1. Introduction
Information technologies, such as web-based e-commerce systems, provide a tremendous transaction platform on which to sell products or services to a large number of potential buyers. According to recent research, retail e-commerce sales worldwide reached 1.86 trillion USD in 2016 and are projected to grow to 4.48 trillion USD in 2021 [
1]. Also, in the Republic of Korea, e-commerce sales were 1.4 trillion KRW in 2001 and have grown to 49.6 trillion KRW in 2017, with a compound annual growth rate of 25% [
2].
Trust plays a vital role in the sustainability of e-commerce transactions. The issue of trust is more critical in e-commerce transactions than traditional commerce transactions due to higher uncertainty [
3]. According to PYMNTS and Signifyd—institutions that track and analyze fraud by examining the transactions of more than 5000 e-commerce merchants in Europe, North America, and Asia—total fraud increased 5.5% from Q2 2016 to Q2 2017, and the value of potential fraud was estimated to be 57.8 billion USD, as of October 2017 [
4]. This implies that e-commerce fraud is becoming more serious, which may lead to consumer anxiety. According to research in 2016, respondents were asked, “How often are you afraid that you might be the victim of a fraud when buying products and services online?” Forty percent of the respondents said that they were afraid of e-commerce fraud to the extent of ‘always’, ‘very often’, and ‘often’ [
5]. Seventeen percent of the respondents said that they had been victims of e-commerce fraud. This shows the level of concern over online shopping fraud in Finland in 2015 [
5]. In the Republic of Korea, the Korean Consumer Agency (KCA) received 3261 complaints of e-commerce fraud in 2005 and 8881 complaints in 2016 [
6].
Figure 1 shows the relationship between e-commerce sales and fraud cases. As shown in this figure, the number of the complaints of e-commerce fraud has been increasing, as the e-commerce market size increases in the Republic of Korea.
The term “distrust” is used in research articles to represent negative feelings of uncertainty [
7,
8,
9]. E-commerce transactions are executed based on trust, but there is plenty of room where distrust resides [
6,
7,
8]. If distrust is an antonym of trust in its concept, the lack of research on distrust matters little. However, if trust and distrust are not simply opposite concepts but play distinct roles, we need a systematic approach for studying trust and distrust separately [
8,
9,
10]. According to McKnight et al. [
11], e-commerce trust is distinguished by three dimensions: intrapersonal-level trust (i.e., buyer), system-level trust (i.e., intermediary), and interpersonal-level trust (i.e., seller). In the e-commerce context, the opportunity for trust and distrust exists between not only the buyer and the seller but also between the buyer and the intermediary through which the monetary transactions flow [
3,
11]. It is crucial to have a comprehensive understanding of how these different levels of trust or distrust (intrapersonal-level, system-level, and interpersonal-level) influence the intention to purchase using e-commerce. This issue of having multiple levels of relationships and trust or distrust at the same time has not been considered in prior research. In this combined approach, we need to consider the concept of trust transfer. Trust is transferable from a better-known party to an associated party [
12]. In e-commerce, the intermediary plays the institutional role of agent in the trust function for trust in a seller [
13]. Trust in the intermediary transfers to trust in the seller.
In 2015, the KCA reported that 90% of the “baek-su-o” products in the market were not genuine and contained fake supplements. “Baek-su-o” had been well known as an antioxidant that provided immune system support, and its market size was three hundred million dollars in Korea. After KCA’s report was published, the “baek-su-o” market crashed amidst a massive refund crisis. In one case, the Hyundai Home Shopping Network (HHSN) took responsibility and paid eight million dollars as refunds to buyers. Did HHSN have to take responsibility even though they did not make the product? What should an intermediary like HHSN do to get trust and prevent distrust? What should sellers do to get trust and prevent distrust? If an intermediary gets trust, does the trust transfer to the seller and vice versa? This event was the trigger for this study.
We are interested in the following three questions: (1) What is the role of trust and distrust in the multilevel nature of e-commerce? (2) Do trust and distrust transfer from the intermediary to the seller? (3) What are the antecedents of trust and distrust in intermediary? To answer these research questions, this article is set out as below: firstly, literature related to the multiple levels of trust and trustor distrust are reviewed as a theoretical background; next, a research model with three levels (buyer, intermediary, and seller) of trustor distrust is proposed; thirdly, the research design and method of data collection is discussed; fourthly, the results of the data analysis are presented; and finally, the paper concludes with a discussion of the results, contributions, limitations, and future directions.
3. Research Model and Hypotheses
In consideration of the multi-level trust model (buyer, intermediary, and seller levels) and distrust, the research model illustrated in
Figure 9 is proposed.
Structural assurance is an institutional trust in which a buyer perceives robust structures that ensure that a successful e-commerce transaction will take place under safe and secure circumstances [
9]. Safe and secure circumstances, inclusive of information quality, privacy protection, and security protection, which are perceived by the buyer, provide a setting that positively affects the buyer’s trust. According to previous studies, structural assurance has a positive effect on the trust of e-marketplace participants, and positively influences buyer’s trust in e-marketplace operators (i.e., intermediaries) [
9,
36,
37]. Recently, third-party seals (TPS) help buyers to build trust. A TPS is a structural assurance by a certifying institution, such as a bank, accountant, or government that positively affect the buyer’s trust [
30,
37]. Also, a parallel distrust concept is defined when a buyer does not perceive that protective structures are in place [
9]. Therefore, we derived the following hypotheses:
Hypothesis 1a: Structural assurance positively affects a buyer’s trust in an intermediary.
Hypothesis 1b: Structural assurance negatively affects a buyer’s distrust in an intermediary.
Based on uncertainty, a buyer tends to trust websites with up-to-date, rich, and easy-to-understand information [
8,
30]. One experiment showed whether buyers’ perception of website quality affected their trust. Participants were provided with eight distinct versions of websites with flaws (style, incompleteness, and language) and asked questions to see how the flaws affected their feelings of trust. The result showed that website pages with more flaws could only attain a low level of trust [
38]. A similar experiment showed that investment in website design affects buyers’ trust. Even when two sites offer the same content, a highly-funded website with a better design attains a higher level of trust from buyers [
39]. Plenty of previous studies have empirically shown the effect of perceived website quality in e-commerce on trust or distrust [
14,
27,
40,
41]. Therefore, we suggest the following hypotheses:
Hypothesis 2a: A buyer’s perceived website quality positively affects his/her trust in an intermediary.
Hypothesis 2b: A buyer’s perceived website quality negatively affects his/her distrust in an intermediary.
Previous research suggests that trust may be transferred from different types of sources, like an individual or an organization. In e-commerce, trust is transferred across hypertext links back and forth through the perceived interaction and similarity, between linked organizations [
28]. Trust in an intermediary is an antecedent to trust in a seller. Potential buyers must develop trust in the intermediary first, and then trust in the intermediary will promote trust in the seller [
42]. Likewise, trust can be transferred from an intermediary to a seller in e-commerce [
13]. Several previous studies have empirically proven the presence of trust transfer in an e-commerce context [
13,
15,
36,
43]. Also, Oh [
44] have shown that distrust in the seller transfers to distrust in the intermediary. This finding implies that distrust can also be transferred.
In e-commerce, perceived risk may act as an entry barrier. Contrary to a retail shop, in e-commerce, the potential buyer must make a decision armed only with information provided by a website. Trustworthiness of an intermediary with institutional operating systems is helpful in building up trust in the seller by reducing perceived risk [
36]. Not only trust but also distrust can affect the perceived risk of the customer. In general, an e-marketplace operator (i.e., intermediary) provides an online marketplace where sellers and buyers can buy and sell goods and adjusts them to make transactions smoothly under the responsibility of the sellers and buyers. Therefore, all the problems that may arise in connection with the transactions between the seller and the buyer belong to themselves. Because of this characteristic, e-marketplaces have a high potential for opportunistic seller behavior and transaction uncertainty, which can act as a factor of distrust that can coexist with trust in a different dimension [
45].
Trust and distrust affect purchase intentions in e-commerce [
7,
8,
27]. Trust and distrust affect the willingness to purchase, intention to use, and willingness to share information [
9]. Based on the above theoretical observations, potential buyers’ perceived risks affect their intention to purchase. We need to consider both trust and distrust of the intermediary and the seller as antecedents to perceived risk and intention to purchase. Along this line, we propose the following nine hypotheses:
Hypothesis 3a: A buyer’s trust in an intermediary positively affects their trustor distrust in the seller.
Hypothesis 3b: A buyer’s trust in an intermediary negatively affects their perceived risk.
Hypothesis 3c: A buyer’s trust in an intermediary positively affects their intention to purchase.
Hypothesis 4a: Distrust in an intermediary negatively affects trustor distrust in the seller.
Hypothesis 4b: Distrust in an intermediary positively affects perceived risk.
Hypothesis 4c: Distrust in an intermediary negatively affects a buyer’s intention to purchase.
Hypothesis 5a: Trustor distrust in a seller positively affects a buyer’s intention to purchase.
Hypothesis 5b: Trustor distrust in a seller negatively affects perceived risk.
Hypothesis 6: Perceived risk negatively affects a buyer’s intention to purchase.
The natural propensity to trust is derived from a potential buyer’s mood or attitude, that is derived from personal experience and his/her culture [
11,
16]. Natural propensity acts as a reference point, so a relative approach is needed rather than an absolute scale for determining trust [
44]. The buyer who has a high level of natural propensity to trust tends to trust the e-commerce intermediary and seller [
34]. Therefore, we posit:
Hypothesis 7a: A buyer’s natural propensity to trust positively affects their trust in an intermediary.
Hypothesis 7b: A buyer’s natural propensity to trust positively affects their trust in a seller.
Hypothesis 8a: A buyer’s natural propensity to distrust positively affects their distrust in an intermediary.
Hypothesis 8b: A buyer’s natural propensity to distrust negatively affects their trust in a seller.
Word-of-mouth (WOM) is the expressed opinions of others as to whether they are favorable or unfavorable to a seller’s products or services. A typical characteristic of WOM is that it is often excessively positive or extremely negative [
46]. People usually share their feelings and opinions when they feel extreme satisfaction or dissatisfaction. WOM is a buyer’s direct opinion of a seller. The term WOM particularly applies to e-commerce. Therefore, we propose the following hypotheses:
Hypothesis 9: Positive eWOM positively affects a buyer’s trust in a seller.
Hypothesis 10: Negative eWOM negatively affects a buyer’s trust in seller.
5. Data Analysis and Results
We tested the hypotheses using SmartPLS 2.0, which is a PLS (Partial Least Squares) analysis tool. PLS is well known for its effectiveness regarding validity and statistical parsimony, even if there are not enough samples [
47]. PLS tests the measurement model for its reliability and validity. Next, the structural model was tested to see the causality of its variables and verify the research hypotheses.
5.1. Testing the Measurement Models
As a first step of PLS analysis, the measurement model was tested for reliability of internal consistency and for construct validity. The reliability of internal consistency was tested using Cronbach’s alpha and composite reliability. Cronbach’s alpha is the coefficient of reliability and composite reliability (CR) indicates internal consistency. Both should be higher than the minimum cutoff score of 0.7.
Table 5 shows that all Cronbach’s alpha results were higher than 0.7 and all CRs were greater than the benchmark of 0.7. This means the measurements were reliable and the factors measured the constructs consistently.
Construct validity was tested using the subcategories of convergent validity and discriminant validity. Convergent validity was examined in terms of average variance extracted (AVE), and it was accepted when AVE was greater than 0.5. Discriminant validity was also examined in terms of AVE, and it was accepted when the square root of AVE was greater than all cases of correlations between each pair of constructs.
Table 5 shows that all AVE were greater than 0.5, indicating that most of the variances were explained by their constructs.
Table 6 shows that every square root of the AVE (diagonal elements) was higher than 0.5, indicating that each measurement explained the intended construct without overlapping an adjacent construct. Factor loading can be used to test convergent validity and discriminant validity as an alternative. A set of confirmatory factor analyses was conducted and we can be reasonably assured that the measurement models had the reliability of internal consistency and construct validity. The next step was testing the structural model.
5.2. Testing the Structural Model
Figure 10 and
Figure 11 and
Table 7 show the structural model results of statistical tests (
t-test) of path coefficients to draw conclusions regarding the research hypotheses and measures of the validity (
R2) of the model that explain how well the model fit. Structural assurance (β = 0.347,
t = 3.67) and perceived website quality (β = 0.476,
t = 5.85) show positive effects on trust in intermediaries (H1a and H2a were supported). Structural assurance (β = −0.277,
t = 1.99) showed a positive effect on distrust in intermediaries (H1b was supported). In contrast, perceived website quality (β = −0.163,
t = 1.05) did not show a negative effect on distrust in intermediaries (H2b was not supported).
For trust transfers between intermediaries and sellers, trust in intermediaries (β = 0.547, t = 6.09) showed a transfer to (or a strong positive effect on) trust in sellers (H3a was supported). However, trust in intermediaries (β = 0.547, t = 0.52) did not show an effect on trust in sellers (H3b was not supported). Also, trust in intermediaries (β = 0.234, t = 1.54) did not show an effect on the trust in sellers (H3c was not supported).
Distrust in intermediaries (β = 0.043, t = 0.43) did not show a transfer to (i.e., any effect on) trust in sellers (H4a was not supported). This result shows that promoting trust in intermediaries is more important than reducing distrust in intermediaries to promote trust in sellers. Distrust in intermediaries (β = 0.545, t = 5.60) shows a strong effect on perceived risk (H4b was supported). This means intermediaries should pay attention to avoid distrust rather than to promote trust when perceived risk needs to be controlled. Distrust in intermediaries (β = 0.100, t = 0.82) did not show an effect on trust in sellers (H4c was not supported).
Trust in sellers (β = 0.395, t = 2.83) showed an effect on intention to purchase, but trust in sellers (β = −0.103, t = 0.73) showed no effect on perceived risk (H5b was not supported). Perceived risk (β = −0.228, t = 2.14) indicated a negative effect on purchase intentions (H6 was supported).
Trust in an intermediary directly affected trust in the seller, which positively influenced the buyer’s purchase intention; whereas, distrust in the intermediary directly impacted on perceived risk, which negatively influenced purchase intentions.
Natural propensity to trust (β = 0.112, t = 1.24) and natural propensity to distrust (β = 0.043, t = 1.04) did not show an effect on trust in sellers (H7b and H8b was not supported). Natural propensity to trust (β = −0.014, t = 0.16) did not show an effect on trust in intermediaries (H7a was not supported), while natural propensity to distrust (β = 0.172, t = 1.99) showed a positive effect on distrust in intermediaries (H8a was supported).
Positive eWOM (β = 0.236, t = 2.49) showed a positive effect on trust in sellers (H9 was supported), but negative eWOM (β = −0.100, t = 0.78) did not show any effect on trust in sellers (H10 was not supported).
6. Discussion
This study proposed an integrated behavioral model based on a differentiated approach that included (1) division of two different dimensions: the intermediary and seller, (2) multi-dimensionally coexistent trust and distrust, unlike the traditional unidimensional approach (i.e., trust and distrust are opposites). To investigate the effects of trust and distrust on purchase intentions in e-commerce, PLS, a structural equation modeling method was used. Results showed several findings. First, trust in an intermediary transferred to trust in a seller. Second, distrust in an intermediary directly impacted on perceived risk, negatively influencing purchase intentions. Third, structural assurance and perceived website quality of an intermediary gave a positive impact on buyer’s trust in the intermediary.
The findings have some theoretical contributions. First, this study considered both trust and distrust to analyze patterns of buyer behavior. E-commerce distrust has not been studied as much as e-commerce trust. If trust and distrust are a unidimensional concept, researching lack of distrust matters little. However, if they are a multidimensional concept, the lack of research on distrust could risk a biased point of view in understanding e-commerce. This study provided a holistic and balanced view, by separating trust and distrust and integrating a research model to better understand e-commerce. Relying on how cognition works against affect, a unidimensional approach to trust in a seller and a multidimensional approach to trust and distrust in an intermediary have shown validity and reliability of measurement. This contribution is expected to act as motivation for further studies. Second, this study also broke down e-commerce into the intermediary and the seller. This dual perspective provided us with a better understanding of buyers’ decision-making behavior, especially empirical evidence of trustor distrust transfer from intermediaries to sellers. Third, this study showed us the effect of the antecedents of trustor distrust in the intermediary and seller. Structural assurance and perceived website quality had a positive impact on trust in an intermediary but did not have an effect on trust in a seller, directly. While this study showed us that trust transfers from an intermediary to seller, trust in an intermediary and seller are both affected by these two antecedents, directly and indirectly.
The findings have some practical contributions. First, the results of the study highlight the management of distrust. Distrust in an intermediary directly impacts on perceived risk, negatively influencing purchase intention. Meanwhile, trust in an intermediary and trust in a seller do not influence distrust at all. This means that no matter how great trust is, a buyer can still have a high perceived risk. The effort of raising trust does not reduce distrust level. This study suggests managing trustor distrust simultaneously. Second, trust in a seller does not break into a dual perspective of trust and distrust, which means that cognitive factors play stronger than affective factors. A buyer is influenced by a seller’s information on the product or service when they make the decision to trust the seller. To raise trust in the seller, providing specific and precise information is highly recommended.
Despite its academic and practical contributions, this study was not without limitations. First, most of the participants were in their 20s because the surveys were collected from a group of students taking undergraduate courses. Survey distribution to a broader population of Internet buyers and a larger sample size will be needed in further research. Second, the data used in this study were not longitudinal. Former transaction experience could affect trust or distrust in the intermediary and the seller. A longitudinal research model is recommended for further study. Third, the antecedents of trust in this study were just a fragment of the trust experience. Structural assurance and perceived website quality are just two possible alternatives. A holistic view, which is more balanced and offers richer antecedents of trust, and especially, of distrust, is expected for further study, including buyers’ trust propensity, and other buyers’ WOM.