Corresponding Author: Evi Rinawati Simanjuntak

INTRODUCTION

The banking sector faced challenges due to several factors, including fluctuations in interest rates and the devaluation of the rupiah against foreign currencies. As a result, Bank Indonesia, the monetary authority, responded by increasing the BI rate. This move heightened competition among banks to attract and retain third-party funds from customers. Consequently, people became more discerning about where they save their money, leading to fierce competition between Indonesian banks, particularly in terms of marketing high-quality and reliable products and services. The primary role of banking is to gather and distribute public funds, acting as an intermediary between those with excess funds and those in need. Banks gain trust from the precautionary principle that is applied because trust is the main keyword for the development of a bank (Manurung & Rahardja, 2004).

With the increasing role of the financial sector, the need for improved marketing management in financial services is also increasing. The effect of marketing on consumer purchasing decisions in various cases, and this research revealed that marketing has a positive influence on consumer purchasing decisions (El Amrani & Correard, 2011). In advertising and product promotion, two main approaches are used in practice: mass (indirect) marketing and direct marketing. Mass marketing uses mass media to broadcast product-related information to current and potential customers. The mass media marketers use include television, radio, magazines, and newspapers. Mass marketing targets large customer groups. It does not discriminate between customers in a group, and the information conveyed to customers is uniform. According to research, direct marketing differs from mass marketing because it targets specific individuals or households so that different customers will receive different marketing information (Wang et al., 2009). The Direct Marketing Association (DMA) defines direct marketing as communication in which data is used systematically to achieve measurable marketing objectives and when direct contact occurs between a company and customers or potential customers.

The effect of direct marketing and advertising on customer enrollment intention in applying for consumer loans for civil servants in one of the BUMDs (Rawung et al., 2015). The results of the study showed that direct marketing and advertising had a positive influence on customer enrollment intentions. Research shows that direct marketing has several advantages, such as high flexibility in adapting to changing environments, being able to develop an extensive database of customers, obtaining a direct response, building relationship with customers, and cost-effective due to less (a smaller) number of intermediaries in the channel (Lim et al., 2022).

The current marketing environment is becoming increasingly competitive and complex, where customer demands are the primary consideration for determining a channel structure that suits customer desires. In line with the financial sector's important role, financial services marketing management experiences higher pressure to achieve success Despite the recession, the financial services sector continues to grow in income and profits and consequently significantly influences other areas of the economy (Wright et al., 2010). Thus, promotion techniques and strategies in financial services are also constantly changing. Direct marketing involves using telephone, mail, fax, e-mail, the internet, and other tools to directly communicate the company's interests to customers (Armstrong et al., 2014). Direct marketing is a marketing system that causes companies or marketers to be able to communicate directly with consumers to produce responses or transactions that are also direct. According to research, the responses generated from direct marketing include inquiry, purchase, or support for consumers in deciding an action (Ku Fan & Wen Cheng, 2009). Marketersï¿½ success through direct marketing is primarily determined by the company's and marketersï¿½ ability to select target markets and design their direct marketing campaigns. The right market will determine the effectiveness of the applied direct marketing, while designing a unique, fun, and inspiring direct marketing campaign will determine the quality of the used direct marketing.

Massive technological developments encourage innovation in banking products that are more accessible, for example, opening a digital account via a mobile phone without the need to visit a branch office. However, on the other hand, digital literacy in Indonesia is still not evenly distributed. According to the report "Digital Literacy Status in Indonesia 2021," released by the Ministry of Communication and Informatics, digital literacy in Indonesia is at a moderate level with an uneven distribution. Revealed in research that financial literacy is the knowledge and skills needed for someone to make the right decisions based on their financial resources (Manurung & Rahardja, 2004). This literacy ability can impact peopleï¿½s success in opening digital accounts, ultimately affecting their decision to buy or try banking products. Based on internal data, the conversion rate for the completion of savings openings to the number of installations is only around 5%. This shows that there are still many difficulties faced by prospective customers when opening digital savings accounts independently. In Wangï¿½s (1999) approach, companies do not establish direct customer relationships for new product offerings. Many customers are either uninterested or do not respond to this sales promotion. Thus, banks, financial services companies, and others are moving away from mass marketing strategies due to their ineffectiveness and targeting a large proportion of their customers with direct marketing for specific product and service offerings. This aligns with research by Zhong and Liu (2003), who found that direct marketing is the primary strategy of many banks and insurance companies to interact with customers.

The success or failure of an organization is primarily determined by performance (Podsakoff & MacKenzie, 2014). Therefore, measuring or improving the sales forceï¿½s performance must be one of the sales manager's priorities. Similarly, research showed that a commercial organization's performance depends on salespeople's performance (Ferdinand & Wahyuningsih, 2018). Hence, an essential management task is determining what characteristics drive sales force performance. This research will focus on examining marketers' characteristics that significantly affect perceived trust and customer enrollment intention. The communication skills, product knowledge, presentation skills, flexibility, empathy, and honesty are the most critical factors (Amor, 2019). Research shows that the essential characteristics of a salesperson are being helpful, having good product knowledge, and being able to communicate in a friendly manner with customers (Fergurson et al., 2021). Meanwhile, Cohen & Prusak (2001) show that affability and comfort do not necessarily generate trust. Nevertheless, research to discuss the characteristics of marketers for digital banking products has not been widely studied at this time. However, research focusing on the characteristics of marketers in the digital banking products sector has not been extensively explored. Therefore, this study aims to further explore the appropriate marketer characteristics that enhance trust and significantly influence customer purchase intentions. The benefits of this research include increased customer trust. By understanding the characteristics of marketers that establish trust, banking institutions can train and develop their employees' skills to build stronger relationships with customers. Additionally, it can enhance customer purchase interest; by understanding the factors influencing customer purchase intentions, companies can design more effective marketing campaigns. Furthermore, other benefits include providing information that can assist companies in designing new products and services tailored to customer needs and preferences, based on marketer characteristics that have been proven successful in enhancing trust and purchase intentions.

METHOD

This quantitative research examines the effect of direct marketing, which consists of product knowledge, affability, and communication skills, on creating perceived trust. The existence of perceived trust will be seen in its influence on decision-making in opening banking customer savings accounts. Research strategies were carried out using a quantitative survey approach, where data collection was carried out through questionnaires from respondents using standard research instruments (Sekaran & Bougie, 2016). The scope of this research was conducted on banking customers who are domiciled in the banking work unit area in Indonesia to determine marketersï¿½ ability to do so with minimal research interference. Furthermore, the sample frame will be limited to male and female customers in all areas of the bank's work units in Indonesia, with an age range of 18 to > 50 years. In this study, the entire or a portion of the population was selected from these individuals, and data were collected to help answer the research questions (Olsen & St George, 2004).

The research conducted was non-probability sampling, which considered the study period. In contrast, the sampling method chosen was purposive sampling, where this technique is also called judgmental sampling (Etikan et al., 2016). The sample size in this study is 233, considering the minimum number of samples, which is 180. The data was collected through an online survey using a Google Form questionnaire distributed to the target population. Respondents were asked to answer questions on a five-point Likert scale from 1 to 5, with responses ranging from "strongly disagree" to "strongly agree," and provide general information consisting of gender, age, and domicile. Based on the results of the questionnaire, its validity and reliability were then tested.

Description of Respondent Characteristics

This research involved 233 respondents as a whole. Based on the results of the data collection in this study, the following is a description of the characteristics of the respondents according to gender, occupation, and age: The results of the analysis showed that of the 233 respondents who were examined in this study, most were male (51.9%). In contrast, the remaining respondents were female (48.1%. Furthermore, according to the type of work of the respondents, most of the respondents worked as students (37.8%). In comparison, the rest worked as private employees, as much as 27.0%, as much as 15.0% as civil servants/BUMN, as much as 14.6% worked as entrepreneurs, and as many as 5.6 respondents did not work. Furthermore, according to the age of the respondents, most of the respondents were aged between 21 and 30 years (36.1%), while the respondents aged 18ï¿½20 were as many as 27.9%, as many as 25.3% of respondents aged 31ï¿½40 years, as many as 7.3% of respondents aged 41ï¿½50 years, and as many as 3.4% of respondents aged > 50 years.

Descriptive analysis was carried out by calculating the mean score of respondents' answers on each research variable. The average score of these answers is then categorized into three categories according to Umar (2012), namely the low category if the mean value is between 1.00 and 2.33; the medium category if the mean value is between 2.33 and 3.67; and the high category if the mean value is between 3.67 and 5.00. Based on the results of completing the questionnaire, the following is a description of the research variables consisting of product knowledge, affability, communication skills, perceived trust, and enrollment intention:

Table 1. Description of Research Variables

Product Knowledge		Affability		Communication Skill		Perceived Trust		Enrollment intention
Kode	Mean	Kode	Mean	Kode	Mean	Kode	Mean	Kode	Mean
PK1	3.373	AFF1	3.313	CS1	3.292	PT1	3.541	EI1	3.167
PK2	3.262	AFF2	3.219	CS2	3.335	PT2	3.103	EI2	3.056
PK3	3.489	AFF3	3.584	CS3	3.382	PT3	3.704	EI3	2.961
PK4	3.584	AFF4	3.167
PK5	3.433
Average	3.428	Average	3.321	Average	3.336	Average	3.449	Average	3.061

Product knowledge is measured using five question items. The analysis results in Table 1 show that overall product knowledge is good. However, in terms of knowledge about product specifications, delivery of information about products, developments about products, and features and benefits of products, there is still room for improvement.

In this study, affability was measured by four question items. The analysis results in Table 1 show that overall affability is good. However, it still needs improvement in understanding different customer needs, non-verbal communication with others, and the ability to influence others.

Three question items measured communication skills in this study. The analysis results show that overall the description of communication skills is good. However, all three can still be improved in terms of presentation and speaking skills, both in terms of articulation and effective word selection.

Three question items measure perceived trust in this study. The analysis results in Table 1 show that overall perceived trust is good. However, it still needs to be improved in terms of building product understanding and the ability to solve consumer problems.

In this study, enrollment intention is measured by three question items. Three question items measured enrollment intention in this study. The analysis results in Table 1 show that overall enrollment intention is also good. However, all three are still very likely to be improved regarding the suitability of product quality, interesting information received, and trust in the information conveyed by the salesperson.

RESULTS AND DISCUSSION

In this study, testing the influence of the impact of banking direct marketing on enrollment intention was carried out using SEM PLS analysis. The stages in the PLS-SEM analysis consist of (1) drawing a path diagram according to the research model framework; (2) performing outer model tests to assess the validity and reliability of indicators in measuring the variables (constructs); (3) assessing the goodness of fit of the model to ensure that the processed data is fit with the estimated model so that the sample used can provide an overview of the actual condition of the population; and (4) conducting inner model testing, which is the stage of testing the effect of inter-variables as a tool for testing research hypotheses (Hair et al., 2019). This research model contains five latent variables: affability (AFF), communication skill (CS), enrollment intention (EI), product knowledge (PK), and perceived trust (PT). The measurement model testing phase includes composite reliability and convergence validity testing. The results of the PLS analysis can be used to test the research hypothesis if all indicators in the PLS model meet the requirements of convergent validity, discriminant validity, and composite reliability. To bring up the results of the outer model test, the PLS model must be estimated using an algorithmic technique. The following is the estimation result of the PLS-SEM model after being estimated using an algorithm technique.

Construct reliability can be assessed by Cronbach's alpha value and the composite reliability value of each construct. The recommended composite reliability and Crombach's alpha values are more significant than 0.7, but in development research, because the loading factor limit used is low (0.5), low composite reliability and Crombach's alpha values can still be accepted as long as the validity requirements converge and discriminant has been met.

Table 2. Composite Reliability

	Cronbach's Alpha	rho_A	Composite Reliability
AFF	0.908	0.914	0.936
CS	0.905	0.906	0.940
EI	0.938	0.948	0.960
PK	0.949	0.957	0.961
PT	0.915	0.921	0.947

Based on the analysis results in Table 2 above, the composite reliability and Cronbach's alpha values of all constructs have also exceeded 0.7, indicating that all constructs have met the required reliability.

The discriminant validity test examined the HTMT (heterotrait-monotrait ratio) values between constructs. HTMT is the recommended alternative method for assessing discriminant validity. This method uses a multitrait-multimethod matrix as the basis for measurement. The HTMT value must be less than 0.9 to ensure discriminant validity between the two reflective constructs (Henseler et al., 2015). In this test, the construct in the PLS model is declared to have met discriminant validity if the HTMT value between the construct and the other constructs does not exceed 0.9.

Table 3. The discriminant validity (HTMT)

	AFF	CS	EI	PK	PT
AFF
CS	0.700
EI	0.640	0.564
PK	0.729	0.733	0.737
PT	0.762	0.732	0.716	0.808

Based on the results of the discriminant validity test (Table 3), none of the HTMT values between constructs exceeds 0.9, which means that all constructs in the PLS model (outer model) meet the required discriminant validity criteria.

The goodness-of-fit model test is conducted to ensure that the compiled PLS model fits with the data being analyzed to explain the actual condition of the population. The goodness of fit of the PLS model can be seen from the R Square value. R Square value > 0.67 indicates the PLS model is robust in predicting endogenous, R Square 0.33ï¿½0.67 indicates the PLS model is in the moderate category, and R Square 0.19ï¿½0.33 indicates that the PLS model is weak in predicting endogenous (Chin, 1998).

Table 4. R Square

Variable	R Square	R Square Adjusted
EI	0.448	0.445
PT	0.666	0.661

The analysis results in Table 4 show that the R square of perceived trust (PT) of 0.666 is in the endogenous category, meaning that the model is robust in predicting perceived trust (PT) from its exogenous sources. This also can be seen from PLS-SEM algorithm model in Figure 2.

Figure 1. The estimation result of the PLS-SEM algorithm Model

The convergent validity test is carried out by looking at the loading factor value of each indicator against the construct. Because this research is a confirmatory study, the loading factor limit is 0.7. Based on the estimation results of the SEM model in Table 5, all variables in the model are valid.

Table 5. Convergent Validity

Variable	Indicator	Factor Loading	Conclusion	AVE	Conclusion
Product Knowledge	PK1	0.908	Valid	0.832	Valid
	PK2	0.823	Valid
	PK3	0.957	Valid
	PK4	0.929	Valid
	PK5	0.937	Valid
Affability	AFF1	0.872	Valid	0.787	Valid
	AFF2	0.937	Valid
	AFF3	0.927	Valid
	AFF4	0.806	Valid
Customer Satisfaction	CS1	0.884	Valid	0.840	Valid
	CS2	0.941	Valid
	CS3	0.924	Valid
Perceived Trust	PT1	0.952	Valid	0.855	Valid
	PT2	0.879	Valid
	PT2	0.942	Valid
Enrollment Intention	EI1	0.933	Valid	0.890	Valid
	EI2	0.965	Valid
	EI3	0.931	Valid

Testing the Effect of Variables

In PLS analysis, testing the effect between variables can be done after the model is proven to fit. Testing the effect includes testing the direct effect, testing the indirect effect, and testing the total effect. The following is the estimation result of the PLS-SEM model using the bootstrapping method:

Direct Influence

The direct effect, often referred to as the direct effect," is the direct effect of exogenous variables on endogenous variables. The significance and direction of direct influence in the PLS-SEM analysis can be seen from the p-value, t-statistic, and path coefficients connecting endogenous to exogenous. Suppose the p-value isï¿½ 0.05, and the T statistic is > 1.96 (t value two tails,ï¿½ 5%). In that case, it can be concluded that the exogenous variable significantly affects the endogen, with the direction of influence according to the sign attached to the path coefficient. Furthermore, suppose the p-value is > 0.05, and the T statistic isï¿½ 1.96 (t value two tails,ï¿½ 5%). In that case, it is concluded that the exogenous variable has no significant effect on the endogen (Hair et al., 2019).

Table 6. Results of the Direct Effect Test

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (\|O/STDEV\|)	P Values
AFF -> PT	0.264	0.265	0.062	4.277	0.000
CS -> PT	0.185	0.184	0.061	3.046	0.002
PK -> PT	0.465	0.467	0.057	8.170	0.000
PT -> EI	0.669	0.671	0.029	22.726	0.000

Based on the test results, the following results were obtained:

1. Affability (AFF) and Perceived Trust (PT)

Affability positively and significantly affects perceived trust, as indicated by sig. = 0.000ï¿½ 0.05, T statistic 4.277 > 1.96, and a positive path coefficient of 0.264, meaning that the higher the affability, the higher the perceived trust, and vice versa, the lower the affability, the lower the perceived trust.

2. Communication Skills (CS) and Perceived Trust (PT)

Communication skill positively and significantly affects perceived trust, as indicated by sig. = 0.002ï¿½ 0.05, T statistic 3.046 > 1.96, and a positive path coefficient of 0.185, meaning that the higher the communication skill, the higher the perceived trust, and vice versa, the lower the communication skill, the lower the perceived trust.

3. Product Knowledge (PK) and Perceived Trust (PT)

Product knowledge has a positive and significant effect on perceived trust, as indicated by sig. = 0.000ï¿½ 0.05, T statistic 8.170 > 1.96, and a positive path coefficient of 0.465, meaning that the higher the product knowledge, the higher the perceived trust, and vice versa, the lower the perceived trust.

4. Perceived Trust = Enrollment intention (EI)

Perceived trust has a positive and significant effect on enrollment intention, as indicated by sig. = 0.000ï¿½ 0.05, T statistic 22.726 > 1.96, and a positive path coefficient of 0.669, meaning that the higher the perceived trust, the higher the enrollment intention, and vice versa, the lower the perceived trust, the lower the enrollment intention.

Mediation Effect Testing

In this study, perceived trust acts as a mediator. To test the role of mediation, a mediation test is carried out as follows:

Table 7. Indirect Effect Test

	Original Sample (O)	T Statistics (\|O/STDEV\|)	P Values
AFF -> PT -> EI	0.177	4.255	0.000
CS -> PT -> EI	0.124	3.057	0.002
PK -> PT -> EI	0.311	7.088	0.000

The explanation of the results of the indirect influence test in Table 7 above is as follows:

1. Affability (AFF)ï¿½ Perceived Trust (PT)ï¿½ Enrollment intention (EI)

In the indirect path, the influence of affability on enrollment intention through perceived trust obtained a p-value of 0.000 with a T statistic of 4.255 and a positive indirect path coefficient of 0.177 because the p-value obtained was 0.05 and the T statistic was > 1. It is concluded that affability can indirectly affect enrollment intention, mediated by perceived trust. In this PLS model, perceived trust is proven to mediate the indirect effect of affability on enrollment intention.

2. Communication skill (CS), perceived trust (PT), and enrollment intention (EI)

In the indirect path, the effect of communication skills on enrollment intention through perceived trust obtained a p-value of 0.002 with a T statistic of 3.057 and a positive indirect path coefficient of 0.124. Because the p-value obtained is 0.05, and the T statistic is > 1.96, it can be concluded that communication skills can indirectly affect enrollment intention mediated by perceived trust. In this PLS model, perceived trust is proven to mediate the indirect effect of communication skills on enrollment intention.

3. Product Knowledge (PK), Perceived Trust (PT), and Enrollment intention (EI)

On the indirect path of the effect of product knowledge on enrollment intention through perceived trust, a p-value of 0.000 is obtained with a T statistic of 7.088 and a positive indirect path coefficient of 0.311. Because the p-value obtained is 0.05, and the T statistic is > 1.96, it is concluded that product knowledge can indirectly influence enrollment intention through perceived trust. In this PLS model, perceived trust is proven to mediate the indirect effect of product knowledge on enrollment intention.

Coefficient of determination

The coefficient of determination shows the contribution of all exogenous factors to endogenous factors. The coefficient of determination can be seen from the adjusted R square value. This value ranges from 0 to 1 or can be interpreted as a percentage (0 to 100%). The greater the coefficient of determination, the greater the endogenous variance explained by the exogenous, while the small coefficient of determination indicates the low influence of the exogenous on the endogenous. This is because there are still quite several factors outside of these exogenous that can affect the endogenous.

Table 8.ï¿½ Coefficient of determination

ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ Variable	R Square	R Square Adjusted
EI	0.448	0.445
PT	0.666	0.661

The results of the analysis in Table 8 show that the adjusted R squared value of 0.661 means that 66.1% of the perceived trust variance is influenced by product knowledge, affability, and communication skills, while the remaining 33.9% of the perceived trust variance is influenced by other factors outside of product knowledge, affability, and communication skills. Furthermore, on the enrollment intention variable, the adjusted R square is 0.445, which means that 44.5% of the variance of enrollment intention is influenced by perceived trust, and other factors outside of perceived trust influence the remaining 55.5% of the variance of enrollment intention.

The results of this study proved that product knowledge has a positive and significant effect on perceived trust, where the higher the salesperson's product knowledge of the products offered will increase consumer confidence in opening an account. Product knowledge is the variable that most significantly affects customer trust compared to other variables studied based on the path coefficient value, which is greater than the affability and communication skill variables. This is in line with the research, which shows that product knowledge is the variable with the most significant influence (Siagian et al., 2020). These results show that a marketer with comprehensive knowledge about various bank products and services instills consumer trust. Communicating various product choices' features, benefits, and advantages builds trust. In addition, this product knowledge enables marketers to answer questions that consumers may have, such as about the security of deposit money, interest rates, and ease of transactions, which can provide certainty and build consumer confidence. By having adequate product knowledge, marketers can effectively communicate product advantages, answer consumer questions, and provide appropriate recommendations for financial solutions needed by prospective customers.

The affability variable also has a positive and significant effect on perceived trust, where the friendlier a salesperson, the greater the consumer's confidence in opening an account. These results indicate that consumers are more likely to trust marketers who show a friendly attitude. Friendly sellers can effectively listen to questions, understand consumers' financial needs, and provide recommendations for effective banking solutions. By combining friendliness with product knowledge, marketers can create a powerful combination for instilling trust in consumers. The friendliness of a seller plays an essential role in building consumer trust when opening a bank account because it creates a positive impression, establishes good relations, and fosters an open communication environment. This is similar to research which shows that friendliness has a positive effect on customer trust (Wasti & Tan, 2010). Research also revealed that customers who perceive sellers as friendly and experts are more likely to trust them (Zhang et al., 2016). That trust has a positive impact on enrollment intention.

Communication skill has a positive and significant effect on perceived trust, meaning that the higher the communication skill, the higher the perceived trust, and vice versa, the lower the communication skill, the lower the perceived trust. Effective communication is essential in building and maintaining relationships with consumers. Marketers with solid communication skills can convey information, listen well to consumers, and adapt their communication style to understand the needs of each customer. Marketers build consumer trust by informing consumers of the benefits and features of various bank products in a concise and easy-to-understand manner. In addition, good listening skills enable marketers to provide financial solutions that meet consumer needs. Perceived trust, which is a moderating variable from the influence of product knowledge, affability, and communication skills on enrollment intention, has a positive and significant influence on enrollment intention, meaning that the higher the perceived trust, the higher the enrollment intention, and vice versa, the lower the perceived trust, the lower the enrollment intention.

The results of this study are in line with the results of research by Doney and Canon (1997), which show that communication made by salespeople in business has a positive effect on trust in the salesperson, so it will also have a positive effect on trust in the company where the salesperson works. In addition, this research is also to Wartini's research (2008), which proves that communication skills will build trust (Siau & Wang, 2018). Research also shows that one of the communication skills, such as listening to customers carefully, can increase customer trust in marketers (Itani et al., 2019). The results of this study indicate that perceived trust can mediate the indirect effect of product knowledge, affability, and communication skills on enrollment intention. The results of this study support the results of previous research conducted by (Moriuchi & Takahashi, 2016), which adopted trust as a mediator variable in studying the relationship between marketing mix and online shopping behavior and found that trust is a potent mediator or intervening variable. Their study shows that trust can be a partial or complete mediator.Research found that customer trust has a mediator effect in their study of Malaysiaï¿½s life insurance policy sector (Panigrahi et al., 2014). Trust was also found to have a mediating effect in a study of restaurant business in Korea (Kim & Shim, 2018). In conclusion, trust is consumer behavior in which consumers are willing to rely on and entrust a product or service and the promises or information made by the seller.

CONCLUSION

The conclusion obtained from the results of this study is that product knowledge, affability, and communication skills affect perceived trust, and where perceived trust has a positive effect on enrollment intention, this means that the higher the product knowledge, affability, and communication skills of marketers, the higher the consumer trust of the product to be purchased, which will further increase consumer interest in buying the product. Perceived trust in this study is proven to mediate the effect of product knowledge, affability, and communication skills on enrollment intention. What can be done to increase perceived trust is to communicate appropriately to the public about company recognition, rewards, and performance and empower credible influencers. Based on the path coefficient value in the direct effect test, product knowledge is the most potent variable in influencing the emergence of perceived trust. Therefore companies need to encourage to increase the capacity of marketers through various efforts of training, personal experience, Q&A forums, coaching & mentoring, and attractive incentives for marketers.

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