thus, the purpose of this study is to investigate the impact of each kind of trust on the intention to purchase products using social media platform.

Design/Methodology/Approach – An online structured questionnaire was used to collect data from respondents. A total of 168 responses from Malaysian social commerce users were collected and analyzed with SmartPLS.

Findings – Results show that perceived integrity and perceived reputation significantly influence trust in seller towards purchase intention in social commerce positively. However, perceived ability and perceived benevolence negatively impact the users’ attitude. In addition, perceived risk significantly affect the relationship between perceived benevolence and trust in seller. Trust in sellers positively affects the consumers in purchase decisions via social commerce.

Originality/Value – This study contributes to the literature of trust’s antecedent and moderating effect of perceived risk in social commerce. A new perspective and variable, perceived reputation and perceived risk was tested in the model. The model showed that Model of Trust and Theory of Planned Behavior have extensive power to explain the extent of trust that influence purchase intention via social commerce. This study investigates the variable that influence the complete life cycle usage of mobile commerce applications. The research findings are useful for social commerce business owners, application developers and business managers to formulate strategies to increase the purchase intention among Malaysian consumers.

Keywords – Social Commerce, Perceived Integrity, Perceived Ability, Perceived Reputation, Perceived Risk, Trust in seller, Intention to Purchase.

Paper Type – Research paper

Introduction

Web 2.0 social media applications, for example, Facebook, Twitter and LinkedIn create new opportunities for e-vendors (M. N. Hajli, 2014) that allow people to participate actively in the marketing and selling of products and services in online marketplaces and communities (Goldenberg et al., 2010). The advanced development of social media has revolutionized the way online shopping or known as e-commerce is accomplished, called social commerce (Yakimin, Talib, & Rusly, 2015). Social media is a tool for sharing information among communities such as product and service information and plays as an important driver of online purchase and sale of goods and social media increased its share of e-commerce referrals by nearly 200% between the first quarters of 2014 and 2015 (Kian, Boon, Fong, & Ai, 2017) (Kassim, Bhattarai, Zamzuri, & Othman, 2017). It is important to study the process and uniqueness of how consumers behave in this setting to harness the power of social commerce (Zhang & Benyoucef, 2016).

Trust has become a valuable and scarce commodity in late modernity (Jalaldeen, Razi, Izzuddin, & Tamrin, 2017). Trust is an essential element in any transaction in which it will occur when there is willingness and trust among sellers and buyers (Kian et al., 2017). According to M. N. Hajli, (2014), trust can now be supported by social commerce as social commerce includes social interactions of consumers in which increase the level of trust. Considering trust as a critical aspect of e-commerce, the study conducted by N. Hajli (2015), is being directed to investigate the role of social interactions of consumers through social commerce constructs in order to establish trust in e-commerce platforms. Therefore, it is purported that trust plays a significant role in successfully conducting transactions in the virtual space of the Internet.

However, although consumers’ trust in seller via social commerce may affect consumers’ online purchase decisions, few studies have examined which factors influence their trust in the seller from social media users. This issue is important since the phenomenon of social shopping is rising and online shoppers are becoming accustomed to sharing their detailed observations (Asur & Huberman, 2010). Accordingly, the reasons why people trust in seller on social media shopping networks provide merit further investigation. Hence this study attempts to examine the impact of each kind of trust on the intention to purchase products using social media platform.

The main research question of this study is to what extent do trust impacts on behavioral intentions of customers to use social commerce? The model of the study is developed by integrating constructs of trust with the behavioral theory. The model developed for this study adapts constructs of trust developed by Mayer et al. (1995) to examine customers’ trust of sellers. Finally, constructs of planned behavior that are used to explore behavioral intentions of customers to purchase using social commerce are adapted from the Behavioral Theory developed by Ajzen (1991).

Literature review

Social Commerce

Social media is a platform of social interaction among people whereas communities create, share or exchange information (Hashim, Nor, & Janor, 2016). Social media can also serve as tools facilitating intra- and inter-organizational activities among peers, customers, business partners, and organizations; such as collaborative product development, creation of knowledge-sharing communities, implementation of corporate dialog at financial institutions, marketing strategies for brand management, and collaborative learning and creativity (Ngai, Tao, & Moon, 2015).

Social Commerce can be defined as the use of social media for business transactions and activities which are driven primarily by social interactions and user contributions (Hew, Lee, Ooi, & Lin, 2016). Social commerce is regarded as a new category of e-commerce (Huang & Benyoucef, 2013), or the birth of a “referral economy” benefiting from the advantages of interactive information technology infrastructure (Wang, 2012). Social commerce has gone way beyond an increased presence within social networks, and now creates economic value as well (Zhou, Zhang, & Zimmermann, 2013).

Social commerce intention is a measurement of how to anticipate a possible consumer’s actions. This new stream, social commerce, tries to use the commercial opportunities in social media with the relationships in networks and communities to gain commercial advantages. These commercial advantages can lead the business to increase their sales or improve customer loyalty. ‘Intention to use’ has been thoroughly investigated before through the introduction of related theories such as theory of reasoned action, theory of technology acceptance model and theory of planned behavior (M. N. Hajli, 2014).

Model of Trust developed by Mayer et al., (1995)

This approach is used to understand why a given party will have a greater or lesser amount of trust for another party is to consider the attributes of the trustee (Mayer, Davis, Schoorman, Mayer, & Davis, 2011). Three-dimensional generic typologies of trust (D. J. Kim, Ferrin, & Rao, 2003) described of in the literature are ability, benevolence, and integrity. In the context of social networks, the ability is also the influential antecedent to establishing trust among members who are usually centered on a specific mutual interest, hobby, event, etc. (Mayer et al., 1995). Benevolence is the perception of a positive orientation of the trustee toward the trustor (Mayer et al., 2011). The relationship between integrity and trust involves a trustee who adheres to moral standards such as honesty could enhance a trustor’s confidence in the trustee’s behavior (Mayer et al., 1995).

The study of Lin et al., (2010), found that cognitive trust positively affects consumers’ attitudes toward building trust in product recommendations through social networks. Y. Lu, Zhao, & Wang (2010) also found that the three interpersonal trust significantly influences C2C e-commerce buyers in purchase intention. Thus, the following hypotheses are proposed:

H1: Perceived ability has a positive impact on trust in seller towards purchasing decisions via social commerce.
H2: Perceived benevolence has positive impacts on trust in seller towards purchasing decisions via social commerce.
H3: Perceived integrity has a positive impact on trust in seller towards purchasing decisions via social commerce.

Perceived Reputation

In terms of the characteristics of social commerce can be described as the extent to which consumers believe that a firm is honest and concerned about its customers whereby a firm with a good reputation or image enjoys a higher level of customers’ trust (S. Kim & Park, 2013). In addition, a good reputation is a valuable intangible asset for many e-retailers and provides consumers with potential cues for enhancing trust (Lin et al., 2010). Thus, creating a positive reputation is particularly important for those companies to be successful.

Broutsou & Fitsilis (2012) claimed that consumers’ perception of the reputation of an e-commerce site plays a key role in building their trust in that site. Therefore, a good reputation has to be forged to increase consumers’ trust. Previous studies of e-commerce have demonstrated a close relationship between reputation and trust (Lin et al., 2010). Social commerce users are likely to consider a firm’s reputation as an important factor in evaluating their trust in the firm when purchasing products or services. In this regard, the following hypothesis is proposed:

H4: Perceived reputation has positive impacts on trust in seller towards purchasing decision via social commerce.

Perceived Risk

It is common that individuals are anxious about the possible risk linked with a new information system. In the case of online shopping the perception of risk significantly affects the customer intention (Ahmed et al., 2013).

According to Verhagen, Meents, & Tan (2006), a behavioral risk which refers to the uncertainties that arise because online seller can behave opportunistically by taking advantage of the distant and impersonal nature of online transactions and the intermediary’s inability to carefully monitor all transactions. Moreover, party risk addresses the uncertainties that arise since one is unsure about the offers of the selling party and the seller’s ability and willingness to perform which are reflective of perceived ability, benevolence and integrity into measures of both trust and trustworthiness (Gefen, Rao, & Tractinsky, 2002). For instance, sellers can include misleading product information, use false identities, ignore warranties or commit fraud (Verhagen et al., 2006). Perceived risk as the buyers’ subjective belief of a probability of suffering a loss when engaging in transaction with members of the community of sellers. By offering the information and services including information about regulations and procedure, the reputation of the seller and privacy statements that consumer demand for, consumers are able to cope with perceptions of risk (Ahmed et al., 2013).

Therefore, those customers who believe that there is low perceived risk while purchasing via social commerce have positive intention to purchase online and the other customers who believe that it is very risky to purchase in social media platform have negative intention to purchase online and consequently, they postpone their purchase or finally drop it (Al-Naseer et al., 2016). In this regard, the following hypotheses are proposed:

H5(a): Perceived risk negatively moderates the relationship between perceived ability and trust in seller.
H5(b): Perceived risk negatively moderates the relationship between perceived benevolence and trust in seller.
H5(c): Perceived risk negatively moderates the relationship between perceived integrity and trust in seller.
H5(d): Perceived risk negatively moderates the relationship between perceived reputation and trust in seller.

Theory of Planned Behavior (TPB)

The Theory of Planned Behavior (TPB) is derived from the Theory of Reasoned Action (TRA) (Ajzen, 2015). TPB can be described as the assumption that human beings usually behave in a sensible manner. Therefore, TPB is remarkable and widely adopted in studies of human behavior, including social commerce (Ajzen, 2014).

Trust in Seller

The definition of trust in seller proposed in this research is the willingness of a party to be vulnerable to the actions of the seller based on the expectation that the seller will perform a particular action important to the trustor, irrespective of the ability to monitor or control their customers (Corbitt, Thanasankit, & Yi, 2003). Trust in seller have been derived from the antecedent of attitude towards behavior in theory of planned behavior (TPB) (McCole, Ramsey, & Williams, 2010). Trust can be viewed as a significant antecedent belief that creates a positive attitude toward the transaction behavior (Rofiq, 2012), which in turn leads to transaction intentions. Trust helps reduce the social complexity and vulnerability that a buyer feels in social commerce by allowing the buyer to subjectively rule out undesirable yet possible behaviors of the sellers (B. Lu, Fan, & Zhou, 2016).

Prior studies related to user behavior and social media have highlighted the crucial role of trust in stimulating favorable responses for any medium to be used in the future (B. Lu et al., 2016) (Jalaldeen et al., 2017). Researchers have shown that online peer recommendations influence trust and affect consumers’ purchase intentions (Lin et al., 2010). Accordingly, Nadeem, Andreini, Salo, & Laukkanen (2015) found that people are more likely to trust information from other consumers than from companies. With the increase of social technologies and the interconnectivity of people on the Internet, there is a need for some sort of trust and security that will allow two parties to reduce their perceived risk in transactions (N. Hajli, 2015).

Our literature review reveals the expectation that a shift in attitude towards trust in seller would increase the purchase intention via social commerce. Thus, we propose that:

H6: Trust in sellers has positive direct impacts on purchasing decisions via social commerce.

Intention to Purchase via Social Commerce

Intention to purchase refers to the degree to which a consumer intends to purchase from a certain vendor through the social media (D. J. Kim et al., 2003). Because of the inherent nature of social commerce, trust plays a central role in transactions (Kian et al., 2017). A survey by Aliyar & Mutambala (2015), investigated the behavior of customers in purchasing cosmetics through online and found that customers’ intention to buy was influenced by trust. Subsequent research drew similar conclusions, indicating that consumers’ trust in seller affected their intention to purchase from the social networking (Chen & Shen, 2015; S. Kim & Park, 2013; B. Lu et al., 2016; Y. Lu et al., 2010; Ng, 2013).

Research methodology

The aim of the study was to determine the impact of trust on purchasing decision via social commerce in Malaysia and to achieve this, survey research design was employed.

As discussed in the previous chapter, the objective of this study is to investigate Malaysian customers’ intentions to purchase via social commerce. Therefore, this research determined the population of the study as all social commerce users in Malaysia. The units being analyzed for this study are the individuals consists of the user of social media who make purchase transactions via social commerce. This study applied non-probability sampling, namely Snowball sampling. Snowball sampling is a type of nonrandom sample in which the researcher begins with one case, then, based on information about interrelationships from that case, identifies other cases, and then repeats the process again and again (Berg, 2014). In this research, G*Power is used to minimize sample size needed (P, 2017). The total sample size requires for this study is 166 respondents who need to answer the online survey provided.

Primary data was collected by one of the main structured questionnaires that captured the various variables of the study. This study collected data from respondents through an online survey. A questionnaire is used as the primary instrument for data collections. This section uses 6-point Likert scales, ranging from one for strongly disagree to six for strongly agree. Scale is considered to be the new approach of Renis Likert rating scale to measure personal attributes which is the respondents cannot choose the moderate value, middle point in this kind of rating scale because the respondents have to choose between one of the two qualifications of the scale to be the answer, thus the respondents have to consider for a level (Abdul, 2010). Secondary data was obtained from literature sources or data collected by other people for some other purposes. Secondary data was collected through review of published literature such as journals articles, published theses and textbooks. PLS-SEM method makes the researchers able to examine the reliability and validity of the construct measures. The hypotheses were tested using Structural equation modelling method.

Findings and result

A structural equation modelling (SEM) approach was chosen to analyze the collected data through the software SmartPLS 3. According to Wong, (2013) this approach is freely available to academics and researchers, but also because it has a friendly user interface and advanced reporting features. Furthermore, SmartPLS is one of the prominent software applications for Partial Least Squares Structural Equation Modeling (PLS-SEM) (Wong, 2013). In this study data analysis was carried out by a two-stage method which are the measurement model and the structure model.

Measurement model

Confirmatory factor analysis (CFA) was used to test the measurement model. As shown in Table 2, all of the Cronbach’s alpha values are above 0.7. Since these values are above the acceptable level of 0.7, these constructs are deemed to display adequate reliability.

Convergent validity measures the agreement among multiple items measuring the same construct. The convergent validity of measures can be verified by three criteria which are all item loadings should be significant and exceed 0.7, composite reliabilities should exceed 0.7, and average variance extracted (AVE) for each construct should exceed 0.50. Table 2 shows that all the item loadings except ITP4 (0.661) exceed the threshold of 0.7. The results demonstrate that the measures are reasonably convergent on their respective constructs.

Discriminant validity checks the degree to which measures of one construct are empirically distinct from the other constructs (S. Kim & Park, 2013b). To check for discriminant validity, we employed the new criterion of the Heterotrait-Monotrait ratio of correlations (HTMT) in Table 3. HTMT is a new method for assessing discriminant validity in partial least squares structural equation modeling in which when constructs are conceptually more distinct, a lower, more conservative, threshold value is suggested (HTMT < 0.85) (Hair, Risher, Sarstedt, & Ringle, 2018). However, Hair et al., (2018) proposed a threshold value of HTMT < 0.90 for structural models with constructs that are conceptually very similar.

Table 2 lists the results, with correlations among constructs and the square roots of AVE on the diagonal. The square roots of AVE on the diagonal are higher than the inter-construct correlations, representing the examination of discriminant validity is acceptable. To sum up, the measurement model demonstrates adequate reliability, convergent validity and discriminant validity.

Structural model

For the structural model analysis, each proposed hypothesis in the research model was examined. Table 4 shows the standardized path coefficients between constructs as well as the variance explained, R2 for each dependent variable. R2 is used to measure predictive accuracy of the model that explain the amount of variation in the variables explained by all the variables linked (S. Kim & Park, 2013). In this study found that the construct explained 25.3 percent of the variance in the intention to purchase via social commerce.

Bootstrapping analysis with subsample test of 2000 used to generate path coefficient, t-value and p-value. This is to examine the relationship between independent variables and dependent variables. PLS-SEM evaluate how good the structural model predicted the hypothesis relationships.

The finding further indicated that perceived ability and perceived benevolence are found not statistically significantly impact on trust in seller, with path coefficients at β=0.103, (p> 0.1) and β=0.437, (p> 0.1) respectively. This is followed by perceived integrity and perceived reputation, which exerts the significant impacts on trust in seller which involve in social commerce with path coefficients at β=0.000, (p< 0.001) and β=0.000, (p< 0.001) respectively. Trust in seller imposes an influence on social commerce intention, with a path coefficient at β=0.000, (p< 0.001).

Table 3 shows the moderating effect of perceived risk on purchase intention. Results show that perceived benevolence has a negative effect on trust in seller (β=0.014, p < 0.05), when perceived risk is higher, supporting H5(b). However, perceived risk does not moderate the relationship between perceived ability and trust in seller (β=0.081, p > 0.05), the relationship between perceived integrity and trust in seller (β=0.122, p > 0.1), as well as the relationship between perceived reputation and trust in seller (β=.483, p > 0.1), respectively not supporting H5(a), H5(c) and H5(d).

In figure 2 indicates the moderating effect of perceived risk on the relationship between perceived benevolence and trust in seller. The graph shows low perceived risk will result on positive impact of the relationship between perceived benevolence and trust in seller. Meanwhile, high perceived risk will attain negative impact of the relationship between perceived benevolence and trust in seller.

Discussion

Hypothesis 1 (H1) and Hypothesis 2 (H2) suggested that perceived ability and perceived benevolence respectively has no significant impacts on trust in seller towards purchasing decision via social commerce. The result of the study not supported the hypotheses, and it was not accepted. This is not corresponding to the finding of Lin et al., (2010) in the context of social shopping.

Moreover, Hypothesis 3 (H3) and Hypothesis 4 (H4) implied that perceived integrity and perceived reputation respectively has positive impacts on trust in seller towards purchasing decision via social commerce. The result of the study supported the hypotheses, and it was accepted. This is corresponding to the previous study of Lin et al., (2010) and Kim & Park, (2013) where their studies focusing on the social commerce have found that perceived integrity and reputation respectively plays a significant role in purchase intention.

Hypothesis 5, H5(b) suggested that perceived risk negatively moderates the relationship between perceived benevolence and trust in seller. The result of the study supported the hypothesis, and it was accepted. This is corresponding to the previous study Ahmed et al., (2013) where it found that moderating role of perceived risk plays a significant role in purchase intention.

Hypothesis 6 (H6) implied that trust in seller has positive direct impacts towards purchasing decision via social commerce. The result of the study supported the hypothesis, and it was accepted. From this study, we know that trust is once of factor leading to attitude towards purchase intention. This is corresponding with the finding in Lu, Zhao, & Wang, (2010); Nadeem, Andreini, Salo, & Laukkanen, (2015); Lin et al., (2010) in the area of e-commerce; consumer online ; and online product recommendation respectively.

Conclusion

This study is aimed to investigate the extent of trust impact purchase decision via social commerce. The results show that both perceived integrity and perceived reputation positively influence trust in seller directly affect purchase intention. However, it is also evident from the results that perceived ability and perceived benevolence do not impact trust in seller to purchase via social commerce. Moreover, this study demonstrates that perceived risk has a negative moderator role in the relationship between perceived benevolence and trust in seller however the other antecedents do not interact with trust in seller to create a negative influence on purchase intention.

However, the presented conceptual model does not capture the alternative antecedents of trust such as knowledge-based, characteristic-based, institution-based, and personality-based with the online environment. Future research may replicate this study by including these factors as antecedent and be able to explore a factor triggering trust factor in purchase intention via social commerce. Therefore, we believe that future researchers will find the area of consumer motivations and perceptions in social commerce a rich and fruitful area for the literature.

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