Consumer Trust as the Antecedent of Online Consumer Purchase Decision

Abstract

The e-commerce industry in Indonesia is growing in line with the increasing number of internet users in Indonesia. Unfortunately, many internet users in Indonesia are still unsure about shopping online because of the lack of buyer trust with sellers and service providers. This study aims to identify the factors that influence online shop consumers to conduct transactions online. This research used a questionnaire survey distributed to customers who had ever used an online shop application. The sample used in this research was 468 respondents. The data collected was then analyzed using Partial Least Square. The results of this research indicated that trust, perceived value, and buying interest positively influence consumers’ decisions to purchase using an online shop application.

1. Introduction

The number of internet users in Indonesia until the second quarter of 2020 reached 196.7 million or 73.7 percent of the population. This number increased by about 25.5 million users compared to the previous year [1]. However, the number of internet users is not comparable to e-commerce activities carried out by Indonesia’s people. Of Indonesia’s total population connected to the internet, only 10.4 million people have ever recorded an online shopping activity [2]. The Wall Street Journal mentioned two factors causing the low online shopping activities of Indonesian people. Firstly, it rarely uses debit and credit cards as a means of payment for Indonesia’s people. Secondly, consumers’ distrust to do an online transaction is due to the many cases of fraud that occur in cyberspace [3].

Although the number of online transactions is still considered small, the development of Indonesia’s e-commerce market and the industry is predicted to continue to increase. The value of e-commerce transactions in Indonesia is predicted to reach 130 billion US dollars or around 1.7 quadrillion rupiahs by 2020 [3]. Internet users in Indonesia in early 2021 reached 202.6 million people. This number is an increase of 15.5 percent or 27 million people when compared to January 2020. The total population of Indonesia itself currently is 274.9 million people. This means that internet penetration in Indonesia in early 2021 will reach 73.7 percent. It is recorded that 96.4 percent or 195.3 million Indonesians access the internet via cell phones [4]. Based on the results of the Central Bureau of Statistics in 2020, it shows that there were 1162 businesses that carried out e-commerce activities during 2019. It was recorded that 45.93 percent of new businesses started operating in the 2017–2019, and only 15.49 percent of businesses that have been operating for more than ten years. Of the 53.52 percent of businesses that immediately carried out e-commerce activities when they started operating, 51.97 percent were businesses engaged in the wholesale and retail trade, repair and automotive maintenance sectors [5]. The data showed that the e-commerce industry in Indonesia continues to experience growth throughout the year. This phenomenon had contributed to the emergence of various e-commerce companies that offer various benefits for users, such as lower prices and secure transaction methods.

An example of success from an Indonesian e-commerce company is Shopee-Indonesia. Shopee Indonesia was established in December 2015 under the auspices of Shopee International Indonesia. Shopee Indonesia has succeeded in developing into one of Indonesia’s e-commerce giants in a relatively short period. As of October 2017, 25 million users had downloaded the Shopee application. Shopee recorded total transactions (gross merchandise value/GMV) worth 4.1 billion US dollars, or around 59.4 trillion rupiahs first of semester 2018. That figure increased 173% compared to the first semester in 2017, which recorded a figure of 1.5 billion dollars. Shopee served 239.4 million orders during the first half of 2018 or increased from 80.6 million in the same period in 2017. 40% of the total orders are transactions that occurred in Indonesia. Going forward, Shopee’s gross merchandise value (GMV) or total transaction target regionally targeted at 8.2 billion dollars or around 117.3 trillion rupiahs in 2018. Besides, Shopee also serves transactions in other countries such as Singapore, the Philippines, Thailand, Malaysia, Vietnam, and Taiwan. As a result, Shopee recorded revenue of 92.4 million dollars. The revenue consisted of 59.2 million dollars from commissions and advertisements and 33.2 million dollars remaining from the sale of goods [6].

The online shop’s success in the Indonesian e-commerce market is an exciting matter to be explored further. The course of buying and selling activities between consumers and companies cannot be separated from consumers’ purchase decisions to use e-commerce services. According to Kotler [7], the purchase decision is consumers’ decision to buy or not buy a product offered. Many factors can influence consumer purchase decisions, such as buying interest, perceived value, price, and trust in the product [8,9,10]. These factors can certainly be the direction of determining e-commerce company strategy in Indonesia in order to increase user transactions.

The increasing lack of trust from cyber consumers is a problem faced by online service providers [11]. The dynamics of the relationship between consumer trusts that have the final consequence on the trust decisions are still a challenge for researchers in the field of e-commerce. There has not been much research examining this, especially the mediation aspect as an explanation between trust and buying behavior. Building trust for cyber consumers is an important thing in building a sales system from e-commerce [11]. This research was conducted to identify the factors influencing consumers’ decisions to use services and buy products using the online shop application. The factors examined in this research were developed and examined in several previous studies and reused in online shop service users’ specific research objects. The factors chosen include consumer trust, perceived value, and buying interest. These research results are expected to contribute to mapping Indonesian e-commerce consumers’ behavior, considering there was very little research that had focused on online purchase decisions in Indonesia. This research is quite significant considering the considerable potential of e-commerce in Indonesia which is predicted to continue to grow. Also, many e-commerce businesses in Indonesia have gone bankrupt because they have not succeeded in attracting the interest of the public using the services provided. Building consumer trust is an important factor in the development of e-commerce. Several studies have examined the role of consumer trusts related to purchase intentions in the online marketplace. The trust variable is also an important aspect that plays a role in influencing online consumer purchase decisions. This research aims to examine the role of consumer trust in influencing online purchasing decisions through the aspect of value and online buying interest. Trust is part of a strategy in maintaining long-term relationships with online consumers [12]. The systematic study conducted Bauman and Bachmann [12] shows there are research opportunities in the development of the consumer trust model. The dynamics of the hierarchical relationship of the trust aspect in explaining purchasing decisions are still very limited, especially through the perceived value factor. The combination of two aspects, namely trust and value, in explaining hierarchical online purchasing decisions is still limited, even though perceived value and trust are the main driver components in explaining purchase intentions [13].

2. Literature Review and Hypothesis

According to Shelly et al. [14], electronic commerce or e-commerce is a business transaction activity in an electronic network such as the internet. Kalakota & Whinston [15] defined e-commerce as a shopping activity, transaction activity, or digital money transfer using the internet. Zhaohao [16] put a group of e-commerce activities that can be classified as B2B (Business to Business), B2C (Business to Consumer), or C2C (Consumer To Consumer). E-commerce provides benefits for consumers to shop without the hindrance of time and place because users can access and transact on the site anytime and anywhere. Besides, e-commerce also provides more comparative information for consideration before buying, such as price comparisons between one seller and another. E-commerce can also be a means or platform for commercial, trade, communication, community, collaboration, business process, service, and learning purposes [17]. Kotler and Armstrong [18] defined purchase decisions as one of the stages experienced by consumers in buying or not buying a product offered. Schiffman and Kanuk [8] defined purchase decisions as a decision taken by someone to choose the best choice from several choices offered. This decision comes with a real follow-up and an evaluation of the decision taken to determine the next purchase’s attitude.

In deciding to purchase a product, the customer’s recent experience in using the product also influences its decision-making process [19]. Chen et al. [20] suggested that many factors influence consumer purchase decisions. Consumers generally consider the quality, price, and product reputation factors for the purchase decision. Knowledge of the factors that influence online purchase decisions can help the industry create strong bonding relationships with customers. Purchase decision factors are also useful in developing and implementing e-commerce strategies to achieve its long-term goals, such as having good relationships and customer loyalty. Trust is an important factor in maintaining long-term relationships with customers [12]. Trust also hierarchically plays a role in shaping perceived value because cognitive and affective trust will shape consumer acceptance of the values conveyed [21]. Bator and Lengyel [22] using sections of the funnel, namely top of the funnel, middle of the funnel, and bottom of the funnel, in measuring online marketing performance. The explanation of the hierarchical model adjusts the stages [22], namely the stage of awareness related to trust, considerations related to perceived value and decisions including interests and online buying decisions.

2.1. Buying Interest

Intention is the closest predictor of behavior, this is confirmed in the model developed by Azjen in the theory of reasoned action as well as the theory of planned behavior. Intention plays a unique role in directing action, namely connecting the deep consideration that a person believes and wants with certain actions. Based on the description above, it can be concluded that intention is a person’s desire to perform an action or bring up a certain behavior accompanied by certain efforts. Intention is considered as an intermediary for motivational factors that have an impact on a behavior [23]. According to Kotler [7], buying interest is a feeling that arises from consumers after seeing or knowing information about the product; consumers would try the product and ultimately have the desire to buy and own the product. Assael [24] defined buying interest as consumers’ tendency to buy or take actions related to the buying of a product that is measured by the likelihood of consumers to make transactions. Buying interest is obtained because of perceptions formed from the learning process and thought process. Buying interest is a motivation that continues to be recorded in consumers’ minds and becomes a desire that must be fulfilled.

The formation of intentions can be explained by a theory of planned behavior which assumes that humans always have a purpose in behaving [23]. Previous research had identified the positive influence of buying interest on the decision to buy a product. As an example of research from Hersche [25], which showed the relationship between buying interest and purchasing decisions, high consumer buying interest will encourage consumers to buy the desired product. Conversely, low buying interest will prevent consumers from buying the product. If the buying interest is high, the consumer will decide to buy the product. Hence it can be hypothesized that:

Hypothesis 1 (H1): 

Buying interest has a positive influence on purchase decisions.

2.2. Value Perception

The perception of value is also a trade-off which is the main focus in the science of marketing, where value acts as an appropriate measure of any exchange, whether it is appropriate or not [18]. Value perception is measured by exchanging consumers’ expectations with the products offered, including whatever products are appropriate or not [7]. Consumers value a product using utilitarian value and experience value. Utilitarian value is the value measured from the product’s functional benefits, for example, the benefits from cost savings, time savings, and the superiority of services provided. While the value of experience represents the benefits gained from experience in the form of entertainment, visual appeal, and interactivity involved when doing shopping activities [26]. When shopping online, the perception of value is a consumer’s assessment of the benefits obtained compared to the costs incurred for online transactions [9]. Value perception can be concluded as consumers’ attitude in assessing a product or service and measured from the comparison of the perceived benefits and the costs incurred for consuming a product. Value transactions are a conceptual form of assessing the benefits [18] that can be obtained when shopping online, in accordance with the concept of total consumer needs that are considered by looking at prices and considering perceived trust so that it raises the intention to make purchases at vendors on the internet.

Lien et al. [27] stated that the value perceived by consumers has a significant impact on interest in buying a product. Value perception positively influences if consumers’ quality perception is higher when compared to the perception of the sacrifice of consumers to obtain the product. Hsin and Wen [10] also showed a positive relationship between the perceived value of consumers and consumer buying behavior. If consumers’ perceived value is significant, this value will direct consumers to the decision to buy products in the form of buying interest. Thus, it can be hypothesized that:

Hypothesis 2 (H2): 

Value perception has a positive influence on buying interest.

2.3. Consumer Trust

Trust is one of the main components that drive purchases [13]. The explanation mechanism regarding trust is related to the risk aspect. Risk is a form of negation that encourages consumers not to behave. The trust that is built will minimize the risk [13] so that consumers can make decisions without impact in the future. Ba and Pavlou [28] defined trust as an assessment of a person’s relationship with another person who will carry out certain transactions according to the expectations of those who carry out transactions in an uncertain environment. This trust cannot just happen but must be built early, developed, and consistently proven. When consumers want to shop or do other online transactions, consumers need assurance that the funds transferred will not just disappear, and the products they receive must be following what was promised and explained on the intended e-commerce page. Consumer trust when conducting online transactions is an essential requirement to have the confidence to make transactions. Online shopping is vulnerable to specific risks such as fraud, malware, and system errors, making many consumers distrust online transactions and cancel their intention to transact. As seen from the public’s sensitivity to the quality and safety of online shopping, trust plays an essential role in influencing the success or failure of online transactions [29].

The content available on the seller’s website also affects consumers’ trust, so e-commerce actors need to provide content and transaction systems that are trusted so that consumers are sure to make transactions and are interested in buying. On the other hand, if the seller’s website cannot convince consumers, it can negatively influence consumer trust in e-commerce service providers [30]. The absence of face-to-face contact between consumers and product providers also adds to the critical aspect of e-commerce services that can be trusted [31].

Online trust is formed from consumers’ perceptions of the risks and benefits of online transactions. If consumer trust to shop online increases, consumer purchase intention tends to be higher [32]. Trust also affects consumers’ psychological value to consider the product to be purchased, including at the level of consumers tolerating the vulnerability received in transactions [33]. Consumers who believe in the services provided online certainly expect to get good quality and guaranteed security. Thus, the reputation of quality e-commerce with guaranteed security will have more value in consumers’ eyes. Based on previous studies, the hypothesis is proposed as follows:

Hypothesis 3 (H3): 

Consumer trust has a positive influence on value perception.

Hypothesis 4 (H4): 

Consumer trust has a positive influence on buying interest.

3. Methodology

This research used primary data obtained directly from research subjects. The population of this research was all the people who had used the online shop application. Samples were taken using a convenience sampling technique of 468 respondents. Data is obtained using the questionnaire method distributed to respondents using the Google Form application. Each variable’s evaluation is measured using a 1–5 Likert Scale to obtain interval data from the questions asked. The data collection instrument was adapted from previous research. The variables measured in this research were variables of purchase decision [20], buying interest [34], value perception [9,33], and trust [35].

The data analysis technique used in this research was the Partial Least Square. The validity test is measured by identifying the standardized loading factor and average variance extracted values. If the standardized loading factor of each indicator variable and average variance extracted (AVE) meets a minimum value of 0.5 and a minimum p-value of 0.05 [36], the data is declared valid. The reliability test is measured by looking at the composite reliability and Cronbach’s Alpha values of each variable. If all variables have a value of 0.7, all indicators are said to be reliable.The model is also tested for its feasibility using several indicators of model testing.

4. Result and Discussion

4.1. Validity Test and Reliability Test

The validity test in this research was carried out using the WarpPLS. The purpose of the validity test is to determine the validity of each statement against the research variables. Data is valid if the standardized loading factor of each indicator variable, and the average variance extracted (AVE) meets a minimum value of 0.5 and a minimum p-value of 0.05 [36]. The validity test results on 468 respondents using the WarpPLS application are outlined in the following

Table 1. Convergent Validity and AVE.

Item	Trust	Value Perception	Buying Interest	Purchase Decision	p-Value	AVE	Result
KP1	0.845				<0.001	0.706	Valid
KP2	0.86				<0.001		Valid
KP3	0.812				<0.001		Valid
KP4	0.831				<0.001		Valid
KP5	0.851				<0.001		Valid
PN1		0.871			<0.001	0.785	Valid
PN2		0.891			<0.001		Valid
PN3		0.854			<0.001		Valid
PN4		0.927			<0.001		Valid
MB1			0.854		<0.001	0.763	Valid
MB2			0.842		<0.001		Valid
MB3			0.885		<0.001		Valid
MB4			0.912		<0.001		Valid
KPB1				0.858	<0.001	0.708	Valid
KPB2				0.839	<0.001		Valid
KPB3				0.799	<0.001		Valid
KPB4				0.868	<0.001		Valid

:

From the validity test, the results showed that all indicators were valid because it meets the loading factor value and the average variance extracted (AVE) of more than 0.5 and the p-value of below 0.05. The reliability test using the WarpPLS version 6 application is presented in

Table 2. Reliability Test.

Variable	Composite Reliability	Cronbach’s Alpha
Consumer trust (KP)	0.923	0.896
Value perception (PN)	0.936	0.908
Buying interest (MB)	0.928	0.896
Puchase decision (KPB)	0.906	0.862

below:

Based on the reliability test results presented in Table 2, it showed that all indicators were reliable because the value obtained was more than 0.7. These results proved that all values had met the minimum value of reliability [36].

4.2. Model of Fit

The structural model analysis is carried out to test the model’s suitability using several indicators obtained from the model test using the WarpPLS application. Based on the goodness of fit model, several model indicators show that all indicators meet the goodness of fit rules, as presented in

Table 3. Godness of Fit.

Index	Result	Model Evaluation
Average path coefficient (APC)	p < 0.001	Good
Average R-squared (ARS)	p < 0.001	Good
Average adjusted R-squared (AARS)	p < 0.001	Good
Average block VIF (AVIF)	1884	Good
Average full collinearity VIF (AFVIF)	2309	Good
Tenenhaus GoF (GoF)	0.611	Good

.

4.3. Hypothesis Test

Hypothesis testing is done to find out the correlation between the variables examined in this research. Each variable was tested using path analysis in WarpPLS. The hypothesis was stated to meet the criteria for support if the p-value was less than 0.05. Simultaneously, the coefficient value β indicated the presence or absence of the influence of the analyzed variables. A value of β equal to zero indicated no influence of the variable being tested. In contrast, the value of β, which was not equal to zero, indicated an influence on the variable being tested. The results of the path analysis are illustrated in Figure 1 below:

The path analysis results using the WarpPLS showed that all hypotheses had passed the minimum p-value < 0.05, so it can be concluded that all proposed hypotheses were supported. The H1 hypothesis was supported by p-value < 0.01 and β of 0.62. The H2 hypothesis was supported by p-value < 0.01 and β of 0.62. The H3 hypothesis was supported by p-value < 0.01 and β of 0.64. The H4 hypothesis was supported by p-value < 0.01 and β of 0.62.

The hypothesis test results illustrated in Figure 1 showed that all hypotheses in this research were supported. Hypothesis 1, with a value of β = 0.62 and p < 0.01, proved that there is a positive influence between buying interest (MB) and purchasing decisions (KPP). This research’s results were consistent with previous studies, including research from Hersche [25], which found a positive influence between buying interest and purchasing decisions. Interest in buying goods online will influence consumers to decide whether to buy the desired item. However, the factors that can influence consumers to buy goods on the internet need to be further identified. Therefore, this research examined two variables that can influence consumer buying interest online, namely value perception and trust.

Hypothesis 2 was supported by β = 0.55 and p < 0.01. This value proved a positive influence between value perception (PN) and online shop consumers’ buying interest (MB). These results were in line with previous studies’ results by Hsin and Wen [10], and Lien at al. [27], which indicated that consumers felt that consumers’ positive value after using, hearing, seeing, or considering a product could support consumer interest in buying or using an e-commerce service. Consumers will consider whether the use-value of an item to be purchased can be proportional to the cost. The analysis showed that the value of an item offered by a vendor could influence the interest to buy online. It should be noted that the value perception of an item offered online can be different from the item directly offered because consumers who buy products online cannot see and hold the goods directly. The value perception can change the way consumers consider the value of goods offered on the internet. It may have different considerations compared to the way consumers value a tangible product because consumers can immediately feel the quality of the item rather than just looking at pictures and reviews on the internet.

Trust turns out to influence the value perception and buying interest positively. This opinion was proven from the results of the hypothesis test conducted. Hypothesis 3 was supported by the value of β = 0.64 and p < 0.01. Similarly, hypothesis 4 was supported by the value of β = 0.33 and p < 0.01. These results indicated that consumer trust (KP) had a positive influence on consumers’ value perception (PN) and buying interest in a product (MB). This finding is in line with research with similar variables conducted by Reichheld and Schefter [31], and Leeraphong and Mardjo [32]. Online transactions eliminate some consumer access to the goods bought; consumers cannot see the goods’ condition before being purchased and cannot see the seller directly. In some conditions, this could make consumers hesitate to make transactions. This phenomenon made consumer trust very important to be maintained for e-commerce service providers. One way to increase trust was to create a professional display of the site [30]. The site’s reputation and testimonials from other consumers also affected increasing consumer trust for transactions with online shop applications.

5. Conclusions and Recommendation

This research’s findings explained that consumer purchase decisions to purchase through the online application were positively influenced by buying interest, value perception, and trust. This finding indicated that online businesses must build customer trust and provide easiness and security for customers to conduct transactions. It is intended that consumers’ benefits and comfort were more significant than the value of sacrifice or risk received by consumers. E-commerce companies are perceived to have better value depending on the perceived benefits, the negligible costs that accompany the purchase, and the risks incurred during the purchase. Trust and positive value perceptions contributed a positive influence to the buying interest of online consumers. This finding could be one of the considerations for Indonesia’s e-commerce companies in determining the company’s strategy going forward. E-commerce consumers in Indonesia were still afraid of doing online shopping because of the rampant fraud cases in cyberspace. A company reputation that was safe and convenient for shopping was undoubtedly a primary consideration for consumers to shop on the internet. E-commerce companies that had an insecure reputation had the potential to reduce buying interest from consumers. Therefore, a company strategy that focuses on increasing consumers’ trust is essential in Indonesia’s e-commerce industry today.

There are critical managerial implications from this study. This research is expected to help the development of the rapidly developing e-commerce industry in Indonesia. Through the findings of this research, the online shop had proven to increase the number of transactions by building good trust for consumers. Online shops can build value perception to generate consumer purchase interest, which impacts purchases. Vacuum perception is constructed using utilitarian value and experience value, such as the benefits from cost savings, time savings, the superiority of services provided, entertainment, visual appeal, and interactivity.

This research needs to be further developed so that academics and practitioners who are struggling in e-commerce can implement better policies in the future. The testing of a similar hypothesis framework on other e-commerce companies might produce different findings. Besides, the different lifestyles of the generations are oriented towards buying online [37] so that the lifestyle based on the generations can be considered for further research. The e-commerce consumer’s repurchase intention can also be the next topic for future research development. Following the sample characteristics in this study that young consumers dominate, this study’s limitation does not consider other characteristics, such as social aspects, communication styles, and risk perception. Future research may consider these aspects, including the role of social media [37], communication pattern [38], and risk [39] for future studies.